similar to: VGAM and cnstraints

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

I am trying to follow this example for multinomial logistic regression http://www.ats.ucla.edu/stat/r/dae/mlogit.htm However, I cannot get it to work properly. This is the output I get, and I get an error when I try to use the mlogit function. Any ideas as to why this happens? > mydata <- read.csv(url("http://www.ats.ucla.edu/stat/r/dae/mlogit.csv")) > attach(mydata) >

Dear All, I know this may be a trivial question. In the past I have used glm to make logistic regressions on data. The output creates an object with the results of the logistic regression. This object can then be used to make predictions. Great. I have a different problem. I need to make predictions from a logistic regression taken from a paper. Thus I need to (by hand) enter the reported odds

Dear R help, Suppose I have a dataframe with three columns named p, v and C. Here C is a function of both p and v. I can plot the surface C(p,v) using the package lattice using the function wireframe. Now if I have another dataframe - with 2 columns named p_ind and v_ind and pind is a function of v_ind. I would like to overlay the plot of the curve p_ind, v_ind and C(p_ind, v_ind) onto my

I want to analyse data with an unordered, multi-level outcome variable, y. I am asking for the appropriate method (or R procedure) to use for this analysis. > N <- 500 > set.seed(1234) > data0 <- data.frame(y = as.factor(sample(LETTERS[1:3], N, repl = T, + prob = c(10, 12, 14))), x1 = sample(1:7, N, repl = T, prob = c(8, + 8, 9, 15, 9, 9, 8)), x2 = sample(1:7, N, repl =

Dear users, I can access an database oracle database using sql developer. This was done by importing an xml file that contains the login details - username, password and specifies that it uses the KERBEROS_AUTHENTICATION. I'm trying to connect R - so that it can access this database - do sql queries and convert the resulting tables into dataframes. I am a novice in SQL and database access

I want to test whether the proportional odds assumption for an ordered regression is met. The UCLA website points out that there is no mathematical way to test the proportional odds assumption (http://www.ats.ucla.edu/stat//R/dae/ologit.htm), and use graphical inspection ("We were unable to locate a facility in R to perform any of the tests commonly used to test the parallel slopes

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

Hello R-Users, I have recently run into several problems using vgam() in the VGAM package. I am hoping someone might have some solutions... Briefly, I have been trying to fit GAM models for zero-altered negative binomial models. 1. When fitting smoothed parameters (e.g. s(X, df=2)) changing the degrees-of-freedom has no effect on the level of smoothing (e.g. number of knots for the

The symptom triggering this email is that an S4 summary method sometimes refuses to be invoked, even when a package is explicitly loaded, if the first load of the package is implicit. It may or may not be specific to 'summary' methods and/or the 'VGAM' package. I've sent to R-devel because (i) it looks like some kind of bug to me, but I'm not sure; (ii) it's not

Hi, R-users Could someone help me to understand this following error. I'm using vglm function in VGAM package Best regards and thank you for your ehlp ######## mydata <- read.table("Data2_overruns.csv", sep =";", header = T, row.names=NULL) > attach(mydata) > > y <- mydata$cat.event > phase.vol <-mydata$phase.vol > pilote <- mydata$pilote

Dear Prof. Thomas Yee I$B!G(Bm very interested in your R program VGAM. I tried below your data: # Nonparametric proportional odds model data(pneumo)pneumo = transform(pneumo, let=log(exposure.time))vgam(cbind(normal,mild,severe) ~ s(let), cumulative(par=TRUE), pneumo) However, the results by Version of VGAM are different; ----------The result by Version 0.7-7

I just discovered that when the VGAM package (not on CRAN) is loaded, glm() doesn't work. This is because VGAM defines a family function() which gets found by glm() in place of the family function from stats. Then VGAM:::family returns an object which doesn't have a $family component, (it has a component $vfamily). I thought namespaces should protect us from this happening? Kjetil --

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

sorry...its the same post but this time with subject dear all: I want try the vgam function in VGAM library, but I'm stucked since I can't define the weights. This is because I dont understand well the concept of "matrix-band" (even after read the reference card). I was wondering if someone have some tutorial examples of how to define weights on a matrix form, in VGAM. thank

Dear, When i multiply the y of a regression by 10, I would expect that the coefficient would be multiply by 10 and the z value to stay constant. Here some reproducible code to support the case. *Ex 1* library(mvtnorm) library(VGAM) set.seed(1) x=rmvnorm(1000,sigma=matrix(c(1,0.75,0.75,1),2,2))

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

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 everyone, using the VGAM package and the following code library(VGAM) bp1 <- vglm(cbind(daten$anzahl_b, daten$deckung_b) ~ ., binom2.rho, data=daten1) summary(bp1) coef(bp1, matrix=TRUE) produced this error message: error in object$coefficients : $ operator not defined for this S4 class I am bit confused because some day ago this error message did not show up and

Hi, I am trying to carry out a multinomial regression using the cumlogit link function. I have tried using the VGAM package, and have gotten some results... fit1 <- vgam(Y ~ X1 + X2 + X3 + X4, cumulative(link=logit,intercept.apply=FALSE,parallel=TRUE), data = data1 ) The problem arrises when I try to get the information out of the fitted object. I can