similar to: Ordinal response model

Hola R-adictos, me gustaría que alguien me ayudara a aplicar la función "quantile" (varios percentiles) a un marco de datos estratificado por algunas variables categóricas (ver fichero adjunto como ejemplo). La idea es obtener varios percentiles de varias variables (primeras columnas del fichero de datos), pero dividiendo el marco de datos en función de los valores de las variables

Apologies for what might seem like an simple question. I have written a model which gives me a frequency distribution for a particular score within a set. What I now want to do is loop this so that I get many different frequency distributions and append them to a table with a collum which specifies which loop the frequency distribution is from. What I wantto end up with would look something like

the data file has such structure: 1992 6245 49 . . 20 1 0 0 8.739536 0 . . . . . . . . "alabama" . 0 . 1993 7677 58 . . 15 1 0 0

Hello! I've just upgraded dovecot from 2.0.18 to 2.1.8 and configured impac. I notice dovecot/imap throws segfault from time to time. I'm not sure i know what steps are needed to reproduce problem. I'm using gentoo, hardened-kernel-3.4.2. Namespaces (and settings for imapc) are keept in database so i'm not sure doveconf would be useful. If would be needed please let me know.

Hi, I am trying to write a simulation of the movements of four animals between six patches. The movement between patches is based on a first-order Markov chain so that the next patch they visit depends on the patch they were in before. I have written code that allows me to simulate the movement of one animal but when I add more there seems to be a problem and all chains come back as N/A. I can

I am attempted to examine the temporal independence of my data set and think I need an unordered multinomial logistic regression (or logit model) with repeated measures to do so. The data in question is location of chickens. Chickens could be in any one of 5 locations when a snapshot sample was taken. The locations of chickens (bird) in 8 pens (pen) were scored twice a day (AMPM) for 20 days

I have this snippet of code from an example in Dr. Harrel's book "Regression Modeling Strategies" p 501 n<-2000 .Random.seed <-c(49,39,17,36,23,0,43,51,6,54,50,1) age <-50 + 12 * rnorm(n) age I get the error message: Error in rnorm(n) : .Random.seed[1] is NOT a valid RNG kind (code) I have tried this on Windows and Linux R versions 1.8.1, 1.9.0, and 1.9.1

Dear Peter. I guess you know that Jim Lindseys code include nordr and ordglm in library gnlm - I attach the htmls which do various linear and nonlinear ordinal regressions - exemplified with just the data mentioned, McCullagh (1980) JRSS B42, 109-142. I had it work very fine. -----Oprindelig meddelelse----- Fra: Peter Malewski <p.malewski at tu-bs.de> Til: Troels Ring <tring at

Hello, all. I'm investigating the rate at which skeletal joint surfaces pass through a series of ordered stages (changes in morphology). Current statistical methods in this type of research use various logit or probit regression techniques (e.g., proportional odds logit/probit, forward/backward continuation ratio, or restricted/unrestricted cumulative probit). Data typically include the

I am trying to incorporate random effects (random intercept is good enough) to a proportional odds logistic regression model for ordinal outcomes. Could lme4 do this? I'd appreciate any input. Hakan Demirtas

Hello All: I know the function loglin() and loglm() from librarary(MASS) performs analysis on nominal categorical data. Are there any libraries, functions or examples available for analysis of ordinal categorical data? I have in mind procedures that can replicate results in Alan Agresti (1984) "Analysis of Ordinal Categorical Data." Thanks, ANDREW

I have a consulting client with data containing a mixture of continuous, nominal, and ordinal variables, who wishes to do some correlational analyses. Ignoring for the moment whether this is wise or useful, is there some way to calculate a matrix using, for example, polychoric correlations for pairs of nominal variables, or correlations based on normal scores for ordinal variables? [The PRELIS

That's the message i get when i try to run "replay media catcher". Any ideas why i'm geting this erorr? Thanks

Hi, I am trying to do a Moran's I test on an ordinal logistic regression model. I have a simple spatial weights matrix listed below I would like to use. Y= 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 1 1 0 0 0 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 1 1 1 1 0 0 0 1 1 0 0 0 0 1 1 0 0 1 0 0 0 0 1 1 I try to run the test as follows- moran.test(order$resid,

Hi I come across an idea of using SVM to fit ordinal label data rather than nominal ones. I am wondering if there is any pacakge that does that. Or if you could please recommend me the package that is flexible enough the let me do so, I'd really appreciate. Thank you. - adschai

Hello: I hope someone can help. I want to run a multinomial logit model where the response variable is ordinal and the covariates are nominal. The particular type of model I wish to run is a "baseline logit model." Is it possible to do so in R? I have seen that lrm() can run "continuation-ratio" and "proportional odds" logit models, but not "baseline"

I am trying to model a clusterd ordinal response data (either 1, 2 or 3) called , the correponding physician of the patient is also in the data. Since it is ordinal, I used the ordinal logit model topbox[i]~discrete with probability P[j,1],p[j,2], p[j,3], j is the corresponding physician of the ith patient C[j] is the physician effect , a1 and a1+theta is the common cutpoints for all

I have a spatial data set with ordinal response variable containing four levels. I would like to know if and how spatial autocorrelation can be taken into account when ordinal logistic regression is used (e.g. the function lrm from the Design package). Thanks for your help! Christof

Dear Dr. Fieberg, you used a regression tree approach to explore ordinal data set in addition to the proportinal odds model. I find this very interesting. I would like to know, how good the results of the regression tree approach turned out in comparison to the proportional odds model. Since people very often treat ordinal data as continuous, I would like to know how successfull this strategy

Hi, Is there any clustering package in R that can cluster with ordinal data? thanks! ____________________________________________________________________________________ Be a better friend, newshound, and [[alternative HTML version deleted]]