search for: wts

hi, we want to replace our nt4 pdc/bdc with samba and we use windows nt4 terminal servers with citrix in our environment. as far as i know, we will get a problem here. (extensiver reading of the mailing list led me to this conclusion) regarding: http://ma.ph-freiburg.de/tng/tng-users/2001-05/msg00224.html >Terminal Server has its own User Manager (which has an extra button -

Hi all, I'm having some difficulty passing arguments into lm() from within a function, and I was hoping someone wiser in the ways of R could tell me what I'm doing wrong. I have the following: lmwrap <- function(...) { wts <- somefunction() print(wts) # This works, wts has the values I expect fit <- lm(weights=wts,...) return(fit) } If I call my function lmwrap, I get the the following error: > lmwrap(a~b) Error in eval(expr, envir, enclos) : object "wts" not found A traceback gives me th...

Dear Community, I have a data set with two columns, bird number and mass. Individual birds were captured 1-13 times and weighed each time. I would like to remove those individuals that were captured only once, so that I can assess mass variability per bird. I¹ve tried many approaches with no success. Can anyone recommend a way to remove individuals that were captured only once? Thanks, Ray

...uot; and now I'd like to use the weigths, from the fitted model, to iterpret some of variables impornatce. I used the following command: mts <- nnet(y=Y,x=X,size =4, rang = 0.1, decay = 5e-4, maxit = 5000,linout=TRUE) X is (m x n) Y is (m x 1) And then I get the coeficients by: Wts<-coef(mts) b->h1 i1->h1 i2->h1 i3->h1 i4->h1 i5->h1 i6->h1 i7->h1 ... b->o h1->o h2->o h3->o h4->o ... I understood that I should get the predicted Y (hat(Y)) by: hat(Y)= (b->o...

...------------------------------ # wtd.var is Hmisc package # you can replace the 3 lines inside for loop as # sdx[i,] = apply(x[(i-4):i,],2,var,na.rm=T) # ------------------------------------------------------- x = matrix(rnorm(200),10,20) sdx = matrix(0,10,20) for(i in 5:nrow(x)){ wts = ( 0.5 )^(i-c(1:i)) wts = wts/sum(wts) sdx[i,] = apply(x[1:i,],2,wtd.var,weights=wts,normwt=T, na.rm=T) } [[alternative HTML version deleted]]

Hi. I am running SAMBA 2.0.7 on HP-UX 11.00 and HP-UX 10.20. I have tried adding the MultipleUsersOnConnection registry key onto my Microsoft Windows NT Server 4 Terminal Server Edition servers, running with Service Pack 6. After I make this change in the registry and reboot my WTS servers all my samba connections from the servers are still being made with one process instead of being split into multiple processes per connection. I have placed the key into HKEY_LOCAL_MACHINE\\SYSTEM\CurrentControlSet\Services\Rdr\Parameters and have tried setting the registry key value to 0...

...ula)); mf$start <- NULL;mf$control <- NULL;mf$algorithm <- NULL; mf$trace <- NULL;mf$model <- NULL; mf$lower <- NULL;mf$upper <- NULL; mf[[1]] <- as.name("model.frame"); mf<-evalq(mf,data); n<-nrow(mf) mf<-as.list(mf); wts <- if (!mWeights) model.weights(mf) else rep(1, n) if (any(wts < 0 | is.na(wts))) stop("missing or negative weights not allowed") m <- switch(algorithm, plinear = nlsModel.plinear(formula, mf, start, wts), port = nlsModel(for...

...re used to perform unweighted and weighted fits. I intentionally used very uneven weights to guarantee large differences in the results #================================CUT=========================== ln <- 100 x <- 1:ln y0 <- exp(-.02*x) y <- rnorm(y0, y0, .01) wts <- rep(1, ln) y[30] <- 1.2*y[30] wts[30] <- 1e3 model <- y ~ exp(-k*x) xydata <- list(x=x, y=y) #simple unweighted fit works as expected: r0 <- nls(model, start = c(k=.01), data = list(x=x, y=y)) #simple weighted fit works as expected: r1 <- nls(model, start = c(k=....

...perform both unweighted and weighted fits. (I intentionally used very uneven weights to guarantee large differences in the results.) #================================CUT=========================== ln <- 100 x <- 1:ln y0 <- exp(-.02*x) y <- rnorm(y0, y0, .01) wts <- rep(1, ln) y[30] <- 1.2*y[30] wts[30] <- 1e3 model <- y ~ exp(-k*x) xydata <- list(x=x, y=y) #simple unweighted fit works as expected: r0 <- nls(model, start = c(k=.01), data = list(x=x, y=y)) #simple weighted fit works as expected: r1 <- nls(model, start = c(k=....

