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

In survHE: Survival Analysis in Health Economic Evaluation

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 = "aic", scale = "absolute", stacked = FALSE)

Arguments

...	Optional inputs. Must include at least one survHE object.
type	should the AIC, the BIC or the DIC plotted? (values = "aic", "bic" or "dic")
scale	If scale='absolute' (default), then plot the absolute value of the *IC. If scale='relative' then plot a rescaled version taking the percentage increase in the *IC in comparison with the best-fitting model
stacked	Should the bars be stacked and grouped by survHE object? (default=F)

Details

Something will go here

Value

A plot with the relevant model fitting statistics

Author(s)

Gianluca Baio

References

G Baio (2019). survHE: Survival analysis for health economic evaluation and cost-effectiveness modelling. Journal of Statistical Software (2020). vol 95, 14, 1-47. doi:10.18637/jss.v095.i14

See Also

fit.models

Examples

## Not run:  
data(bc)

mle = fit.models(formula=Surv(recyrs,censrec)~group,data=bc,
    distr=c("exp","wei","lno"),method="mle")
model.fit.plot(mle)

## End(Not run)

survHE documentation built on March 31, 2023, 11:37 p.m.