similar to: modEvA D-squared for gamma glm

Hi All, I have two files. 1. with only one column 2. data matrix I need to compare first columns of both files and print the rows from second file for the overlapping entries. I have solutions for awk and sed, but I need how to do it in R. Thanks Regards Anchal -- Anchal Sharma, PhD Postdoctoral Fellow 195, Little Albany street, Cancer Institute of New Jersey Rutgers University NJ-08901

hello dear list! since we want to do a model analysis and some people would like to see pseudo-R^2 values for different types of glm of a logistic regression, i've decided to write a function that computes either nagelkerkes normed pseudo-R or cox & snells pseudo-R. however, i am not clear as in the decisive step, i need to calculate the log of (maximum likelihood estimates of model

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 all, can somebody please help me how to calculate "McFadden R-square" for a LOGIT model? Corresponding definition can be found here: http://publib.boulder.ibm.com/infocenter/spssstat/v20r0m0/index.jsp?topic=%2Fcom.ibm.spss.statistics.help%2Falg_plum_statistics_rsq_mcfadden.htm Here is my data: Data <- structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,

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

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

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 =

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

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: >

Dear R-Helpers: I have tried everything I can think of and hope not to appear too foolish when my error is pointed out to me. I have some real data (18 points) that look linear on a log-log plot so I used them for a comparison of lm() and survreg. There are no suspensions. survreg.df <- data.frame(Cycles=c(2009000, 577000, 145000, 376000, 37000, 979000, 17420000, 71065000, 46397000,

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

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",

Hello everyone, I wanted to ask if there is an R-package to fit the following Poisson regression model log(\lambda_{ijk}) = \phi_{i} + \alpha_{j} + \beta_{k} i=1,\cdots,N (subjects) j=0,1 (two levels) k=0,1 (two levels) treating the \phi_{i} as nuinsance parameters. Thank you very much -- -Tony [[alternative HTML version deleted]]

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 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 users, I want to apply zero inflated models with continuous and categorical variables and I used pscl package from R and the zeroinf() function. My question are the follow: a) The value of fitted.values is mu or (1-p)*mu? where p is the probability of zero came form a zero point mass b) If mu is zero, how do i know if it is a zero from the zero point mass or from the count process?

Hello, I'm using GLMM on the pscl package and i'm not getting the AIC on the summary. The code i'm using is (example) : mmall3 <-glmmPQL(allclues ~ cycloc + male, data=dados, family=poisson, random=~1|animal/idfid) and the results: Linear mixed-effects model fit by maximum likelihood Data: dados AIC BIC logLik NA NA NA Random effects: Formula: ~1 | animal

Hi @all, backing up and verifying _printing_ databases uses 20-96% cpu and takes a long time (there are only few entries and smbd and nmbd were not running): for i in /var/lock/samba/?*.tdb /var/lock/samba/printing/?*.tdb /etc/?*.tdb do date echo $i.samba.bak rm -f $i.samba.bak tdbbackup -s .samba.bak $i

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",