similar to: Use glm coefficients for other datasets

I'm trying to do this in several ways but havent had any result. Im asked to install python, or perl.... etc. Can anybody suggest a direct, easy and understandable way? Every help would be appreciated. Thx. -- View this message in context: http://r.789695.n4.nabble.com/Export-R-dataframes-to-excel-tp3330399p3330399.html Sent from the R help mailing list archive at Nabble.com.

Dear list members, I was reminded of what I see as a problem with summary.glm this week when some of my students fell into a trap in a homework exercise, defining a logit model in which coefficients were aliased. When this happens in lm, summary.lm prints a message ["Coefficients: (1 not defined because of singularities)"], but summary.glm is silent. In both instances, the print

Hi, I am running glm logit regressions with R and I would like to test a linear combination of coefficients (H0: beta1=beta2 against H1: beta1<>beta2). Is there a package for such a test or how can I perform it otherwise (perhaps with logLik() ???)? Additionally I was wondering if there was no routine to calculate pseudo R2s for logit regressions. Currently I am calculating the pseudo R2

I am a graduate student who's just starting to use more advanced statistics and is completely new to R. ?I'm looking for whether parasite prevalence varies with region. ?I'd like to try using just a subset of the data defined by one these covariates, sex, looking at data only from adults, without using it as a covariate in the analyses. ?I tried using subset=sex=='A' in the

# Hello R Users, # # I would like to fit a glm model with quasi family and # logistical link function, but this does not seam to work # with binary data. # # Please don't suggest to use the quasibinomial family. This # works out, but when applied to the true data, the # variance function does not seams to be # appropriate. # # I couldn't see in the # theory why this does not work. # Is

glm(A~B+C+D+E+F,family = binomial(link = "logit"),data=tre,na.action=na.omit) Error in `contrasts<-`(`*tmp*`, value = "contr.treatment") : contrasts can be applied only to factors with 2 or more levels however, glm(A~B+C+D+E,family = binomial(link = "logit"),data=tre,na.action=na.omit) runs fine glm(A~B+C+D+F,family = binomial(link =

Hello everyone, I am currently teaching an intermediate stats. course at UCSD Extension using R. We are using Venables and Ripley as the primary text for the course, with Freund & Wilson's Statistical Methods as a secondary reference. I recently gave a homework assignment on logistic regression, and I had a question about glm. Let n be the number of trials, p be the estimated

Hi I am trying to make glm() work to analyze a toy logit system. I have a dataframe with x and y independent variables. I have L=1+x-y (ie coefficients 1,1,-1) then if I have a logit relation with L=log(p/(1-p)), p=1/(1+exp(L)). If I interpret "p" as the probability of success in a Bernouilli trial, and I can observe the result (0 for "no", 1 for

m1.logit <-lm(default ~ amt.fac + age.fac + duration + chk_acct + history + purpose + sav_acct + employment + install_rate + pstatus

Hi, I am using glm(). I'd like to know what the command means. For example, glm(family=binomial(link=logit)) means logit model. Then, glm(family=gaussian(link=logit)), does this mean? Thank you in advance. Kenji. A Analysis Manager SPI - Strategy, Productivity, Insight., Japan

Hello, I searched everywhere but I didn't find what I want, that is why I as the question here. Threads discussing this issue on this mailing list are already quite old. Does anybody know of a function in R which allows to estimate ordered probit/logit model with random coefficients. The only mixed effect model I found was clmm of the ordinal package but it only provides random intercepts. I

I am creating a model attempting to predict the probability someone will reoffend after being caught for a crime. There are seven total inputs and I planned on using a logistic regression. I started with a null deviance of 182.91 and ended up with a residual deviance of 83.40 after accounting for different interactions and such. However, I realized after that my code is different from that in

Hi R-help, Is there such a thing as a function in R for fitting a GLM where the response is distributed as a Beta distribution? In my case, the response variable is a percentage ([0,1] and continuous). The current glm() function in R doesn't include the Beta distribution. Thank you for any help on this topic. Sincerely, Sharon K?hlmann

Hi all, I have to run a logit regresion over a large dataset and I am not sure about the best option to do it. The dataset is about 200000x2000 and R runs out of memory when creating it. After going over help archives and the mailing lists, I think there are two main options, though I am not sure about which one will be better. Of course, any alternative will be welcome as well. Actually, I

Hi, I had a logit regression, but don't really know how to handle the "Warning message: non-integer #successes in a binomial glm! in: eval(expr, envir, enclos)" problem. I had the same logit regression without weights and it worked out without the warning, but I figured it makes more sense to add the weights. The weights sum up to one. Could anyone give me some hint? Thanks a lot!

Dear all, I have a question about the glm. When the events of an observation is 0, the logit function on it is Inf. I wonder how the glm solve it. An example: Treat Events Trials A 0 50 B 7 50 C 10 50 D 15 50 E 17 50 Program: treat <- factor(c("A", "B", "C", "D", "E")) events <- c(0, 7, 10, 15,

Dear all, I am using glm function in order to estimate a logit model i.e. glm(Y ~ data[,2] + data[,3], family = binomial(link = "logit")). I also created a function that estimates logit model and I would like it to compare it with the glm function. So, does anyone know what optimizer or optimization method glm uses in order to derive the result? Thank you Dimitris -- View this

Many thanks for taking the time to read this! I am looking at the repeatability of behaviour between re-sighted individuals across discrete time periods (annual breeding seasons). My approach was to run a GLM (with a logit link - the data are proportional, presence v. absence of behaviour) for each breeding season. I included the re-sighted individuals as a factor (categorical variable) (i.e.

Hi All, When modeling with glm and family = binomial (link = logit) and response values of 0 and 1, I get the predicted probabilities of assigning to my class one, then I would like to compare it with my vector y which does have the original labels. How should I change the probabilities into values of zero and 1 and then compare it with my vector y to find out about the accuracy of my

Posted about this earlier. Didn't receive any response But, some further research leads me to believe that MAYBE a GLMM or a GEE function will do what I need. Hello, I have a bit of a tricky puzzle with trying to implement a logit model as described in a paper. The particular paper is on horseracing and they explain a model that is a logit trained "per race", yet somehow the