Booklet of Code and Output for STAD29/STA 1007 Midterm Exam
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1 Booklet of Code and Output for STAD29/STA 1007 Midterm Exam
2 List of Figures in this document by page: List of Figures 1 NBA attendance data Regression model for NBA attendances Residual plots for NBA attendances Ozone layer data Ozone layer data processing part Ozone layer data processing part Ozone layer scatterplot New Zealand bird data Analysis 1 of NZ bird data Analysis 2 of NZ bird data Analysis 3 of NZ bird data Space shuttle data (summary) Space shuttle part Space shuttle part Space shuttle part Predictions Prediction Primary biliary cirrhosis data Survival analysis part Survival analysis part Survival analysis part Survival analysis part
3 nba=read.table("nba-attendance.txt",header=t) nba team wins market.share finals attendance 1 Atlanta no Boston yes Charlotte no Chicago no Cleveland yes Dallas yes Denver no Detroit yes GoldenState no Houston no Indiana no LAClippers no LALakers yes Memphis no Miami yes Milwaukee no Minnesota no NewJersey no NewOrleans no NewYork no OklahomaCity no Orlando yes Philadelphia no Phoenix no Portland no Sacramento no SanAntonio yes Toronto no Utah no Washington no Figure 1: NBA attendance data 2
4 nba.1=lm(attendance~wins+market.share+finals,data=nba) summary(nba.1) Call: lm(formula = attendance ~ wins + market.share + finals, data = nba) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) e-10 *** wins market.share finalsyes Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2113 on 26 degrees of freedom Multiple R-squared: ,Adjusted R-squared: F-statistic: on 3 and 26 DF, p-value: Figure 2: Regression model for NBA attendances 3
5 par(mfrow=c(2,2)) plot(nba.1) Residuals vs Fitted Normal Q Q Residuals Standardized residuals Fitted values Theoretical Quantiles Standardized residuals Scale Location Standardized residuals Residuals vs Leverage 8 26 Cook's distance Fitted values Leverage par(mfrow=c(1,1)) Figure 3: Residual plots for NBA attendances 4
6 ozone=read.table("inhibit.txt",header=t) ozone inhibit.deep uvb.deep inhibit.surface uvb.surface NA NA NA NA NA NA suppressmessages(library(dplyr)) Figure 4: Ozone layer data ozone %>% mutate(inhibit=inhibit.deep,uvb=uvb.deep) %>% select(c(inhibit,uvb)) -> deep ozone %>% mutate(inhibit=inhibit.surface,uvb=uvb.surface) %>% select(c(inhibit,uvb)) -> surface Figure 5: Ozone layer data processing part 1 5
7 v=bind_rows(deep=deep,surface=surface,.id="depth") v Source: local data frame [20 x 3] depth inhibit uvb (chr) (dbl) (dbl) 1 deep deep deep deep deep deep deep deep deep deep surface surface surface surface surface surface surface surface NA NA 19 surface NA NA 20 surface NA NA Figure 6: Ozone layer data processing part 2 6
8 library(ggplot2) ggplot(v,aes(x=uvb,y=inhibit,colour=depth))+ geom_point()+geom_smooth(method="lm") Warning: Removed 3 rows containing non-finite values (stat smooth). Warning: Removed 3 rows containing missing values (geom point) depth inhibit 20 deep surface uvb Figure 7: Ozone layer scatterplot 7
9 nzbirds=read.table("nzbird.txt",header=t) str(nzbirds) 'data.frame': 67 obs. of 15 variables: $ Species: Factor w/ 67 levels "Aca_cann","Aca_flam",..: $ Status : int $ Length : int $ Mass : num $ Range : num $ Migr : int $ Insect : int $ Diet : int $ Clutch : num $ Broods : int $ Wood : int $ Upland : int $ Water : int $ Release: int $ Indiv : int Figure 8: New Zealand bird data 8
10 nzbirds.1=glm(status~length+mass+range+migr+insect+diet+ Clutch+Broods+Wood+Upland+Water+Release+Indiv, data=nzbirds,family="binomial") summary(nzbirds.1) Call: glm(formula = Status ~ Length + Mass + Range + Migr + Insect + Diet + Clutch + Broods + Wood + Upland + Water + Release + Indiv, family = "binomial", data = nzbirds) Deviance Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error z value Pr(> z ) (Intercept) Length Mass Range Migr Insect Diet Clutch Broods Wood Upland Water Release Indiv Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: on 66 degrees of freedom Residual deviance: on 53 degrees of freedom AIC: Number of Fisher Scoring iterations: 8 Figure 9: Analysis 1 of NZ bird data 9
