## Not run:
# ## variable names in the input data.frame aeanonym
# ## RAND treatment as randomized
# ## PREF adverse event symptom name
# ## SN number of patients in treatment group
# ## SAE number of patients in each group for whom the event PREF was observed
# ## OrgSys Organ System
# ##
# ## Input sort order is PREF/RAND
#
# data(aeanonym)
# head(aeanonym)
#
# ## variable names are hard-wired in the program
# ## names(aeanonym) <- c("RAND", "PREF", "SAE", "SN", "OrgSys")
#
#
# ## Calculate log relative risk and confidence intervals (95
# ## AElogrelrisk sets the sort order for PREF to match the relative risk.
# aeanonymr <- AElogrelrisk(aeanonym) ## PREF sorted by relative risk
# head(aeanonymr)
# class(aeanonymr$PREF)
# levels(aeanonymr$PREF)
#
# AEdotplot(aeanonym)
# \dontrun{
# AEdotplot(aeanonym, sort=FALSE)
# }
# AEdotplot(aeanonym, conditionVariable=aeanonym$OrgSys)
#
# aefake <- rbind(cbind(aeanonym, group="ABC"), cbind(aeanonym, group="DEF"))
# aefake$SAE[67:132] <- sample(aefake$SAE[67:132])
# aefake$OrgSys.group <- with(aefake, interaction(OrgSys, group))
#
# ## fake 2
# KEEP <- aefake$OrgSys %in% c("GI","Resp")
#
# AEfakeGR <- AEdotplot(aefake[KEEP,], conditionVariable=aefake$OrgSys.group[KEEP],
# sub=list("ABC and DEF have different sort orders for PREF", cex=.7))
# AEfakeGR ## ABC and DEF have different sort orders for PREF
#
# AEfakeGR1 <- AEdotplot(aefake[KEEP & (1:132) <= 66,],
# conditionVariable=aefake$OrgSys.group[KEEP & (1:132) <= 66])
# AEfakeGR2 <- AEdotplot(aefake[KEEP & (1:132) >= 67,],
# conditionVariable=aefake$OrgSys.group[KEEP & (1:132) >= 67])
#
# AEfakeGR1
# AEfakeGR2
#
# AEfakeMatched <- AEmatchSortorder(AEfakeGR1, AEfakeGR2)
# update(do.call(c, AEfakeMatched),
# main="ABC sorted by Relative Risk; DEF matches ABC order")
# ## End(Not run)
## Please see ?AEdotplot for examples using the formula method
##
## Many more examples are in demo("AEdotplotManyExamples")
Run the code above in your browser using DataLab