Description Usage Arguments Details Value Author(s) See Also Examples
Plot of interaction effects for all possible proportional interactions models.
1 2 3 4 5 6 7  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 
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 
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 multiplicitycorrected 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 
legend 
logical value indicating whether legend for significant values should be included 
subgroup.text 

subgroup.axis 

subgroup.title 
character for title over inclusion/exclusion grid 
effects.text 

effects.axis 

confint 
logical indicating whether to include 95 percent confidence intervals on effects plot 
segments.gpar 

subgroup 
logical indicator of whether fitted object is the result of 
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  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)

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