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| #read in data after downloading the csv file | ||
| #NB edit directory to match file location on your computer | ||
| ppnLR <- read.csv("~/tempi/BT2012/ppnLR.csv", sep=";") | ||
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| #define variables and put into a data frame | ||
| sex<-as.factor(ppnLR$sex) | ||
| height<-ppnLR$height | ||
| ppn<-as.factor(ppnLR$ppn) | ||
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| ppnData = data.frame(ppn,height,sex) | ||
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| #call graphics library (install first if not already present) | ||
| library(ggplot2) | ||
| #investigate distribution of height using histogram with 20 bins | ||
| ggplot(ppnData,aes(x=height))+geom_histogram(bins=20) | ||
| #split the histogram by sex | ||
| ggplot(ppnData,aes(x=height))+geom_histogram(aes(color = sex, fill = sex), position = "identity",bins=15, alpha = 0.4) | ||
| #investigate frequency of ppn for females and males | ||
| #create first a frequency table by combining the two variables sex and ppn | ||
| sex_ppn <- table(ppnData$sex,ppnData$ppn) | ||
| sex_ppn | ||
| prop.table(sex_ppn,1) | ||
| prop.table(sex_ppn,2) | ||
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| #fit logistic regression model to explain ppn risk as a function of height | ||
| ppnLR <- glm(ppn ~ height, family = "binomial") | ||
| #examine model fitting results | ||
| summary(ppnLR) | ||
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| #fit another logistic regression model to compare ppn risk between females and males | ||
| ppnLR2 <- glm(ppn ~ sex, family = "binomial") | ||
| #examine model fitting results (no significant difference associated with sex) | ||
| summary(ppnLR2) | ||
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| #include now both height and sex as an explanatory variables in an additive model | ||
| ppnLR3 <- glm(ppn ~ height+sex, family = "binomial") | ||
| summary(ppnLR3) | ||
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| #examine last what happens if an interaction effect between sex and height is included in the model | ||
| ppnLR4 <- glm(ppn ~ height+sex+height:sex, family = "binomial") | ||
| summary(ppnLR4) | ||
| #note how inclusion of the additional term changes the estimate of height effect and the baseline (Intercept) | ||
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| #use boxplots to compare height distributions between diagnosed vs non-diagnosed and females/males | ||
| ggplot(ppnData,aes(x=ppn,y=height,group=ppn))+geom_boxplot(aes(fill=sex))+facet_grid(. ~ sex) |
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