similar to: Multivariate response methods question

Hi everyone! I've recently begun to learn R for my job as the IT department suffers from lack of funding for new software. I was talking to the guy in charge of Requisitions and have found out the budget for books is in great shape. So, I'm curious what books people know of that have R examples and are good for: 1.) Uni and Multivariate Time Series Analysis/Forecasting 2.) GLMs (at

Hi - I am using polr. I can get a result from polr fit by calling result.plr <- polr(formula, data=mydata, method="probit"); However, from the 'result.plr', how can I access standard error of the estimated coefficients as well as the t statistics for each one of them? What I would like to do ultimately is to see which coefficients are not significant and try to refit the

I'd like to model the relationship between m responses Y1, ..., Ym and a single set of predictor variables X1, ..., Xr. Each response is assumed to follow its own regression model, and the error terms in each model can be correlated. My understanding is that although lm() handles vector Y's on the left-hand side of the model formula, it really just fits m separate lm models. What should

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

Dear R-helpers, I need to compute probabilties of multinomial observations, eg by doing the following: y=sample(1:3,15,1) prob=matrix(runif(45),15) prob=prob/rowSums(prob) diag(prob[,y]) However, my question is whether this is the most efficient way to do this. In the call prob[,y] a whole matrix is computed which seems a bit of a waste. Is there maybe a vectorized version of dmultinom which

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

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

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

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

Hi Everyone, I did a quick search of the list and it looks like this may not have been asked before... I'm trying to generate a matrix of random numbers between 0 and 1, with 6 columns, 10000 rows. About all I know is that runif(1) gives me the random number I'm looking for. Any help would be great! thanks, -Max

Hi Everyone, I did a quick search of the list and it looks like this may not have been asked before... I'm trying to generate a matrix of random numbers between 0 and 1, with 6 columns, 10000 rows. About all I know is that runif(1) gives me the random number I'm looking for. Any help would be great! thanks, -Max

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

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

Dear R-users, Is there a function in R that classifies data in more than 2 groups using logistic regression/classification? I want to compare the c-indices of earlier research (lrm, binary response variables) with new c-indices obtained from 'multiple' (more response variables) logistic regression. Best regards, Stephanie Delalieux Department Biosystems M?-BIORES Group of Geomatics

Dear all R-users, I am a new user of R and I am trying to build a discrete choice model (with more than two alternatives A, B, C and D) using logistic regression. I have data that describes the observed choice probabilities and some background information. An example below describes the data: Sex Age pr(A) pr(B) pr(C) pr(D) ... 1 11 0.5 0.5 0 0 1 40 1 0 0 0 0 34 0 0 0 1 0 64 0.1 0.5 0.2 0.2 ...

Is it possible to use function "glm" in case when my outcome variable has 5 different classes? I have seen examples only when using binomial outcome variable. What about using function "multinom"? How do I to get the signifigance and the confidence levels of the coefficients and the value of goodness of the model with this function? Thank You for Your help! -- View this

? Hallo, which function can i use to do (baseline) logistic regression + goodness of fit tests? so far i found: # logistic on binary data lrm combined with resid(model,'gof') # logistic on binary data glm with no gof-test # baseline logit on binary data

Hi, how can I do multinomial logistic regression in R? I think glm() can only handle binary response variable, and polr() can only handle ordinal response variable. how to do logistic regression with multinomial response variable? Thanks __________________________________

Greetings, I'm interested in generating data from various bivariate or mulitivariate distributions (e.g. gamma, t, etc), where I can specify the parameter values, including the correlations among the variables. I haven't been able to dig anything up on the faq, but I probably missed something. A nudge in the right direction would be appreciated. David --

Is there any implicit function in R to minimize a function with many variables? Many thanks Raquel ---------------------- R Granell, Rheumatology Unit & School of Mathematics University of Bristol Raquel.Granell at bristol.ac.uk Tel:07968079410