similar to: Error in describe Unused Argument(s)

Hi- I want to fit a model with crossed random effects and heteroskedastic level-1 errors where inferences about fixed effects are of primary interest. The dimension of the random effects is making the model computationally prohibitive using lme() where I could model the heteroskedasticity with the "weights" argument. I am aware that the weights argument to lmer() cannot be used to

hi all i'm new to the area of neural networks. i've been reading some references and seem to understand some of the learning algorithms. i am very familiar with regression and would just like to see how neural nets handle this problem so i've been using the nnet package. i simply want to use a 3 layer neural net, ie 1 input, 1 hidden layer (where the hidden layer is linear, since i

ES function gives the below error. > ES(sim, p=.95, method=c("modified"),portfolio_method=c("component"), > weights=w1) /Error in checkData(R, method = "xts", ...) : The data cannot be converted into a time series. If you are trying to pass in names from a data object with one column, you should use the form 'data[rows, columns, drop = FALSE]'.

I would like you consider that the function ecdf could be extended in the following way to handle weights when computing Empirical distribution Functions. There exist particular cases that supports this kind of extension, see for example: Rao, C. R., 1997. Statistic and True. Putting chance to work. World Scientific Publishing. Cox, D. R., 1969. Some Sampling Problems in Technology. New

Dear R helpers I have following data stocks <- c("ABC", "DEF", "GHI", "JKL") prices_df <- data.frame(ABC = c(17,24,15,22,16,22,17,22,15,19),                                          DEF = c(22,28,20,20,28,26,29,18,24,21),                                           GHI = c(32,27,32,36,37,37,34,23,25,32),                                          

for unweighted fits using `nls' I compute confidence intervals for the fitted model function by using: #------------------- se.fit <- sqrt(apply(rr$m$gradient(), 1, function(x) sum(vcov(rr)*outer(x,x)))) luconf <- yfit + outer(se.fit, qnorm(c(probex, 1 - probex))) #------------------- where `rr' contains an `nls' object, `x' is the independent variable vector, `yfit'

dear list members, I apologize in advance for posting a second time, but probably after one week chances are, the first try went down the sink.. my question concerns computation of confidence intervals in nonlinear fits with `nls' when weigthing the fit. the seemingly correct procedure does not work as expected, as is detailed in my original post below. any remarks appreciated. greetings

ComRades: I am geeting the error message Error in ldots[[which(MET)]] : attempt to select less than one element when I try to fit the geostatistical model with the likfit() function of geoR. I have tried with old data for which likfit() successfully maximised the likelihood in previous versions of geoR, and yet the current version fails. I have tried in Windows Vista and Windows XP (I haven't

Hi all, I'm working on a target whose registers have equal-sized subregisters and all of those subregisters can be named (or the other way round: registers can be grouped into super registers). So for instance we've got 16 registers W (as in wide) W0..W15 and 32 registers N (as in narrow) N0..N31. This way, W0 is made by grouping N0 and N1, W1 is N2 and N3, W2 is N4 and N5, ..., W15 is

dear list members, my question concerns computation of confidence intervals in nonlinear fits with `nls' when weigthing the fit. the seemingly correct procedure does not work as (I) expected. I'm posting this here since: (A) the problem might suggest a modification to the `m' component in the return argument of `nls' (making this post formally OK for this list) and (B) I got no

I have a bit of an issue with rsync. I am using to keep directories in sync via another server for backup. Here is the server config [w1] path = /w1 comment = w1 web dir [w2] path = /w2 comment = w2 web dir Now on the client i run this command rsync -avv --delete --force domain.com::w1/ /w1/ It will NOT delete anything that is no on the server anymore.. for example on the server/client there

Suppose that I have a function fff which calls a couple of functions foo and bar, and that foo takes optional arguments a and b, and bar takes optional arguments u and v. Is there an elegant way of passing ***both*** sets of optional arguments via a ``...'' argument for fff? I'd like to be able to have a structure like: fff(x,y,z,...) { . a1 <- foo(x,y,...) b1

Hi, I would like to create wireframe plots conditional on 2 variables and use different limits for the 3-axes in each plot. I thought I could do this with subscripts and the panel.wireframe but I haven't been successful. I am getting this error "...multiple actual arguments..." so I definitely don't have something set up correctly. Listed below is the code to generate the

I have huge list of edges with weights. a1 b1 w1 a2 b2 w2 a3 b3 w3 a1 b1 w4 a3 b1 w5 I have to convert it into 2 dim matrix b1 b2 b3 a1 max(w1,w4) 0 0 a2 0 w2 0 a3 w5 0 w3 if edges repeated take the maximum weights. How do this efficiently without using for loop? Any idea. thanks Avi [[alternative

Jim has been exceedingly patient (and may well continue to be so), but this smells like "failure to launch". At what point will you start showing your (failed) attempts at solving your own problems so we can help you work on your specific weaknesses and become self-sufficient? -- Sent from my phone. Please excuse my brevity. On February 25, 2018 7:55:55 AM PST, Val <valkremk at

Hello, I'm trying to get percentiles (PERCENTRANK for excel users) by factor in the following data.frame: myExample <- data.frame(Ret=seq(-2, 2.5, by=0.5),PE=seq(10,19),Sectors=rep(c("Financial","Industrial"),5)) myExample <- na.omit(myExample) Thanks to Patrick I I managed to put together the following lines which does it for the "Ret" column: myecdf

Dear R-Help List, I'm trying to simulate data from a conditional distribution, and haven't been able to modify my existing code to do so. I searched the archives, but didn't find any previous post that matched my question. n=10000 pop = data.frame(W1 = rbinom(n, 1, .2), W2 = runif(n, min = 3, max = 8), W3 = rnorm(n, mean=0, sd=2)) pop = transform(pop, A = rbinom(n, 1,

Dear R Help, I read through the archives pretty extensively before sending this email, as it seemed there were several threads on using predict with GLM. However, while my issue is similar to previous posts (cannot get it to predict using new data), none of the suggested fixes are working. The important bits of my code: set.seed(644) n0=200 #number of observations

HI Jim and all, I want to put one more condition. Include col2 and col3 if they are not in col1. Here is the data mydat <- read.table(textConnection("Col1 Col2 col3 K2 X1 NA Z1 K1 K2 Z2 NA NA Z3 X1 NA Z4 Y1 W1"),header = TRUE,stringsAsFactors=FALSE) The desired out put would be Col1 Col2 col3 1 X1 0 0 2 K1 0 0 3 Y1 0 0 4 W1 0 0 6 K2 X1

I keep getting the 'memory.size' error message when I run a program I have been writing. It always it cannot allocate a vector of a certain size. I believe the error comes in the code fragement below where I have multiple arrays that could be taking up space. Does anyone know a good way around this? w1 <- outer(xk$xk1, data[,x1], function(y,z) abs(z-y)) w2 <- outer(xk$xk2,