forest.subsets: Subsets forest plot for proportional interactions models
In anoint: Analysis of Interactions

Description Usage Arguments Details Value Author(s) See Also Examples

Plot of interaction effects for all possible proportional interactions models.

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)

`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`

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

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.

Stephanie Kovalchik <s.a.kovalchik@gmail.com>

pim.subsets

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)