rubinRules2: Assessing the three Rubin Rules.

In Ngendahimana/sensitivityR5: This package supports implementation of sensitivity analyisis using objects from Match, MatchIt and bmatch

Description

This function allows you to assess how sensitive your results are to unmeasured variable.

Usage

rubinRules2(data, Treatment, matchscore = "ps", covlist)

Arguments

data	data set to be used.
Treatment	A variables defining exposure group.
matchscore	Variable containing matching distance.Default is propensity score.
covlist	list of variables to be balanced. Note: All variable should be of numeric type.

Examples

data(toy)
psmodel <- glm(treated ~ covA + covB + covC + covD + covE + covF + Asqr + BC + BD, family=binomial(), data=toy)

toy$ps <- psmodel$fitted
toy$linps <- psmodel$linear.predictors

covlist1=c('covA', 'covB', 'covC', 'covD', 'covE', 'covF.Middle', 'covF.High', 'Asqr','BC', 'BD')

rubinRules(data=toy,Treatment='treated',covlist=covlist1)

Ngendahimana/sensitivityR5 documentation built on June 24, 2020, 4:09 a.m.