check_model

check_model.R

library(magrittr)
library(tidyverse)
library(ChainLadder)

############" Fits mack AND my model in parallel to check if it's ok : 
check_model <- function(triangle){

  df <- triangle %>% 
    as.data.frame %>% 
    mutate(
      weight = cbind(rep(NA,nrow(triangle)),triangle %>% as.matrix) %>% .[,1:nrow(triangle)] %>% as.vector,
      f = value / weight,
      phi.start = 1
     ) %>% 
    drop_na


  model <- lm(data = df,formula=f~factor(dev)+0,weights=weight/phi.start)
  df %<>% mutate(estimated.f = model$fitted.values,
                       rez2 = weight * (f - estimated.f)^2)
  
  model2 <- df %>% 
    filter(dev != nrow(triangle)) %>%
    {lm(data =., formula = rez2 ~ factor(dev))}
  
  df %<>% mutate(
    estimated.phi = model2$fitted.values %>% {c(.,min(.[length(.)],.[length(.)-1],.[length(.)]^2/.[length(.)-1]))}
  )

  model3 <- lm(data = df,formula=f~factor(dev)+0,weights=weight/estimated.phi)
  
  model4 <- MackChainLadder(triangle,est.sigma="Mack")
  resultat <- 
    df %>% 
      group_by(dev) %>% 
      summarise(phi=mean(estimated.phi)) %>% 
      mutate(
        mack = model4$sigma^2,
        degree.of.freedom = max(dev) - dev, # correction for degree of freedom
        phi = (phi * (degree.of.freedom+1)/(degree.of.freedom)) %>% .[1:(length(.)-1)] %>% {c(.,min(.[length(.)],.[length(.)-1],.[length(.)]^2/.[length(.)-1]))},
        f = model3$coefficients[1:(dim(triangle)[1]-1)],
        f.cl = model4$f[1:(dim(triangle)[1]-1)]
        ) %>%
      select(f,f.cl,phi,mack) %>%
      as.data.frame

  return(list(mod.f = model, mod.phi = model2, mod.final = model3,rez = resultat))
}


data(ABC)
check_model(ABC)