similar to: Is there any way to get score vector in each iteration in glm??

Hello I am running a project but I encounter a problem . I would be happy to receive help : problem: I have a binary dependent variable and some covariates logit(y)=a+bx+cz . I want to estimate the score vectors and their covariance by the usage of logit function and so glm in R .The vlaue of one of the coefficient ( like b) is known previously and I want to extract a and c and covariance

Hello; Does anybody knows that R have a function for Generelized Negative Binomial model, something like "gnbreg" in "STATA" where dispersion parameter itself is a function of covaraites ? Thanks; [[alternative HTML version deleted]]

Hello ; I am doing a regression of count data (number of award and there are some covariates) I have estiamted the parameters of negative binomial distribuion (lambda is a function of covaraites, GLM model) by glm.nb function and training dataset. Now I want to predict the number of award (for example y=0, y=1, y=2,) or testing dataset. I dont know how to calculate this numbers? I would be very

I have just committed some code to the r-devel branch to implement the Rao efficient score test. This is asymptotically equivalent to the LRT, but there is some indication that it might have better properties in smaller samples since it is based more directly on the distribution of the sufficient sums under the null hypothesis (e.g., if you have a divergent fit to the model under the alternative,

Hi everyone, I am trying to get the factor score for each individual case from a principal component analysis, as I understand, both princomp() and prcomp() can not produce this factor score, the principal() in psych package has this option: scores=T, but after running the code, I could not figure out how to show the factor score results. Here is my code, could anyone give me some advice please?

Dear all, I use the Andersen plot to check for proportional hazards assumption for a factor (say x) in the Cox regression model and obtained a straight line that pass through the origin. However, the formal test done by the R-function cox.zph, which is based on the plot of Schonefeld residuals against time, indicates that proportional hazards assumption is violated. Further, a plot of the score

Is there a way to compute score residuals from glm() fits (my current example is Poisson regression)? I'm referring to the types of residuals used in computing the sandwich covariance matrix estimator. The sandwich package will compute the Huber-White sandwich estimator but I need to use the cluster sandwich estimator to account for intra-cluster correlation. Thanks Frank ----- Frank

Hi, I am new to R and AIC scores but what I get from coxme seems wrong. The AIC score increases as p-values decrease. Since lower AIC scores mean better models and lower p-values mean stronger effects or differences then shouldn't they change in the same direction? I found this happens with the data set rats as well as my own data. Below is the output for two models constructed with the rats

Dear All, I've been using step function to find me the best model.this basically works by using AIC score fucntion that is implemented on step(). The problem I'm facing with lots of variables on the model for example : step(lm(x1~x2,x3,x4,......x13)) sometimes gives me a warning message which is : AIC=- inf Coefficients: (Intercept) wnt3.values wnt6.values wnt10b.values

I am running a series of candidate mixed models using lmer (package lme4) and I'd like to be able to compile a list of the AIC scores for those models so that I can quickly summarize and rank the models by AIC. When I do logistic regression, I can easily generate this kind of list by creating the model objects using glm, and doing: > md <- c("md1.lr", "md2.lr",

Hi -- I have found that by default the function print.glm() uses 3 significant figures when printing out null and model deviances and the aic. Of course, this is not wrong. But if a person fitted two nested models and compared the resulting deviances obtained from print.glm(), the resulting hypothesis test could indeed be wrong because of this rounding. The function summary() applied to a glm

Dear all, I have the following dataset with results from an experiment with individual bats that performed two tasks related to prey capture under different conditions: X variables: indiv - 5 individual bats used in the experiment; all of which performed both tasks task - 2 tasks that each individual bat had to perform dist - 5 repeated measures of individual bats at 5 different distances from

Hello, I ran AIC for some competing models I created. I get df and an AIC score from the AIC procedure. Can I use the models with the lowest AIC scores from this procedure to choose my 'best' models? If not, what else do I need to do (and know) and how can I do it in R to chose the 'best' models? Thank you kindly, Michael [[alternative HTML version deleted]]

When I do t.test on two distributions (see example below), it outputs numerous data about the t.test. What I'd like to do is individually capture some of this data and assign it to other variables. However, I am unable to find anything in the help section. In the example below, the t value is -4.0441 and the p-value is 0.006771 How can I assign these values to two variables, let's

Hello. I am still a newbie in R. Excuse me if I am asking something obvious. My efforts to get an answer through browsing the mailing archives failed. I want to perform an augmented Dickey-Fuller test and to obtain AIC and BIC and to be able to impose some linear restrictions on the ADF regression so as to decide the correct order of autoregression. However I could find no obvious way to impose

Dear R community, Confused by some of my lab results I ask for the definition of the null hypothesis of a two-way analysis of variance in R (anova() and aov()). Starting with the following model y = a_i + b_j , i in A and j in B is the tested null hypothesis H_0: a_i = 0 for all i in A or H_0: a_m = a_n for any m and n in A? Consequently the same questions for interaction effects.

> -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch]On Behalf Of Thomas Petzoldt > Sent: 06 September 2005 06:34 > Cc: petzoldt at rcs.urz.tu-dresden.de; R-Help > Subject: Re: [R] model selection vs. H0 based testing > > > Hello, > > I wish to thank Douglas Bates very much for clarification and

Hi all, I can't find the error in the binomial GLM I have done. I want to use that because there are more than one explanatory variables (all categorical) and a binary response variable. This is how my data set looks like: > str(data) 'data.frame': 1004 obs. of 5 variables: $ site : int 0 0 0 0 0 0 0 0 0 0 ... $ sex : Factor w/ 2 levels "0","1": NA NA NA

Hi There, There are 300 boy students and 100 girl students in a class. One interesting question is whether boy is smarter than girl or not. first given the exam with a difficulty level 1, the number of the student who got A is below 31 for boy, 10 for girl. Then we increase the difficulty level of the exam to level 2, the number of the student who got A is below 32 for boy, 10 for girl. We

Hi, I have probability estimates from two predictive models. I have these estimates and also a binary outcome for a validation data set not used in calibrating either model. I would like to calculate the Brier score for both models on this binary outcome and test the hypothesis that the Brier scores are equal from the two models. I have not been able to find an R function to do this, can