# Models with ordered and unordered factors

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## Models with ordered and unordered factors

 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]] ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.
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## Re: Models with ordered and unordered factors

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