similar to: R vglm new family writing: mix Poisson/multinomial

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.

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

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))

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 , 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 +

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

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,

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

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)

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

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

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

Dear useRs, upon request, the VGAM package (currently version 0.7-1) has been officially released on CRAN (the package has been at my website http://www.stat.auckland.ac.nz/~yee/VGAM for a number of years now). VGAM implements a general framework for several classes of regression models using iteratively reweighted least squares (IRLS). The key ideas are Fisher scoring, generalized linear and

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" >

Hello R users, I have a puzzle with the VGAM package, on my first excursion into generalized additive models, in that this very nice package seems to want to do either more or less than what I want. Precisely, I have a 4-component outcome, y, and am fitting multinomial logistic regression with one predictor x. What I would like to find out is, is there a single nonlinear function f(x) which acts

Dear All I am trying to fit a multinomial logistic regression to a data set with a size of 94279 by 14 entries. The data frame has one "sample" column which is the categorical variable, and the number of different categories is 9. The size of the data set (as a csv file) is less than 10 MB. I tried to fit a multinomial logistic regression, either using vglm() from the VGAM package or

Dear all, I am new to R and would like to run a multinomial logistic regression on my dataset (3 predictors for 1 dependent variables) I have used the vglm function from the VGAM package and got some results. Using the predict() function, I obtained the probability table I was looking for. However, I would like to fully understand how the predict() function generates the probabilities or in

I'm attempting to creat multinomial logistic regression model using the following code: mlogit<- vglm(ME ~ HIST,family=multinomial(),na.action = na.pass) ME (dependent variable) has three levels (0,1,2) How do I declare a reference outcome (either 0,1,2)?

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