[R] Models with ordered and unordered factors

Bert Gunter
2011-11-15T16:32:33Z

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.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?
>
> 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

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