# [R] Comparison of age categories using contrasts

Mark Difford mark_difford at yahoo.co.uk
Tue Feb 17 17:35:51 CET 2009

```Hi Dylan, Chuck,

Mark Difford wrote:
>> Coming to your question [?] about how to generate the kind of contrasts
>> that Patrick wanted
>> using contrast.Design. Well, it is not that straightforward, though I may
>> have missed
>> something in the documentation to the function. In the past I have
>> cobbled them together
>> using the kind of hack given below:

Here is a much simpler route, and a correction to my earlier posting (helped
by a little off-list prompting from Prof. Harrell). All that is required to
get successive difference (i.e. sdif) contrasts using contrast.Design is the
following:

contrast(l, a=list(f=c("a","b","c")), b=list(f=c("b","c","d")))

## Run a full example
set.seed(10101010)
x <- rnorm(100)
y1 <- x[1:25] * 2 + rnorm(25, mean=1)
y2 <- x[26:50] * 2.6 + rnorm(25, mean=1.5)
y3 <- x[51:75] * 2.9 + rnorm(25, mean=5)
y4 <- x[76:100] * 3.5 + rnorm(25, mean=5.5)

d <- data.frame(x=x, y=c(y1,y2,y3,y4), f=factor(rep(letters[1:4], each=25)))

l <- ols(y ~ x * f, data=d)

## compare with multcomp by setting x=0
summary(glht(l, linfct=mcp(f="Seq")))
contrast(l, b=list(x=0, f=c("a","b","c")), a=list(x=0, f=c("b","c","d")))

Regards, and apologies for any confusion, Mark.

Mark Difford wrote:
>
> Hi Dylan,
>
>>> Am I trying to use contrast.Design() for something that it was not
>>> intended for? ...
>
> I think Prof. Harrell's main point had to do with how interactions are
> handled. You can also get the kind of contrasts that Patrick was
> interested in via multcomp. If we do this using your artificial data set
> we see that the contrasts are the same as those got by fitting the model
> using contr.sdif, but a warning is generated about interactions in the
> model &c. [see example code]
>
> Part of Prof. Harrell's "system" is that in generating contrasts via
> contr.Design an appropriate value is automatically chosen for the
> interacting variable (in this case the median value of x) so that a
> reasonable default set of contrasts is calculated. This can of course be
> changed.
>
> Coming to your question [?] about how to generate the kind of contrasts
> that Patrick wanted using contrast.Design. Well, it is not that
> straightforward, though I may have missed something in the documentation
> to the function. In the past I have cobbled them together using the kind
> of hack given below:
>
> ## Exampe code
> x <- rnorm(100)
> y1 <- x[1:25] * 2 + rnorm(25, mean=1)
> y2 <- x[26:50] * 2.6 + rnorm(25, mean=1.5)
> y3 <- x[51:75] * 2.9 + rnorm(25, mean=5)
> y4 <- x[76:100] * 3.5 + rnorm(25, mean=5.5)
>
>
> d <- data.frame(x=x, y=c(y1,y2,y3,y4), f=factor(rep(letters[1:4],
> each=25)))
>
>
> # now try with contrast.Design():
> library(multcomp)
> l <- ols(y ~ x * f, data=d)
>
> ## model fitted using successive difference contrasts
> library(MASS)
> T.lm <- lm(y ~ x * f, contrasts=list(f="contr.sdif"), data=d)
> summary(T.lm)
>
> ## show the warning: model fitted using ols() and treatment contrasts
> summary(glht(l, linfct=mcp(f="Seq")))
>
> ## "custom" successive difference contrasts using contrast.Design
> TFin <- {}
> for (i in 1:(length(levels(d\$f))-1)) {
>     tcont <- contrast(l, a=list(f=levels(d\$f)[i]),
> b=list(f=levels(d\$f)[i+1]))
>     TFin <- rbind(TFin, tcont)
>     rownames(TFin)[i] <-  paste(levels(d\$f)[i], levels(d\$f)[i+1], sep="-")
> }
> TFin[,1:9]
>
> Regards, Mark.
