Having searched CRAN and the mailing list archives, I'm pretty sure
there is no function in R that implements constrained clustering.
Before I start writing my own, have I overlooked something?
Thanks,
Tyler
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On 2008-02-07, Birgit Lemcke <[EMAIL PROTECTED]> wrote:
>
> Am 06.02.2008 um 21:00 schrieb Tyler Smith:
>>
>>> My dataset contains variables of the classes factor and numeric. Is
>>> there another function that is able to handle this?
>>
>> The nume
On 2008-02-06, Birgit Lemcke <[EMAIL PROTECTED]> wrote:
>
> I am using R 2.6.1 on a PowerBook G4.
> I would like to perform a discriminant function analysis. I found lda
> in MASS but as far as I understood, is it only working with
> explanatory variables of the class factor.
I think you are
Hi,
I've added several pages of worked solutions for the book Ecological
Detective by Hilborn and Mangel to the R-wiki. My hope is that this
will be of use to others working through this book without access to a
local expert. I am certainly not an expert, local or otherwise.
I have posted soluti
On 2007-12-21, [EMAIL PROTECTED] <[EMAIL PROTECTED]> wrote:
>
> Dear R users,
>
> I have just moved to R2.6.1 under Opensuse linux 10.3. I used to
> work with R under XPpro. Is it "normal" to have a visual aspect of R
> under linux different ?
Yes, that's normal. Under windows, you get a GUI int
On 2007-12-21, Louis Martin <[EMAIL PROTECTED]> wrote:
> Hi,
>
> I am running the following loop, but it takes hours to run as n is big. Is
> there any way "apply" can be used? Thanks.
> ### Start
> nclass <- dim(data)[[2]] - 1
> z <- matrix(0, ncol = nclass, nrow = nclass)
> n <- dim(
On 2007-11-29, John Fox <[EMAIL PROTECTED]> wrote:
>
> Anova() creates an object of class "anova" which then gets printed by the
> print method for "anova" objects. The reason that the sums of squares for
> the type II tests are rounded like this is because of the large value for
> the intercept. A
Hi,
When I use Anova(car) to produce type III SS, 'Sum Sq' is reported in
integers:
> Anova(bot.lm3, type ="III")
Anova Table (Type III tests)
Response: bottemp
Sum Sq DfF value Pr(>F)
(Intercept) 45295 1 29436.4440 < 2e-16
fungroup
On 2007-11-23, hadley wickham <[EMAIL PROTECTED]> wrote:
>>
>> What I need is a reference to the tests implemented in glht, so I can
>> decide which one is appropriate for my data. Sequen, Changepoint et
>> al. may be common terms in some fields, but not in the references I'm
>> working from.
>
> H
On 2007-11-22, Dieter Menne <[EMAIL PROTECTED]> wrote:
> Tyler Smith mail.mcgill.ca> writes:
>
>> I'm trying to figure out how anova works in R by translating the
>> examples in Sokal And Rohlf's (1995 3rd edition) Biometry. I've hit a
>> snag wit
Thank you for your response. I think you have misunderstood what I'm
asking, though.
On 2007-11-23, Emmanuel Charpentier <[EMAIL PROTECTED]> wrote:
>
> - Tukey HSD will enable you to test the p(p-1)/2 pair differences one
> can create with p groups ;
> - Dunnett's procedure is made to compare (p-
Hi,
I'm trying to make sense of the options for multiple comparisons
options in R. I've found the following options:
pairwise.t.test, which provides standard t-tests, with options for
choosing an appropriate correction for multiple comparisons
TukeyHSD, which provides the usual Tukey test
glht(
On 2007-11-22, Peter Alspach <[EMAIL PROTECTED]> wrote:
> Tyler
>
> For balanced data like this you might find aov() gives an output which
> is more comparable to Sokal and Rohlf (which I don't have):
>
>> trtCont <- C(sugars$treatment, matrix(c(-4,1,1,1,1, 0,-1,3,-1,-1), 5,
> 2))
>> sugarsAov <-
Hi,
I'm trying to figure out how anova works in R by translating the
examples in Sokal And Rohlf's (1995 3rd edition) Biometry. I've hit a
snag with planned comparisons, their box 9.4 and section 9.6. It's a
basic anova design:
treatment <- factor(rep(c("control", "glucose", "fructose",
On 2007-11-20, jim holtman <[EMAIL PROTECTED]> wrote:
> Does something like this work for you? You can vary the mgp parameter
> for placement of the label.
>
You can also use mtext to place the axis label separately:
barplot(rev(modelledprofile),horiz=TRUE,xlim=c(0,1),col="cornflowerblue",names.
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