ch15: Print examples of chapter 15 of 'R for Dummies'. In rfordummies: Code Examples to Accompany the Book "R for Dummies"

Description

To print a listing of all examples of a chapter, use `ch15()`. To run all the examples of `ch15()`, use `example(ch15)`.

Usage

 `1` ```ch15() ```

`toc`
Other Chapters: `ch01`, `ch02`, `ch03`, `ch04`, `ch05`, `ch06`, `ch07`, `ch08`, `ch09`, `ch10`, `ch11`, `ch12`, `ch13`, `ch14`, `ch16`, `ch17`, `ch18`, `ch19`, `ch20`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142``` ```# 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') ```