Models comparison
The clmplus package
was created to provide actuarial scientists the tool-box that we
illustrate in our paper, Pittarello G., Hiabu M., and Villegas A.,
Replicating and extending chain-ladder via an age-period-cohort
structure on the claim development in a run-off triangle, (pre-print,
2023). This vignette is made to make the readers able to recreate
the type of analysis we make in the data application section of our
paper. Please observe that the results we obtained in the paper
were performed on a larger number of run-off triangles.
We believe it is extremely important to provide this vignette to disclose to the public the code we used to perform our analysis. Our results can be replicated by just running this vignette with the following seed. This vignette is organized as follows:
In the bake-off section we provide numerical results to compare
clmplusmodels performances to theapcpackage models performances on the test set.In the rankings section we try to rank
clmplusmodels and theapcpackage models based on their performances on the validation set.
During the analysis we suggest how practitioners should split the data in order to perform the studies we just described.
For the case study in the paper we want to make our analysis as close
as possible to a real life reserving problem. In order to do so, we
decided to select data sets available to the public from the R packages
clmplus, ChainLadder, and
apc.
list.of.datasets <- list(
GenIns=GenIns,
sifa.mod=sifa.mod,
sifa.gtpl=sifa.gtpl,
sifa.mtpl=sifa.mtpl,
amases.gtpl=amases.gtpl,
amases.mod=amases.mod,
amases.mtpl=amases.mtpl,
bz = incr2cum(data.loss.BZ()$response),
ta = incr2cum(data.loss.TA()$response),
xl = incr2cum(data.loss.XL()$response),
vnj = incr2cum(data.loss.VNJ()$response),
abc=ABC,
autoC= auto$CommercialAutoPaid,
autoP = auto$PersonalAutoPaid,
autoBI = AutoBI$AutoBIPaid,
mclpaid= MCLpaid,
medmal=MedMal$MedMalPaid,
mortgage=Mortgage,
mw08=MW2008,
mw14=MW2014,
ukmotor = UKMotor,
usapaid=USAApaid
)Bake-off
Within this section we provide the code to measure the models extrapolation accuracy. In order to do so, we need to coherently split the data set into training, validation and testing. We show an example for a 12x12 run-off triangle.
J=12
df<-data.frame(expand.grid(c(0:(J-1)),c(0:(J-1))),c(1:(J^2)))
colnames(df) <- c("origin","dev","value")
df$value[df$origin+df$dev==(J-1)]=c(3)
df$value[df$origin+df$dev<(J-2)]=c(1)
df$value[df$origin+df$dev==(J-2)]=c(2)
df$value[df$origin+df$dev>=J]=c(NA)
#nas in the lower
df[J,3]=c(NA)
df[J-1,3]=c(NA)
df[J+J-1,3]=c(NA)
df[J*J-J+1,3]=c(NA)
df[J*J-J+1,3]=c(NA)
#nas in the upper tail
df[J*J-J+1-12,3]=c(NA)
df[J*J-J+2-12,3]=c(NA)
ggplot(data=df, aes(x=as.integer(dev), y=as.integer(origin))) +
geom_tile(aes(fill = as.factor(value),color="#000000"))+scale_y_reverse()+
scale_fill_manual(values=c("royalblue", "darkred", "darkgreen","white"),
na.value = "white",
labels=c("Train","Validation","Test",""))+
theme_classic()+
labs(x="Development year", y="Accident year",fill="")+
theme(axis.title.x = element_text(size=8), axis.text.x = element_text(size=7))+
theme(axis.title.y = element_text(size=8), axis.text.y = element_text(size=7))+
scale_color_brewer(guide = 'none')
best.of.the.bests <- function(df1,df2){
"
Util to turn character columns values into numeric.
"
df1=apply(df1,MARGIN=2,FUN=as.numeric)
df2=apply(df2,MARGIN=2,FUN=as.numeric)
df3 <- rbind(df1,df2)
df3=apply(df3,FUN=abs.min,MARGIN = 2)
return(df3)
}
modelcomparison.1d <- function(cumulative.payments.triangle){
"
Function to compare the clmplus package age-period-cohort models with apc package age-period-cohort models performances across different triangles.
This function takes a triangle of cumulative payments as input.
It returns the accuracy measures for the two families on the triangle.
