similar to: cumVar and cumSkew

Dear userRs, when applied the summary function to a glm fit (e.g Poisson) the parameter table provides the categorical variables assuming that the first level estimate (in alphabetical order) is 0. What is the standard error for that variable then? Are the standard errors calculated assuming a normal distribution? Many thanks, -- View this message in context:

Hi, I am trying to fi a glm-poisson model to 400.000 records. I have tried biglm and glmulti but i have problems... can it really be the case that 400.000 are too many records??? I am thinking of using random samples of my dataset..... Many thanks, -- View this message in context: http://r.789695.n4.nabble.com/glm-poisson-fitting-400-000-records-tp3925100p3925100.html Sent from the R help

Hi userRs! I am trying to fit some GLM-poisson and neg.binomial. The neg. Binomial model is to account for over-dispersion. When I fit the poisson model i get: (Dispersion parameter for poisson family taken to be 1) However, if I estimate the dispersion coefficient by means of: sum(residuals(fit,type="pearson")^2)/fit$df.res I obtained 2.4. This is theory means over-dispersion since

Hi! Is there an easy way to add to the scree-plot labels to each value pertaining to the cumulative proportion of explained variance? Thanks and a happy new year Anne ---------------------------------------------------- Anne Piotet Tel: +41 79 359 83 32 (mobile) Email: anne.piotet@m-td.com --------------------------------------------------- M-TD Modelling and Technology Development PSE-C

I need to calculate a large number of t statistics, and would like to do so via matrix operations. So far I have figured out a way to calculate the mean of each row of the matrix: d <- matrix(runif(100000,1,10), 1000, 10) # some test data s <- rep(1,ncol(d)) # a sum vector to use for matrix multiplication means <- (d%*%s)/ncol(d) This is at least 1 order of magnitude faster than

I've uploaded a package colSums_1.0.tar.gz to CRAN /src/contrib/Devel. It contains functions colSums, colMeans, colVars, colStdevs, rowSums, rowMeans, rowVars, and rowStdevs. These do simple, fast arithmetic on columns/rows of a matrix, or more generally across dimensions of an array, e.g. colSums(m) = apply(m, 2, sum) but faster. They should be compatible with the corresponding S-Plus

Hello all! I have a problem to extract values greater that for example 1820. I try this code: x[x[,1]>1820,]->x1 Please help me! Thank you! The data structure is: structure(c(2.576, 1.728, 3.434, 2.187, 1.928, 1.886, 1.2425, 1.23, 1.075, 1.1785, 1.186, 1.165, 1.732, 1.517, 1.4095, 1.074, 1.618, 1.677, 1.845, 1.594, 1.6655, 1.1605, 1.425, 1.099, 1.007, 1.1795, 1.3855, 1.4065, 1.138, 1.514,

Greeting All I am using Filebench benchmark in an "Interactive mode" to test ZFS performance with randomread wordload. My Filebench setting & run results are as follwos ------------------------------------------------------------------------------------------ filebench> set $filesize=5g filebench> set $dir=/hdd/fs32k filebench> set $iosize=32k filebench> set

Hello, I'm fairly new to R so please excuse me if I am asking something obvious. I have looked in the FAQ, Introduction, and help pages, and searched the archives, but I don't know much about graphics yet. I'm running Red Hat Linux 2.14.18 on a machine blessed with dual 1.5 Xeon processors and 3.7GB of RAM. I have a very large dataset with 27 variables, and in exploring the data

Dear R-help, I am having trouble with your scatterplot3d program. For help with this problem I was directed to your address by Martin Maechler at " r-core-bounces at r-project.org." I'm also sending a CC to " r-core-owner at r-project.org" as I'm not yet certain of the proper address to use for this. I have R version 2.8.1 and have downloaded 'scatterplot3d.'

Hi, I fit both Poisson and NB (negative binomial) models to some empirical data. Although models provide me with sensible parameters, in the case of the NB models i get three inconsistencites: - First, the total number of occurrences predicted by the model (i.e. fitted(fit)) is much greater than those of the data. I realise that poisson and NB models are different in the sense that