>
>
>
> Dylan Beaudette-2 wrote:
>>
>> On Mon, Feb 16, 2009 at 5:28 PM, Patrick Giraudoux
>> <patrick.giraudoux at univ-fcomte.fr> wrote:
>>> Greg Snow a écrit :
>>>> One approach is to create your own contrasts matrix:
>>>>
>>>>
>>>>> mycmat <- diag(8)
>>>>> mycmat[ row(mycmat) == col(mycmat) + 1 ] <- -1
>>>>> mycmati <- solve(mycmat)
>>>>> contrasts(agefactor) <- mycmati[,-1]
>>>>>
>>>>
>>>> Now when you use agefactor, the intercept will be the first age group
>>>> and the slopes will be the differences between the pairs of groups
>>>> (make sure that the order of the levels of agefactor is correct).
>>>>
>>>> The difference between this method and the contr.sdif function in MASS
>>>> is how the intercept will end up being interpreted (and the dimnames).
>>>>
>>>> Hope this helps,
>>>>
>>>>
>>>
>>> Actually, exactly what I needed including the reference to contr.sdif in
>>> MASS I did not spot before (although I am a faithful reader of the
>>> yellow book... but so many things still escape to me). Again thanks a
>>> lot.
>>>
>>> Patrick
>>>
>>
>> Glad you were able to solve your problem. Frank replied earlier with
>> the suggestion to use contrast.Design() to perform the tests after the
>> initial model has been fit. Perhaps I am a little denser than normal,
>> but I cannot figure out how to apply contrast.Design() to a similar
>> model with several levels of a factor. Example data:
>>
>> # need these
>> library(lattice)
>> library(Design)
>>
>> # replicate an important experimental dataset
>> set.seed(10101010)
>> x <- rnorm(100)
>> y1 <- x[1:25] * 2 + rnorm(25, mean=1)
>> y2 <- x[26:50] * 2.6 + rnorm(25, mean=1.5)
>> y3 <- x[51:75] * 2.9 + rnorm(25, mean=5)
>> y4 <- x[76:100] * 3.5 + rnorm(25, mean=5.5)
>>
>> d <- data.frame(x=x, y=c(y1,y2,y3,y4), f=factor(rep(letters[1:4],
>> each=25)))
>>
>> # plot
>> xyplot(y ~ x, groups=f, data=d, auto.key=list(columns=4, title='Beard
>> Type', lines=TRUE, points=FALSE, cex=0.75), type=c('p','r'),
>> ylab='Number of Pirates', xlab='Distance from Land')
>>
>> # standard comparison to base level of f
>> summary(lm(y ~ x * f, data=d))
>>
>> # compare to level 4 of f
>> summary(lm(y ~ x * C(f, base=4), data=d))
>>
>>
>> # now try with contrast.Design():
>> l <- ols(y ~ x * f, data=d)
>>
>> # probably the wrong approach, as the results do not look too familiar:
>> contrast(l, list(f=levels(d\$f)))
>>
>>  x          f Contrast  S.E.      Lower       Upper     t     Pr(>|t|)
>>  -0.3449623 a 0.3966682 0.1961059 0.007184856 0.7861515  2.02 0.0460
>>  -0.3449623 b 0.5587395 0.1889173 0.183533422 0.9339456  2.96 0.0039
>>  -0.3449623 c 4.1511677 0.1889170 3.775962254 4.5263730 21.97 0.0000
>>  -0.3449623 d 4.3510407 0.1888820 3.975904726 4.7261766 23.04 0.0000
>>
>>
>> This is adjusting the output to a given value of 'x'... Am I trying to
>> use contrast.Design() for something that it was not intended for? Any
>> tips Frank?
>>
>> Cheers,
>> Dylan
>>
>> ______________________________________________
>> R-help at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>>
>
>

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