similar to: multivariate ordinal probit regression vglm()

Hello. I have yet to receive a response to my previous post, so I may have done a poor job asking the question. So, here is the general question: how can I run a run a multivariate (more than one non-independent, response variables) ordered probit regression model? I've had success doing this in the univariate case using the vglm() function in the VGAM package. For example:

Dear all, For my package "ref" I have implemented extensive regression testing. It now fails to compile since primitives "dim" and "dimnames" (and their assignment methods) no longer allow for additional arguments. I was using an additional argument "ref" with several methods. For "].refdata" it still works, with "dim.refdata" no longer.

I have following the message "dim(refdata) and dimnames(refdata) no longer allow parameter ref=TRUE, use dim(derefdata(refdata)), dimnames(derefdata(refdata)) instead" when I loaded data.table. Is it from the package ref? Could it be fixed? Or there is something wrong with my installation? > library(data.table) Loading required package: ref dim(refdata) and dimnames(refdata) no

Hi dear R users, 1) I would like to know if there is a simple way to define a vglm family which would be a mix of poisson variables and bernoulli variables (0/1 response) for idea this would be invoked like this: vglm(...,family=mixpoissonmultinom(npoisson,n01response)) where the n's give the number of each type of response. 2) and a simpler question : How to use constraints in rrvglm?

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Hi Folks, I wonderif someone who is familiar with the details of vglm in the VGAM package can assist me. I'm new to using it, and there doesn;t seem much in the documentation that's relevant to the question below. Say I have a vector x of 0/1 responses and another vector y of 0/1 responses, these in fact being a bivariate set of 0/1 responses equivalent to cbind(x,y). E.g.

Anyone know how to get p-values for the t-values from the coefficients produced in vglm? Attached is the code and output ? see comment added to output to show where I need p-values + print(paste("********** Using VGAM function gamma2 **********")) + modl2<- vglm(MidPoint~Count,gamma2,data=modl.subset,trace=TRUE,crit="c") + print(coef(modl2,matrix=TRUE))

Hello R users, I am performing a multinomial logit regression and would like to constrain a few model coefficients to be equal. Here is my model: multi <- vglm(case123con ~ SNP_A1+SNP_A2+age, multinomial, work.analy) where case123con is a four level categorical variable (case 1, case 2, case 3, control) and SNP_A1 and SNP_A2 are indicator functions (yes/no). The output of this

Hi , I know this question has been asked twice in the past but to my knowldege, it still hasn't been solved. I am doing a zero inflated binomial model using the VGAM package, I need to obtain p values for my Tvalues in the vglm output. code is as follows > mod2=vglm(dmat~Season+Diel+Tidal.phase+Tidal.cycle,zibinomial, data=mp1) > summary(mod2) Call: vglm(formula = dmat ~ Season +

Dear all, I'm facing a problem in estimation of glm model with weibull distribution. I run this : eqn0<-formula(fdh~cup1+cup2+cup3+cup4+fin1+vd1+cm2+cm4+milieu+cpro1+cpro2+cpro3a+cpro3b+schef+log(y)) regWeib0<-vglm(eqn0,family=weibull,subset(br, fdh<1)) I have en estimation but there is a message saying that regularity conditions are violated : Message d'avis : In eval(expr,

Hi All, I'm fitting an proportional odds model using vglm() from VGAM. My response variable is the severity of diseases, going from 0 to 5 (the severity is actually an ordered factor). The independent variables are: 1 genetic marker, time of medical observation, age, sex. What I *need* is a p-value for the genetic marker. Because I have ~1.5 million markers I'd rather not faffing

Hi All, could anybody help me to understand what is this error means ? mydata=read.table("C:/Documents and Settings/angieb/Desktop/CommercialGL/cl_ilf_claimdata.csv",header=TRUE,sep=",") > names(mydata) [1] "ILFTable" "liabLimit" "AnnAggLimit" "DedAmt" "Loss" "TIL" >

Hi, I just want to know how to get leverage values in VGLM(Adjacent category logit model, *acat()*) model [[alternative HTML version deleted]]

Dear: I try to analysis multinomial logistic regression using vglm in VGAM package. However, I wonder how many levels of responses variable this command is suitable. I saw the examples in google search works for 3 levels, say 1,2,3. However, my response variable is more than 3 levels. Is it working for my case or have alternative approach? Many thanks! Xin [[alternative HTML version

Hello , I need to estimate the parameters of generalized poisson regression model. I found that I could use : vglm(formula, family,.., data), but I dont know how to interpret the output!!! min 1Q Median 3Q Max elogit (lambda) -.66 -.61 -.46 -.06 35.33 log(theta) -10.2 -.02 .11 .64 1.2

Dear R developers: I am trying to fit a PPO model using vglm from the library VGAM, and get an error while executing the code. Here is the data, code, and error: Data: first row is the column names. a = age, and 1,2,3, 4 and 5 are condition grades. a 1 2 3 4 5 1 1 0 0 0 0 2 84 2 7 10 2 3 16 0 6 6 2 4 13 0 3 4 0 5 0 0 0 1 0 Library(VGAM)

R-Experts: I am using the vglm function of the VGAM library to perform proportional odds ordinal logistic regression. The issue that I would like help with concerns the format in which the response variable must be provided for this function to work correctly. Consider the following example: ------ library(VGAM) library(MASS) attach(pneumo) pneumo # Inspect the format of the original dataset

Hi, all. I've written a function that returns the survival function for a Gompertz mortality model. I've specified the two model parameters. Using a simple integration, I can calculate the life expectancy at any age. Is there a way I can use a loop with the integration that will quickly return life expectancy over a range of ages, say 0 to 80, so that I don't have to manually type

Hello R-help. I believe I may be coding the offset incorrectly in my formula. I am testing significance between different group pairings using the VGAM package. My data are zero-truncated and poisson-distributed and overdispersed, so I'm using the posnegbinomial function in vglm (code below). I've spent a very long time working out this model and am pretty sure it's the best for

I found it easy to use R when typing data manually into it. Now I need to read data from a file, and I get the following errors: > refdata = > read.table("K:\\MerchantData\\RiskModel\\refund_distribution.csv", header > = TRUE) Error in scan(file, what, nmax, sep, dec, quote, skip, nlines, na.strings, : line 1 did not have 42 elements > refdata = >