anoint.subgroups {anoint} | R Documentation |
Perform one-by-one subgroup analyses
Description
Computes all interaction effects one variable at a time.
Usage
anoint.subgroups(formula,trt,data,family="binomial",na.action=na.omit,fwer=0.05,...)
Arguments
formula |
formula for covariate model as given in |
trt |
character name of treatment assignment indicator |
data |
data.frame containing the variables of |
family |
character specifying family of |
na.action |
function, na.action to perform for handling observations with missing variables among variables in formula. Default is |
fwer |
numeric value for the desired familywise error rate, should be between 0 and 1. |
... |
additional arguments passed to |
Value
Returns a list with
- subset
indicator of the covariates included in the fitted model
- interaction
value of the of treatment-covariate interaction effect (using model with treatment-covariate product term)
- LRT
value of likelihood ratio test of treatment-covariate interaction
- lower
lower endpoints of 95 percent confidence interval for interaction parameter
- upper
upper endpoints of 95 percent confidence interval for interaction parameter
- pvalue
pvalue for 1-df chi-squared test
- include.exclude.matrix
matrix of same rows as covariates and columns as covariates with logical entries indicating which covariates (columns) were include in the fitted model (row)
- covariates
vector of covariate names as in formula
- reject
indicator of rejected hypotheses using a Bonferroni multiple testing correction such that familywise error is controlled at level
fwer
.
Author(s)
Stephanie Kovalchik <s.a.kovalchik@gmail.com>
Examples
set.seed(11903)
# NO INTERACTION CONDITION, LOGISTIC MODEL
null.interaction <- data.anoint(
alpha = c(log(.5),log(.5*.75)),
beta = log(c(1.5,2)),
gamma = rep(1,2),
mean = c(0,0),
vcov = diag(2),
type="survival", n = 500
)
head(null.interaction)
anoint.subgroups(Surv(y, event)~V1+V2,trt="trt",data=null.interaction,family="coxph")
# PROPORTIONAL INTERACTION WITH THREE COVARIATES AND BINARY OUTCOME
pim.interaction <- data.anoint(
n = 5000,
alpha = c(log(.2/.8),log(.2*.75/(1-.2*.75))),
beta = rep(log(.8),3),
gamma = rep(1.5,3),
mean = c(0,0,0),
vcov = diag(3),
type="binomial"
)
anoint.subgroups(y~V1+V2+V3,trt="trt",data=pim.interaction,family="binomial")