Dear Erin, What you characterize as "snaky" might also be called bimodal and short-tailed. Try, e.g., plot(density(residuals(dog1.aov))) to see the bimodality more clearly. A Box-Cox transformation can correct skewness, but won't help here.
I hope this helps, John ------------------------------ John Fox, Professor Department of Sociology McMaster University Hamilton, Ontario, Canada web: socserv.mcmaster.ca/jfox > -----Original Message----- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On > Behalf Of Erin Hodgess > Sent: December-02-08 4:28 PM > To: r-help@r-project.org > Subject: [R] QQ plots and boxcox > > Dear R People: > > In the DASL library, there is a story about hot dogs. > > Here are the data: > Beef 186 495 > Beef 181 477 > Beef 176 425 > Beef 149 322 > Beef 184 482 > Beef 190 587 > Beef 158 370 > Beef 139 322 > Beef 175 479 > Beef 148 375 > Beef 152 330 > Beef 111 300 > Beef 141 386 > Beef 153 401 > Beef 190 645 > Beef 157 440 > Beef 131 317 > Beef 149 319 > Beef 135 298 > Beef 132 253 > Meat 173 458 > Meat 191 506 > Meat 182 473 > Meat 190 545 > Meat 172 496 > Meat 147 360 > Meat 146 387 > Meat 139 386 > Meat 175 507 > Meat 136 393 > Meat 179 405 > Meat 153 372 > Meat 107 144 > Meat 195 511 > Meat 135 405 > Meat 140 428 > Meat 138 339 > Poultry 129 430 > Poultry 132 375 > Poultry 102 396 > Poultry 106 383 > Poultry 94 387 > Poultry 102 542 > Poultry 87 359 > Poultry 99 357 > Poultry 107 528 > Poultry 113 513 > Poultry 135 426 > Poultry 142 513 > Poultry 86 358 > Poultry 143 581 > Poultry 152 588 > Poultry 146 522 > Poultry 144 545 > > > Here is my work: > > dog1.df <- read.table(file="dog1.dat",as.is=F,header=F, > + col.names=c("type","cal","sodium")) > > dog1.aov <- aov(cal~type,data=dog1.df) > > summary(dog1.aov) > Df Sum Sq Mean Sq F value Pr(>F) > type 2 17692.2 8846.1 16.074 3.862e-06 *** > Residuals 51 28067.1 550.3 > --- > Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > TukeyHSD(dog1.aov) > Tukey multiple comparisons of means > 95% family-wise confidence level > > Fit: aov(formula = cal ~ type, data = dog1.df) > > $type > diff lwr upr p adj > Meat-Beef 1.855882 -16.82550 20.53726 0.9688129 > Poultry-Beef -38.085294 -56.76667 -19.40391 0.0000277 > Poultry-Meat -39.941176 -59.36515 -20.51720 0.0000239 > > > plot(dog1.aov) > > When I look at the QQ plot, it's quite "snaky". > I thought that doing a transformation (based on boxcox) would help > with the snakiness. > > However, when I re-run with the transformation, it's still snaky. > > Any suggestions, please? > > Thanks, > Sincerely, > Erin > > > -- > Erin Hodgess > Associate Professor > Department of Computer and Mathematical Sciences > University of Houston - Downtown > mailto: [EMAIL PROTECTED] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
