ggplot_fic: Plot focused model comparison statistics: ggplot2 method
In fic: Focused Information Criteria for Model Comparison

Description Usage Arguments See Also Examples

This only works if the focus estimates are available. The focus estimates are plotted against the root MSE. One plot is made for each covariate value defining different focuses. If the wide model estimate is available, this is illustrated as a solid line on the plot, and if the narrow model estimate is available, this is showm as a dashed line.

1 2	ggplot_fic(x, ci = TRUE, legend = TRUE, ylab = NULL, xlab = NULL, xlim = NULL, ylim = NULL)

`x`	Output from `fic`.
`ci`	Plot interval estimates? (`TRUE` or `FALSE`). These are calculated as plus / minus twice the standard error of the submodel focus under the wide model. These are rough estimates of uncertainty intended to illustrate the bias-variance tradeoff, and exclude any uncertainty associated with the choice between models.
`legend`	Show a legend, identifying a) the line types for the wide and narrow models b) the names of the terms of the wide model. This is used when the `inds` object supplied to `fic` was constructed by `all_inds`, so has row names made out of a string of 0s and 1s that identify the terms included in the submodel. These strings are plotted as text labels against the estimate for each submodel. The legend identifies which 0s and 1s correspond to which model terms.
`ylab`	y-axis label.
`xlab`	x-axis label.
`xlim`	x-axis limits (pair of numbers)
`ylim`	y-axis limits

plot.fic, summary.fic

## Example from the main vignette, see there for more details

wide.glm <- glm(low ~ lwtkg + age + smoke + ht + ui + smokeage + smokeui,
                data=birthwt, family=binomial)
vals.smoke <-    c(1, 58.24, 22.95, 1, 0, 0, 22.95, 0)
vals.nonsmoke <- c(1, 59.50, 23.43, 0, 0, 0, 0, 0)
X <- rbind("Smokers" = vals.smoke, "Non-smokers" = vals.nonsmoke)
inds0 <- c(1,1,0,0,0,0,0,0)
combs <- all_inds(wide.glm, inds0)
ficres <- fic(wide = wide.glm, inds = combs, inds0 = inds0,
              focus = prob_logistic, X = X)
ggplot_fic(ficres)
summary(ficres)