similar to: Vectorizing integrate()

Hi, I've got an array, say with i,jth entry = A_ij, and a vector, say with jth entry= v_j. I would like to multiply each column of the array by the corresponding vector component, i,e. find the array with i,jth entry A_ij * v_j This seems so basic but I can't figure out how to do it without a loop. Any suggestions? Michal.

List: Thank you for the replies to my post yesterday. Gabor and Phil also gave useful replies on how to improve the function by relying on mapply rather than the explicit for loop. In general, I try and use the family of apply functions rather than the looping constructs such as for, while etc as a matter of practice. However, it seems the mapply function in this case is slower (in terms of CPU

Hi, In the help files in the ?mgcv package for the gam.control() function, there is an option irls.reg. The help files describe this option as: For most models this should be 0. The iteratively re-weighted least squares method by which GAMs are fitted can fail to converge in some circumstances. For example, data with many zeroes can cause problems in a model with a log link, because a mean of

Hi all, I am also confused about the manual: a. The input arguments: wt.method are the weights case weights (giving the relative importance of case, so a weight of 2 means there are two of these) or the inverse of the variances, so a weight of two means this error is half as variable? w (optional) initial down-weighting for each case. init (optional) initial values for the

Hello -- I am curious if lmer can be used to fit a multilevel factor model such as a two-parameter item response model. The one parameter model is straightforward. A two-factor model requires a set of factor loadings multiplying a single random effect. For example, a logit model for the ith subject responding correctly to the jth item (j=1,..,J) is logit[p(ij)] = a1*item1(i) + ... + aJ *

I have matrices stored within a list like something as follows: a <- list(matrix(rnorm(50), ncol=5), matrix(rnorm(50), ncol=5)) b <- list(matrix(rnorm(50), nrow=5), matrix(rnorm(50), nrow=5)) I don't recall how to perform matrix multiplication on each list element such that the result is a new list result <- list(a[[1]]%*%b[[1]], a[[2]]%*%b[[2]]) I think I'm close with

Hi, I am trying to parameterize the following mixed model (following Piepho and Ogutu 2002), to test for a trend over time, using multiple sites: y[ij]=mu+b[j]+a[i]+w[j]*(beta +t[i])+c[ij] where: y[ij]= a response variable at site i and year j mu = fixed intercept Beta=fixed slope w[j]=constant representing the jth year (covariate) b[j]=random effect of jth year, iid N(0,sigma2[b]) a[i]=random

Dear List I am having a bit of a problem getting a program to work. For each of i=1 to n persons I have a matrix (different for each person) with m rows. What I want to do, is create m new data sets such that the first is made up of the first row for each person from the original matrices, the second contains the second row for each person from the original matrices etc etc up to the mth

Dear All, I have the following error message when I fitted lmer to a binary data with the "AGQ" option: Error in family$mu.eta(eta) : NAs are not allowed in subscripted assignments In addition: Warning message: IRLS iterations for PQL did not converge Any help? Thanks in advance, Abderrahim [[alternative HTML version deleted]]

Hi, I have two matrices, A (axd) and B (bxd). I want to get another matrix C (axb) such that, C[i,j] is the Euclidean distance between the ith row of A and jth row of B. In general, I can say that C[i,j] = some.function (A[i,], B[j,]). What is the best method for doing so? (assume a < b) I have been doing some exploration myself: Consider the following function: get.f, in which,

I can run one-sample t-test on an array, for example a matrix myData1, with the following apply(myData1, 2, t.test) Is there a similar fashion using apply() or something else to run 2-sample t-test with datasets from two groups, myData1 and myData2, without looping? TIA, Gang

Dear R developers, I have noticed a discrepancy between the coefficients returned by R's glm() for logistic regression and SAS's PROC LOGISTIC. I am using dist = binomial and link = logit for both R and SAS. I believe R uses IRLS whereas SAS uses Fisher's scoring, but the difference is something like 100 SE on the intercept. What accounts for such a huge difference? Thank you for

Dear list members, In re-running with GLMM() from the lme4 package a generalized-linear mixed model that I had previously fit with glmmPQL() from MASS, I'm getting a warning of a convergence failure, even when I set the method argument of GLMM() to "PQL": > bang.mod.1 <- glmmPQL(contraception ~ as.factor(children) + cage + urban, + random=~as.factor(children) + cage +

I need to wrap a loop inside a function and am having a small bit of difficulty getting the results I need. Below is a replicable example. # define functions pcm <- function(theta,d,score){ exp(rowSums(outer(theta,d[1:score],'-')))/ apply(exp(apply(outer(theta,d, '-'), 1, cumsum)), 2, sum) } foo <- function(theta,items, score){ like.mat <-

R-List, I'm trying to compute pairwise distances among pairs of observations, which each pair containing data from 2 groups. There are more than 100000 unique pairs. I have programmed a distance function that has three parameters, a vector of covariates from the ith observation in Group 1, a vector of covarites from the jth observation in Group 2, and a weighting matrix. I have used

Dear list, I'm trying to fit a glm model using family=poisson(link = "identity"). The problem is that the glm function fits a model with a negative intercept, which sounds like a nonsense to me, being the response a Poisson variable. >From a previous discussion on this list I've understood that the glm function uses IRLS for the fitting without any constraint so it is

Dear List I apologize for the multiple postings. After being in the weeds on this problem for a while I think my original post may have been a little cryptic. I think I can be clearer. Essentially, I need the following a <- c(2,3) b <- c(4,5,6) (2*4) + (2*5) + (2*6) + (3*4) + (3*5) +(3*6) But I do not know of a built in function that would do this. Any suggestions? -----Original

The integral of any probability density from -Inf to Inf should equal 1, correct? I don't understand last result below. > integrate(function(x) dnorm(x, 0,1), -Inf, Inf) 1 with absolute error < 9.4e-05 > integrate(function(x) dnorm(x, 100,10), -Inf, Inf) 1 with absolute error < 0.00012 > integrate(function(x) dnorm(x, 500,50), -Inf, Inf) 8.410947e-11 with absolute error <

Is anyone else concerned with the way in which the R function parser relocates comments that occur after condional expressions in functions to before, i.e. fred <- function(x) { # wonder what x is like if (x>0) stop("Sorry non-positive x only") # that showed x big-time! x } but then fred is parsed and stored as "fred" <- function (x) { # wonder what x is

Fellow R Users: I'm not extremely familiar with lda or R programming, but a recent editorial review of a manuscript submission has prompted a crash cousre. I am on this forum hoping I could solicit some much needed advice for deriving a classification equation. I have used three basic measurements in lda to predict two groups: male and female. I have a working model, low Wilk's lambda,