pim.subsets {anoint} | R Documentation |
Perform all subsets proportional interactions modeling
Description
Computes all possible proportional interactions model among p
covariates.
Usage
pim.subsets(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 |
Details
Under the proportional interaction model the coef
of the main covariate effects in the control arm are multiplied by the interaction
effect to get the covariate effects for the treatment group.
Value
Returns a list with
- subset
indicator of the covariates included in the fitted model
- interaction
value of the interaction effect of the proportional interaction model, see details
- LRT
value of likelihood ratio test of proportional 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 subsets and columns as covariates with logical entries indicating which covariates (columns) were include in which subset model (row)
- covariates
vector of covariate names as in formula
- reject
indicator of rejected hypotheses using a multiple testing correction such that familywise error is controlled at level
fwer
.
Author(s)
Stephanie Kovalchik <s.a.kovalchik@gmail.com>
References
Follmann DA, Proschan MA. A multivariate test of interaction for use in clinical trials. Biometrics 1999; 55(4):1151-1155
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)
pim.subsets(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"
)
pim.subsets(y~V1+V2+V3,trt="trt",data=pim.interaction,family="binomial")