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table_2_intercept_model.R
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table_2_intercept_model.R
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rm(list=ls())
# libraries ----
library(data.table)
library(dplyr)
library(ChainLadder)
library(ggplot2)
source('conditional-aj-reserving\\helper_functions_ajr.R')
# Width 4 ----
input.pbties <- c(-7.11758383885783e-06, 1.39183828093592e-06, 5.73888289022062e-08, 5.66835672901971e-06,
0, -1.16363390439916e-05, 1.9869412608886e-06, 9.64939778310298e-06,
0, 0, -4.55533160623821e-06, 4.55533160623821e-06,
0, 0 ,0, 0)
results <- matrix(ncol=6)
trial <- 1
ix2 = 0
V=1200
width=4
lambda <- function(t){
A <- matrix(c(1*mark_dist(1, t, 0),
1*mark_dist(2, t, 0),
1*mark_dist(3, t, 0),
# 1*mark_dist(4, t, 0),
# 1*mark_dist(5, t, 0),
rep(0, width)),
nrow = width, ncol = width, byrow = TRUE)
diag(A) <- -rowSums(A)
A
}
imx <- matrix(input.pbties, byrow = T,ncol=width)
diag(imx) <- 0
jrate <- apply(imx,1,sum)
jmx <- matrix(rep(jrate,width),byrow = F,ncol=width)
imx[1:(width-1),] <- imx[1:(width-1),]/jmx[1:(width-1),]
ap.volumes <- rep(V,width-1) -seq(0,100*(width-2),by=100)
{set.seed(1)
X <- sort(1/sample(11:(width-1+10),sum(ap.volumes),replace=T))
}
for(trial in 1:20){
set.seed(trial)
sim.2.fit <- simulate_mkvian_dataX(ap.volumes=ap.volumes,
tsh=1e-06,
stdize=1,
X=X)
input.data <- sim.2.fit
data.list <- input.data$sim.2.fit
rbns.data <- input.data$censored.data
rbns.data[["states"]] <- as.vector(as.character(rbns.data$states))
closed.data <- rbns.data %>% group_by(id) %>% filter("4"%chin%states) %>% as.data.table()
rbns.data <- rbns.data %>% group_by(id) %>% filter(!("4"%chin%states)) %>% as.data.table()
fit <- AalenJohansen::aalen_johansen(data.list)
yhat <- sapply(fit$p, last)
cond <- (yhat > 1 | yhat <0) | is.na(yhat)
yhat[cond] <- 1
x.vals <- fit$t
cond2 = diff(c(0,yhat))<0
while(sum(cond2)!=0){
yhat <-yhat[!cond2]
x.vals <-x.vals[!cond2]
cond2 = diff(c(0,yhat))<0
}
rbns.data <- rbns.data[,.(k=last(times)),by=.(id)]
actual <- input.data$actual.data[,.(ultimate=last(times)),by=id]
rbns.data <-merge(actual,rbns.data,by='id')
colnames(rbns.data)[2] <- 'true.ultimate'
out <- rbns.data[,c('ultimate','variance','crps_i'):=individual_results_nf(k,
true.ultimate=true.ultimate,
x.vals=x.vals,
yhat=yhat),
by=.(id)]
closed <- closed.data[,.(ultimate=last(times)),by=id]
actual.tot <- sum(actual$ultimate)
pred.tot <- sum(out$ultimate) +sum(closed$ultimate)
cl.tot <- chain_ladder_computer(input.data$actual.data,width=width)
actual.tot <- sum(actual$ultimate)
pred.tot <- sum(out$ultimate) +sum(closed$ultimate)
cl.tot <- chain_ladder_computer(input.data$actual.data,width=width)
results = rbind(results,c(actual.tot,
pred.tot,
cl.tot$ultimate,