"
# function internal variables
leave.out=2
rmse = NULL
mae = NULL
error.pc = NULL
model.name = NULL
error.incidence = NULL
model.family = NULL
mre = NULL
# data pre-precessing ----
J <- dim(cumulative.payments.triangle)[2]
reduced.triangle <- c2t(t2c(cumulative.payments.triangle)[1:(J-leave.out),1:(J-leave.out)])
newt.rtt <- AggregateDataPP(reduced.triangle)
newt.apc <- apc.data.list(response=newt.rtt$incremental.payments.triangle,
data.format="CL")
## stmomo -----
to.project <- t2c(cumulative.payments.triangle)[1:(J-leave.out-1),J-leave.out]
true.values <- t2c(cumulative.payments.triangle)[2:(J-leave.out),(J-leave.out+1):J]
for(ix in c('a','ac','ap','apc')){ ##names(models)
hz.fit <- StMoMo::fit(models[[ix]],
Dxt = newt.rtt$occurrance,
Ext = newt.rtt$exposure,
wxt=newt.rtt$fit.w,
iterMax=as.integer(1e+05))
hz.rate = fcst.fn(hz.fit,
hazard.model = ix,
gk.fc.model = 'a',
ckj.fc.model= 'a')$rates[,1:leave.out]
J.new=dim(reduced.triangle)[2]
fij = (2+hz.rate)/(2-hz.rate)
pred.mx = fij
pred.mx[,1]=fij[,1]*c(NA,to.project)
temp=unname(pred.mx[1:(J.new-1),1][!is.na(pred.mx[1:(J.new-1),1])])
pred.mx[,2]=fij[,2]*c(rep(NA,J.new-length(temp)),temp)
true.mx= rbind(rep(NA,2),true.values)
# this is meant to be NA
true.mx[2,2]=NA
sq.errors = (pred.mx-true.mx)^2
abs.errors = abs(pred.mx-true.mx)
r.errors = (pred.mx-true.mx)/true.mx
error.inc.num = apply(pred.mx-true.mx,sum,MARGIN=2,na.rm=T)
error.inc.den = apply(true.mx,sum,MARGIN=2,na.rm=T)
model.name.ix = c(paste0(ix,".val"),paste0(ix,".test"))
model.name = c(model.name,model.name.ix)
model.family = c(model.family,rep(ix,2))
rmse = c(rmse,sqrt(apply(sq.errors,MARGIN = 2,mean,na.rm=T)))
mae = c(mae,apply(abs.errors,MARGIN = 2,mean,na.rm=T))
mre = c(mre,apply(r.errors,MARGIN = 2,mean,na.rm=T))
error.incidence = c(error.incidence,error.inc.num/error.inc.den)
}
## stmomo results ----
out1 <- data.frame(
model.name,
model.family,
mre,
error.incidence,
rmse,
mae)
temp.ix <- grepl(".val", model.name)
temp.df <- out1[temp.ix,]
out2 <- data.frame(
rmse=temp.df$model.name[which(abs(temp.df$rmse)==min(abs(temp.df$rmse)))],
mre=temp.df$model.name[which(abs(temp.df$mre)==min(abs(temp.df$mre)))],
mae=temp.df$model.name[which(abs(temp.df$mae)==min(abs(temp.df$mae)))],
error.incidence=temp.df$model.name[which(abs(temp.df$error.incidence)==min(abs(temp.df$error.incidence)))])
temp.ix <- grepl(".test", model.name)
out3 <- out1[temp.ix,]
best.df = out2
best.df[1,]=NA
out.test.min <- data.frame(
rmse=out3$model.name[which(abs(out3$rmse)==min(abs(out3$rmse)))],
mre=out3$model.name[which(abs(out3$mre)==min(abs(out3$mre)))],
mae=out3$model.name[which(abs(out3$mae)==min(abs(out3$mae)))],
error.incidence=out3$model.name[which(abs(out3$error.incidence)==min(abs(out3$error.incidence)))])
temp.mx=matrix((sub("\\..*", "", out2) == sub("\\..*", "", out.test.min)),nrow=1)
choices.mx.clmplus=matrix(sub("\\..*", "", out2),nrow=1)
agreement.frame.clmplus=data.frame(temp.mx)
choices.frame.clmplus=data.frame(choices.mx.clmplus)
colnames(agreement.frame.clmplus)=colnames(out2)
colnames(choices.frame.clmplus)=colnames(out2)
for(col.ix in colnames(out2)){
res=out1$model.family[out1$model.name == out2[1,col.ix]]
res.test = out3$model.family == res
best.df[1,col.ix] = out3[res.test,col.ix]}
families.set=c('a','apc') #'ap',
temp.ix = out3$model.family %in% families.set
comparison.df = out3[temp.ix,]
comparison.df = cbind(comparison.df,
approach=rep('clmplus',length(families.set)))
## apc ----
rmse = NULL
mae = NULL
error.pc = NULL
model.name = NULL
