similar to: calculate likelihood based on logit regression

I got the following warning messages when I did a binomial logit regression using glm(): Warning messages: 1: Algorithm did not converge in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, 2: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, Can some one share your thoughts on how to

Hi. I'm attempting to fit a logistic/binomial model so I can determine the influence of landscape on the probability that a box gets used by a bird. I've looked at a few sources (MASS text, Dalgaard, Fox and google) and the examples are almost always based on tabular predictor variables. My data, however are not. I'm not sure if that is the source of the problems or not because the

Dear Adrian and Ian (and r-helpers), I encountered a curious result in developing an example using the bicreg function in the BMA package: I noticed that pairs of models with equal R^2 and equal numbers of predictors had nevertheless different BIC values. Looking at the bicreg function, the definition of BIC appears to be the usual one, or close to it [bic <- n * log(1 - r2/100) + (size - 1) *

I thought I would have another try at explaining my problem. I think that last time I may have buried it in irrelevant detail. This output should explain my dilemma: > dim(S) [1] 1455 269 > summary(as.vector(S)) Min. 1st Qu. Median Mean 3rd Qu. Max. -1.160e+04 0.000e+00 0.000e+00 -4.132e-08 0.000e+00 8.636e+03 > sum(as.vector(S)==0)/(1455*269) [1]

Following p.206 of "Statistical Models in S", I wish to change the code for summary.glm() so that it estimates the dispersion for binomial & poisson models when the parameter dispersion is set to zero. The following changes [insertion of ||dispersion==0 at one point; and !is.null(dispersion) at another] will do the trick: "summary.glm" <- function(object, dispersion =

Hello, I am trying to run a mixed effects nested ANOVA but none of my codes are giving me any meaningful results and I am not sure what I am doing wrong. I am a new user on R and would appreciate some help. The experimental design is that I have some frogs that have been exposed to three acoustic Treatments and I am measuring neural activity (egr), in 12 brain regions. Some frogs also called

Dear all, Currently I have the following output > mydensity <- density(x) > print(mydensity) x y Min. : -92.14 Min. :0.000e+00 1st Qu.: 356.66 1st Qu.:5.530e-09 Median : 805.45 Median :4.681e-05 Mean : 805.45 Mean :5.564e-04 3rd Qu.:1254.24 3rd Qu.:3.370e-04 Max. :1703.04 Max. :5.541e-03 How can I access the Max value of

Hi there Sorry for what may be a naive or dumb question. I have the following data: > x <- c(1,2,3,4) # predictor vector > y <- c(2,4,6,8) # response vector. Notice that it is an exact, perfect straight line through the origin and slope equal to 2 > error <- c(0.3,0.3,0.3,0.3) # I have (equal) ``errors'', for instance, in the measured responses Of course the

Hi, I am new to model selection by coefficient shrinkage method such as lasso. And I became particularly interested in variable selection in Cox regression by lasso. I became aware of the coxpath() in R package glmpath does lasso on Cox model. I have tried the sample script on the help page of coxpath(), but I have difficult time understanding the output. Therefore, I would greatly appreciate if

Dear list and Martin, I'm testing different approaches to fit an electricity demand time series and come upon the fracdiff package (v 1.3-1) for fitting fractional ARIMA models. The following questions are motivated by this package. 1. Despite having a help page, the residuals and fitted functions don't seem to have implementation, or did i miss something obvious? Alternatively, having a

Dear all, I have run into a problem when running some code implemented in the Bioconductor panp-package (applied to my own expression data), whereby gene expression values of known true negative probesets (x) are interpolated onto present/absent p-values (y) between 0 and 1 using the *approxfun - function*{stats}; when I have used R version 2.8, everything had worked fine, however, after updating

This is a simple R program that I have been trying to run. I keep running into the "singular matrix" error. I end up with no sensible results. Can anyone suggest any changes or a way around this? I am a total rookie when working with R. Thanks, Rasika > library(survival) Loading required package: splines > args(coxph) function (formula, data, weights, subset, na.action, init,

Hello everybody I want to compare the proportions of germinated seeds (seed batches of size 10) of three plant types (1,2,3) with a glm with binomial data (following the method in Crawley: Statistics,an introduction using R, p.247). The problem seems to be that in two plant types (2,3) all plants have proportions = 0. I give you my data and the model I'm running: success failure

Dear all, This question is partly statistics and partly R and I apologise in advance for my (usual) verbosity! My data is a little more complicated that this suggests, but essentially I have proportion data from different studies (id), each from a specific country and region of the World. I would like to examine the variables that affect the proportion, but these factors are hierarchical. In case

Hello, I just ran the predict.StructTS function using the AirPassengers data and got a ridiculous result. Here's what I ended up with: http://24.210.155.111/PredictWhat!.pdf Who wrote this? Am I seriously supposed to think this function would accurately predict a time series? -AnalogKid

Hi My code is as following: #include <R.h> #include <Rinternals.h> //* the Projector part *// void Projector(double *L, int *dimL, double *G, int *dimG, double *W, int *dimW, int *xymod, int *dimxy, double *modif, int *dimif, double *Lsum) { ...} //* the interface part *// #define getDim(A) INTEGER(coerceVector(getAttrib(A,R_DimSymbol), INTSXP)) SEXP Projector5(SEXP L, SEXP G,

> Subject: [R] Question on running tseries::garch on Mac OSX > Date: Sat, 18 Jan 2003 15:58:50 -0800 > From: Nicholas Waltner <nwaltner at attbi.com> > To: <R-help at stat.math.ethz.ch> > > Hello, > > When I run the garch examples, I get the following output: > > > dax.garch <- garch(dax) > > ***** ESTIMATION WITH ANALYTICAL GRADIENT *****

In my post at https://stat.ethz.ch/pipermail/r-help/2011-October/292019.html I included an undefined term "ej". The problem code should be as follows. It seems like a simple linear programming problem, but for some reason my code is not finding the solution. obj <- c(rep(0,3),1) col1 <-c(1,0,0,1,0,0,1,-2.330078923,0) col2 <-c(0,1,0,0,1,0,1,-2.057855981,0) col3

I have a file with 4 columns per line, all pipe delimited. $ wc -l cmie_firm_data.text 89325 cmie_firm_data.text $ ls -al cmie_firm_data.text -rw-r--r-- 1 ajayshah ajayshah 4415637 Jul 5 15:25 cmie_firm_data.text $ awk -F\| '(NF != 4)' cmie_firm_data.text $ head cmie_firm_data.text All figures are for the year 20030331||| Company|GVA Less Interest (Rs. thousand)|Interest (Rs.

Hello, I have a short question about the prcomp function. First I cite the associated help page (help(prcomp)): "Value: ... SDEV the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix). ROTATION the matrix of variable loadings