sum(out$variance),
cl.tot$process.se,
mean(out$crps_i,
na.rm=T)))
trial <- trial+1
# }
}
L <- matrix(input.pbties, byrow = T,ncol=width)
diag(imx) <- 0
alpha <- c(1,rep(0,width-2))
ev=sum(alpha%*%solve(-L[1:(width-1),1:(width-1)]))
results <- data.frame(results[-1,])
colnames(results) <- c('actual.tot',
'pred.tot',
'cl.tot',
'variance',
'cl.msep',
'mcrps')
locn = "conditional-aj-reserving\\results_csv\\%s"
finame=paste0("simulation_",
'aytrend_interceptmodel_all_records_',
width,
"_",
format(Sys.time(),
"%Y_%m_%d_%H_%M"),
".csv")
fwrite(results, file=sprintf(locn,finame))
results %>% reframe(pred.tot=mean(pred.tot),
simulated.tot=mean(actual.tot),
cl.ultimate=mean(cl.tot),
exact.tot=ev*sum(V*(width-1)),
msep= mean(sqrt(variance)),
msep.cl=mean(cl.msep),
aj.error=1-mean(pred.tot/exact.tot),
cl.error=1-mean(cl.tot/exact.tot),
) %>% xtable::xtable()
# Width 5 ----
input.pbties <-c(-3.87473643786946e-06, 7.75530168016601e-07, 1.92727077396385e-08, 9.37832380696512e-10, 3.07899572973252e-06,
0, -6.0461063538271e-06, 1.40368477593149e-06, 3.24343294024007e-08, 4.60998724849321e-06,
0, 0, -4.59531201287676e-06, 2.33954270788095e-06, 2.25576930499581e-06,
0, 0, 0, -2.37851748950939e-06, 2.37851748950939e-06,
0, 0, 0, 0, 0)
width <- 5
imx <- matrix(input.pbties,
byrow = T,
ncol=width)
diag(imx) <- 0
jrate <- apply(imx,1,sum)
jmx <- matrix(rep(jrate,width),byrow = F,ncol=width)
imx[1:(width-1),] <- imx[1:(width-1),]/jmx[1:(width-1),]
apply(imx,1,sum)
lambda <- function(t){
A <- matrix(c(1*mark_dist(1, t, 0),
1*mark_dist(2, t, 0),
1*mark_dist(3, t, 0),
1*mark_dist(4, t, 0),
# 1*mark_dist(5, t, 0),
rep(0, width)),
nrow = width, ncol = width, byrow = TRUE)
diag(A) <- -rowSums(A)
A
}
V=1200
results <- matrix(ncol=6)
ap.volumes <- rep(V,width-1) -seq(0,100*(width-2),by=100)
{set.seed(1)
X <- sort(1/sample(11:(width-1+10),sum(ap.volumes),replace=T))
}
for(trial in 1:20){
set.seed(trial)
sim.2.fit <- simulate_mkvian_dataX(ap.volumes=ap.volumes,
tsh=1e-06,
stdize=1,
X=X)
input.data <- sim.2.fit
data.list <- input.data$sim.2.fit
rbns.data <- input.data$censored.data
rbns.data[["states"]] <- as.vector(as.character(rbns.data$states))
closed.data <- rbns.data %>% group_by(id) %>% filter(as.character(width)%chin%states) %>% as.data.table()
rbns.data <- rbns.data %>% group_by(id) %>% filter(!(as.character(width)%chin%states)) %>% as.data.table()
fit <- AalenJohansen::aalen_johansen(data.list)
yhat <- sapply(fit$p, last)
cond <- (yhat > 1 | yhat <0) | is.na(yhat)
yhat[cond] <- 1
x.vals <- fit$t
cond2 = diff(c(0,yhat))<0
while(sum(cond2)!=0){
yhat <-yhat[!cond2]
x.vals <-x.vals[!cond2]
cond2 = diff(c(0,yhat))<0
}
rbns.data <- rbns.data[,.(k=last(times)),by=.(id)]
actual <- input.data$actual.data[,.(ultimate=last(times)),by=id]
