Thanks a lot for your answers and reading suggestions, now I know my guess
was completely wrong.

I guess in my case it will be more informative to keep the unordered
factors. That way I can know not only that days differ in general, but also
get information on which day is differing from day 1.

Cheers;

Catarina


On 15 November 2011 17:54, Paul Johnson <pauljohn32@gmail.com> wrote:

> On Tue, Nov 15, 2011 at 9:00 AM, Catarina Miranda
> <catarina.miranda@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?
>
> Dear Catarina:
>
> I have had the same question, and I hope my answers help you
> understand what's going on.
>
> The short version:
>
> http://pj.freefaculty.org/R/WorkingExamples/orderedFactor-01.R
>
> The longer version, "Working with Ordinal Predictors"
>
> http://pj.freefaculty.org/ResearchPapers/MidWest09/Midwest09.pdf
>
> HTH
> pj
>
> >
> > 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.
> >
> >
>
>
>
> --
> Paul E. Johnson
> Professor, Political Science
> 1541 Lilac Lane, Room 504
> University of Kansas
>

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