# Fits random effects meta-analysis models including robust models Robust generalized linear models (GLM) using a mixture method, as described in Beath (2018) . package ## The robmixglm function This is the main function that allows fitting the models. The robmixglm objects may be tested for outliers using outlierTest. The results of test.outliers may also be plotted. ## Examples ```r # for the following cores is set to 1 to satisfy the CRAN testing requirements # removing will reduce the time taken depending on number of cores available # animal brain vs body weight library(MASS) data(Animals) Animals$logbrain <- log(Animals$brain) Animals$logbody <- log(Animals$body) lm1 <- lm(logbrain~logbody, data = Animals) lm2 <- robmixglm(logbrain~logbody, data = Animals, cores = 1) plot(Animals$logbody, Animals$logbrain) abline(lm1, col = "red") abline(lm2, col = "green") plot(outlierProbs(lm2)) outlierTest(lm2, cores = 1) # Forbes data on relationship between atmospheric pressure and boiling point of water library(MASS) data(forbes) forbes.robustmix <- robmixglm(100*log10(pres)~bp, data = MASS::forbes, cores = 1) summary(forbes.robustmix) plot(outlierProbs(forbes.robustmix)) outlierTest(forbes.robustmix, cores = 1) # diabetes diabdata.robustmix <- robmixglm(glyhb~age+gender+bmi+waisthip+frame, data = diabdata, cores = 1) summary(diabdata.robustmix) # this will take about 5-10 minutes diabdata.step <- step(diabdata.robustmix, glyhb~age+gender+bmi+waisthip+frame) summary(diabdata.step) plot(outlierProbs(diabdata.step)) outlierTest(diabdata.step, cores = 1) # Hawkins' data library(forward) data(hawkins) hawkins.robustmix <- robmixglm(y~x1+x2+x3+x4+x5+x6+x7+x8, cores = 1, data=hawkins) summary(hawkins.robustmix) plot(outlierProbs(hawkins.robustmix)) outlierTest(hawkins.robustmix, cores = 1) # carrot damage library(robustbase) data(carrots) carrots.robustmix <- robmixglm(cbind(success, total-success)~logdose+factor(block), family = "binomial", data = carrots, cores = 1) summary(carrots.robustmix) plot(outlierProbs(carrots.robustmix)) outlierTest(carrots.robustmix, cores = 1) # train derailment library(forward) data(derailme) derailme$cYear <- derailme$Year-mean(derailme$Year) derailme$TrainKm100 <- derailme$TrainKm*100.0 derailme.robustmix <- robmixglm(y~cYear+factor(Type), offset = log(TrainKm100), family = "truncpoisson", quadpoints = 51, data = derailme, cores = 1) summary(derailme.robustmix) plot(outlierProbs(derailme.robustmix)) outlierTest(derailme.robustmix, cores = 1) # hospital costs hospcosts.robustmix <- robmixglm(costs~adm+age+dest+ins+loglos+sex, family = "gamma", data = hospcosts, cores = 1) summary(hospcosts.robustmix) plot(outlierProbs(hospcosts.robustmix)) outlierTest(hospcosts.robustmix, cores = 1) ``` ## Author(s) Ken Beath ## References Beath, K. J. A mixture-based approach to robust analysis of generalised linear models, Journal of Applied Statistics, 45(12), 2256-2268 (2018) DOI: 10.1080/02664763.2017.1414164

robmixglm-package