rbns.data <-merge(actual,rbns.data,by='id')
colnames(rbns.data)[2] <- 'true.ultimate'
out <- rbns.data[,c('ultimate','variance','crps_i'):=individual_results_nf(k,
true.ultimate=true.ultimate,
x.vals=x.vals,
yhat=yhat),
by=.(id)]
closed <- closed.data[,.(ultimate=last(times)),by=id]
actual.tot <- sum(actual$ultimate)
pred.tot <- sum(out$ultimate) +sum(closed$ultimate)
cl.tot <- chain_ladder_computer(input.data$actual.data,width=width)
actual.tot <- sum(actual$ultimate)
pred.tot <- sum(out$ultimate) +sum(closed$ultimate)
cl.tot <- chain_ladder_computer(input.data$actual.data,width=width)
results = rbind(results,c(actual.tot,
pred.tot,
cl.tot$ultimate,
sum(out$variance),
cl.tot$process.se,
mean(out$crps_i,
na.rm=T)))
trial <- trial+1
# }
}
L <- matrix(input.pbties, byrow = T,ncol=width)
diag(imx) <- 0
alpha <- c(1,rep(0,width-2))
ev=sum(alpha%*%solve(-L[1:(width-1),1:(width-1)]))
results <- data.frame(results[-1,])
colnames(results) <- c('actual.tot','pred.tot','cl.tot','variance','cl.msep','mcrps')
locn = " conditional-aj-reserving\\results_csv\\%s"
finame=paste0("simulation_",
'aytrend_interceptmodel_all_records_',
width,
"_",
format(Sys.time(),
"%Y_%m_%d_%H_%M"),
".csv")
fwrite(results, file=sprintf(locn,finame))
results %>% reframe(pred.tot=mean(pred.tot),
simulated.tot=mean(actual.tot),
cl.ultimate=mean(cl.tot),
exact.tot=ev*sum(V*(width-1)),
msep= mean(sqrt(variance)),
msep.cl=mean(cl.msep),
aj.error=1-mean(pred.tot/exact.tot),
cl.error=1-mean(cl.tot/exact.tot),
) %>% xtable::xtable()
# Width 6 ----
input.pbties <-c(-3.92969143285773e-06, 9.46146090007938e-07, 1.63469251967611e-08, 1.05743452770889e-09, 9.82949116106126e-11, 2.96604268821371e-06,
0, -6.26893145831146e-06, 1.25354737215752e-06, 3.00799715378682e-08, 0, 4.98530411461608e-06,
0, 0, -4.78554760053172e-06, 2.34236472738395e-06, 0, 2.44318287314777e-06,
0, 0, 0, -4.02223209153801e-06, 2.21897636190889e-06, 1.80325572962912e-06,
0, 0, 0, 0, -9.71814465069587e-07, 9.71814465069587e-07,
0, 0, 0, 0, 0, 0)
width <- 6
imx <- matrix(input.pbties, byrow = T,ncol=width)
diag(imx) <- 0
jrate <- apply(imx,1,sum)
jmx <- matrix(rep(jrate,width),byrow = F,ncol=width)
imx[1:(width-1),] <- imx[1:(width-1),]/jmx[1:(width-1),]
apply(imx,1,sum)
lambda <- function(t){
A <- matrix(c(1*mark_dist(1, t, 0),
1*mark_dist(2, t, 0),
1*mark_dist(3, t, 0),
1*mark_dist(4, t, 0),
1*mark_dist(5, t, 0),
# 1*mark_dist(6, t, 0),
# 1*mark_dist(7, t, 0),
# 1*mark_dist(8, t, 0),
rep(0, width)),
nrow = width, ncol = width, byrow = TRUE)
diag(A) <- -rowSums(A)
A
}
V=1200
results <- matrix(ncol=6)
ap.volumes <- rep(V,width-1)-seq(0,100*(width-2),by=100)
{set.seed(1)
X <- sort(1/sample(11:(width-1+10),sum(ap.volumes),replace=T))
}
for(trial in 1:20){
set.seed(trial)
sim.2.fit <- simulate_mkvian_dataX(ap.volumes=ap.volumes,
