... 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 <[email protected]> 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 <
> [email protected]> 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]]
>>
>>
>> ______________________________________________
>> [email protected] 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
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