forest.subsets: Subsets forest plot for proportional interactions models

Description

Plot of interaction effects for all possible proportional interactions models.

Usage

forest.subsets(object, index = 1:(min(length(object$interaction),  30)), labels = NULL, exclude.fill = "white", include.fill = "grey30",  signif.fill = "red", percent.inner = 0.9, xlimits = NULL,  legend = TRUE, subgroup.text = NULL, subgroup.axis = NULL,  subgroup.title = "Included Covariates",  effects.text = NULL, effects.axis = NULL, confint = TRUE,  segments.gpar = NULL, subgroup = FALSE)

Arguments

object

result of pim.subsets

index

vector indicating which subset models to include in plot, maximum of 30 of the best subsets if not specified.

labels

vector of names for subgroups. If NULL, covariates of pim.subsets is used.

exclude.fill

color for grid squares of excluded covariates

include.fill

color for grid squares of included covariates

signif.fill

color for plot circles indicating multiplicity-corrected significance

percent.inner

percentage of graphic device window for plot region

xlimits

vector of two elements indicating minimum and maximum value for effects plot. Values and confidence intervals outside xlimits will be clipped.

legend

logical value indicating whether legend for significant values should be included

subgroup.text

gpar list for modifying title of subgroup grid

subgroup.axis

gpar list for modifying text of subgroup grid labels

subgroup.title

character for title over inclusion/exclusion grid

effects.text

gpar list for modifying title of effects plot

effects.axis

gpar list for modifying text of effects plot axis

confint

logical indicating whether to include 95 percent confidence intervals on effects plot

segments.gpar

gpar list for rendering of confidence interval segments

subgroup

logical indicator of whether fitted object is the result of anoint.subgroups

Value

Returns a plot of the results of all subsets of proportional interactions models. On the lefthand side we plot a grid describing the subsets models. This is a grid showing the included and exclude covariates of each proportional interactions model. Each row corresponds to a particular model. Colored squares in each row indicate the covariates given a proportional interaction effect, while unfilled (exclude.fill) indicate covariates left out of the model. The righthand side shows the interaction effect estimates (effects) for the corresponding subset model.

Details

The significance level is the multiplicity corrected criterion with fwer control as specified by pim.subsets.

Examples

Run this code


set.seed(11903)

# NO INTERACTION CONDITION, LOGISTIC MODEL
# SUPPOSE 5 HYPOTHESIZED EFFECT MODIFIERS

null.interaction <- data.anoint(
                             alpha = c(log(.5),log(.5*.75)),
                             beta = log(rep(1.5,5)),
                             gamma = rep(1,5),
                             mean = rep(0,5),
                             vcov = diag(5),
                             type="survival", n = 500
                             )

head(null.interaction)

fit <- pim.subsets(Surv(y, event)~V1+V2+V3+V4+V5,trt="trt",
		data=null.interaction,family="coxph")

forest.subsets(fit)