search for: sozpsi

Dear List, I work with multilevel data from psychological group experiments and have frequently encountered a situation for which I haven't found an elegant solution: I need to assign the value of a specific group member to all members of the group. For example, I have a group leader (identified by a binary vector) and some attribute for all group members. I want to create a new

Dear R users, I have a newbie-question that I couldn't resolve after reading through several pieces of documentation and searching the archive. I have a data.frame containing experimental data from a group experiment in psychology. Each line represents a single participant, but participants were assigned to groups of three or four persons. One variable indicates each participants'

Hello lmer() - users, A call to the lmer() function causes my installation of R (2.11.1 on Mac OS X 10.5.8) to crash and I am trying to figure out the problem. I have a data set with longitudinal data of four subsequent performance measures of 1133 individuals nested in 88 groups. The data is in long format. I hypothesize a performance increase for each individual over time and intend to

Dear R users, I still feel new to R so please apologize if I am doing something stupid here. My use of the polygon() function produces a result that I cannot comprehend: In a plot, I would like to shade the area below a normal distribution. However, I do not want the entire area to be shaded, but just the area on the right side of a vertical line that I draw through the distribution (in

Hello, I have a problem with selecting the right type of sums of squares for an ANCOVA for my specific experimental data and hypotheses. I do have a basic understanding of the differences between Type-I, II, and III SSs, have read about the principle of marginality, and read Venable's "Exegeses on Linear Models" (http://www.stats.ox.ac.uk/pub/MASS3/Exegeses.pdf). I am pretty new to

Dear List I need to transform a large matrix M with many NAs into a list L with one row for each non missing cell. Every row should contain the cell value in the first column, and its coordinates of the matrix in column 2 and 3. M: x1 x2 y1 1 2 y2 4 5 y3 7 8 L: v x y 1 1 1 4 1 2 7 1 2 2 2 1 5 2 2 8 2 3 I'm trying to do this with a loop, but since my matrix is quite large (around

Dear List I have two large matrices A and B. Both have the same dimensions, let's say 20k x 30k. About half the cells of B are missing. Now I'm looking for an efficient way to merge them, so that the missing values in B are replaced by the corresponding values of A. Matrix A [,1] [,2] [,3] [1,] 1 2 3 [2,] 4 5 6 merged with Matrix B [,1] [,2] [,3] [1,] 10 NA NA [2,] NA 50 60

Hello everyone, I am trying to regress applicants' performance in an assessment center (AC) on their gender (individual level) and the size of the AC (group level) with a multi-level model: model.0 <- lme(performance ~ ACsize + gender, random = ~1 | ACNumber, method = "ML", control = list(opt = "optim")) I have 1047 applicants in 118 ACs: >

Dear R-Users, I have a trivial problem, but extensive googling and ??'ing did not solve it: I want to obtain the sums of squares from a summary.aov() object for later use. Example: > DV <- rnorm(100) > IV1 <- as.factor(rep(c("male", "female"), times = 50)) > IV2 <- as.factor(rep(c("young", "old"), times = 50)) > >

Thank you for reading. I am trying to get sphericity values, and I understood I need to use mlm, but how do I implement a nested within subject design in mlm? I already read the R newsletter, fox chapter appendix, EZanova, and whatever I could find online. My original ANOVA anova(aov(resp ~ sucrose*citral, random =~1 | subject, data = p12bl, subset = exps==1)) Or anova(aov(resp ~

DeaR list I would like to predict the values of each column of a matrix A by regressing it on all other columns of the same matrix A. I do this with a for loop: A <- B <- matrix(round(runif(10*3,1,10),0),10) A for (i in 1:length(A[1,])) B[,i] <- as.matrix(predict(lm( A[,i] ~ A[,-i] ))) B It works fine, but I need it to be faster. I've looked at *apply but just can't