### Example: Smoking Behaviors Lesson 3 ## ### Simple line by line R code ### Needs LRStats.R and Gamma.f.R functions too ### Uses {VCD} package to plot the expected counts and residuals ### Nice R code that corresponds to SAS code and output ####################################################### smoke=matrix(c(400,416,188,1380,1823,1168), ncol=2, dimnames=list(parent=c("both", "one","neither"), child=c("yes", "no"))) smoke #### Chi-Square Independence Test result=chisq.test(smoke) result #### Let us look at the Percentage, Row Percentage and Column Percentage #### of the total observations contained in each cell. Contingency_Table=list(Frequency=smoke,Expected=result\$expected,Percentage=prop.table(smoke),RowPercentage=prop.table(smoke,1),ColPercentage=prop.table(smoke,2)) Contingency_Table #### Likelihood Ratio Test LRstats(smoke) #### a function assocstats in package vcd that computes these association measures #### along with the Pearson and LR chi-square tests. If this doesn't run then you #### need to install package 'colorspace' too. library(vcd) ## produces independence statistics assocstats(smoke) result\$expected ## expected counts; compare with the plot below ##plot an area proportional visualization of a (possibly higher-dimensional) table of expected frequencies. mosaic(smoke) #produce Pearson residuals then compare to the plot below result\$residuals ##produce an association plot indicating deviations in terms of Pearson residuals from a specified independence model in possibly high-dimensional contingency table. assoc(smoke) #### A function for computing Goodman and Kruskal's gamma adapted #### from S-Plus Manual to Accompany Agresti's Categorical Data Analysis(Laura A. Thompson 2001). Gamma.f(smoke)