## ---- warning=FALSE, message=FALSE, error=FALSE, comment=FALSE, echo=FALSE----
# Set knitr options to not evaluate anything
knitr::opts_chunk$set(eval=FALSE, warning=FALSE, message=FALSE, error=FALSE, comment=FALSE)
## ------------------------------------------------------------------------
# # Set the correct directory
# #setwd("~/Documents/Projekte/epiet")
#
# # Initalize the epiet package
# library(epiet)
#
# # Load data
# load(campy)
## ------------------------------------------------------------------------
# str(campy)
## ------------------------------------------------------------------------
# campy$diluted <- ifelse(campy$concentrated==1 | campy$powder==1, 1, 0)
#
# # stata commands
# # generate diluted = 1 if concentrated==1 | powder==1
# # replace diluted = 0 if concentrated==0 & powder==0
## ------------------------------------------------------------------------
# tabulate case
# bysort case: summarize age, detail
# To test whether the age distribution is normal among both cases and controls, you can run:
# bysort case: swilk age //Shapiro-Wilk test1 for both cases and controls swilk age if case==1 //Shapiro-Wilk test for cases
# swilk age if case==0 //Shapiro-Wilk test for controls
# You can also visualise the distribution of age among cases and controls:
# histogram age if case==1, frequency
# histogram age if case==0, frequency
# Age does not appear to be very normally distributed.
# Now that you are absolutely sure that the hypothesis of normality in the variable age is not really the case, you choose to run Wilcoxon’s ranksum test2:
# ranksum age, by(case)
# If you had gone for the t-test, the command would have been:
# ttest age, by(case)
#
## ------------------------------------------------------------------------
# plotEpicurve()
## ------------------------------------------------------------------------
# # cc case breastfeeding
# # cc case dishwasher
# # cc case supply
# # cctable case supply
# # cctable case tap-diluted
# # cctable case tap-diluted, or
# # the latter command runs cctable for all variables between tap and diluted in the dataset. the option or in the cctable command sorts the results by the odds ratio.
# # Another way to see whether there is an association between two dichotomous variables is, of course, the χ2 test. This does not give you the odds ratio, though.
# # tab case bottled, chi2
# # Adding the options col or row, which would provide you the percentages by column or row
# # respectively, can help you identify the direction of the association.
# # The results (odds ratios and 95% confidence intervals) for the univariate analysis are shown in Table 5 on the next page.
#
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