similar to: Problems completely reading in a "large" sized data set

Hi, I added the Windows::WSA module to windows-pr recently (it''s in the latest git repo). However, I can''t seem to make WSAStartup work. I tried the following bit of code, only to get "The Windows Sockets version requested is not supported" on my Vista box. Is it really not supported on Vista? The docs say Win2k or later. Or is there another issue? require

I can consistently segfault the 0.10.4 gem, so I''m trying to get the subversion version working with hopes towards tracking the problem down. I have a fresh SVN checkout but: a) the version (in ferret.rb) claims to be 0.9.6; and b) Ferret::Index::FieldInfos and a couple other classes are missing at run time. It looks like this is because they''re not exported in the C

Greetings to r-help land. I've run into some program crashes and I've traced them back to methods() behavior after the package gdata is loaded. I provide now a minimal re-producible example. This seems bugish to me. How about you? dat <- data.frame(x = rnorm(100), y = rnorm(100)) lm1 <- lm(y ~ x, data = dat) methods(class = "lm") ## OK so far library(gdata)

Dear friends, I'm probably wrong but is there anything better than bootstrap to get a confidence interval of the median from a population with unspecified distribution ? Best wishes Troels Ring, Aalborg, Denmark

Hi all, The nobs method of (MASS:::polr class) takes into account of weight, but nobs method of glm does not. I wonder what is the rationale of such design behind nobs.glm. Thanks in advance. Best Regards. > library(MASS) > house.plr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) > house.logit <- glm(I(Sat=='High') ~ Infl + Type + Cont, binomial,weights

Dear all, I am studying a bit the various support functions that exist for extracting information from fitted model objects. From the help files it is not completely clear to me whether the number returned by nobs() should be the same as the "nobs" attribute of the object returned by logLik(). If so, then there is a slight inconsistency in the methods for 'nls' objects with

In accordance with Venables and Ripley, SAS documentation and other sources AIC with sigma^2 unknown is calculated as: AIC = -2LL + 2* #parameters = n log(RSS/n) + 2p For the fitness data: (http://support.sas.com/ctx/samples/index.jsp?sid=927), SAS gets an AIC of 64.534 with model oxygen = runtime. (SAS STAT User's Guide. Chapter 61. pp 3956, the REG Procedure). This value of AIC accords

Hi! The nobs() method for glm objects always returns the number of cases with non-null weights in the data, which does not correspond to the number of observations for Poisson regression/log-linear models, i.e. when family="poisson" or family="quasipoisson". This sounds dangerous since nobs() is, as the documentation states, primarily aimed at computing the Bayesian

It seems that confint.default returns an empty data.frame for objects of class mlm. For example: ``` nobs <- 20 set.seed(1234) # some fake data datf <- data.frame(x1=rnorm(nobs),x2=runif(nobs),y1=rnorm(nobs),y2=rnorm(nobs)) fitm <- lm(cbind(y1,y2) ~ x1 + x2,data=datf) confint(fitm) # returns: 2.5 % 97.5 % ``` I have seen proposed workarounds on stackoverflow and elsewhere, but

Hi, I have a data set like this: Mutant Rep Time OD 02H02 1 0 0.029 02H02 2 0 0.029 02H02 3 0 0.023 02H02 1 8 0.655 02H02 2 8 0.615 02H02 3 8 0.557 02H02 1 12 1.776 02H02 2 12 1.859 02H02 3 12 1.668 02H02 1 16 3.379 02H02 2 16 3.726 02H02 3 16 3.367 306 1 0 0.033 306 2

I've been trying to figure out how to accomplish the following... I've got a list (returned from a function) and I would like to "cbind()" the lists together to create a "cross tab" report or simply bind them together somehow the function returns a list that looks like the following: > all$BM $species [1] "BM" $vbar.nobs [1] 3 $vbar.sum [1] 54.05435

Hi, in R-3.0 I get the following error when calling lsfit with more observations than variables, which seems to come from an error in the formatting of the error message (note that this was not happening in 2.15.3): > nobs <- 5; nvar <- 6; lsfit(matrix(runif(nobs*nvar), ncol=nvar), runif(nobs), intercept=FALSE) Error in sprintf(ngettext(nry, "%d response", "%d

I have a factor (with "n" observations and "k" levels), with only "nobs" < n of the observations not missing. I would like to produce a (n x k) model matrix with treatment contrasts for this factor, with rows of NAs placeholding the missing observations. If I use model.matrix() I get back a (nobs x k) matrix. Is there an easy way to get the (n x k) without

Dear R friends: Can someone help me with the following problem? Many thanks in advance. # Problem Description: # I want to write functions which take a (character) vector of dataframe names as input argument. # For example, I want to extract the number of observations from a number of dataframes. # I tried the following: nobs.fun <- function (dframe.vec) { nobs.vec <-

I was wondering if it is possible to do the following in a smarter way. I want get the mean value across the columns of a matrix, but I want to do this on subrows of the matrix, given by some vector(same length as the the number of rows). Something like nObs<- 6 nDim <- 4 m <- matrix(rnorm(nObs*nDim),ncol=nDim) fac<-sample(1:(nObs/2),nObs,rep=T) ##loop trough different

Hello R-Community, I have the following matching problem: I have two data.frames, one with an observation every month (per company ID), and one with an observation every quarter (per company ID; note that quarter means fiscal quarter; therefore 1Q = Jan, Feb, Mar is not necessarily correct and also, a fiscal quarter is not necessarily 3 month long). For every month and company, I want to get the

Hello, how is, for example, the Schwarz criterion is defined for kmeans? It should be something like: k <- 2 vars <- 4 nobs <- 100 dat <- rbind(matrix(rnorm(nobs, sd = 0.3), ncol = vars), matrix(rnorm(nobs, mean = 1, sd = 0.3), ncol = vars)) colnames(dat) <- paste("var",1:4) (cl <- kmeans(dat, k)) schwarz <- sum(cl$withinss)+ vars*k*log(nobs) Thanks

Full_Name: Ju-Sung Lee Version: 2.2.0 OS: Windows XP Submission from: (NULL) (66.93.61.221) BIC() requires the attribute $nobs from the logLik object but the logLik of a glm(formula,family=binomial()) object does not include $nobs. Adding attr(obj,'nobs') = value, seems to allow BIC() to work. Reproducing the problem: library(nmle); BIC(logLik(glm(1~1,family=binomial())));

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 list, i'm trying to test if a linear combination of coefficients of glm is equal to 0. For example : class 'cl' has 3 levels (1,2,3) and 'y' is a response variable. We want to test H0: mu1 + mu2 - mu3 =0 where mu1,mu2, and mu3 are the means for each level. for me, the question is how to get the covariance matrix of the estimated parameters from glm. but perhaps there