error.incidence = NULL
model.family = NULL
mre = NULL
true.inc.values <- t2c(cum2incr(cumulative.payments.triangle))[2:(J-leave.out),(J-leave.out+1):J]
for(apc.mods in c("AC","APC")){ #,"AP"
fit <- apc.fit.model(newt.apc,
model.family = "od.poisson.response",
model.design = apc.mods)
if(apc.mods == "AC"){fcst <- apc.forecast.ac(fit)$trap.response.forecast}
# if(apc.mods == "AP"){fcst <- apc.forecast.ap(fit)$trap.response.forecast}
if(apc.mods == "APC"){fcst <- apc.forecast.apc(fit)$trap.response.forecast}
plogram.hat = t2c.full.square(incr2cum(t(fcst)))
pred.mx = plogram.hat[,(J-leave.out+1):J]
# true.mx= rbind(rep(NA,2),true.inc.values)
# # this is meant to be NA
# true.mx[2,2]=NA
sq.errors = (pred.mx-true.mx)^2
abs.errors = abs(pred.mx-true.mx)
r.errors = (pred.mx-true.mx)/true.mx #use same benchmark
error.inc.num = apply(pred.mx-true.mx,sum,MARGIN=2,na.rm=T)
error.inc.den = apply(true.mx,sum,MARGIN=2,na.rm=T) #use same benchmark
model.name.ix = c(paste0(apc.mods,".val"),paste0(apc.mods,".test"))
model.name = c(model.name,tolower(model.name.ix))
model.family = c(model.family,tolower(rep(apc.mods,2)))
rmse = c(rmse,sqrt(apply(sq.errors,MARGIN = 2,mean,na.rm=T)))
mae = c(mae,apply(abs.errors,MARGIN = 2,mean,na.rm=T))
mre = c(mre,apply(r.errors,MARGIN = 2,mean,na.rm=T))
error.incidence = c(error.incidence,error.inc.num/error.inc.den)}
out4 <- data.frame(
model.name,
model.family,
mre,
error.incidence,
rmse,
mae)
temp.ix <- grepl(".val", model.name)
temp.df <- out4[temp.ix,]
out5 <- data.frame(
rmse=temp.df$model.name[which(abs(temp.df$rmse)==min(abs(temp.df$rmse)))],
mre=temp.df$model.name[which(abs(temp.df$mre)==min(abs(temp.df$mre)))],
mae=temp.df$model.name[which(abs(temp.df$mae)==min(abs(temp.df$mae)))],
error.incidence=temp.df$model.name[which(abs(temp.df$error.incidence)==min(abs(temp.df$error.incidence)))])
temp.ix <- grepl(".test", model.name)
out6 <- out4[temp.ix,]
out.test.min2 <- data.frame(
rmse=out6$model.name[which(abs(out6$rmse)==min(abs(out6$rmse)))],
mre=out6$model.name[which(abs(out6$mre)==min(abs(out6$mre)))],
mae=out6$model.name[which(abs(out6$mae)==min(abs(out6$mae)))],
error.incidence=out6$model.name[which(abs(out6$error.incidence)==min(abs(out6$error.incidence)))])
temp.mx=matrix((sub("\\..*", "", out5) == sub("\\..*", "", out.test.min2)),nrow=1)
choices.mx.apc=matrix(sub("\\..*", "", out5),nrow=1)
choices.frame.apc=data.frame(choices.mx.apc)
agreement.frame.apc=data.frame(temp.mx)
colnames(agreement.frame.apc)=colnames(out5)
colnames(choices.frame.apc)=colnames(out5)
best.df.apc = out5
best.df.apc[1,]=NA
for(col.ix in colnames(out5)){
res=out4$model.family[out4$model.name == out5[1,col.ix]]
res.test = out6$model.family == res
best.df.apc[1,col.ix] = out6[res.test,col.ix]}
families.set=c('ac','apc') #'ap',
temp.ix = out6$model.family %in% families.set
comparison.df.apc = out6[temp.ix,]
comparison.df.apc = cbind(comparison.df.apc,
approach=rep('apc',length(families.set)))
out = list(
best.model.clmplus = best.df,
best.model.apc = best.df.apc,
agreement.frame.clmplus=agreement.frame.clmplus,
agreement.frame.apc=agreement.frame.apc,
choices.frame.clmplus=choices.frame.clmplus,
choices.frame.apc=choices.frame.apc,
comparison.df = rbind(comparison.df,
comparison.df.apc))
return(out)}
modelcomparison<-function(list.of.datasets){
"This functions returns the datasets to plot the bake-off section of the paper.
The input is a list of datasets that constitue the sample.
The output is datasets that contain accuracy measures.
