search for: default_prob

...t;AA","A","BBB","BB","B","CCC","AAA","AA","A","BBB","BB","B","CCC","AAA","AA","A","BBB","BB","B","CCC"), default_probability = c(0.0027,0.0029,0.0031,0.0034,0.0037,0.004,0.0043,0.0025,0.0024,0.0024,0.0023,0.0022,0.0021,0.0021,0.003,0.0031,0.0032,0.0033,0.0034,0.0035,0.0036)) n_name = as.character(unique(df$basel_asset_class)) IPD = df$default_probability internal_category = c(1:7) # Total types of ratings...

...ers 2 and 3 respectively. I understand it is like a spoon feeding, but I have really taken the efforts to find the solution and invested myself on whole of Saturday and Sunday and tried different options but just couldn't find the way. I will be grateful if someone guides me. I understand the default_probability should be between 0 and 1. However, just for disply checking purpose I have taken different figures. Regards Akshata # Here is the part of my code which generates the graphs - library(plyr) internal_scale = c("AAA","AA","A","BBB","BB",&...

...t_lnODF <- sum(log(ODF)) tot_k2 <- sum(k^2) slope <- exp((n * tot_klnODF - tot_k * tot_lnODF)/(n * tot_k2 - tot_k^2)) intercept <- exp((tot_lnODF - log(slope)* tot_k)/n) IPD <- intercept * slope^k return(data.frame(ratings = ratings, default_probability = round(IPD, digits = 4))) } result = DP(k = df$k, ODF = df$default_frequency, ratings = df$ratings) # ________________________________________________________________________________________ The above code fetches me following result. However, I am dealing with only one set of data here...