# NOT RUN {
library(emmeans)
library(lme4)
data(datafake)
#Simple lm model
mod=lm(Petal.Width~Species,data=iris)
raw.lsm=emmeans(mod,~Species)
report.lsmeans(raw.lsm)
# You can display the Statistics in columns
report.lsmeans(raw.lsm,transpose=TRUE)
# In case of just one intercept
mod=glm(Species~1,data=iris,family=binomial)
raw.lsm=emmeans(mod,~1)
report.lsmeans(raw.lsm)
# Display statistics in columns
report.lsmeans(raw.lsm,transpose=TRUE)
#Mixed model example using lme4
mod=lmer(y_numeric~GROUP+TIMEPOINT+GROUP*TIMEPOINT+(1|SUBJID),data=datafake)
raw.lsm=emmeans(mod,~GROUP|TIMEPOINT)
report.lsmeans(lsm=raw.lsm,at="TIMEPOINT")
# Display statistics in columns
report.lsmeans(lsm=raw.lsm,at="TIMEPOINT",transpose=TRUE)
# LM model with specific contrast
warp.lm <- lm(breaks ~ wool+tension+wool:tension, data = warpbreaks)
warp.emm <- emmeans(warp.lm, ~ tension | wool)
contr=contrast(warp.emm, "trt.vs.ctrl", ref = "M")
report.lsmeans(lsm=contr,at="wool")
# Display statistics in columns
report.lsmeans(lsm=contr,at="wool",transpose=TRUE)
# Cox model
library(survival)
data(time_to_cure)
fit <- coxph(Surv(time, status) ~ Group, data = time_to_cure)
em=emmeans(fit,~Group,type="response")
pairs=pairs(em,adjust="none",exclude="Untreated")
pairs
report.lsmeans(pairs)
# Display statistics in columns
report.lsmeans(pairs,transpose=TRUE)
# }
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