"
best.fit=NULL
families.fit=NULL
agreement.clmplus=NULL
agreement.apc=NULL
choices.clmplus=NULL
choices.apc=NULL
for(df.ix in names(list.of.datasets)){
cat(paste0(".. Comparison on dataset: ",df.ix))
out.ix = modelcomparison.1d(list.of.datasets[[df.ix]])
best.of.the.bests.df=best.of.the.bests(out.ix$best.model.clmplus,
out.ix$best.model.apc)
out.ix$best.model.clmplus['package']= 'clmplus'
out.ix$best.model.apc['package']= 'apc'
best.of.the.bests.df['package']='overall.best'
best.fit=rbind(best.fit,
out.ix$best.model.clmplus,
out.ix$best.model.apc,
best.of.the.bests.df)
families.fit=rbind(families.fit,
out.ix$comparison.df)
agreement.clmplus=rbind(agreement.clmplus,
out.ix$agreement.frame.clmplus)
agreement.apc=rbind(agreement.apc,
out.ix$agreement.frame.apc)
choices.clmplus=rbind(choices.clmplus,
out.ix$choices.frame.clmplus)
choices.apc=rbind(choices.apc,
out.ix$choices.frame.apc)
}
best.fit[,1:4]=apply(best.fit[,1:4],MARGIN = 2,FUN = as.numeric)
families.fit[,c('mre',
'error.incidence',
'rmse',
'mae')]=apply(
families.fit[,c('mre',
'error.incidence',
'rmse',
'mae')],
MARGIN = 2,
FUN = as.numeric)
out = list(best.fit=best.fit,
families.fit=families.fit,
agreement.clmplus=agreement.clmplus,
agreement.apc=agreement.apc,
choices.clmplus=choices.clmplus,
choices.apc=choices.apc)
return(out)
}
bake.off <- function(models.comparison){
"
This function plots out the results from the previous computations.
It takes as input the resulting dataframes of model.comparison.
The output is the boxplots of the paper's bake-off section.
"
p1<- models.comparison$best.fit[,c("rmse","mae","package")] %>%
tidyr::pivot_longer(-c(package)) %>%
ggplot(aes(x=package,y=value))+
geom_boxplot()+
facet_wrap(.~name,nrow = 1,strip.position = 'bottom')+
theme_bw()+
theme(strip.placement = 'outside',strip.background = element_blank())
p2<- models.comparison$best.fit[,c("mre","error.incidence","package")] %>%
tidyr::pivot_longer(-c(package)) %>%
ggplot(aes(x=package,y=value))+
geom_boxplot()+
facet_wrap(.~name,nrow = 1,strip.position = 'bottom')+
theme_bw()+
theme(strip.placement = 'outside',strip.background = element_blank())
abs.best=models.comparison$best.fit[,c("mre","error.incidence","package")]
abs.best[,c("mre","error.incidence")]=apply(abs.best[,c("mre","error.incidence")],
MARGIN=2,
FUN=abs)
p3<- abs.best %>%
tidyr::pivot_longer(-c(package)) %>%
ggplot(aes(x=package,y=value))+
geom_boxplot()+
facet_wrap(.~name,nrow = 1,strip.position = 'bottom')+
theme_bw()+
theme(strip.placement = 'outside',strip.background = element_blank())
only.ei=models.comparison$best.fit[,c("error.incidence","package")]
only.ei[,c("error.incidence")]=abs(only.ei[,c("error.incidence")])
p4<- abs.best %>%
tidyr::pivot_longer(-c(package)) %>%
ggplot(aes(x=package,y=value))+
geom_boxplot()+
# facet_wrap(.~name,nrow = 1,strip.position = 'bottom')+
theme_bw()+
theme(strip.placement = 'outside',strip.background = element_blank())
out = list(p1=p1,
p2=p2,
p3=p3,
p4=p4)
return(out)
}The models in the clmplus package are compared to those
in the apc package. Below it can be found the code we used
to create the bake-off plot in our paper.
out=modelcomparison(list.of.datasets = list.of.datasets)
#> .. Comparison on dataset: GenIns
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: sifa.mod
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: sifa.gtpl
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: sifa.mtpl
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 860.063891
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49353.589600
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 29251.193380
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 36106.295210
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 40125.390780
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 44498.942240
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 45490.189570
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 48040.321560
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49991.357650
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49694.294990
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 20880.666980
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 12463.983790
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 13441.333110
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 12951.176400
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 15370.423630
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 15339.426370
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 17842.901760
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 19570.203660
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 10047.067150
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5144.085289
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5868.234632
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 6033.755464
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5593.853546
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5478.076757
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 7035.199194
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 3933.606063
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2726.947823
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2881.972323
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 3009.655856
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2615.516340
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2540.639658
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2906.368627
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2359.362462
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2421.987896
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1264.396267
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1984.311792
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2136.804699
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1333.856105
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 918.004039
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1249.952517
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1134.706143
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1238.429974
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1075.978340
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 733.741025
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 941.440475
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 860.063891
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49353.589600
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 29251.193380
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 36106.295210
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 40125.390780
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 44498.942240
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 45490.189570
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 48040.321560
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49991.357650
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49694.294990
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 20880.666980
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 12463.983790
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 13441.333110
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 12951.176400
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 15370.423630
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 15339.426370
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 17842.901760
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 19570.203660
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 10047.067150
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5144.085289
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5868.234632
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 6033.755464
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5593.853546
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5478.076757
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 7035.199194
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 3933.606063
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2726.947823
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2881.972323
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 3009.655856
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2615.516340
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2540.639658
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2906.368627
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2359.362462
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2421.987896
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1264.396267
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1984.311792
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2136.804699
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1333.856105
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 918.004039
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1249.952517
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1134.706143
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1238.429974
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1075.978340
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 733.741025
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 941.440475
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 860.063891
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49353.589600
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 29251.193380
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 36106.295210
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 40125.390780
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 44498.942240
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 45490.189570
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 48040.321560
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49991.357650
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49694.294990
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 20880.666980
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 12463.983790
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 13441.333110
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 12951.176400
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 15370.423630
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 15339.426370
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 17842.901760
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 19570.203660
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 10047.067150
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5144.085289
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5868.234632
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 6033.755464
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5593.853546
