similar to: Logit / ms

Hello colleagues, This is a follow up to a question I posed in November regarding an analysis I was working on. Thank you to Dr. Brian Ripley and Dr. John Fox for helping me out during that time. I am conducting logistic regression on data set on psi (ESP) ganzfeld trials. The response variable is binary (correct/incorrect), with a 25% guessing base rate. Dr. Ripley suggested that I

I apologize for my earlier posting that, unbeknownst to me before, apparently was not in the correct format for this list. Hopefully this attempt will go through, and no-one will hold the newbie mistake against me. I could really use some help in writing a new glm link function in order to run an analysis of daily nest survival rates. I've struggled with this for weeks now, and can at least

Dear R Users/Experts, I am using a function called logitreg() originally described in MASS (the book 4th Ed.) by Venebles & Ripley, p445. I used the code as provided but made couple of changes to run a 'constrained' logistic regression, I set the method = "L-BFGS-B", set lower/upper values for the variables. Here is the function, logitregVR <- function(x, y, wt =

Sorry, I deleted my old post. Pasting the new query below. Dear R Users/Experts, I am using a function called logitreg() originally described in MASS (the book 4th Ed.) by Venebles & Ripley, p445. I used the code as provided but made couple of changes to run a 'constrained' logistic regression, I set the method = "L-BFGS-B", set lower/upper values for the variables. Here

Hello, Is there a package that implements the Cochrane-Orcutt itterative procedure for dealing with autocorrelation in a regression model? Thanks, John. -- ========================================== John Janmaat Department of Economics Acadia University, Wolfville, NS, B0P 1X0 (902)585-1461 All opinions stated are personal, unless otherwise indicated.

Hello, I need to solve a non-linear optimization with non-linear constraints. The 'nlm' routine does not seem to allow constraints. Is there a package for solving such problems in R? Thanks, John. -- ========================================== John Janmaat Department of Economics Acadia University, Wolfville, NS, B0P 1X0 (902)585-1461 All opinions stated are personal, unless

Hello, I have a linear model with different slopes for different treatment groups. I need to pairwise compare the different slope estimates for the different treatment groups. Is there a package that does pairwise comparisons of slope coefficients, making the appropriate adjustments in the P values? Thanks, John. -- ========================================== John Janmaat Department of

Hello, I am trying to get a time derivative on a plot title. I prefer to have it in the form \dot{s_i}, but \partial s_i/\partial t would be O.K. In the graphics demo I cannot find either a dot or a partial equivalent. Thanks, John. -- ========================================== John Janmaat Department of Economics Acadia University, Wolfville, NS, B0P 1X0 (902)585-1461 All opinions stated

Hi, I'm getting an error message using polr(): Error in optim(start, fmin, gmin, method = "BFGS", hessian = Hess, ...) : initial value in 'vmmin' is not finite The outcome variable is ordinal and factored, and the independant variable is continuous. I've checked the source code for both polr() and optim() and can't find any variable called

Dear all, I get an error message when I use polr() in MASS. These are my data: skugg grupp frekv 4 1 gr3 0 5 2 gr3 3 6 3 gr3 6 10 1 gr5 1 11 2 gr5 12 12 3 gr5 1 > > summary(polr(skugg ~ grupp, weights=frekv, data= skugg.cpy1.dat)) Error in optim(start, fmin, gmin, method = "BFGS", hessian = Hess, ...) :

Hi all!! I am using a proportinal odds model to study some ordered categorical data. I am trying to predict one ordered categorical variable taking into account only another categorical variable. I am using polr from the R MASS library. It seems to work ok, but I'm still getting familiar and I don't know how to assess goodness of fit. I have this output, when using response ~ independent

Hi all!! I am using a proportinal odds model to study some ordered categorical data. I am trying to predict one ordered categorical variable taking into account only another categorical variable. I am using polr from the R MASS library. It seems to work ok, but I'm still getting familiar and I don't know how to assess goodness of fit. I have this output, when using response ~ independent

I tried the following ordered logistic regression in R: mod1 <- polr(altitude~sp + wind_dir + wind_speed + hr, data=altioot) But when I asked The summary of my regression I got the folloing error message: > summary (mod1) Re-fitting to get Hessian Error in optim(start, fmin, gmin, method = "BFGS", hessian = Hess, ...) : the initial value of 'vmin' is not

Hello, I am new to R and have a couple of questions. My data set contains the variables "Bwt" and "Hwt", which are bodyweight and heartweight, respectively, of a group of cats. With the following code, I am making two plots, both to be viewed in the same plot window in R: library(MASS) maleData <- subset(cats, Sex == "M") linreg0 <- lm(maleData$Hwt ~

Hello again I am trying to fit an ordinal logistic model using the polr function from MASS. When I run model.loan.ordinal <- polr(loancat~age + sex + racgp + yrseduc + needlchg + gallery + sniffball + smokeball + sniffher + smokeher + nicocaine + inject + poly(year.of.int,3) + druginj + inj.years) I get an error Error in optim(start, fmin, gmin, method = "BFGS", hessian =

Hello, # Full disclosure. I am not sure if my problem is a bug(s) in the code, or a fundamental misunderstanding on my part about what I am trying to do with these statistics. I am not familiar with maximum likelihood tests. # I currently have two vectors Aequipecten<-c(0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,

Dear Sir, I get an error message when I use polr() in MASS package. My data is "ord.dat". I made "y" a factor. y y1 y2 x lx 1 0 0 0 3.2e-02 -1.49485 2 0 0 0 3.2e-02 -1.49485 3 0 0 0 1.0e-01 -1.00000 4 0 0 0 1.0e-01 -1.00000 5 0 0 0 3.2e-01 -0.49485 6 0 0 0 3.2e-01 -0.49485 7 1 1 0 1.0e+00 0.00000 8 0 0 0 1.0e+00 0.00000 9 1 1 0

Dear All, For logistic regression models: is it possible to use validate (rms package) to compute bias-corrected AUC, but have variable selection with AIC use step (or stepAIC, from MASS), instead of fastbw? More details: I've been using the validate function (in the rms package, by Frank Harrell) to obtain, among other things, bootstrap bias-corrected estimates of the AUC, when variable

Hey, everyone, I am using proportional odds model for ordinal responses in dose-response experiments. For some samll data, SAS can successfully provide estimators of the parameters, but the built-in function polr() in R fails. Would you like to tell me how to make some change so I can use polr() to obtain the estimators? Or anyone can give me a hint about the conditions for the existance of MLE

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 <-