11 nzbirds.2=update(nzbirds.1,.~.-length-range-insect-clutch -Broods-Water-Release) summary(nzbirds.2) Call: glm(formula = Status ~ Mass + Migr + Diet + Wood + Upland + Indiv, family = "binomial", data = nzbirds) Deviance Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error z value Pr(> z ) (Intercept) Mass * Migr * Diet Wood Upland * Indiv *** --- Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: on 66 degrees of freedom Residual deviance: on 60 degrees of freedom AIC: Number of Fisher Scoring iterations: 7 Figure 10: Analysis 2 of NZ bird data 10
12 anova(nzbirds.2,nzbirds.1,test="chisq") Analysis of Deviance Table Model 1: Status ~ Mass + Migr + Diet + Wood + Upland + Indiv Model 2: Status ~ Length + Mass + Range + Migr + Insect + Diet + Clutch + Broods + Wood + Upland + Water + Release + Indiv Resid. Df Resid. Dev Df Deviance Pr(>Chi) Figure 11: Analysis 3 of NZ bird data space.shuttle=read.table("space-shuttle.txt",header=t) str(space.shuttle) 'data.frame': 23 obs. of 5 variables: $ flight : int $ distress : Factor w/ 3 levels "0","1-2","3+": $ temp : int $ date : int $ z.computed.: num Figure 12: Space shuttle data (summary) library(mass) Attaching package: MASS The following object is masked from package:dplyr : select space.shuttle.1=polr(distress~temp+date,data=space.shuttle) space.shuttle.2=polr(distress~date,data=space.shuttle) space.shuttle.3=polr(distress~temp,data=space.shuttle) Figure 13: Space shuttle part 1 11
13 anova(space.shuttle.2,space.shuttle.1) Likelihood ratio tests of ordinal regression models Response: distress Model Resid. df Resid. Dev Test Df LR stat. Pr(Chi) 1 date temp + date vs Figure 14: Space shuttle part 2 anova(space.shuttle.3,space.shuttle.1) Likelihood ratio tests of ordinal regression models Response: distress Model Resid. df Resid. Dev Test Df LR stat. Pr(Chi) 1 temp temp + date vs Figure 15: Space shuttle part 3 temps=c(67,75) dates=c(8600,9300) new=expand.grid(temp=temps,date=dates) pp=predict(space.shuttle.1,new,type="p") cbind(new,pp) temp date Figure 16: Predictions 1 12
14 new=data.frame(temp=31,date=9524) pp=predict(space.shuttle.1,new,type="p") cbind(new,pp) temp date pp e e e-01 Figure 17: Prediction 2 13
15 pbc1=read.table("pbc.txt",header=t) str(pbc1) 'data.frame': 312 obs. of 9 variables: $ id : int $ days : int $ status : int $ drug : int $ age : int $ edema : num $ bilirubi: num $ albumin : num $ prothom : num Figure 18: Primary biliary cirrhosis data library(survival) attach(pbc1) y=surv(days,status==2) head(y,n=20) [1] [12] Figure 19: Survival analysis part 1 14
16 y.1=coxph(y~drug+age+edema+bilirubi+albumin+prothom) summary(y.1) Call: coxph(formula = y ~ drug + age + edema + bilirubi + albumin + prothom) n= 312, number of events= 125 coef exp(coef) se(coef) z Pr(> z ) drug e e e age 9.036e e e *** edema 8.198e e e ** bilirubi 1.160e e e e-14 *** albumin e e e e-07 *** prothom 2.658e e e *** --- Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 exp(coef) exp(-coef) lower.95 upper.95 drug age edema bilirubi albumin prothom Concordance= (se = ) Rsquare= (max possible= ) Likelihood ratio test= on 6 df, p=0 Wald test = on 6 df, p=0 Score (logrank) test = on 6 df, p=0 Figure 20: Survival analysis part 2 15
17 drugs=c(1,2) ages=c(15000,20000) edemas=1 bilirubis=3 albumins=3.5 prothoms=10.5 new=expand.grid(drug=drugs,age=ages,edema=edemas,bilirubi=bilirubis, albumin=albumins,prothom=prothoms) colours=c("red","blue","green","black") cbind(new,colours) drug age edema bilirubi albumin prothom colours red blue green black Figure 21: Survival analysis part 3 16
18 pp=survfit(y.1,new) plot(pp,col=colours) Figure 22: Survival analysis part 4 17
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