similar to: Data handling/optimum glm method.

Hi There, I am trying to fit a logit model to some data in a CSV file in R. Here is my code: Prepared_Data = read.csv("Prepared_Data.csv", header=TRUE) Prepared_Data attach(Prepared_Data) lrfit<-glm(C3~A1*B2*D4*E5,family = binomial) anova(lrfit, test="Chisq") write.csv(anova(lrfit, test="Chisq"), file="CWModelA.csv") shell.exec("CWModelA.csv")

Hello, I am wondering if there is an easy way to combine loess() with glm() to produce a locally fitted generalised regression. I have a data set of about 5,000 observations and 5 explanatory variables, with a binary outcome. One of the explanatory variables (lets call it X) is much more predictive than the others. A single glm() regression over the entire data set produces rather poor results,

Dear R users, I'm hoping that more experienced users will be able to assist me in examining the model fit of a mixed generalised linear model. The example using the data 'bacteria' within the MASS package will hopefully illustrate what I would like to acheive; library(MASS) library(nlme) attach(bacteria) # y being output and the trt - treatment group being an explanatory variable.

> -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of David Firth > Sent: Tuesday, March 16, 2004 1:12 PM > To: Paul Johnson > Cc: r-help at r-project.org > Subject: Re: [R] glm questions > > > Dear Paul > > Here are some attempts at your questions. I hope it's of some help.

Dear list, Is there a generalised form of rbind/cbind for combining matrices/arrays into higher-D structures? ie. if I have: a <- matrix(2,2,2) b <- matrix(3,2,2) how can I get array(rep(c(3,2), each=4), c(2,2,2)) ? It seems like this would be the job of a generalised abind function: abind(a,b, along=1) == rbind(a,b) abind(a,b, along=2) == cbind(a,b) abind(a,b, along=3)

Hi all, Could somebody be so kind to explain to me what is the saturated model on which deviance and degrees of freedom are calculated when fitting a binomial glm? Everything makes sense if I fit the model using as response a vector of proportions or a two-column matrix. But when the response is a factor and counts are specified via the "weights" argument, I am kind of lost as far as

Dear R users, I have used the following model: M1 <- gls(Nblad ~ Concentration+Season + Concentration:Season, data=DDD, weights=varIdent(form=~ 1 | Season*Concentration)) to assess the effect of Concentration and Season on nitrogen uptake by leaves (Nblad). I accounted for the difference in variance across the factor levels by using the varIdent function. Then I wanted to perform multiple

Greetings, everybody. Can I ask some glm questions? 1. How do you find out -2*lnL(saturated model)? In the output from glm, I find: Null deviance: which I think is -2[lnL(null) - lnL(saturated)] Residual deviance: -2[lnL(fitted) - lnL(saturated)] The Null model is the one that includes the constant only (plus offset if specified). Right? I can use the Null and Residual deviance to

Hi Allstaters, Does anybody know if the R package can fit Generalised Additive Models?. Thanks indeed, Aurelio. atobias at ole.com ___________________________________________________________________ Consigue tu e-mail gratuito TERRA.ES Haz click en http://www.terra.es/correo/ -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Readers, I have been reading 'the r book' by Crawley and think that the generalised additive model is appropriate for this problem. The package 'gam' was installed using the command (as root) install.package("gam") ... library(gam) > library(gam) Loading required package: splines Loading required package: akima > library(mgcv) This is mgcv 1.3-25 Attaching

Hello, I am wondering if someone can direct me to the syntax of the Generalised Additive Model in R? In Splus this is gam(formula, ... inputs would be pretty much the same as glm I suspect..) Any response on that (or what package should I grap) would be appreciated very much. Thanks, Peppy Adi-Purnomo -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help

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, I have recently been attempting to find the LD50 from two predicted fits (For male and females) in a Generalised linear model which models the effect of both sex + logdose (and sex*logdose interaction) on proportion survival (formula = y ~ ldose * sex, family = "binomial", data = dat (y is the survival data)). I can obtain the LD50 for females using the dose.p() command in the MASS

Dear R users I am analysing data from a group of twins and their siblings. The measures that we are interested in are all correlated within families, with the correlations being stronger between twins than between non-twin siblings. The measures are all calculated from survey answers and by definition have limited ranges (e.g. -5 to +5), though within the range they are approximately normally

Dear R community I have a question regarding the value of cost complexity parameter "k" used in "tree" package for pruning purpose. Any help in finding the optimum value of "k" is requested. Please give some suggestion in this regard. In the example below i used k=0 but i don't know why? But if i use k=NULL, then it will not plot the resultant tree.

I've noticed that glm and lrm give extremely different results if you attempt to fit a saturated model to a dataset with zero cells. Consider, for instance the data from, Agresti's Death Penalty example [0]. The crosstab table is: , , PENALTY = NO VIC DEF BLACK WHITE BLACK 97 52 WHITE 9 132 , , PENALTY = YES VIC DEF BLACK WHITE BLACK 6 11

I'm trying to figure out if I'm the only one with these problems with them. I recently had a few customers that switched to them because of the price, of course that means that they have to use FXO ports, but it is realy cheaper, so customers don't really care. In any case, there are 2 issues that I can't get solved, and they are not interested in helping. 1. When they tell you

hi, I am required to take text/doc files uploaded to a share/directory in a static ip, do some processing on this queue, convert the file format and save the results in a directory for it to be downloaded (or I could upload the same to another share/folder in another static ip). volume of files is about 1-5 files/second of about 50KB to 100KB each and the converting and delivery has to be in

On Wed, 19 Aug 2015, Jatin Davey wrote: > Hi All > > We use CentOS 6.6 for our application. I have profiled the application and > find that we have a heavy requirement in terms of Disk writes. On an average > when our application operates at a certain load i can observe that the disk > writes / second is around 2 Mbps (Average). Initial thought is, do you really care? 2Mbps

On 8/19/2015 1:54 PM, John Hodrien wrote: > On Wed, 19 Aug 2015, Jatin Davey wrote: > >> Hi All >> >> We use CentOS 6.6 for our application. I have profiled the >> application and find that we have a heavy requirement in terms of >> Disk writes. On an average when our application operates at a certain >> load i can observe that the disk writes / second