similar to: Extracting variance components from lmer

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

All, How does one extract the level-1 variance from a model fit via lmer()? In the code below the level-2 variance component may be obtained via subscripting, but what about the level-1 variance, viz., the 3.215072 term? (actually this term squared) Didn't see anything in the archives on this. Cheers, David > fm <- lmer( dv ~ time.num*drug + (1 | Patient.new), data=dat.new )

Hello, I am trying to perform a two-factor ANOVA analysis using a blocking design with "Vol" as the response variable. My intent is to have "Rater" treated as the treatment variable and the "Pipe" treated as the blocking variable. I am reading and preparing my dataset using the following three lines of code: values <- read.table("filename",

Dear R-ers, I am having trouble understanding why I am getting an error using glmmPQL (library MASS). I am getting the following error: iteration 1 Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 The long story: I have data from an experiment on pairwise comparisons between 3 treatments (a, b, c). So a typical run of an experiment

Hi All, Is there an easy way to reduce a data.frame to 1 'id' per row while keeping information from the other rows of that same variable, if applicable? e.g.: # data multi[1:15,] id r n wi wi.tau z k alliance a.rater eml treatment outcome o.rater german 1 100 0.2800000 44 41 21.72514 0.2876821 210 <NA> <NA> <NA> <NA>

Hi: I have some problems when I use the function "solve" function in a loop. In the following code, I have a diagonal martix "ttt" whose elements change in every iteration in a loop. I defined a "dpoMatrix"class before the loop so I do not need to define this class every time in the loop. The reason is to save some computing time. The code is below. The inverse

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

In the following example, how can I drop the group index from the list after I perform a split? n <- 3 nn <- 10 g <- factor(round(n * runif(n * nn))) x <- rnorm(n * nn) + sqrt(as.numeric(g)) df <- data.frame(g,x) df.s <- split(df,g) Thanks! Rick DeShon [[alternative HTML version deleted]]

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, I have an experiment where 5 raters assessed the quality of 24 web sites. (each rater rated each site once). I want to come up with a measure of reliability of the ratings for the web sites ie to what extent does each rater give the same (or similar) rating to each web site. My idea was to fit a random effects model using lme and from that, calculate the intraclass correlation as a

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. 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 am using fleiss kappa for inter rater agreement. Are there any know issues with Fleiss kappa calculation in R? Even when I supply mock data with total agreement among the raters I do not get a kappa value of 1. instead I am getting negative values. I am using the irr package version 0.70 Any help is much appreciated. Thanks and Regards M [[alternative HTML version deleted]]

Is there a function that calculates the inter-rater agreement index (kappa) in R? Thanks ../Murli [[alternative HTML version deleted]]

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 All: Could anyone point me to a package that can calculate stratified kappa? My design is like this, 4 raters, 30 types of diagnosis scores, 20 patients. Each rater will rate each patient for each type of diagnosis score. The rater's value is nominal. I know I can measure the agreement between raters for each type of diagnosis score, e.g., calculate out 30 kappa values. My problem is I

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

Dear all, I'm trying to store/extract the mean& standard error of the fixed effects parameter and the variance of the random effects parameter from "lmer" procedure from mlmre4 package developed by bates n pinheiro. while storing fixed effects parameter is straight forward, the same is not true for storing the variance parameter of the random effects. kindly help me ~prabhu

Dear R user, I am trying to reproduce the results in Montgomery D.C (2001, chap 13, example 13-1). Briefly, there are three suppliers, four batches nested within suppliers and three determinations of purity (response variable) on each batch. It is a two stage nested design, where suppliers are fixed and batches are random. y_ijk=mu+tau_i+beta_j(nested in tau_i)+epsilon_ijk Here are the

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