similar to: How to remove index from list after split?

Hi All. I need a bit of help extracting the residual error variance from the VarCorr structure from lmer. #Here's a 2-way random effects model lmer.1 <- lmer(rating ~ (1|person)+(1|rater), data = dat) #Get the structure vc.fit <- VarCorr(lmer.1) #results in..... $person 1 x 1 Matrix of class "dpoMatrix" (Intercept) (Intercept) 0.7755392 $rater 1 x 1 Matrix

Hi All. I need to create a first-order autoregressive covariance matrix (AR(1)) for a longitudinal mixed-model simulation. I can do this using nested "for" loops but I'm trying to improve my R coding proficiency and am curious how it might be done in a more elegant manner. To be clear, if there are 5 time points then the AR(1) matrix is 5x5 where the diagonal is a constant

Hi All. I'd like to change the default plotting colors used to construct a scatterplot with regression line in the CAR package. So, scatterplot(y~pred,smooth=FALSE, xlab="X", ylab="Y", lwd=2) If I change the palette (e.g., palette(ranbow(6)), I can change the color of the lines and points. However, the axes and labels remain in black (i.e., the first color in the

Hi All. I've run into a problem with the plinear algorithm in nls that is confusing me. Assume the following reaction time data over 15 trials for a single unit. Trials are coded from 0-14 so that the intercept represents reaction time in the first trial. trl RT 0 1132.0 1 630.5 2 1371.5 3 704.0 4 488.5 5 575.5 6 613.0 7 824.5 8 509.0 9

Hi All. Imagine you have a large block diagonal matrix. I'd like to replace the zeros in this matrix with small random (runif) numbers. Any ideas for a simple and efficient way to do this? Best regards, Rick DeShon

Hi All. I'd like to generate a sample of n observations from a k dimensional multivariate normal distribution with a random correlation matrix. My solution: The lower (or upper) triangle of the correlation matrix has n.tri=(d/2)(d+1)-d entries. Take a uniform sample of n.tri possible correlations (runi(n.tr,-.99,.99) Populate a triangle of the matrix with the sampled correlations Mirror the

Hi All. I'm trying to run nlsList an getting an error that makes no sense to me. I have accuracy and reaction time data over many trials for each person (id) When I use nlsList code that is virtually identical to the example in the doc file I get the following error. I've tried everything I could think of and can't get around it. Any ideas what I'm doing wrong? **************

Hi All. I'm trying to run a simple model from Baayan, Davidson, & Bates and getting a confusing error message. Any ideas what I'm doing wrong here? # Here's the data..... Subj <- factor(rep(1:3,each=6)) Item <- factor(rep(1:3,6)) SOA <- factor(rep(0:1,3,each=3)) RT <- c(466,520,502,475,494,490,516,566,577,491,544,526,484,529,539,470,511,528) priming

Hi, I am using split function and wonder how to add the factor to the splitted results. #Example n <- 3; nn <- 10 g <- factor(round(n * stats::runif(n * nn))) #factor x <- rnorm(n * nn) + sqrt(as.numeric(g)) #value xg <- split(x, g) xg $`0` [1] 0.82513702 -0.03911584 2.32955347 0.36745335 1.75572642 2.65461438 0.41675829 $`1` [1] 0.8583493 2.4264804 -0.3622378

Hi All. I'm new to R and trying to learn the data manipulation routines. At the moment, this is the one that has me stumped. Imagine n test takers provide responses to k multiple choice items. The result is a (n+1) x k matrix of responses where the first row of the matrix is a correct answer key. How could you transform the raw item responses into 1s (correct response) and 0s (incorrect

Hi All. I would like to compute a separate covariance matrix for a set of variables for each of the levels of a factor and then compute the average covariance matrix over the factor levels. I can loop through this computation but I need to perform the calculation for a large number of levels and am looking for something more elegant. To be concrete.... u <- 3 n <- 10 x <-

Hello, I use nnet to do prediction for a continuous variable. after that, I calculate correlation coefficient between predicted value and real observation. I run my code(see following) several time, but I get different correlation coefficient each time. Anyone know why? In addition, How to calculate prediction accuracy for prediction of continuous variable? Aimin thanks, > m.nn.omega

Rsync Ver: 2.5.5 OS: HPUX-11 Hello List, I'm using rsync with ssh with this command line: rsync -avz -e ssh some_file user-b@hostname-b:/home/user-b Which does work, however I get the following error output: rsync error: protocol incompatibility (code 2) at main.c(578) Since the file does transfer, is the error output just some sort of bug? I looked in the maillist archives, and I found

Hi! I'm running 1.1rc4 on a system and this happens occasionally: --8<-- mail.info; dovecot: auth(default): client in: AUTH 1 PLAIN service=imap lip=NN.NN.NN.NN rip=NNN.NN.NNN.NN lport=143 mail.info; dovecot: auth-worker(default): pam(XXXXXXXXXXXX,NNN.NN.NNN.NN): lookup service=imap kern.alert; kernel: grsec: From NN.NN.NN.NN: denied resource overstep by requesting

I have the following problem. I have an automatically generated named list with "stringified" names: a <- list("A"=..., "B"=..., "C"=..., ) then I want to refer to the elements of the list, stored as an vector of names: nn <- c("A", "B", "C"), so that I could get list elements like a$nn[1], a$nn[2], etc. Obviously it

Hello, (Apologies for length, wanted to get all the relevant detail in that I know of). I've been having a lot of trouble with some code for an inventory analysis problem I was doing, and finally came to the conclusion that it appears that choose() is returning incorrect values. Specifically: ------------- Browse[1]> nn [1] 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3

Outbound calls on my Telus PRI aren't taking the Name portion of the callerID. I've looked at the logs, and it is being set (see below), but the PRI debug output doesn't show the name being sent anywhere. As a result, received calls always display from Unknown (or just the number). Is there some config that I've missed somewhere? I'm running NI-1 (Telus says NI-2 doesn't

Group It's unlikely I'm trying this the best way, but I'm trying to create a data structure from elements like nNode = 2 nn = vector("list",nNode) nn[[1]] = list(Node = "1", Connect.up = c(NULL), Connect.down = c(2,3)) nn[[2]] = list(Node = "2", Connect.up = c(1), Connect.down = c(4,5)) .... #( and eventually many more nodes) NodeList =

Is this a bug, or have I misunderstood the proper use of lm? Thanks, Whit code: x <- rnorm(50) y <- matrix(as.logical(round(runif(100),0)),ncol=2) NROW(x)==NROW(y) lm(x~y) > x <- rnorm(50) > y <- matrix(as.logical(round(runif(100),0)),ncol=2) > NROW(x)==NROW(y) [1] TRUE > lm(x~y) Error in "[[<-.data.frame"(`*tmp*`, nn, value = c(2, 1, 2, 1, 1, 1, 2, :

Dear R-list, I try to transform a mathematica script to R. #######relevant part of the Mathematica script (* p_sv *) dd = NN (DsD - DD^2); lownum = NN (L-DD)^2; upnum = NN (H-DD)^2; low = lownum/(2s^2); up = upnum/(2s^2); psv = NIntegrate[1/(s^NN) Exp[-dd/(2s^2)] (Gamma[1/2,0,up] + Gamma[1/2,0,low]),{s,sL,sH}, MinRecursion->3]; PSV = psv/Sqrt[2NN]; Print["------------- Results