[R-sig-ME] Unstandardizing GLMM Regression Coefficients

[Timothy Lau]

Hello,

lme4 mentioned that I should consider rescaling some of my predictor
variables because of the scale differences:

*fit warnings:*
*Some predictor variables are on very different scales: consider rescaling*



I have since been trying to expand Ben Bolker's function:

*#
http://stackoverflow.com/questions/23642111/how-to-unscale-the-coefficients-from-an-lmer-model-fitted-with-a-scaled-respon
<http://stackoverflow.com/questions/23642111/how-to-unscale-the-coefficients-from-an-lmer-model-fitted-with-a-scaled-respon>*
*rescale.coefs <- function(beta,mu,sigma) {*
*  beta2 <- beta ## inherit names etc.*
*  beta2[-1] <- sigma[1]*beta[-1]/sigma[-1]*
*  beta2[1]  <- sigma[1]*beta[1]+mu[1]-sum(beta2[-1]*mu[-1])*
*  beta2*
*}*
*# regular model*
*m1 <- lm(Illiteracy~.,as.data.frame(state.x77))*
*b1 <- coef(m1)*
*# Make a scaled version of the data*
*ss <- scale(state.x77)*
*# Scaled coefficients:*
*m1S <- update(m1,data=as.data.frame(ss))*
*b1S <- coef(m1S)*
*# rescaling*
*icol <- which(colnames(state.x77)=="Illiteracy")*
*p.order <- c(icol,(1:ncol(state.x77))[-icol])*
*m <- colMeans(state.x77)[p.order]*
*s <- apply(state.x77,2,sd)[p.order]*
*all.equal(b1,rescale.coefs(b1S,m,s))  ## TRUE*



to work with GLMM models (e.g., count data) that can't have the outcome
standardized, only the predictors:
*# function assumes scaled data is within model; no NA's and only used
variables are in orig.data*
*unscale.coef.mer <- function(model, orig.data) {*
*  require(lme4)*
*  ran.ef <- as.character(attr(attr(model at frame,
"terms"),"predvars.random"))[-1:-2] # random effects*
*  offset <- attr(attr(model at frame, "terms"), "offset")*
*  scaled.data <- model at frame[,!names(model at frame) %in% c(ran.ef, offset)]
# the scaled covariate(s) and outcome used in the model*
*  orig.data <- orig.data[,!names(orig.data) %in% c(ran.ef, offset)]*
*  beta <- fixef(model) # the fixed effects*
*  outvar <- names(model at frame)[attr(attr(m3 at frame, "terms"), "response")]
# the outcoem var. name*
*  icol <- which(colnames(orig.data) == outvar) # outcome variable column
number*
*  p.order <- c(icol, (1:ncol(orig.data))[-icol]) # full list of variable
names with outcome 1st*
*  mu <- colMeans(x = orig.data)[p.order] # variable means with outcome 1st*
*  sigma <- apply(X = orig.data, MARGIN = 2, FUN = sd)[p.order] # variable
SDs with outcome 1st*
*  beta2 <- beta # inherit names etc.*
*  beta2[-1] <- sigma[1] * beta[-1] / sigma[-1] # the fixed effects except
intercept*
*  beta2[1]  <- sigma[1] * beta[1] + mu[1] - sum(beta2[-1] * mu[-1]) # the
FE intercept*
*  return(beta2)*
*}*


​Could someone help me ​with backing out or unscaling the coefficeints so
that m2 coef matches m1 even though m2 used the standardized data (example
data):
*# warnings due to scaling*
*m1 <- glmer(formula = awards ~ 1 + write + (1 | cid), family = poisson,
data = foreign::read.dta(file =
"http://www.ats.ucla.edu/stat/data/hsbdemo.dta
<http://www.ats.ucla.edu/stat/data/hsbdemo.dta>"))*

*​# fixed scaling but need to back out coefficients to original scale for
interpretative reasons​m​2​ <- glmer(formula = awards ~ 1 +
​scale(​write​)​ + (1 | cid), family = poisson, data =
foreign::read.dta(file = "http://www.ats.ucla.edu/stat/data/hsbdemo.dta
<http://www.ats.ucla.edu/stat/data/hsbdemo.dta>"))*



Best,
Timothy



"The will to win means nothing without the will to prepare."
http://en.wikipedia.org/wiki/Juma_Ikangaa

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