similar to: 3.0.8pre2 and tdbackup

Hi, testparm: --------- testparm shows: ERROR: the 'passwd program' (/usr/bin/passwd %u) requires a '%u' parameter. You can see, passwd program _is_ /usr/bin/passwd %u smbstatus: ---------- smbstatus command always shows processes _and_ shares even if using the switches '-p' for showing processes only or '-S' for showing shares only or '-B' for

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

Hello All,  I have used logistic regression glm in R and I am evaluating two models both learned with glm but with different predictors. model1 <- glm (Y ~ x4+ x5+ x6+ x7, data = dat, family = binomial(link=logit))model2 <- glm (Y~ x1 + x2 +x3 , data = dat, family = binomial(link=logit))  and I would like to compare these two models based on the prediction that I get from each model: pred1 =

Dear R users, I'm having a hard time with some very simple things. I have a time series where the dates are in the format 7-Oct-04. I imported the file with read.csv so the date column is a factor. The series is rather long and I want to plot it piece by piece. The function below works fine, except that the labels for date are meaningless (ie 9.47e+08 or 1098000000 - apparently the number of

Greets, I'm using a data frame that looks like: > head(pr2) X1 X2 X3 X4 Y fit res 1 44 33.2 5 30 41.2 39.22201 1.977991 2 43 33.8 4 41 31.7 38.48476 -6.784761 3 48 40.6 3 38 39.4 44.78278 -5.382783 4 52 39.2 7 48 57.5 51.48134 6.018656 5 71 45.5 11 53 74.8 68.25585 6.544153 6 44 37.5 9 65 59.8 53.27743 6.522569 Along with the command: >

Hello, I would like to know if there is a clever way to avoid the problem illustrated below within the xyplot function. x <- seq(1:10) y <- seq(1:10) pr1 <- xyplot(x ~ y) u <- seq(1:12) v <- seq(1:12) pr2 <- xyplot(u ~ v, col = "red", more = FALSE) prts <- list(pr1, pr2) for(i in prts) print(i, more = TRUE) I realize that one possibility is to

Hi, When attempting to restore to LINUX RHEL5 - OCFS2 filesystem received the following error during RMAN restore for nearly all of the datafiles attempted to restore with exception of a couple of smaller datafiles which were smaller < 2GB. ORA-19870: error reading backup piece /db/dumps/TR1_1/rmanbackup/TR1_88_1 ORA-19502: write error on file "/db/devices/db1/PR2/pr2_1/pr2.data1",

Dear Rhelpers I have the problem with initial values, could you please tell me how to solve it? Thank you June > p = summary(maxLik(fr,start=c(0,0,0,1,0,-25,-0.2))) Error in maxRoutine(fn = logLik, grad = grad, hess = hess, start = start, : NA in the initial gradient > p = summary(maxLik(fr,start=c(0,0,0,1,0,-25,-0.2),method="BFGS")) Error in optim(start, func, gr =

Dear R experts Sorry for this question M1 <- 1:10 lcd1 <- c(11, 22, 33, 44, 11, 22, 33, 33, 22, 11) lcd2 <- c(22, 11, 44, 11, 33, 11, 22, 22, 11, 22) lcd3 <- c(12, 12, 34, 14, 13, 12, 23, 23, 12, 12) #generating variables through sampling pvec <- c("PR1", "PR2", "PR3", "PR4", "PR5", "PR6", "PR7",

Hello. I am running 9 poisson regressions with 5 predictors each, using glm with family=gaussian. Gaussian distribution fits better than linear regression on fit indices, and also for theoretical reasons (e.g. the dependent variables are counts, and the distribution is highly positively skewed). I want to determine pseudo R^2 now. However, using the pR2() of the pscl package offers drastically

Dear r helpers I run the following code for nested logit and got a message that Error in optim(c(0, 0, 0, 0, 0.1, -2, -0.2), fr, hessian = TRUE, method = "BFGS") : initial value in 'vmmin' is not finite What does this mean? and how can I correct it? Thank you June > yogurt = read.table("yogurtnp.csv", header=F,sep=",")> attach(yogurt)>

Dear R Users on a self-written function for calculating maximum likelihood probability (plz check function code at the bottom of this message), one value, wden, suddenly jump to zero. detail info as following: w[11]=2.14 lnw =2.37 2.90 3.76 ... regw =1.96 1.77 1.82 .... wden=0.182 0.178 0.179... w[11]=2.14 lnw=2.37 2.90 3.76 ... regw =1.96 1.77 1.82 .... wden=0.182

Help needed with lm function: Dear R's, Could anyone tell me how to replace the link function (probit logit, loglog etc.) in lm with an abitrary user-defined function? The task is to perform ML Estimation of betas for a dichotome target variable. Maybe there is already a package for this (I did not find one). Any hints or a code excerpt would be welcome! Thank you -Jeff jeff.pr2 (at)

I am running 1.3.1 on a Windows (NT 4.0) machine. I've fit a nonlinear model intended to predict crop yield from nutrient information, and want to use the profile function. If I type say, profile(simparj.fm) I get the following error message: "Error in prof$getProfile(): number of iterations exceeded maximum of 5.25515e-308" I used the profiler function to profile simparj,fm step

I have noticed an anomaly with symbolic links. umask 027 mkdir folder1 echo 'hello world' > folder1/file1 cd folder1 ln -s file1 softfile1 rsync -a ../folder1/ ../folder2/ ls -lF ../folder* ../folder1: total 16 -rw-r----- 1 alan alan 12 Apr 26 00:00 file1 lrwxr-x--- 1 alan alan 5 Apr 26 00:02 softfile1@ -> file1 ../folder2: total 16 -rw-r----- 1 alan alan 12 Apr 26

Hi All, I am running a generalized linear model with gamma distribution in R (glm, family=gamma ) for my data (gene expression as response variable and few predictors). I want to calculate r-squared for this model. I have been reading online about it and found there are multiple formulas for calculating R2 (psuedo) for glm (in R) with gaussian (r2 from linear model), logistic regression

Running a binary logit model on the data df <- data.frame(y=sample(letters[1:3], 100, repl=T), x=rnorm(100)) reveals some residual deviance: summary(glm(y ~ ., data=df, family=binomial("logit"))) However, running a multinomial model on that data (multinom, nnet) reveals a residual deviance: summary(multinom(y ~ ., data=df)) On page 203, the MASS book says that "here the

Hey, R users I do not know how to describe my question. I am a new user for R and write the following?code for a dynamic labor economics?model and use OPTIM to get optimizations and parameter values. the following code does not work due to the?equation: ?? wden[,i]<-dnorm((1-regw[,i])/w[5])/w[5] where w[5]?is one of the parameters (together with vector a, b and other elements in vector

Hi guys, I need some help to analyzing my data. I start to describe my data: I have 21 matrices, every matrix on the rows has users and on columns has items, in my case films. Element of index (i, j) represent the rating expressed by user i about item j. I have a matrix for each of professions. An example of a this type of matrix is: item 1 item 2 item 3 item4 id

Hi I have been using a Zero-Inflated negative binomial model fitted using the pscl zeroinfl command but I would like to extract a goodness of fit measure are there any suitable pseudo R^2 measures available for this type of analysis to try and assess the amount of variation in the data explained by the model? I have tried with the pR2 command in pscl (for computing various pseudo R2