similar to: Problem with indexing

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 helpers in this forum, This is a clarified version of my previous questions in this forum. I really need your generous help on this issue. > Suppose I have the following data set: > > id x y > 023 1 2 > 023 2 5 > 023 4 6 > 023 5 7 > 412 2 5 > 412 3 4 > 412 4 6 > 412 7 9 > 220 5 7 > 220 4 8 > 220 9 8 > ...... > Now I want to compute the

hello everybody I use the following code for my programming & it runs with the error as specified below.Any help that would disolve the error will be highly appreciated. Thanks in advance my code looks like this #### R programme for simulating the power of the two sample t test vs various #### non-parametric alternatives sim.size <- 200 sample.size <- 10 set.seed(231) mu1 <- 0 delta

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,

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

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 *

Suppose I have a mixed-effects model where yij is the jth sample for the ith subject: yij= beta0 + beta1(age) + beta2(age^2) + beta3(age^3) + beta4(IQ) + beta5(IQ^2) + beta6(age*IQ) + beta7(age^2*IQ) + beta8(age^3 *IQ) +random intercepti + eij In R how can I get an F test against the null hypothesis of beta6=beta7=beta8=0? In SAS I can run something like contrast age*IQ 1,

Hello, I have two vectors x1 and x2 both in increasing order. I want to select the x1[j]th entry which is the max min of the x2[i]th entry. I can do this using if and for statements but is there a quick way to do it without running a loop? Thank you in advance, Pantelis

Dear R helpers,   I have following input files. (Actually they are more than 10 rates but here i am considering only 2 rates to write my problem)   rate1.csv min1        max1            min2          max2          min3           max3 1.05        1.30               1.30          1.65             1.65          1.99   rate2.csv min1        max1            min2          max2          min3          

On Saturday 15 September 2007, Tanya Lattner wrote: > 2) Download llvm-2.1, llvm-test-2.1, and the llvm-gcc4.0 source. > Compile everything. Run "make check" and the full llvm-test suite > (make TEST=nightly report). I tried to do this, but ran into trouble. LLVM itself compiled fine. GCC compilation is broken when Java support is enabled. I can file a detailed bug report

Hi, I have constructed a dataframe as follows: Oil <- rep(c("Oil1","Oil2","Oil3"),8) Comp <- rep(rep(c("C1","C2"),c(4,4)),3) Mth <- rep(c("M1","M1","M2","M2"),6) Meas <- rep(c(1,2),12) Result <- rnorm(24,mean=100, sd=5) df <- data.frame(Oil, Comp, Mth, Meas, Result) The same compound

Summary ======= After an unclean shutdown the file /var/run/dovecot/master.pid remained behind. This prevented dovecot from starting, and gave a misleading error message. To be more resilient and fault-tolerant, I recommend that dovecot also check the validity of the PID in /var/run/dovecot/master.pid in order to determine whether or not another dovecot process is running. Detail ====== In

I have a large dataset organised in choice groups see below. Each choice represents a separate occasion with 1 product chosen out of the 6 offered.            +-------------------------------------------------------------------------------------------------+      | dn   obs   choice      acid   br                 date       cdate   situat~n   mth   year   set |     

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.

Dear List members I have a large dataset organised in choice groups see sample below +-------------------------------------------------------------------------------------------------+ | dn obs choice acid br date cdate situat~n mth year set |

Hi there, I am trying to run simulations using R with linear mixed model (lme). There are two factors in my fixed effect model, educ (treatment and control) and mth (visit 1, 2, and 3). What I want to obtain is the estimated treatment difference (treatment - control) at visit 3, plus the standard error and p-value. This can be easily obtained in SAS using lsmeans or estimate statements, but I

hello everyone, I would like to as for advice for the use of ?lmer? (package ?lme4?) and writing the proper syntax to best describe my data using a mixed-effects model. I have just started to use these models, and although I have read some good examples (Extending the Linear Model with R, Faraway 2005; and the R book, Crawley 2007), I am still not sure of the syntax to test my hypothesis.

Hi all! I have a list of matrices and I want to multiply the ith element of the list with the ith element of a another vector. That is, > LL <- list(A=diag(3),B=diag(3),C=diag(3)) > vec <- 1:3 > for(i in 1:3) + { + LL[[i]] <- LL[[i]]*vec[i] + } > LL $A [,1] [,2] [,3] [1,] 1 0 0 [2,] 0 1 0 [3,] 0 0 1 $B [,1] [,2] [,3] [1,] 2 0

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

Hi, I am traying to implement an STAR model, but I have some problems. I am following the instruction of the model, that they are in: http://bm2.genes.nig.ac.jp/RGM2/R_current/library/tsDyn/man/star.html that they are from: http://bm2.genes.nig.ac.jp/RGM2/pkg.php?p=tsDyn The model is: star(x, m=2, noRegimes, d = 1, steps = d, series, rob = FALSE, mTh, thDelay, thVar, sig=0.05, trace=TRUE,