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5478.076757
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 7035.199194
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 3933.606063
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2726.947823
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2881.972323
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 3009.655856
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2615.516340
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2540.639658
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2906.368627
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2359.362462
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2421.987896
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1264.396267
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1984.311792
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2136.804699
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1333.856105
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 918.004039
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1249.952517
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1134.706143
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1238.429974
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1075.978340
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 733.741025
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 941.440475
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 860.063891
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49353.589600
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 29251.193380
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 36106.295210
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 40125.390780
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 44498.942240
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 45490.189570
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 48040.321560
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49991.357650
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49694.294990
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 20880.666980
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 12463.983790
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 13441.333110
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 12951.176400
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 15370.423630
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 15339.426370
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 17842.901760
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 19570.203660
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 10047.067150
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5144.085289
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5868.234632
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 6033.755464
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5593.853546
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5478.076757
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 7035.199194
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 3933.606063
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2726.947823
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2881.972323
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 3009.655856
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2615.516340
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2540.639658
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2906.368627
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2359.362462
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2421.987896
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1264.396267
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1984.311792
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2136.804699
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1333.856105
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 918.004039
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1249.952517
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1134.706143
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1238.429974
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1075.978340
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 733.741025
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 941.440475
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: amases.gtpl
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: amases.mod
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: amases.mtpl
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: bz
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: ta
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: xl
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 153 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 17
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 153 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 17
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 153 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 17
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 153 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 17
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: vnj
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: abc
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: autoC
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: autoP
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: autoBI
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 15 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -5 -4 -3 -2 -1 5
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 15 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -5 -4 -3 -2 -1 5
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 15 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -5 -4 -3 -2 -1 5
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 15 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -5 -4 -3 -2 -1 5
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: mclpaid
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 10 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -4 -3 -2 -1 4
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 10 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -4 -3 -2 -1 4
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 10 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -4 -3 -2 -1 4
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 10 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -4 -3 -2 -1 4
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: medmal
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 15 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -5 -4 -3 -2 -1 5
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 15 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -5 -4 -3 -2 -1 5
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 15 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -5 -4 -3 -2 -1 5
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 15 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -5 -4 -3 -2 -1 5
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: mortgage
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 21 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -6 -5 -4 -3 -2 -1 6
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 21 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -6 -5 -4 -3 -2 -1 6
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 21 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -6 -5 -4 -3 -2 -1 6
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 21 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -6 -5 -4 -3 -2 -1 6
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: mw08
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 21 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -6 -5 -4 -3 -2 -1 6
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 21 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -6 -5 -4 -3 -2 -1 6
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 21 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -6 -5 -4 -3 -2 -1 6
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 21 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -6 -5 -4 -3 -2 -1 6
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: mw14
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 105 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 14
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 105 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 14
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 105 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 14
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 105 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 14
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: ukmotor
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 10 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -4 -3 -2 -1 4
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 10 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -4 -3 -2 -1 4
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 10 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -4 -3 -2 -1 4
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 10 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -4 -3 -2 -1 4
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> .. Comparison on dataset: usapaid
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
cake = bake.off(out)
In detail, having the three set at disposal, for each case we are able to pick the best model based on the error incidence.
abs.best=out$best.fit[,c("error.incidence","package")]
abs.best[,c("error.incidence")]=abs(abs.best[,c("error.incidence")])
abs.best[,'data.source']=sort(rep(seq(1,dim(abs.best)[1]/3),3))
p3<- ggplot(abs.best,aes(x=package,
y=error.incidence,
fill=package,
label=data.source)) +
geom_boxplot(outlier.shape = NA)+
viridis::scale_fill_viridis(discrete=T, alpha=0.6) +
geom_jitter(color="black",
size=1,
alpha=0.9,
position = position_jitter(seed = 1)) +
geom_text(aes(label=ifelse(data.source%in% c(15,24,17,18),
as.character(data.source),'')),
hjust=0,
vjust=0,
size=5,
position = position_jitter(seed = 1))+
# geom_text(position = position_jitter(seed = 42))+
coord_flip()+
theme_bw()+
ggplot2::labs(x="Package", y="Error Incidence")+
theme(axis.text.y = element_text(size=15),
axis.text.x = element_text(size=15))+
theme(axis.title.y = element_text(size=20),
axis.title.x = element_text(size=20))
p3Rankings
Within this section we provide practitioners with the code to perform models ranking. Now we have a different training validation split. The training validation split we use is represented in the following picture.
