similar to: Reduced Error Logistic Regression, and R?

Hello, I am trying to run the APE program COMPAR.GEE with a model containing a categorical response variable and a mixture of continuous and categorical independent variables. The model runs when I have categorical (binary) response and two continuous independent variables (VAR1 and VAR2), but when I include a categorical (binary) independent variable (VAR3), I receive the following output with

Dear all, is it possible to perform logstic type of geographical weighted regression in R software? thanks in advance. robert. [[alternative HTML version deleted]]

Hi I am working on corpora of automatically recognized utterances, looking for features that predict error in the hypothesis the recognizer is proposing. I am using the glm functions to do logistic regression. I do this type of thing: * logistic.model = glm(formula = similarity ~., family = binomial, data = data) and end up with a model: > summary(logistic.model) Call:

Hi, Dear R-help I know there are some R package to deal with zero-inflated count data. But I am now looking for R package to deal with zero-inflated continuous data. The response variable (Y) in my dataset contains a larger mount of zero and the Non-zero response are quite right skewed. Now what i am doing is first to use a logistic regression on covariates (X) to estimate the probability of Y

Hi, I have a problem when checking the package 'probdistr' (on probability distributions). I got this warning * checking index information ... WARNING Vignettes with missing or empty \VignetteIndexEntry: [1] "probdistr-chi" "probdistr-contextra" "probdistr-discrete" [4] "probdistr-discrextra" "probdistr-exp"

Thanks for telling me that you could not get my message, I hope this work better... so my question was: I built a population matrix to which I applied the fonction eigen in order to find the main parameters about my population. I know that the first eigen value correspond to lambda or exponential growth rate of my population. My problem is that I want to have the 95% confidence interval of the

I got some R code that I don't understand. Question as comment in code //where is t comming from, what is phi inverse rAC <- function(name, n, d, theta){ #generic function for Archimedean copula simulation illegalpar <- switch(name, clayton = (theta < 0), gumbel = (theta < 1), frank = (theta < 0), BB9 = ((theta[1] < 1) | (theta[2] < 0)), GIG = ((theta[2] < 0) |

Hi, I have this string, in which I want to extract some of it's element: > x <- "Best-K Gene 11340 211952_at RANBP5 Noc= 3 - 2 LL= -963.669 -965.35" yielding this array [1] "211952_at" "RANBP5" "2" In Perl we would do it this way: __BEGIN__ my @needed =(); my $str = "Best-K Gene 11340 211952_at RANBP5 Noc= 3 - 2 LL= -963.669

Hello, Is there an example out there that shows how to treat each of the predictor variable types when doing logistic regression in R? Something like this: glm(y~x1+x2+x3+x4, data=mydata, family=binomial(link="logit"), na.action=na.pass) I'm drawing mostly from: http://www.ats.ucla.edu/stat/r/dae/logit.htm ...but there are only two types of variable in the example given. I'm

Hi, I'm designing a experiment in order to compare the growing of several clones of a tree specie. It will be a complete randomized block design. How can I decide what model of mean comparision to choose? LSD, HSD,TukeyHSD, Duncan,...? Thanks in advance

Dear all, I'm interested in extracting the score from multinomial logistic regression models fit using multinom, to assess the stregth of assocation of the parameter with the response (akin to the score from clogit/cox regression). currently I'm using R 1.8.1. Is there a function that will extract the score from a multinom object or how i can get back to it? or from using glm? I

Hi, I was usuing the fExtemes package, and wanted to obtain some of the values returned from the function gumbelFit(). For example, in the following code, I would like to access 'mu' and 'beta' from the object 'para'. How should I go about doing this? Is there any generic method to access the object? ----------------------------------- >library("fExtremes")

Dear all R-users, I am a new user of R and I am trying to build a discrete choice model (with more than two alternatives A, B, C and D) using logistic regression. I have data that describes the observed choice probabilities and some background information. An example below describes the data: Sex Age pr(A) pr(B) pr(C) pr(D) ... 1 11 0.5 0.5 0 0 1 40 1 0 0 0 0 34 0 0 0 1 0 64 0.1 0.5 0.2 0.2 ...

Hi folks, I was wondering how to run a mixed models approach to analyze a linear regression with a user-defined covariance structure. I have my model y = xa +zb +e and b ~ N (0, C*sigma_square). (and a is a fixed effects) I would like to provide R the C (variance-covariance) matrix I can easily provide an example, but at this point I am first trying to know what is the best package the

Hi Folks, I've just come across a kind of problem which leads me to wonder how to approach it in R. Basically, each a set of items is subjected to a series of "impacts" until it eventually "fails". The "force" of each impact would depend on covariates X,Y say; but as a result of preceding impacts an item would be expected to have a "cumulative

Hello: If the question is how likely an event will occur at a give time point, can we use logistic regression with time t as a predictor variable? For example, if the data is ID Gender Tenure Churn 1 M 17 0 2 M 3 1 3 M 6 0 4 F 10 1 5 F 9 0 6 F

Dear R-users, I would like to calculate elasticities and sensitivities of each parameters involved in the following transition matrix: A <- matrix(c( sigma*s0*f1, sigma*s0*f2, s, v ), nrow=2, byrow=TRUE,dimnames=list(stage,stage)) The command "eigen.analysis" avaliable in package "popbio" provides sensibility matrix and elasticity matrix

Dear all, I have been trying to fit my data (only right censored) with gumbel distribution using fitdistrplus. I am getting very high standard error. I have been wondering why. The followings are the outputs: fit1=fitdistcens(dr0, "gumbel", start=list(a=99, b=0.6), optim.method= "L-BFGS-B", lower = 0.0, upper = Inf) > summary(fit1) FITTING OF THE DISTRIBUTION ' gumbel

Hi all, I have a problem with concatinating strings while taking as a path here the problem is i have to take path as FPATH<-"D:\\Kiran" and file name as Fname<-"FINDINGS.CSV" and while I am reading this table I have to take path with using these two strings because in "FPATH" there is many files like findings.csv, and path will be

Dear list, I want to perform a logistic regression analysis with multiple categorical predictors (i.e., a logit) on some data where there is a very definite relationship between one predicator and the response/independent variable. The problem I have is that in such a case the p value goes very high (while I as a naive newbie would expect it to crash towards 0). I'll illustrate my problem