similar to: glm

# 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

Hi, I have the following data data1 <- data.frame(count = c(0,1,1,2,4,5,13,16,14), weeks = 1:9,                     treat=c(rep("1mg",3),rep("5mg",3),rep("10mg",3))) and I am using library(splines) to fit glm.m <- glm(count~bs(weeks)+as.factor(treat),family=poisson,data=data1) and I am interested in predicting the count variale for the weeks 10, 11 and

Hello, I have 12 sample each sample has got 1000 observation, i.e I have a matrix X with 1000 rows and 12 columns! m <- svm(t(X)) p <- predict (m) Can anyone tell me how to use svmtrain() in R! Many Yhanks, Samuel [[alternative HTML version deleted]]

Hello, I have got the following problem: > setwd("C:/temp") > library(xlsReadWrite) > MyData <- read.xls(file="Mappe1.xls", colNames = TRUE,dateTimeAs = "isodatetime") > attach(MyData) > MyData name value times 1 A1 2 2006-05-12 2 A2 3 2006-05-16 3 A3 1 2006-05-12 4 A4 4 2006-05-12 5 A5 2

Hello, if we suppose that times <- c("2006-05-14", "2006-06-12", "2006-06-12", "2006-05-14", "2006-05-14", "2006-06-12") value <- c(2,3,1,4,3,1) then with plot(times, value) we have two boxplots in one graph for 2006-05-14 and 2006-06-12 respectively! Is it possible to have them in a scatterplot? and

Hello, I am trying to read the SAS file MyData.sa7bdat in R! This file is saved under D:\data! I therefore wrote > path <-"D:/SasData" > sashome <- "C/Progra, Files/SAS Institute/9_1/SAS" > sascmd <- file.path(sashome, "sas.exe") > MyData <- read.ssd(path, "MyData", sascmd=sascmd) The results what I get:

In this real example (below), all four of the replicates in one treatment combination had zero failures, and this produced a very high standard error in the summary.lm. =20 Just adding one failure to one of the replicates produced a well-behaved standard error. =20 I don't know if this is a bug, but it is certainly hard for users to understand. =20 I would value your comments=20 =20 Thanks =20

Hello, I am interested in Figure 2 in http://www-stat.stanford.edu/~tibs/ftp/cus.pdf Can anyone tell please how to create this plot? Many thanks Samuel [[alternative HTML version deleted]]

Dear all, I am new to R and my question may be trivial to you... I am doing a GLM with binomial errors to compare proportions of species in different categories of seed sizes (4 categories) between 2 sites. In the model summary the residual deviance is much higher than the degree of freedom (Residual deviance: 153.74 on 4 degrees of freedom) and even after correcting for overdispersion by

I am unable to calculate confidence intervals for the slope estimate in a quasibinomial glm using confint(). Below is the output and the package info for MASS. Thanks in advance! R 2.9.2 MASS 7.2-48 > confint(glm.palive.0.str) Waiting for profiling to be done... Error: dims [product 37] do not match the length of object [74] > glm.palive.0.str Call: glm(formula = cbind(alive, red) ~ str,

Hello R-list. I am a "long time listener - first time caller" who has been using R in research and graduate teaching for over 5 years. I hope that my question is simple but not too foolish. I've looked through the FAQ and searched the R site mail list with some close hits but no direct answers, so... I would like to estimate QAIC (and QAICc) for a glm fit using the

Hello, Is there is any R function computes the AUC for paired data? Many thanks, Samuel [[alternative HTML version deleted]]

Hello everyone, I am confused about how the dependent variable should be specified, e.g. say S and F denote series of successes and failures. Is it share<-S/(S+F) glm(share~x,family=quasibinomial) or glm(cbind(S,F)~x,family=quasibinomial) The two variants produce very different dispersion parameter and deviances. The book by Crawley, the only one R-book a have, says the second variant is

I'm trying to plot an extrapolated logistic sigmoid curve using glm(..., family=binomial) as follows, but neither the fitted() points or the predict()ed curve are plotting correctly: > year <- c(2003+(6/12), 2004+(2/12), 2004+(10/12), 2005+(4/12)) > percent <- c(0.31, 0.43, 0.47, 0.50) > plot(year, percent, xlim=c(2003, 2007), ylim=c(0, 1)) > lm <- lm(percent ~ year)

Hello, suppose one has the following values x1 <- rnorm(10,5,1) x2 <- rgamma(10,5,1) y <- rnorm(10,4,1) mydat <- data.frame(y,x1,x2) then one can use glm like mod <- glm(y~x1+x2, data=mydat, family=gaussian) But how could I estimate y_hat? Thanks alot! Sam --------------------------------- [[alternative HTML version deleted]]

Hi R-people: When I use the command to fit a model with an intercept, only: glm ( formula=haspdata ~ 1, data=dat, family=binomial, weights= dat$hy.wgt.s, subset=(dat$haspdat0!=3) ) I get the message: Warning message: non-integer #successes in a binomial glm! in: eval(expr, envir, enclos) Does anyone know what this means?? The data for this command is listed below. Thanks, Phil Smith CDC

Hi, my dependent variable is a proportion ("prob.bind"), and the independent variables are factors for group membership ("group") and a covariate ("capacity"). I am interested in the effects of group, capacity, and their interaction. Each subject is observed on all (4) levels of capacity (I use capacity as a covariate because the effect of this variable is normatively

Hello, I have got the following problem: > times <- c("02.07.2007", "03.07.2007","03.09.2007", "04.07.2007","05.07.2007") > mode(times) [1] "numeric" > tim <- as.character(times) > mode(tim) [1] "character" > sort(times) [1] "02.07.2007" "03.07.2007" "03.09.2007"

Problems with predict and lines in plotting binomial glm Dear R-helpers I have found quite a lot of tips on how to work with glm through this mailing list, but still have a problem that I can't solve. I have got a data set of which the x-variable is count data and the y-variable is proportional data, and I want to know what the relationship between the variables are. The data was

Hi, I have a question about logistic regression in R. Suppose I have a small list of proteins P1, P2, P3 that predict a two-class target T, say cancer/noncancer. Lets further say I know that I can build a simple logistic regression model in R model <- glm(T ~ ., data=d.f(Y), family=binomial) (Y is the dataset of the Proteins). This works fine. T is a factored vector with levels cancer,