# models ranking
J=12
df<-data.frame(expand.grid(c(0:(J-1)),c(0:(J-1))),c(1:(J^2)))
colnames(df) <- c("origin","dev","value")
df$value[df$origin+df$dev==(J-1)]=c(2)
df$value[df$origin+df$dev<(J-1)]=c(1)
df$value[df$origin+df$dev>=J]=c(NA)
df[J,3]=c(NA)
df[J*J-J+1,3]=c(NA)
ggplot(data=df, aes(x=as.integer(dev), y=as.integer(origin))) +
geom_tile(aes(fill = as.factor(value),color="#000000"))+scale_y_reverse()+
scale_fill_manual(values=c("royalblue", "darkred", "white"),
na.value = "white",
labels=c("Train","Validation",""))+
theme_classic()+
labs(x="Development year", y="Accident year",fill="")+
theme(axis.title.x = element_text(size=8), axis.text.x = element_text(size=7))+
theme(axis.title.y = element_text(size=8), axis.text.y = element_text(size=7))+
scale_color_brewer(guide = 'none')
modelsranking.1d <- function(data.T){
"
Function to rank the clmplus package and apc package age-period-cohort models.
This function takes a triangle of cumulative payments as input.
It returns the ranking on the triangle.
"
leave.out=1
model.name = NULL
error.incidence = NULL
mre = NULL
#pre-processing
triangle <- data.T$cumulative.payments.triangle
J <- dim(triangle)[2]
reduced.triangle <- c2t(t2c(triangle)[1:(J-leave.out),1:(J-leave.out)])
newt.rtt <- AggregateDataPP(reduced.triangle)
to.project <- t2c(triangle)[1:(J-leave.out-1),J-leave.out]
true.values <- t2c(triangle)[2:(J-leave.out),J]
for(ix in c('a','ac','ap','apc')){
hz.fit <- StMoMo::fit(models[[ix]],
Dxt = newt.rtt$occurrance,
Ext = newt.rtt$exposure,
wxt=newt.rtt$fit.w,
iterMax=as.integer(1e+05))
hz.rate = fcst.fn(hz.fit,
hazard.model = ix,
gk.fc.model = 'a',
ckj.fc.model= 'a')$rates[,1]
fij = (2+hz.rate)/(2-hz.rate)
pred.fij = fij[(leave.out+1):length(fij)]
pred.v=to.project*pred.fij
r.errors = (pred.v-true.values)/true.values
error.inc.num = sum(pred.v-true.values,na.rm = T)
error.inc.den = sum(true.values)
model.name = c(model.name,
paste0('clmplus.',ix))
error.incidence = c(error.incidence,error.inc.num/error.inc.den)
mre = c(mre,mean(r.errors))
}
# ix='lc'
# hz.fit <- fit.lc.nr(data.T = newt.rtt,
# iter.max = 3e+04)
# if(hz.fit$converged==TRUE){hz.rate = forecast.lc.nr(hz.fit,J=dim(newt.rtt$cumulative.payments.triangle)[2])$rates[,1:leave.out]
# fij = (2+hz.rate)/(2-hz.rate)
# pred.fij = fij[(leave.out+1):length(fij)]
# pred.v=to.project*pred.fij
# r.errors = (pred.v-true.values)/true.values
#
# error.inc.num = sum(pred.v-true.values,na.rm = T)
# error.inc.den = sum(true.values)
#
# model.name = c(model.name,
# paste0('clmplus.',ix))
# error.incidence = c(error.incidence,error.inc.num/error.inc.den)
# mre = c(mre,mean(r.errors))}
out1 <- data.frame(
model.name,
# mre,
error.incidence)
## APC package
newt.apc <- apc.data.list(response=newt.rtt$incremental.payments.triangle,
data.format="CL")
## apc
rmse = NULL
mae = NULL
error.pc = NULL
model.name = NULL
error.incidence = NULL
model.family = NULL
mre = NULL
true.inc.values <- t2c(data.T$incremental.payments.triangle)[2:(J-leave.out),(J-leave.out+1):J]
for(apc.mods in c("AC","APC")){ #,"AP"
fit <- apc.fit.model(newt.apc,
model.family = "od.poisson.response",
model.design = apc.mods)
if(apc.mods == "AC"){fcst <- apc.forecast.ac(fit)$trap.response.forecast}
# if(apc.mods == "AP"){fcst <- apc.forecast.ap(fit)$trap.response.forecast}
if(apc.mods == "APC"){fcst <- apc.forecast.apc(fit)$trap.response.forecast}
plogram.hat = t2c.full.square(incr2cum(t(fcst)))
pred.v = plogram.hat[,(J-leave.out+1):J]
pred.v = pred.v[2:length(pred.v)]
r.errors = (pred.v-true.values)/true.values
error.inc.num = sum(pred.v-true.values)
error.inc.den = sum(true.values)
model.name = c(model.name,
paste0('apc.',tolower(apc.mods)))
error.incidence = c(error.incidence,error.inc.num/error.inc.den)
mre = c(mre,mean(r.errors))
}
out2 <- data.frame(
model.name,
# mre,
error.incidence)
out3 <- rbind(out1,out2)
out3 <- out3[order(abs(out3$error.incidence),decreasing = F),]
out3[,'ei.rank']=c(1:dim(out3)[1])
# out3[,'mre.rank']=order(abs(out3$mre),decreasing = F)
#fix it manually
r2set=min(out3$ei.rank[out3$model.name=='apc.ac'],
out3$ei.rank[out3$model.name=='clmplus.a'])
out3$ei.rank[out3$model.name=='apc.ac']=r2set
out3$ei.rank[out3$model.name=='clmplus.a']=r2set
if( out3$ei.rank[out3$model.name=='apc.ac'] < max(out3$ei.rank)){
cond=out3$ei.rank>out3$ei.rank[out3$model.name=='apc.ac']
out3$ei.rank[cond]=out3$ei.rank[cond]-1
}
return(list(models.ranks=out3))
}
modelsranking <- function(list.of.datasets){
"
This functions returns the datasets to plot in the ranking section of the paper.
