Chapter 4 Validation

In this forth component, we check the internal validity of our Sick-Sicker model before we move on to the analysis components. To internally validate the Sick-Sicker model, we compare the model-predicted output evaluated at posterior parameters against the calibration targets. This is all done in the 04_validation.R script in the analysis folder.

In section 04.2 Compute model-predicted outputs, we compute the model-predicted outputs for each sample of posterior distribution as well as for the MAP estimate. We then use the function data_summary to summarize the model-predicted posterior outputs into different summary statistics.

print.function(data_summary)
## function(data, varname, groupnames){
##   summary_func <- function(x, col){
##     c(mean = mean(x[[col]], na.rm = TRUE),
##       median = quantile(x[[col]], probs = 0.5, names = FALSE),
##       sd = sd(x[[col]], na.rm=TRUE),
##       lb = quantile(x[[col]], probs = 0.025, names = FALSE),
##       ub = quantile(x[[col]], probs = 0.975, names = FALSE))
##   }
##   data_sum <- plyr::ddply(data, groupnames, .fun = summary_func, 
##                     varname)
##   data_sum <- plyr::rename(data_sum, c("mean" = varname))
##   return(data_sum)
## }
## <bytecode: 0x7fb559f97eb0>
## <environment: namespace:darthpack>

This function is informed by three arguments, data, varname and groupnames.

The computation of the model-predicted outputs using the MAP estimate is done by inserting the v_calib_post_map data into the previously described calibration_out function. This function creates a list including the estimated values for survival, prevalence and the proportion of sicker individuals at cycles 10, 20 and 30.

In sections 04.6 Internal validation: Model-predicted outputs vs. targets, we check the internal validation by plotting the model-predicted outputs against the calibration targets (Figures 4.1-4.3). The generated plots are saved as .png files in the figs folder. These files can be used in reports without the need of re-running the code.

Survival data: Model-predicted outputs vs targets.

Figure 4.1: Survival data: Model-predicted outputs vs targets.

Prevalence data of sick individuals: Model-predicted output vs targets.

Figure 4.2: Prevalence data of sick individuals: Model-predicted output vs targets.

Proportion who are Sicker, among all those afflicted (Sick + Sicker): Model-predicted output.

Figure 4.3: Proportion who are Sicker, among all those afflicted (Sick + Sicker): Model-predicted output.