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.
C
On Tue, Nov 15, 2011 at 9:00 AM, Catarina Miranda
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 rea
... 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
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 s
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 o
Val
> Sent: Monday, November 09, 2009 4:08 PM
> To: r-help@r-project.org
> Subject: [R] Models
>
> Hi all,
> I hope that there might be some statistician out there to help me for
> a
> possible explanation for the following simple question.
>
> Y1~ lm(y~ t1 + t2 +
Iuri Gavronski wrote:
Frank,
I certainly can't speak for Emmanuel. I don't know his reasons.
The reason I've posted this question is the fact that (as far as I
understood), ordinal regression is based on logistic regression (or
probit), and logistic regression expects a formula like dichotomous
Frank,
I certainly can't speak for Emmanuel. I don't know his reasons.
The reason I've posted this question is the fact that (as far as I
understood), ordinal regression is based on logistic regression (or
probit), and logistic regression expects a formula like dichotomous ~
ratio1 + ratio2 + ...
Hi all,
I hope that there might be some statistician out there to help me for a
possible explanation for the following simple question.
Y1~ lm(y~ t1 + t2 + t3 + t4 + t5,data=temp) # oridnary linear model
library(gam)
Y2~ gam(y~ lo(t1) +lo(t2) +lo(t3) +lo(t4) +lo(t5),data=temp) # additive
mode
Emmanuel Charpentier wrote:
Le dimanche 08 novembre 2009 à 19:05 -0600, Frank E Harrell Jr a écrit :
Emmanuel Charpentier wrote:
Le dimanche 08 novembre 2009 à 17:07 -0200, Iuri Gavronski a écrit :
Hi,
I would like to fit Logit models for ordered data, such as those
suggested by Greene (2003)
Le dimanche 08 novembre 2009 à 19:05 -0600, Frank E Harrell Jr a écrit :
> Emmanuel Charpentier wrote:
> > Le dimanche 08 novembre 2009 à 17:07 -0200, Iuri Gavronski a écrit :
> >> Hi,
> >>
> >> I would like to fit Logit models for ordered data, such as those
> >> suggested by Greene (2003), p. 736
Emmanuel Charpentier wrote:
Le dimanche 08 novembre 2009 à 17:07 -0200, Iuri Gavronski a écrit :
Hi,
I would like to fit Logit models for ordered data, such as those
suggested by Greene (2003), p. 736.
Does anyone suggests any package in R for that?
look up the polr function in package MASS
Le dimanche 08 novembre 2009 à 17:07 -0200, Iuri Gavronski a écrit :
> Hi,
>
> I would like to fit Logit models for ordered data, such as those
> suggested by Greene (2003), p. 736.
>
> Does anyone suggests any package in R for that?
look up the polr function in package MASS (and read the releva
Hi,
I would like to fit Logit models for ordered data, such as those
suggested by Greene (2003), p. 736.
Does anyone suggests any package in R for that?
By the way, my dependent variable is ordinal and my independent
variables are ratio/intervalar.
Thanks,
Iuri.
Greene, W. H. Econometric Anal
Hi Michael,
Can you also build the PMML model on the cloud with R, paying for the
processor ,memory usage. Any plans to extend the abilty to model, or is it
just deploy PMML models on the cloud servers.
Regards,
Ajay
http://www.decisionstats.com
On Thu, Jan 22, 2009 at 4:29 AM, MZ wrote:
> Fo
Following the recent NYT article about R, I thought this group is not
only ready for R but ready to take it one step further.
Got models in R? Deploy and score them in ADAPA in minutes on the
Amazon EC2 cloud computing infrastructure!
Zementis ( http://www.zementis.com ) has been working with the
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