The input is a list of datasets that constitue the sample.
The output is the rankings across different data sources.
"
full.ranks=NULL
for(df.ix in names(list.of.datasets)){
out.df=modelsranking.1d(AggregateDataPP(list.of.datasets[[df.ix]]))
out.df$models.ranks[,'data.source']=rep(df.ix,dim(out.df$models.ranks)[1])
full.ranks=rbind(full.ranks,out.df$models.ranks)
}
return(list(full.ranks=full.ranks))
}full.ranks=modelsranking(list.of.datasets)
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 860.063891
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 458.156066
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 281.869679
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49353.589600
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 50605.820000
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 29251.193380
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 36106.295210
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 40125.390780
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 44498.942240
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 45490.189570
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 48040.321560
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49991.357650
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49694.294990
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 20880.666980
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 18304.246140
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 12463.983790
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 13441.333110
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 12951.176400
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 15370.423630
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 15339.426370
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 17842.901760
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 19570.203660
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 10047.067150
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 8201.559214
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5144.085289
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5868.234632
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 6033.755464
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5593.853546
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5478.076757
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 7035.199194
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 3933.606063
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5749.890057
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2726.947823
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2881.972323
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 3009.655856
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2615.516340
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2540.639658
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2906.368627
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2725.682936
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2359.362462
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2421.987896
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1264.396267
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1984.311792
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2136.804699
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1294.338023
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1333.856105
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 918.004039
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1249.952517
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1134.706143
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1184.355428
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1238.429974
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1075.978340
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 733.741025
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 904.371165
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 941.440475
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 860.063891
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 458.156066
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 281.869679
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49353.589600
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 50605.820000
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 29251.193380
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 36106.295210
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 40125.390780
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 44498.942240
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 45490.189570
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 48040.321560
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49991.357650
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49694.294990
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 20880.666980
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 18304.246140
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 12463.983790
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 13441.333110
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 12951.176400
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 15370.423630
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 15339.426370
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 17842.901760
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 19570.203660
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 10047.067150
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 8201.559214
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5144.085289
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5868.234632
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 6033.755464
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5593.853546
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5478.076757
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 7035.199194
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 3933.606063
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5749.890057
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2726.947823
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2881.972323
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 3009.655856
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2615.516340
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2540.639658
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2906.368627
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2725.682936
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2359.362462
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2421.987896
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1264.396267
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1984.311792
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2136.804699
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1294.338023
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1333.856105
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 918.004039
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1249.952517
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1134.706143
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1184.355428
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1238.429974
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1075.978340
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 733.741025
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 904.371165
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 941.440475
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 860.063891
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 458.156066
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 281.869679
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49353.589600
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 50605.820000
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 29251.193380
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 36106.295210
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 40125.390780
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 44498.942240
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 45490.189570
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 48040.321560
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49991.357650
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49694.294990
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 20880.666980
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 18304.246140
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 12463.983790
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 13441.333110
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 12951.176400
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 15370.423630
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 15339.426370
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 17842.901760
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 19570.203660
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 10047.067150
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 8201.559214
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5144.085289
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5868.234632
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 6033.755464
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5593.853546
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5478.076757
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 7035.199194
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 3933.606063
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5749.890057
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2726.947823
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2881.972323
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 3009.655856
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2615.516340
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2540.639658
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2906.368627
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2725.682936
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2359.362462
