similar to: new package glm.predict

Dear R users, I am pleased to announce the release on CRAN of brant 0.1-1: https://cran.r-project.org/package=brant. I implemented the brant test (Brant, Rollin 1990) in R together with Prof. Marco Steenbergen at our Institute. The function brant() tests the parallel regression assumption for ordinal logistic regressions. Any comments, suggestions or queries are gratefully received. Best

Dear R users, I am pleased to announce the release on CRAN of brant 0.1-1: https://cran.r-project.org/package=brant. I implemented the brant test (Brant, Rollin 1990) in R together with Prof. Marco Steenbergen at our Institute. The function brant() tests the parallel regression assumption for ordinal logistic regressions. Any comments, suggestions or queries are gratefully received. Best

Hi, I am searching for methods to compare regression models with an ordered categorical response variable (polr versus multinom). The pattern of predictions of both methods (using the same predictor variables) is quite different and the AIC is smaller for the multinom approach. I guess polr has more strict premises for the structure of the response variable, which methods can be used to test for

Hello, I am trying to run an ordinal logistic regression (polr) using the package 'MASS'. I have successfully run other regression classes (glm, multinom) without much problem, but with the 'polr' class I get the following error: " Error in svd(X) : infinite or missing values in 'x' " which appears when I run the "summary" command. The data file is

Hi, there, Is there a R routine which can fit multinomial logistic regression for nominal outcomes? Not the multinom() of log-linear model, neither the polr() for ordinal outcomes. Thanks. Jun Han

Hello list, Is there any mean to fit models for ordinal response other than multinomial polytomous ("multinom" from nnet ) and cumulative logit ("polr" from MASS)? I am particularly interested in continuation-ratio model and adjacent-category logit model. It is for the sake of epidemiology in wild-living populations! Many thanks, Emmanuelle Fromont

I'm running polr() and getting warning messages from glm.fit(). It seems reasonable to use glm.control() to turn on the trace and follow what glm.fit() does when called by polr(); or is it? glm.control(maxit=10, trace=TRUE) polr(act~., data=mm) The glm.control() sets the trace TRUE, but there's no change in the output from polr(). Many thanks in advance for any help/pointers.

Dear community, I'm currently attempting to predict the occurence of an event (factor) having more than 2 levels with several continuous predictors. The model being ordinal, I was waiting the glm function to return several intercepts, which is not the case when looking to my results (I only have one intercept). I finally managed to perform an ordinal polytomous logisitc regression with the

Dear Colleagues, I am doing two class classification using logistic regression. I realized that I can either use "glm" function or "multinom" function. I know "multinom" is used for multiclass classification. But if I was it for binary classification, I was wondering if there is an difference in the results compared to "glm" results. Thanks in advance.

Hi all, The nobs method of (MASS:::polr class) takes into account of weight, but nobs method of glm does not. I wonder what is the rationale of such design behind nobs.glm. Thanks in advance. Best Regards. > library(MASS) > house.plr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) > house.logit <- glm(I(Sat=='High') ~ Infl + Type + Cont, binomial,weights

Hi, has anyone seen this before? [schlegel@gauss schlegel]$ smbpasswd Old SMB password: New SMB password: Retype new SMB password: Server did not provide 'target information', required for NTLMv2 rpc_pipe_bind: rpc_send_auth_reply failed. machine 127.0.0.1 does not support SAMR connections, but LANMAN password changed are disabled Failed to change password for schlegel PDC ist Fedora

Hello, This is the fitted model: > fit Call: multinom(formula = resp ~ pred$cls + pred$smoke) Coefficients: (Intercept) pred$cls2 pred$cls3 pred$cls4 pred$cls5 pred$smoke2 pred$smoke3 Proteinuria -1.140520 0.1616644 0.05554898 -0.01584927 0.02574805 -0.4057245 -0.2898425 Hypertension -2.691215 -0.3699690 -0.22582107 0.01615898 0.26318005 0.1239051 0.2413282

I have a student that I'm encouraging to use R rather than SAS or Stata and within just 2 weeks he has come up with a question that stumps me. What does a person do about endogeneity in generalized linear models? Suppose Y1 and Y2 are 5 category ordinal dependent variables. I see that MASS has polr for estimation of models like that, as long as they are independent. But what if the

It has always been my understanding that deviance for GLMs is defined by; D = -2(loglikelihood(model) - loglikelihood(saturated model)) and this can be calculated by (or at least usually is); D = -2(loglikelihood(model)) As is done so in the code for 'polr' by Brian Ripley (in the package 'MASS') where the -loglikehood is minimised using optim; res <-

I want to fit a multinomial model with logit link. For example let this matrix to be analyzed: male female aborted factor 10 12 1 1.2 14 14 4 1.3 15 12 3 1.4 (this is an example, not the true data which are far more complex...) I suppose the correct function to analyze these data is polr from MASS library. The data have been

Hello, since I installed the package VGAM I have problems useing the predict for othere methods. for example I have a model from glm and polr the command predict(model) I get the error: unable to find an inherited method for function "predict", for signature "polr". Has perhaps anybody a solution, because Iwould need vglm and also other methods like tree in a loop. Thanks a

Hi I would like to analyse an ordinal categorical variable. I know how I can analyse a nominal categorical variable (with multinom or if there are only two levels with glm). Does somebody know which command I need in R to analyse an ordinal categorical variable? I want to describe the variable y with the variables x1,x2,x3 and x4. So my model looks like: y ~ x1+x2+x3+x4. y: ordinal factor

I have used plor() to model a rather large 3-category dataset (~1500 data points, ~15 independent variables); from the resulting model (with a deviance slightly below the residual degrees of freedom), the training data are placed in only the two extreme categories. Though the result appears to indicate that there's only a relative 'narrow' bin for the medium group, [and when the

Is there a way to get confidence intervals around an ordered regression like polr() in the MASS package? ------------------------------------------- Joe King, M.A. Ph.D. Student University of Washington - Seattle 206-913-2912 <mailto:jp@joepking.com> jp@joepking.com ------------------------------------------- Ad astra per aspera - "Through hardships to the stars"

Dear Terry, It's not surprising that different modeling functions behave differently in this respect because there's no articulated standard. Please see my response to Martin for my take on the singular.ok argument. For a highly sophisticated user like you, singular.ok=TRUE isn't problematic -- you're not going to fail to notice an NA in the coefficient vector -- but I've