similar to: size of a glm-like object

Dear all, I am dealing with the following (apparently simple problem): For some reasons I am interested in passing variables from a dataframe to a specific environment, and in fitting a standard glm: dati<-data.frame(y=rnorm(10),x1=runif(10),x2=runif(10)) KK<-new.env() for(i in 1:ncol(dati)) assign(names(dati[i]),dati[[i]],envir=KK) #Now the following two lines work correctly:

Dear all, The help file for the generic function vcov states "Classes with methods for this function include: 'lm', 'glm', 'nls', 'lme', 'gls', 'coxph' and 'survreg' (the last two in package 'survival')." Since, I am not able to use vcov.coxph(), I am wondering whether I am missing something (as I suspect..) regards, vito

Hi all, I'd like to fit model where the terms both are in the data.frame, mydata say, and are vectors *not in the data.frame*. >obj<-glm(y~x, data=mydata) #works >Z<-pmax(mydata$x-20,0) >(length(Z)==length(obj$y)) >[1] TRUE >update(obj,.~.+Z) #works However for some subset it doesn't works: >obj<-glm(y~x, data=mydata, subset=f==1) #works

my function is glm(a~log(b)+c+d+e,family=binomial,data=f)->aa I want to extract the original data set of aa. How to do it ? You may suggest the model.frame() function. In fact ,i have tried it. model.frame returns a data frame of containing a,log(b) NOT b,c,d,e I want to extract a data frame containing a,b,c,d,e,which is exactly the same as "f" How can I achieve this

Dear all, I would like to write a function like: myfun<-function(x,fn) {xx<-exp(x); x*fn(xx)} where fn is a symbolic description of any function with its argument to be specified. Therefore myfun(5,"2+0.3*y^2") should return 5*(2+0.3*exp(5)^2), myfun(5,"log(y)") should return 5*log(exp(5)) and so on. I tried with "expression" and others, but without success.

I've redirected this reply from r-help to the bugs list. On 11/3/2006 8:25 AM, vito muggeo wrote: > Dear all, > I am dealing with the following (apparently simple problem): > For some reasons I am interested in passing variables from a dataframe > to a specific environment, and in fitting a standard glm: > > dati<-data.frame(y=rnorm(10),x1=runif(10),x2=runif(10)) >

Hi, I make an analysis sequence on R. In some cases the function glm.nb fail to ajust the model. Its Ok. The problem is that this error stop the program. I need treat this error and not stop the program. Something like this: ... model <- glm.nb(y~x,maxit=1000) if(glm.nb fail) { teste[i] <- 0 } else { teste[i] <- anova(modelo)$"P(>|Chi|)"[2] } ... I try this: ...

Hello, I am currently working with the betareg package, which allows the fitting of a variable dispersion beta regression model (Simas et al. 2010, Computational Statistics & Data Analysis). I was wondering whether there is any package in R that allows me to fit variable dispersion parameters in the standard logistic regression model, that is to make the dispersion parameter contingent upon

Hello R users I ran more than 100 logistic regression analyses. Some of the analyses gave me this kind warning below. ########################################################### Warning messages: 1: algorithm did not converge in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, ... 2: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y,

Hi all, Although I'm able to manage functions in R, because I'm writing my first package I'd like to optmize them, but I'm not so expert to do it and I spent a lot of my time without success. So I was wonder whether someone could give me some advice. Let the function myfn<-function(glm.obj,....){ #glm.obj is a glm object ..... class(out)<- c("myclass",class(obj))

dear all, Given a matrix A, say, I would like to apply a bivariate function to each combination of its colums. That is if myfun<-function(x,y)cor(x,y) #computes simple correlation of two vectors x and y then the results should be something similar to cor(A). I tried with mapply, outer,...but without success Can anybody help me? many thanks in advance, vito

Hello all, I've been having trouble with assessing a breakpoint in a logistic GLM with two explanatory variables. For this analysis I've been using the 'segmented' package version 0.2-9.1. But I keep getting an error and I don't see where I would be going awry. The situation is the following: Two explanatory variables: bedekking - a variable with possible values between 0 and

Hi all, I am using the package ?Segmented? to estimate logistic regression models with unknown breakpoints (see Muggeo 2003 Statistics in Medicine 22:3055-3071). In the documentation it suggests that it might be possible to include several variables with breakpoints in the same model: ?Z = a vector or a matrix meaning the (continuous) explanatory variable(s) having segmented relationships with

Dear all, It appears that MASS::polr() and Design::lrm() return the same point estimates but different st.errs when fitting proportional odds models, grade<-c(4,4,2,4,3,2,3,1,3,3,2,2,3,3,2,4,2,4,5,2,1,4,1,2,5,3,4,2,2,1) score<-c(525,533,545,582,581,576,572,609,559,543,576,525,574,582,574,471,595, 557,557,584,599,517,649,584,463,591,488,563,553,549) library(MASS) library(Design)

Hi all, I am experiencing the following quite strange (at least in my knowledge) problem in a simple function like the following: fn<-function(y,v=2){ n<-length(y) y<-y[(v+1):(n-v)] plot(y,type="l",lty=3,xlab="Time",ylab=deparse(substitute(y))) } fn(rnorm(50)) #look at the plot!!! The plot appears with numbers on the left side! If I delete the

Hello, I used the glm function in R to fit a dose-response relationship and then have been using dose.p to calculate the LC50, however I would like to calculate the NOEL (no observed effect level), ie the lowest dose above which responses start occurring. Does anyone know how to do this? [[alternative HTML version deleted]]

Dear all, I'm looking for someone that help me to write an R function to simulate survival data under complex situations, namely time-varying hazard ratio, marginal distribution of survival times and covariates. The algorithm is described in the reference below and it should be not very difficult to implement it. However I tried but without success....;-( Below there the code that I used; it

I am encountering an error when I attempt to reference a glm model within a function. The function uses the segmented.glm command (package = segmented). Within the segmented.glm command one specifies an object, in this case a logistic regression model, and specifies a starting threshold term (psi). I believe this is an environment problem, but I do not have a solution. Any assistance

Dear members, here is my trouble: My data consists of counts of trapped insects in different attractive traps. I usually use GLMs with a poisson error distribution to find out the differences between my traitments (and to look at other factor effects). But for some dataset where one traitment contains only zeros, GLM with poisson family fail to find any difference between this particular traitment

Ok, so basically I have a dataframe named data_frame data_frame contains: startdate startprice endpricethreshold1 endpricethreshold2 endpricethreshold3 all of these endpricethresholds are true/false binary vectors. They are true or false depending on whether the endprice was above or below whatever the endpricethreshold is. now I want to try to use lets say the general linear model to have