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Suggestion: predicted effect size corrected for unbalanced covariates #225

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Suggestion: predicted effect size corrected for unbalanced covariates #225

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25 changes: 21 additions & 4 deletions R/diffMeth.R
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Original file line number Diff line number Diff line change
Expand Up @@ -201,8 +201,8 @@ QValuesfun<-function(rawp,pi0)

logReg<-function(counts, vars, treatment,
overdispersion=c("none","MN","shrinkMN"),
effect=c("wmean","mean","predicted"), parShrinkMN=list(),
test=c("F","Chisq")){
effect=c("wmean","mean","predicted","predicted2"),
parShrinkMN=list(), test=c("F","Chisq")){

# correct counts and treatment factor for NAs in counts
treatment<-ifelse(is.na(counts),NA,treatment)[1:length(treatment)]
Expand Down Expand Up @@ -240,13 +240,13 @@ logReg<-function(counts, vars, treatment,
phi=switch(overdispersion,
none=1,
MN={
mu=fitted(obj)
#mu=fitted(obj) # already calculated above
uresids <- (y-w*mu)/sqrt(mu*(w-w*mu)) # pearson residuals
phi=sum( uresids^2 )/(length(w)-nprm) # correction factor
ifelse(phi>1,phi,1)
},
shrinkMN={
mu=fitted(obj)
#mu=fitted(obj) # already calculated above
uresids <- (y-w*mu)/sqrt(mu*(w-w*mu)) # pearson residuals
phi=sum( uresids^2 )/(length(w)-nprm) # correction factor

Expand Down Expand Up @@ -300,6 +300,23 @@ logReg<-function(counts, vars, treatment,
},
predicted={
tapply(mu, treatment, FUN = mean)
},
predicted2={
# Add effect estimate calculated on balanced set of covariates.
# Use sets of covariates seen in any treatment group for each group:
covars = vars[,-1,drop=FALSE]
# optionally, remove duplicate covariate sets:
#covars = unique(covars)
# if treatment is a factor, ii will be a factor with same levels:
ii = sort(unique(treatment))
newdata = cbind(treatment=ii[1], covars)
for(i in ii[-1]) newdata = rbind(newdata, cbind(treatment=i, covars))
# predict:
# (this should take care of factor covariates as well)
newMat = model.matrix(formula, as.data.frame(newdata))
eta <- as.matrix(newMat) %*% as.matrix(obj$coef)
mu_pred = as.vector(obj$family$linkinv(eta))
tapply(mu_pred, newdata$treatment, FUN=mean)
}
)

Expand Down