...t(formula)); mf$start <- NULL;mf$control <- NULL;mf$algorithm <- NULL; mf$trace <- NULL;mf$model <- NULL; mf$lower <- NULL;mf$upper <- NULL; mf[[1]] <- as.name("model.frame"); mf<-evalq(mf,data); n<-nrow(mf) mf<-as.list(mf); wts <- if (!mWeights) model.weights(mf) else rep(1, n) if (any(wts < 0 | is.na(wts))) stop("missing or negative weights not allowed") m <- switch(algorithm, plinear = nlsModel.plinear(formula, mf, start, wts), port = nlsModel(formula, m...

...ula)); mf$start <- NULL;mf$control <- NULL;mf$algorithm <- NULL; mf$trace <- NULL;mf$model <- NULL; mf$lower <- NULL;mf$upper <- NULL; mf[[1]] <- as.name("model.frame"); mf<-evalq(mf,data); n<-nrow(mf) mf<-as.list(mf); wts <- if (!mWeights) model.weights(mf) else rep(1, n) if (any(wts < 0 | is.na(wts))) stop("missing or negative weights not allowed") m <- switch(algorithm, plinear = nlsModel.plinear(formula, mf, start, wts), port = nlsModel(for...

...[!sapply(xlev, is.null)] } else xlev <- NULL resp <- model.response(mf) qtx <- model.matrix(Terms, mf, contrasts) cons <- attr(qtx, "contrasts") dnx <- colnames(qtx) asgn.t <- attr(qtx, "assign") if(length(wts <- model.weights(mf))) { wts <- sqrt(wts) resp <- resp * wts qtx <- qtx * wts } qty <- as.matrix(qr.qty(qr.e, resp)) if((nc <- ncol(qty)) > 1) { dny <- colnames(resp) if(is.null(dny)) dny &lt...

...of mmlcr. For your reference, this is relevant code that I identified in mmlcr, using getAnywhere{mmlcrfit.multinomlong}: function (object, weights, classnumber) { data <- object$data form.full <- formula(attr(data, "terms")) form <- object$shortform data$.wts <- weights[match(getGroups(data, form.full, 1), row.names(weights)), classnumber] component.multinom.result <- multinom(form, data = data, weights = .wts, na.action = na.exclude, subset = (.wts > 0), maxit = 150, trace = FALSE) object$coef[[classnum...

...quot;mlbench") > index <- 1:nrow(Glass) > testindex <- sample(index, trunc(length(index)/5)) > testset <- Glass[testindex, ] > trainset <- Glass[-testindex, ] > datatrain <- subset(trainset,select=-Type) > classestrain <- subset(trainset,select=Type) > Wts <- 1.0/table(Glass$Type); > model <- svm(datatrain,classestrain,type="C-classification",kernel="radial",cost=100,class.weights=Wts) > datatest = subset(testset,select=-Type) > classestest <- subset(testset,select=Type) > pred.test=predict(model,datatest)...

I found a post from a year ago which matches my current situation almost exactly, except that we are using samba 2.09. Anyone resolve this yet? Here's the previous text... *********************************** I'm running NT 4.0 Terminal Server (Service Pack 4) with Citrix MetaFrame 1.8 on a PC, and Samba 2.0.3 on an HP-UX box. We've been having occasional problems with files not

...} else xlev <- NULL > resp <- model.response(mf) > qtx <- model.matrix(Terms, mf, contrasts) > cons <- attr(qtx, "contrasts") > dnx <- colnames(qtx) > asgn.t <- attr(qtx, "assign") > if(length(wts <- model.weights(mf))) { > wts <- sqrt(wts) > resp <- resp * wts > qtx <- qtx * wts > } > qty <- as.matrix(qr.qty(qr.e, resp)) > if((nc <- ncol(qty)) > 1) { > dny <- colnames(resp) &...

I've got a Wildcard T100P along with a Zhone Zplex 10 24S/O which has been working fine for me now for a while. These have been pulled out of a working Asterisk installation (as they were no longer required) to use at home only to find that the fan noise is too loud. As such I'm looking to sell off this hardware and replace it with some combination of fanless hardware that will allow me to

I want to create a neural network, and then everytime it receives new data, instead of creating a new nnet, i want to use a backpropagation algorithm to adjust the weights in the already created nn. I'm using nnet package, I know that nn$wts gives the weights, but I cant find out which weights belong to which conections so I could implement the backpropagation algorithm myself. But if anyone knows some function to do this, it would be even better. In anycase, thank you! Filipe Rocha [[alternative HTML version deleted]]

Hi everyone, I admit I am a bit of an R novice, and I was hoping someone could help me with this error message: Warning message: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : extra arguments weigths are just disregarded. My equation is: lm( Y ~ X1 + X2 + X3, weigths = seq(0.1, 1, by = 0.1)) -- View this message in context:

Hi, I am having trouble with the survival library, particualrily the coxph function. the following works coxph(jtree9$cph.call,z,rep(1,dim(z)[1])) Call: coxph(formula = jtree9$cph.call, data = z, weights = rep(1, dim(z)[1])) coef exp(coef) se(coef) z p SM 0.2574 1.294 0.0786 3.274 1.1e-03 Sex -0.1283 0.880 0.1809 -0.709