similar to: Coefficients in a polynomial glm with family poisson/binomial

I've been working on a small personal project that needs to select files for manipulation from various directories and move them around in planned ways. file.choose() is a nice way to select files. However, I've noticed that if file.choose() is called within a function, it is the directory from which that calling function has been invoked that is displayed by file.choose(). This is not

Try choose.files choose.files(default = file.path(mydir, "*.*"), multi = FALSE) On Wed, Dec 18, 2024 at 10:33?AM J C Nash <profjcnash at gmail.com> wrote: > > I've been working on a small personal project that needs to select files for manipulation from > various directories and move them around in planned ways. file.choose() is a nice way to select > files.

I believe file.choose() remembers the last choice, and repeats that location the next time you call it. This is true when it is called at top level or from within a function. The first time you call it in a session, it will default to the current working directory, but not after that. Front ends are allowed to replace the underlying function, so that may depend on which front end you are

Dear R's! I am strugling with cancor procedure in R. I cannot figure out the meaning of xcoef and of yxcoef. Are these: 1. standardized coefficients 2. structural coefficients 3. something else? I have tried to simulate canonical correlation analysis by checking the eigenstructure of the expression: Sigma_xx %*% Sigma_xy %*% Sigma_yy %*% t(Sigma_xy). The resulting eigenvalues were the same

R-helpers: I would like to measure the correlation coefficient between the repeated measures of a single variable that is measured over time and is unbalanced. As an example, consider the Orthodont dataset from package nlme, where the model is: fit <- lmer(distance ~ age + (1 | Subject), data=Orthodont) I would like to measure the correlation b/t the variable "distance" at

Hi everyone, Can I suggest an additional output component in cancor, from package mva? It would be useful to have the number of canonical correlation vectors, equivalently the rank of the covariance between x and y (label "rank"). This would usually be min(dx, dy), where dx and dy have already been computed for the svd function, but there might be situations where it was less than

I'm sorry to bother you but I'm stuck I can not make a bridge work for me. My current configuration is: eth0 Link encap:Ethernet Hardware Adresse 00:1d:7d:d1:06:bd inet Adresse:192.168.1.1 Bcast:192.168.1.255 Maske:255.255.255.0 inet6-Adresse: fe80::21d:7dff:fed1:6bd/64 G?ltigkeitsbereich:V according to all what I could find in the net. The following should

I am attempting to run a glm with a binomial model to analyze proportion data. I have been following Crawley's book closely and am wondering if there is an accepted standard for how much is too much overdispersion? (e.g. change in AIC has an accepted standard of 2). In the example, he fits several models, binomial and quasibinomial and then accepts the quasibinomial. The output for residual

This is a question regarding the 'regTermTest' function in the 'survey' package. Imagine Z as a three level factor variable, and code ZB and ZC as the two corresponding dummy variables. X is a continuous variable. In a 'glm' of Y on Z and X, say, how do the two test specifications test.terms = c("ZB:X","ZC:X") # and test.terms = ~ ZB:X + ZC:X in

Hi! I would like to perform an F-Test over more than one variable within a generalized mixed model with Gamma-distribution and log-link function. For this purpose, I use the package mgcv. Similar tests may be done using the function "anova", as for example in the case of a normal distributed response. However, if I do so, the error message "error in eval(expr, envir, enclos) :

Hi all I'm getting a NaN returned on using dffits, as explained below. To me, there seems no obvious (or non-obvious reason for that matter) reason why a NaN appears. Before I start digging further, can anyone see why dffits might be failing? Is there a problem with the data? Consider: # Load data dep <-

Thanks for your reply Mark, but no, using predict on the new data.frame does not help here. ? I had first thought that the probelm was due?the?explanatory variable (age)?and?the offset one (date) being?very similar (highly?correlated, I am trying to tease their effect apart, and hoped offset would help in this since I know the relationship with age already). But this appears not to be the case.

Hi, Suppose I have a vector: > names.select [1] "Idd13" "Idd14" "Idd8.12" "Idd7" automatically generated by some selection criteria. Now, if I have a data frame with many variables, of which the variables in "names.select" are also variables from the data frame. e.g. > all.df[1:5,] Mouse Idd5 Idd6.19.20 Idd13 Idd14 Idd8.12

Ted Harding said: > I can get the estimated RRs from > RRs <- exp(summary(GLM)$coef[,1]) > but do not see how to implement confidence intervals based > on "robust error variances" using the output in GLM. Thanks for the link to the data. Here's my best guess. If you use the following approach, with the HC0 type of robust standard errors in the

Hi i would like to use some graphs or tables to explore the data and make some sensible guesses of what to expect to see in a glm model to assess if toxin concentration and sex have a relationship with the kill rate of rats. But i cant seem to work it out as i have two predictor variables~help?Thanks.:) Here's my data. >

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 am sorry for this simple question, but... How do I extract something from a data.frame? The following is my Problem: I have got a dataframe "a" with various columns. One of those columns is called V3 and contains elements of the following levels: > levels(a$V3) [1] "C" "CA" "CB" "CD" "CD1" "CD2"

Hello: I'd like to know how and if the GLM convergence problems are addressed in gamlss. For simplicity, let's focus on Normal and Negative Binomial with log link. The convergence issues of the glm() function were alleviated in 2011 when glm2 package was released. Package gamlss was released in 2012, so it might still use the glm-like solution or call glm() directly. Is that the case or

Hi all, In a simulation context, I'm applying some my function, "myfun" say, to a list of glm obj, "list.glm": >length(list.glm) #number of samples simulated [1] 1000 >class(list.glm[[324]]) #any component of the list [1] "glm" "lm" >length(list.glm[[290]]$y) #sample size [1] 1000 Because length(list.glm) and the sample size are rather large,

Dear all, I am comparing logistic regression models to evaluate if one predictor explains additional variance that is not yet explained by another predictor. As far as I understand Baron and Li describe how to do this, but my question is now: how do I report this in an article? Can anyone recommend a particular article that shows a concrete example of how the results from te following simple