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2421.987896
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1264.396267
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1984.311792
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2136.804699
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1294.338023
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1333.856105
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 918.004039
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1249.952517
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1134.706143
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1184.355428
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1238.429974
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1075.978340
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 733.741025
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 904.371165
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 941.440475
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 860.063891
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 458.156066
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 281.869679
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49353.589600
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 50605.820000
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 29251.193380
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 36106.295210
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 40125.390780
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 44498.942240
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 45490.189570
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 48040.321560
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49991.357650
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 49694.294990
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 20880.666980
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 18304.246140
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 12463.983790
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 13441.333110
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 12951.176400
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 15370.423630
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 15339.426370
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 17842.901760
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 19570.203660
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 10047.067150
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 8201.559214
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5144.085289
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5868.234632
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 6033.755464
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5593.853546
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5478.076757
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 7035.199194
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 3933.606063
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 5749.890057
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2726.947823
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2881.972323
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 3009.655856
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2615.516340
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2540.639658
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2906.368627
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2725.682936
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2359.362462
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2421.987896
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1264.396267
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1984.311792
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 2136.804699
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1294.338023
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1333.856105
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 918.004039
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1249.952517
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1134.706143
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1184.355428
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1238.429974
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 1075.978340
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 733.741025
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 904.371165
#> Warning in dpois(y, mu, log = TRUE): non-integer x = 941.440475
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 55 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 10
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 171 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 18
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 171 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 18
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 171 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 18
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 171 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 18
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 45 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -9 -8 -7 -6 -5 -4 -3 -2 -1 9
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 21 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -6 -5 -4 -3 -2 -1 6
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 21 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -6 -5 -4 -3 -2 -1 6
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 21 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -6 -5 -4 -3 -2 -1 6
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 21 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -6 -5 -4 -3 -2 -1 6
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 15 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -5 -4 -3 -2 -1 5
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 15 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -5 -4 -3 -2 -1 5
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 15 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -5 -4 -3 -2 -1 5
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 15 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -5 -4 -3 -2 -1 5
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 21 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -6 -5 -4 -3 -2 -1 6
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 21 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -6 -5 -4 -3 -2 -1 6
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 21 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -6 -5 -4 -3 -2 -1 6
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 21 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -6 -5 -4 -3 -2 -1 6
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 28 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -7 -6 -5 -4 -3 -2 -1 7
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 120 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 15
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 120 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 15
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 120 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 15
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 120 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 15
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 15 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -5 -4 -3 -2 -1 5
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 15 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -5 -4 -3 -2 -1 5
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 15 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -5 -4 -3 -2 -1 5
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 15 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -5 -4 -3 -2 -1 5
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnm
#> Warning in fit.StMoMo(models[[ix]], Dxt = newt.rtt$occurrance, Ext = newt.rtt$exposure, : StMoMo: 36 missing values which have been zero weighted
#> StMoMo: The following ages have been zero weigthed: 1
#> StMoMo: The following years have been zero weigthed: 1
#> StMoMo: The following cohorts have been zero weigthed: -8 -7 -6 -5 -4 -3 -2 -1 8
#> StMoMo: Start fitting with gnm
#> StMoMo: Finish fitting with gnmThe following picture is the models ranks plot we included in the paper.
p_min_expd0 <- ggplot(full.ranks$full.ranks, aes(model.name, data.source)) +
geom_tile(aes(fill = cut(ei.rank, breaks=0:6, labels=1:6)), colour = "grey") +
ggtitle(" ") +
theme_classic()+
geom_text(aes(label = ei.rank))+
scale_y_discrete(limits=names(list.of.datasets)) +
scale_fill_manual(drop=FALSE, values=colorRampPalette(c("white","#6699CC"))(6), na.value="#EEEEEE", name="Rank") +
xlab("Model") + ylab("Data source")
p_min_expd0
It can be useful to inspect the average rank of the different models we were comparing.
tbl=full.ranks$full.ranks %>%
dplyr::group_by(model.name) %>%
dplyr::summarise(mean.rank = mean(ei.rank))
tbl
#> # A tibble: 6 × 2
#> model.name mean.rank
#> <chr> <dbl>
#> 1 apc.ac 3.09
#> 2 apc.apc 4.14
#> 3 clmplus.a 3.09
#> 4 clmplus.ac 2.32
#> 5 clmplus.ap 3
#> 6 clmplus.apc 2.45library(dplyr)
temp.df=full.ranks$full.ranks[,c('model.name','ei.rank')] %>%
group_by(model.name, ei.rank) %>% summarise(count = n())
#> `summarise()` has grouped output by 'model.name'. You can override using the
#> `.groups` argument.The following picture was not included in the paper but it shows the models ranks counts. It provides additional consistency to the results we included in our work.
ggplot(temp.df, aes(y=count, x=factor(ei.rank))) +
geom_bar(position="stack", stat="identity",fill='#6699CC') +
scale_y_continuous(limits=c(0,15))+
facet_wrap(~model.name, scales='free')+
theme_classic()+
ylab("")+
xlab("Rank")
Conclusions
In this vignette we wanted to show how the tool-box of actuarial
scientists is enriched thanks to clmplus.
It is important to perform different analysis to validate results. In this work we showed two.
There is no best model in absolute terms: our purpose is to show that more options can improve claims reserving.