similar to: Samba - winXP lookup

Hi again, I posted this message some time ago in the list, however nobody replied yet. I find this kinda weird because the problem happens with both my XP computers, so I am pretty sure there must be at least a couple of guys out there with the same problem... Basically the problem is that when using Samba shares from XP from time to time I experience a ~30 second lockup of the shares. This

Hello I am using sem to look at the direct effect of one variable on another but i am uncertain if i am progressing correctly. An example: covar1<-? matrix(c(0.4,-0.2,3,-0.2 , 0.3,-2 , 3 ,-2 , 60), nrow=3,byrow=T) rownames(covar1)<-colnames(covar1)<-c("endo","exo","med") path1<-matrix(c(? ? "exo -> endo",? "g1", NA,

> Might not be it...but the '30 second lockup' triggered a memory... > > Could this be the 'looking for scheduled tasks' problem? > Here's a blurb from the > windows-net-mag site > (http://www.win2000mag.net/Articles/Index.cfm?ArticleID=24546) . Like Windows 2000, XP suffers from a little-documented browse delay. When you browse to a Windows 9x computer

Hi, I am brand new to R and not familiar with the language, though I have been reading the manuals and making some slow going progress. I am working with some source code from a Global Vector Auto -Regressive program written by Ranier Puhr from the R-forge group. I need help interpreting the processes of the following code. I am going to post in parts since it's pretty long: GVAR

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, it's my first time to write a loop with R for my homework. This loop is part of the function. I wanna assign values for hll according to panel [ii,1]=pp. I didn't get any error message in this part. but then when I further calculate another stuff with hll, the function can't return. I think it must be some problem in my loop. Probably something stupid or easy. But I tried to look

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