tsh=1e-06,
stdize=1,
X=X)
ix2=ix2+1
input.data <- sim.2.fit
data.list <- input.data$sim.2.fit
rbns.data <- input.data$censored.data
rbns.data[["states"]] <- as.vector(as.character(rbns.data$states))
closed.data <- rbns.data %>% group_by(id) %>% filter(as.character(width)%chin%states) %>% as.data.table()
rbns.data <- rbns.data %>% group_by(id) %>% filter(!(as.character(width)%chin%states)) %>% as.data.table()
fit <- AalenJohansen::aalen_johansen(data.list)
yhat <- sapply(fit$p, last)
cond <- (yhat >1 | yhat <0) | is.na(yhat)
trial<-trial+1
yhat[cond]<-1
x.vals <- fit$t
cond2 = diff(c(0,yhat))<0
while(sum(cond2)!=0){
yhat <-yhat[!cond2]
x.vals <-x.vals[!cond2]
cond2 = diff(c(0,yhat))<0
}
rbns.data <- rbns.data[,.(k=last(times)),by=.(id)]
actual <- input.data$actual.data[,.(ultimate=last(times)),by=id]
rbns.data <-merge(actual,rbns.data,by='id')
colnames(rbns.data)[2] <- 'true.ultimate'
out <- rbns.data[,c('ultimate','variance','crps_i'):=individual_results_nf(k,
true.ultimate=true.ultimate,
x.vals=x.vals,
yhat=yhat),
by=.(id)]
closed <- closed.data[,.(ultimate=last(times)),by=id]
actual.tot <- sum(actual$ultimate)
pred.tot <- sum(out$ultimate) +sum(closed$ultimate)
cl.tot <- chain_ladder_computer(input.data$actual.data,width=width)
results = rbind(results,c(actual.tot,
pred.tot,
cl.tot$ultimate,
sum(out$variance),
cl.tot$process.se,
mean(out$crps_i,
na.rm=T)))
}
L <- matrix(input.pbties, byrow = T,ncol=width)
diag(imx) <- 0
alpha <- c(1,rep(0,width-2))
ev=sum(alpha%*%solve(-L[1:(width-1),1:(width-1)]))
results <- data.frame(results[-1,])
colnames(results) <- c('actual.tot',
'pred.tot',
'cl.tot',
'variance',
'cl.msep',
'mcrps')
locn = "conditional-aj-reserving\\results_csv\\%s"
finame=paste0("simulation_",
'aytrend_interceptmodel_all_records_',
width,
"_",
format(Sys.time(),
"%Y_%m_%d_%H_%M"),
".csv")
fwrite(results, file=sprintf(locn,finame))
results %>% reframe(pred.tot=mean(pred.tot),
simulated.tot=mean(actual.tot),
cl.ultimate=mean(cl.tot),
exact.tot=ev*sum(V*(width-1)),
msep= mean(sqrt(variance)),
aj.error=1-mean(pred.tot/exact.tot),
cl.error=1-mean(cl.tot/exact.tot),
) %>% xtable::xtable()
1-pred.tot/actual.tot
1-cl.tot$ultimate/actual.tot
# Width 7 ----
# model parameters
input.pbties <-c(-3.97809678024541e-06, 9.51943523703866e-07, 1.61328746890491e-08, 9.85506966190774e-10, 1.3547224236955e-10, 0, 3.00889940264394e-06,
0, -6.79208351973386e-06, 1.21175308945881e-06, 3.27015353265806e-08, 5.42628524413257e-09, 0, 5.54220260970434e-06,
0, 0, -5.52419512708527e-06, 2.64769885127375e-06, 1.90551855895997e-08, 0, 2.85744109022192e-06,
0, 0, 0, -6.40163496831553e-06, 4.21428114058748e-06, 0, 2.18735382772805e-06,
0, 0, 0, 0, -2.59150524018556e-06, 1.72767016012371e-06, 8.63835080061855e-07,
