similar to: How to view or export values of 'names' in a lm

Hello I am trying to get out of an lm model the fstatistics, however after I run the model I write > names(Model) and the fstatistic does not appear only these. names(Model) [1] "coefficients" "residuals" "effects" "rank" "fitted.values" [6] "assign" "qr" "df.residual"

# Your mailer is set to "none" (default on Windows), # hence we cannot send the bug report directly from R. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs@r-project.org # ###################################################### The multcomp functions work on "lm" objects as anticipated. They do not work on

Full_Name: David Firth Version: 1.3.1 OS: linux and others Submission from: (NULL) (163.1.103.121) help(lm) says that An object of class `"lm"' is a list containing at least the following components: ... formula: the formula supplied. But is the "formula" component necessarily present? An example: > x<-rnorm(10) > y<-rnorm(10) >

I think there are two bugs in aov() that shows up when the right hand side of `formula' contains both `-1' and an Error() term, e.g., aov(y ~ a + b - 1 + Error(c), ...). Without `-1' or `Error()' there is no problem. I've included and example, and the source of aov() with suggested fixes below. The first bug (labeled BUG 1 below) creates an extra, empty stratum inside

I believe you are right, but can you please explain why anyone would want to fit this model? It differs only in the coding from aov(y ~ a + b + Error(c), data=test.df) and merely lumps together the top two strata. There is a much simpler fix: in the line if(intercept) nmstrata <- c("(Intercept)", nmstrata) remove the condition (and drop the empty stratum later if you

Dear all, glht (from the multcomp package) needs a term and a model component in it's fitted model. In fitted models from e.g. repeated measurements ANOVAs I do not find neither model nor term. Is it possible to build together a model and term component myself, so that glht will work for repeated measurements ANOVAs? If so, how would I do that? Best regards, Michael Zehetleitner

Win2k, R1.6.2. I've been using Splus 6.1 and wanted to try the same regression analysis in R. Using "names( blah.lm )" in R yields [1] "coefficients" "residuals" "effects" "rank" [5] "fitted.values" "assign" "qr" "df.residual" [9] "xlevels"

Below is a SIMEX object that was generated with the "simex" function from the "simex" package applied to a logistic regression fit. From this mountain of information I would like to extract all of the values summarized in this line: .. ..$ variance.jackknife: num [1:5, 1:4] 1.684 1.144 0.85 0.624 0.519 ... Can someone suggest how to go about doing this? I can extract the

Hello, I am using the glm model with a poisson distribution. The model runs just fine but when I try to get the null deviance for the model of the null degrees of freedom I get the following errors: > null.deviance(pAmeir_1) Error: couldn't find function "null.deviance" > df.null(pAmeir_1) Error: couldn't find function "df.null" When I do: >

Hi, While exploring how summary.lm generated its output I came across a section that left me puzzled. at around line 57 R <- chol2inv(Qr$qr[p1, p1, drop = FALSE]) se <- sqrt(diag(R) * resvar) I'm hoping somebody could explain the logic of these to steps or alternatively point me in the direction of a text that will explain these steps. In particular I'm puzzled

Dear friends, After fitting a generalized linear models ,i hope to get the variance of alpha,variance of beta and their covariance, that is , the variance-covariance matrix/information of alpha and beta , suppose *B* is the object of GLMs, i use attributes(B) to look for the options ,but can't find it, anybody knows how to get it? > attributes(B) $names [1] "coefficients"

R developers, I have a first attempt to make an aov function. Eventually I want to build in Error() structure, but first I am trying to get this presentable for balanced data with only a single stratum, just using residual error. I am following R. M. Heiberger's Computation for the Analysis of Designed Experiments, Wiley (1989) I a using a wrapper (aov.bal) to call the

Hi all, I am using glm function with family binomial(logit) to fit logistic regression model. My data is very big and the algorithm is such that it has to run glm function hundreds of times. Now *I need only the **estimates of the coefficients and std. error in my output, *but apparently glm function is computing several other statistics and parameters (mentioned below) which increases the

Hi, I've been running out of memory while using the lm.fit function - have solved the problem and thought there might be interest in incorporating some of the changes. Looked at the source and changed the following lines storage.mode(x) <- "double" storage.mode(y) <- "double" z <- .Fortran("dqrls", qr = x, n = n, p = p, y = y, ny = ny,

Greetings, In my opinion the documentation or behaviour of qr.solve, qr.coef, qr.resid, and qr.fitted is not easily comprehensible and unfortunate. We all know that a linear system Ax=b can have 0, one or infinitely many solutions. To treat all these cases uniformly we can rephrase the problem as x = argmin_u||Au-b||,

Hi, I am using robust regression, i.e. model.robust<-ltsreg(MXD~ORR,data=DATA). My question:- is there any way to determine the Robust Multiple R-Squared (as returned in the summary output in splus)? I found an equivalent model in the rrcov package which included R-square, residuals etc in it's list of components, but when I used this package the only results returned were equivalent to

Hi, all. I want to write some functions like glm() so i studied it. In glm.fit(), it calls a fortran subroutine named "dqrfit" to compute least squares solutions to the system x * b = y To learn how "dqrfit" works, I just follow how glm() calls "dqrfit" by my own example, my codes are given below: > qr <- >

I had a look at some influence measures, and it seems to me that currently several methods handle multiple lm (mlm) objects wrongly in R. In some cases there are separate "mlm" methods, but usually "mlm" objects are handled by the same methods as univariate "lm" methods, and in some cases this fails. There are two general patterns of problems in influence measures:

G'day all, I had a look at the GLM code of R (1.4.1) and I believe that there are problems with the function "glm.fit" that may bite in rare circumstances. Note, I have no data set with which I ran into trouble. This report is solely based on having a look at the code. Below I append a listing of the glm.fit function as produced by my system. I have added line numbers so that I

Dear r-devel, Among the R functions I have written and later shared with colleagues, there are five that I hope will become a part of the R base package. The tasks are neither specific nor marginal, so rather than creating one more 'misc' package, I would be happy if the R Development Core Team would adopt these functions and hammer them into shape. The functions are available from