## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----warning=FALSE, message=FALSE--------------------------------------------- library(clustord) library(multgee) head(arthritis) ## ----------------------------------------------------------------------------- arthritis$y <- factor(arthritis$y) ## ----------------------------------------------------------------------------- fit <- osm(y ~ baseline + sex + age, data=arthritis, subset = (time == 1)) fit ## ----------------------------------------------------------------------------- fit <- osm(y ~ baseline + sex + age, data=arthritis, subset = (time == 1), control=list(maxit=5000)) fit ## ----------------------------------------------------------------------------- summary(fit) ## ----------------------------------------------------------------------------- arthritis$y_merged <- as.numeric(as.character(arthritis$y)) arthritis$y_merged[arthritis$y_merged %in% c(2,3)] <- 2.5 arthritis$y_merged <- factor(arthritis$y_merged) fit_merged <- osm(y_merged ~ baseline + sex + age, data=arthritis, subset = (time == 1), control = list(maxit = 5000)) summary(fit_merged) ## ----------------------------------------------------------------------------- arthritis$y_merged2 <- as.numeric(as.character(arthritis$y_merged)) arthritis$y_merged2[arthritis$y_merged2 %in% c(4,5)] <- 4.5 arthritis$y_merged2 <- factor(arthritis$y_merged2) fit_merged2 <- osm(y_merged2 ~ baseline + sex + age, data=arthritis, subset = (time == 1), control = list(maxit = 5000)) fit_merged2 ## ----eval=FALSE--------------------------------------------------------------- # remotes::install_github("lfmcmillan/effects") ## ----eval=FALSE--------------------------------------------------------------- # fit1 <- osm(y ~ x + z, data=df) # plot(Effect(focal.predictors = c("x"), fit1)) ## ----eval=FALSE--------------------------------------------------------------- # detach("package:effects") # remotes::install_github("lfmcmillan/effects") ## ----eval=FALSE--------------------------------------------------------------- # library(effects) # plot(Effect(focal.predictors = c("baseline"), fit))