0, 0, 0, 0, 0, -4.85907232534793e-07, 4.85907232534793e-07,
0, 0, 0, 0, 0, 0, 0)
width <- 7
imx <- matrix(input.pbties, byrow = T,ncol=width)
diag(imx) <- 0
jrate <- apply(imx,1,sum)
jmx <- matrix(rep(jrate,width),byrow = F,ncol=width)
imx[1:(width-1),] <- imx[1:(width-1),]/jmx[1:(width-1),]
apply(imx,1,sum)
lambda <- function(t){
A <- matrix(c(1*mark_dist(1, t, 0),
1*mark_dist(2, t, 0),
1*mark_dist(3, t, 0),
1*mark_dist(4, t, 0),
1*mark_dist(5, t, 0),
1*mark_dist(6, t, 0),
# 1*mark_dist(7, t, 0),
# 1*mark_dist(8, t, 0),
# 1*mark_dist(9, t, 0),
rep(0, width)),
nrow = width, ncol = width, byrow = TRUE)
diag(A) <- -rowSums(A)
A
}
V=1200
results <- matrix(ncol=6)
ap.volumes <- rep(V,width-1)-seq(0,100*(width-2),by=100)
{set.seed(1)
X <- sort(1/sample(11:(width-1+10),sum(ap.volumes),replace=T))
}
for(trial in 1:20){
set.seed(trial)
sim.2.fit <- simulate_mkvian_dataX(ap.volumes=ap.volumes,
tsh=1e-06,
stdize=1,
X=X)
ix2=ix2+1
input.data <- sim.2.fit
data.list <- input.data$sim.2.fit
rbns.data <- input.data$censored.data
rbns.data[["states"]] <- as.vector(as.character(rbns.data$states))
closed.data <- rbns.data %>% group_by(id) %>% filter(as.character(width)%chin%states) %>% as.data.table()
rbns.data <- rbns.data %>% group_by(id) %>% filter(!(as.character(width)%chin%states)) %>% as.data.table()
fit <- AalenJohansen::aalen_johansen(data.list)
yhat <- sapply(fit$p, last)
cond <- (yhat >1 | yhat <0) | is.na(yhat)
trial<-trial+1
yhat[cond]<-1
x.vals <- fit$t
# x.vals<- x.vals[cond]
cond2 = diff(c(0,yhat))<0
while(sum(cond2)!=0){
yhat <-yhat[!cond2]
x.vals <-x.vals[!cond2]
cond2 = diff(c(0,yhat))<0
}
rbns.data <- rbns.data[,.(k=last(times)),by=.(id)]
actual <- input.data$actual.data[,.(ultimate=last(times)),by=id]
rbns.data <-merge(actual,rbns.data,by='id')
colnames(rbns.data)[2] <- 'true.ultimate'
out <- rbns.data[,c('ultimate','variance','crps_i'):=individual_results_nf(k,
true.ultimate=true.ultimate,
x.vals=x.vals,
yhat=yhat),
by=.(id)]
closed <- closed.data[,.(ultimate=last(times)),by=id]
actual.tot <- sum(actual$ultimate)
pred.tot <- sum(out$ultimate) +sum(closed$ultimate)
cl.tot <- chain_ladder_computer(input.data$actual.data,width=width)
results = rbind(results,c(actual.tot,
pred.tot,
cl.tot$ultimate,
sum(out$variance),
cl.tot$process.se,
mean(out$crps_i,
na.rm=T)))
# }
}
L <- matrix(input.pbties, byrow = T,ncol=width)
diag(imx) <- 0
alpha <- c(1,rep(0,width-2))
ev=sum(alpha%*%solve(-L[1:(width-1),1:(width-1)]))
results <- data.frame(results[-1,])
colnames(results) <- c('actual.tot',
'pred.tot',
'cl.tot',
'variance',
'cl.msep',
'mcrps')
locn = "conditional-aj-reserving\\results_csv\\%s"
finame=paste0("simulation_",
'aytrend_interceptmodel_all_records_',
width,
"_",
format(Sys.time(),
"%Y_%m_%d_%H_%M"),
".csv")
fwrite(results, file=sprintf(locn,finame))