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ch15 | R Documentation |
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if (interactive()) { # Chapter 15 # Testing Differences and Relations # Taking a Closer Look at Distributions ## Observing beavers str(beaver2) ## Testing normality graphically library(lattice) histogram(~temp | factor(activ), data=beaver2) ## Using quantile plots ### Comparing two samples qqplot(beaver2$temp[beaver2$activ==1], beaver2$temp[beaver2$activ==0]) ### Using a QQ plot to check for normality qqnorm( beaver2$temp[beaver2$activ==0], main='Inactive') qqline( beaver2$temp[beaver2$activ==0] ) ## Testing normality in a formal way shapiro.test(beaver2$temp) result <- shapiro.test(beaver2$temp) result$p.value with(beaver2, tapply(temp, activ, shapiro.test)) # Comparing Two Samples ## Testing differences ### Carrying out a t-test t.test(temp ~ activ, data=beaver2) activetemp <- beaver2$temp[beaver2$activ==1] inactivetemp <- beaver2$temp[beaver2$activ==0] t.test(activetemp, inactivetemp) ### Dropping assumptions wilcox.test(temp ~ activ, data=beaver2) ### Testing direction ## Comparing paired data t.test(extra ~ group, data=sleep, paired=TRUE) # Testing Counts and Proportions ## Checking out proportions survivors <- matrix(c(1781,1443,135,47), ncol=2) colnames(survivors) <- c('survived','died') rownames(survivors) <- c('no seat belt','seat belt') survivors result.prop <- prop.test(survivors) result.prop ## Analyzing tables ### Testing contingency of tables chisq.test(survivors) ### Testing tables with more than two columns str(HairEyeColor) HairEyeMargin <- margin.table(HairEyeColor, margin=c(1,2)) HairEyeMargin chisq.test(HairEyeMargin) ## Extracting test results str(result) t.test(temp ~ activ, data=beaver2)$p.value # Working with Models ## Analyzing variances str(InsectSprays) ### Building the model AOVModel <- aov(count ~ spray, data=InsectSprays) ### Looking at the object AOVModel ## Evaluating the differences summary(AOVModel) ### Checking the model tables model.tables(AOVModel, type='effects') ### Looking at the individual differences Comparisons <- TukeyHSD(AOVModel) Comparisons$spray['D-C',] ### Plotting the differences plot(Comparisons, las=1) ## Modeling linear relations ### Building a linear model Model <- lm(mpg ~ wt, data=mtcars) ### Extracting information from the model coef.Model <- coef(Model) coef.Model plot(mpg ~ wt, data = mtcars) abline(a=coef.Model[1], b=coef.Model[2]) ## Evaluating linear models ### Summarizing the model Model.summary <- summary(Model) Model.summary coef(Model.summary) ### Testing the impact of model terms Model.anova <- anova(Model) Model.anova Model.anova['wt','Pr(>F)'] ## Predicting new values ### Getting the values new.cars <- data.frame(wt=c(1.7, 2.4, 3.6)) predict(Model, newdata=new.cars) ### Having confidence in your predictions predict(Model, newdata=new.cars, interval='confidence') predict(Model,newdata=new.cars, interval='prediction') }
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