similar to: logistic regression - what is being predicted when using predict - probabilities or odds?

Hi all - my first time here and am having an issue with the Predict function. I am using a tutorial as a guide, locate here: http://www.ats.ucla.edu/STAT/R/dae/mlogit.htm My code gives this error > newdata1$predicted <- predict(mlogit,newdata=newdata1,type="response") Error in `$<-.data.frame`(`*tmp*`, "predicted", value = c(0.332822934960197, : replacement has

Dear R users I have the matrix of the centres of some clusters, e.g. 20 clusters each with 100 dimentions, so this matrix contains 20 rows * 100 columns numeric values. I have collected new data (each with 100 numeric values) and would like to keep the above 20 centres fixed/'unmoved' whilst just see how my new data fit in this grouping system, e.g. if the data is close to cluster 1

Hi R-helpers, My real data is a panel (unbalanced and with gaps in years) of thousands of firms, by year and industry, and with financial information (variables X, Y, Z, for example), the number of firms by year and industry is not always equal, the number of years by industry is not always equal. #reproducible example firm1<-sort(rep(1:10,5),decreasing=F) year1<-rep(2000:2004,10)

Dear all, I have a matrix, called newdata1, > dim(newdata1) [1] 34176 83 It looks like: EntrezID Name S1 S2 S3 S4 S5..... 1 4076 CAPRIN1 0.1 0.2 0.3... 2 139170 WDR40B 0.4 0.5 0.6... 3 5505 PPP1R2P1 0.3 0.3 0.7... 4 4076 CAPRIN1 0.7 0.3 0.2... 5 139170 WDR40B null 0.8

Dear list, I tried to apply the logistic regression to different response variables from a dataframe and would like to store the results using the function logistic.display from the "epicalc" package in a list, but got an error message "Error in eval(expr, envir, enclos) : y values must be 0 <= y <= 1". All the response variables have value of 0 or 1. It worked

Dear R Wizards, After much frustration and days of confusion I have finally broken down and am asking for help, which I don’t like doing, but I just can’t figure this one out on my own. I’ve conducted a laboratory experiment testing the effects of temperature and salinity on whether or not a biological event will occur (Go or NoGo). I’ve coded the factors temperature and salinity as factors for

Hi everyone. I have two questions. I’ve found some other questions and answers similar to these but they didn’t solve my problem. I’m working with a panel of firm/years observations (see my reproducible example). I’m using the plm package. My panel not only is unbalanced but also have some gap’s in years. #reproducible example

Hello! I have a matrix: > dim(data1) [1] 34176 581 of 34176 rows and 581 columns, I want to make a new matrix data2 by taking the 2nd column, 9th column, 16th column.... the column number is in the order of: 2 9 (2+7) 16 (9+7) 23 (16+7) ... the new matrix data2 should include 83 columns, is there an easy way to do that using cbind() function? Thanks a lot! Allen [[alternative HTML

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

To the list: What R commands will perform the score test on an ordered multinomial logit model to evaluate the proportional odds assumption? Many thanks. Mtichell Wachtel [[alternative HTML version deleted]]

The code library(Design) f <- lrm(y~x1+x2+x1*x2, data=data) plot(f) produces a plot of log odds vs x2 with 0.95 confidence intervals. How do I get a plot of odds ratios vs x2 instead? Thanks -- View this message in context: http://r.789695.n4.nabble.com/Odds-ratios-from-lrm-plot-tp4033340p4033340.html Sent from the R help mailing list archive at Nabble.com.

Dear community, I am currently attempting to perform a (L1) penalized ordinal logistic regression with proportional odds. For the moment I only found R packages allowing to perform forward or backward continuation ratio model with several penalizations. Does anyone have a clue of what R package I could use ? I am not even quite sure that penalized logistic regression with proportional odds has

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

Hello dear R-help mailing list, I wish to perform a non-parametric repeated measures anova. If what I read online is true, this could be achieved using a mixed Ordinal Regression model (a.k.a: Proportional Odds Model). I found two packages that seems relevant, but couldn't find any vignette on the subject: http://cran.r-project.org/web/packages/repolr/

mantelhaen.test() gives the exact conditional p-value (for independence) and confidence intervals (CIs)for the common odds ratio for a stratified 2x2 table. The epitools package by Tomas Aragon (available via CRAN) contains functions which use fisher.test() to calculate mid-p exact p-values and CIs for the CMLE odds ratio for a single 2x2 table. The mid-p p-value for independence for a stratified

For the following model, library(vcd) arth.polr <- polr(Improved ~ Sex + Treatment + Age, data=Arthritis) summary(arth.polr) where Improved is an ordered, 3-level response I'm looking for a *simple* way to test the validity of the proportional odds assumption, typically done via a score test for equal slopes/effects over the predictors. I do find a po.test= option in the repolr package

Dear all, I am looking for an R function which does stepwise selection cox model in r (delta chisq likelihood ratio test) similar to the stepwise, pe (0.05) lr: stcox in STATA. I am very thankful for any reply. Regards, Linda [[alternative HTML version deleted]]

I am trying to do logistic regression with a categorical predictor variable with the glm() function, family=binomial. Using glm() I would like to be able to calculate the confidence intervals of all three possible odds ratios for a factor (the factor has three categories). Three categories imply two columns of 0's and 1's in the design matrix, and two parameter estimates with their

Hi Since the 'equal slope' assumption doesn't hold in my data I cannot use a proportional odds model ('Design' library, together with 'Hmisc'). I would like to try therefore a partial proportional odds model Please, could anybody tell me, where to find the code and how to specify such a model ..or any potential alternatives many thanks for your kind help christoph

Please don't post in HTML. Your post is almost unreadable Also,lease have a look at http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example and/or http://adv-r.had.co.nz/Reproducibility.html for some suggestions about what you should include in your question. Welcome to R. John Kane Kingston ON Canada > -----Original Message----- > From: rosita21 at