... In addition, the following may also be informative. > f <- paste("day", 1:3) > contrasts(ordered(f)) .L .Q [1,] -7.071068e-01 0.4082483 [2,] -7.850462e-17 -0.8164966 [3,] 7.071068e-01 0.4082483
> contrasts(factor(f)) day 2 day 3 day 1 0 0 day 2 1 0 day 3 0 1 Cheers, Bert On Tue, Nov 15, 2011 at 8:32 AM, Bert Gunter <bgun...@gene.com> wrote: > Ordered factors use orthogonal polynomial contrasts by default. The .L and > .Q stand for the linear and quadratic terms. Unordered factors use > "treatment" contrasts although (they're actually not contrasts), that are > interpreted as you described. > > If you do not know what this means, you need to do some reading on linear > models/multiple regression. Try posting on > http://stats.stackexchange.com/ or, as always, consult your local > statistician for help. V&R's MASS book also contains a useful but terse > discussion on these issues. > > Cheers, > Bert > > On Tue, Nov 15, 2011 at 7:00 AM, Catarina Miranda < > catarina.mira...@gmail.com> wrote: > >> Hello; >> >> I am having a problems with the interpretation of models using ordered or >> unordered predictors. >> I am running models in lmer but I will try to give a simplified example >> data set using lm. >> Both in the example and in my real data set I use a predictor variable >> referring to 3 consecutive days of an experiment. It is a factor, and I >> thought it would be more correct to consider it ordered. >> Below is my example code with my comments/ideas along it. >> Can someone help me to understand what is happening? >> >> Thanks a lot in advance; >> >> Catarina Miranda >> >> >> y<-c(72,25,24,2,18,38,62,30,78,34,67,21,97,79,64,53,27,81) >> >> Day<-c(rep("Day 1",6),rep("Day 2",6),rep("Day 3",6)) >> >> dataf<-data.frame(y,Day) >> >> str(dataf) #Day is not ordered >> #'data.frame': 18 obs. of 2 variables: >> # $ y : num 72 25 24 2 18 38 62 30 78 34 ... >> # $ Day: Factor w/ 3 levels "Day 1","Day 2",..: 1 1 1 1 1 1 2 2 2 2 ... >> >> summary(lm(y~Day,data=dataf)) #Day 2 is not significantly different from >> Day 1, but Day 3 is. >> # >> #Call: >> #lm(formula = y ~ Day, data = dataf) >> # >> #Residuals: >> # Min 1Q Median 3Q Max >> #-39.833 -14.458 -3.833 13.958 42.167 >> # >> #Coefficients: >> # Estimate Std. Error t value Pr(>|t|) >> #(Intercept) 29.833 9.755 3.058 0.00797 ** >> #DayDay 2 18.833 13.796 1.365 0.19234 >> #DayDay 3 37.000 13.796 2.682 0.01707 * >> #--- >> #Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 >> # >> #Residual standard error: 23.9 on 15 degrees of freedom >> #Multiple R-squared: 0.3241, Adjusted R-squared: 0.234 >> #F-statistic: 3.597 on 2 and 15 DF, p-value: 0.05297 >> # >> >> dataf$Day<-ordered(dataf$Day) >> >> str(dataf) # "Day 1"<"Day 2"<"Day 3" >> #'data.frame': 18 obs. of 2 variables: >> # $ y : num 72 25 24 2 18 38 62 30 78 34 ... >> # $ Day: Ord.factor w/ 3 levels "Day 1"<"Day 2"<..: 1 1 1 1 1 1 2 2 2 2 >> ... >> >> summary(lm(y~Day,data=dataf)) #Significances reversed (or "Day.L" and >> "Day.Q" are not sinonimous "Day 2" and "Day 3"?): Day 2 (".L") is >> significantly different from Day 1, but Day 3 (.Q) isn't. >> >> #Call: >> #lm(formula = y ~ Day, data = dataf) >> # >> #Residuals: >> # Min 1Q Median 3Q Max >> #-39.833 -14.458 -3.833 13.958 42.167 >> # >> #Coefficients: >> # Estimate Std. Error t value Pr(>|t|) >> #(Intercept) 48.4444 5.6322 8.601 3.49e-07 *** >> #Day.L 26.1630 9.7553 2.682 0.0171 * >> #Day.Q -0.2722 9.7553 -0.028 0.9781 >> #--- >> #Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 >> # >> #Residual standard error: 23.9 on 15 degrees of freedom >> #Multiple R-squared: 0.3241, Adjusted R-squared: 0.234 >> #F-statistic: 3.597 on 2 and 15 DF, p-value: 0.05297 >> >> [[alternative HTML version deleted]] >> >> >> ______________________________________________ >> 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. >> >> > > > -- > > Bert Gunter > Genentech Nonclinical Biostatistics > > Internal Contact Info: > Phone: 467-7374 > Website: > > http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm > > > -- Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm [[alternative HTML version deleted]]
______________________________________________ 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.