model.fit.plot: Graphical representation of the measures of model fitting...

In expertsurv: Incorporate Expert Opinion with Parametric Survival Models

Graphical representation of the measures of model fitting based on Information Criteria

Description

Plots a summary of the model fit for all the models fitted.

Usage

model.fit.plot(..., type = "dic")

Arguments

...	Optional inputs. Must include an expertsurv object.
type	should the DIC, WAIC, PML be plotted (AIC, BIC also allowed but only valid for frequentist approach).

Value

A plot with the relevant model fitting statistics plotted in order of fit.

Examples

require("dplyr")
param_expert_example1 <- list()
param_expert_example1[[1]] <- data.frame(dist = c("norm"),
                                         wi = c(1), # Ensure Weights sum to 1
                                         param1 = c(0.1),
                                         param2 = c(0.05),
                                         param3 = c(NA))
timepoint_expert <- 14 # Expert opinion at t = 14


data2 <- expertsurv::data %>% rename(status = censored) %>% 
mutate(time2 = ifelse(time > 10, 10, time),
status2 = ifelse(time> 10, 0, status))
example1  <- fit.models.expert(formula=Surv(time2,status2)~1,data=data2,
                              distr=c("wei", "gomp"),
                              method="mle",
                              pool_type = "linear pool", 
                              opinion_type = "survival",
                              times_expert = timepoint_expert, 
                              param_expert = param_expert_example1)


model.fit.plot(example1, type = "aic")

expertsurv documentation built on Oct. 5, 2023, 5:09 p.m.