similar to: Incorrect behavior for ordering timepoints in "reshape" (PR#7669)

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 )

All, I'm trying to plot an all.effects() object, as shown in the help for all.effects and also Crawley's R book (p.178, 2007). The data has a repeated measures structure, but I'm using all.effects for the simple lm() fit here. Below is a reproducible example that yields the error message. fm.ex = lm(dv ~ time.num*drug*X, data = dat.new) fm.effects = all.effects(fm.ex, xlevels =

Hi, i do have a dataframe representing data from a repeated experiment. PID is a subject identifier, Time are timepoints in an experiment which was repeated twice. For each subject and all three timepoints there are 2 sets of four values. df <- data.frame(PID = c(rep("A", 12), rep("B", 12), rep("C", 12)), Time = rep(c(0, 0, 0, 0, 30, 30, 30,

Dear R Community, I am trying to create an heatmap for the following set of data: ##example of data matrix o4 V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 green 27 28 29 29 28 28 26 25 25 23 23 22 22 21 21 22 22 22 yellow 6 8 8 7 7 7 6 6 6 7 7 7 6 6 6 6 6 5 red 15 15 15 15 15 15 14 13 12 11 12 10 9 8 7 6 8 9 pink

On 1/7/2015 8:59 AM, Chandler Carruth wrote: > > Essentially, I think target-independent optimizations are still > attractive, but we might want to just force them to go through an > actual target-implemented API to interpret the scopes rather than > making the interpretation work from first principles. I just worry > that the targets are going to be too different and we may

Hi Folks, I have repeated measures for data on association time (under 2 acoustic condtions) in male and female frogs as they grow to adulthood (6 timepoints). Thus, two within-subject variables (Acoustic Condition: 2 levels, Timepoint: 6 levels) and one between-subject variable (Sex:male or female). I am pretty sure my distributions depart from normality but I would first like to simply run a

I have a data frame with two variables that are factors. One is actually a TRUE/FALSE factor, and I have coded it as 1/0, a continuous variable, but I could turn it back into a factor. The second is an ordered factor and consists of five timepoints. There are several continuous variables as well. Now I want to fit a linear model to my data, using lm (or another R procedure if recommended).

Hi, I'm having difficulty importing my textfile that looks something like this: #begin text file Timepoint 1 ObjectNumber Volume SurfaceArea 1 5.3 9.7 2 4.9 8.3 3 5.0 9.1 4 3.5 7.8 Timepoint 2 ObjectNumber Volume SurfaceArea 1 5.1

dear list, i am using a multifactorial design with two treatments (factor A: drugs, three levels; factor B: theraphy, two levels) and a time factor (three levels, different timepoint). hypothetically, i measured the same subjects for all treatements and timepoints, so its a repeated measurement design. now i ran an anova in R and also some Tukey post-hoc tests using glht. but what i am actually

Hello, I have a number of files output1.dat, output2.dat, ... , output20.dat, each of which monitors several variables over a fixed number of timepoints. From this I want to create a data frame which contains the mean value between all files, for each timepoint and each variable. The code below works, but it seems like I should be able to do the second part without a for loop. I played

dear list, i am running a within-design ANOVA with 4 factors (4,4,2 and 2 levels each). the last one is a time factor comprising two different treatment timepoints. i fit a mixed-effects model using lme and apply the anova function to the outcome. according to this analysis, there are highly significant main effect on the first and the time factor. i then checked the boxplots for the two 4-level

Hi, I cannot find a 'vectorized' solution to this 'for loop' kind of problem. Do you see a vectorized, fast-running solution? Objective: Take the value of X at each timepoint and calculate the corresponding value of Y. Leading 0's and all 1's for X are assigned to Y; otherwise Y is incremented by the number of 0's adjacent to the last 1. The frequency and

I have two points of collection across 20 subjects (pre and post for each), so 20 pairs of data points. I would like to plot the actual raw data points for each subject for both pre and post and connect lines between these two points (20 in all) to depict real change between the two timepoints. I have tried using stripchart which adequately plots the two lines of subject data points. Attempting

Dear all R users, I want to realize 800 000 ANOVAS and to store Sum of Squares of the effects. Here is an extract of my table data Product attribute subject rep t1 t2 t3 … t101 P1 A1 S1 R1 1 0 0 … 1 I want to realize 1 ANOVA per timepoint and per attribute, there are 101 timepoints and 8 attributes so I want to realize 808 ANOVAS. This will be an ANOVA with two factors : Here is one example:

Hi all, I wonder if there is any way to calculate a moving average on an irregular time series, or use the rollapply function in zoo? I have a set of dates where I want to check if there has been an event 14 days prior to each time point in order to mark these timepoints for removal, and can't figure out a good way to do it. Many thanks in advance! Gustaf Example data:

>> (odd, I did get this reply for Silvia, but not the original post) > > Hmm, it was properly CCed to the list. Yes, I found it in the spam bucket for some reason... > Chapters are a list of timepoints stored in the metadata. They are an > information for player software that is usually used to allow the user > to jump to certain significant points within a stream. This

Dear all, I am having trouble creating a "jagged array" in R. I have found no commentary on how to do it in the help files, but maybe I am misunderstanding the purpose of some of the array functions (e.g. tapply). I am using a longitudinal dataset where each individual has a different number of time points (observations). I need to create correlation structures for each

Hi all, Looking for tips on how I might more optimally solve this. I have time series data (samples from a force sensor) that are not guaranteed to be sampled at the same time values across trials. ex. trial time x 1 1 1 1 5 4 1 7 9 1 12 20 2 1 0 2 3 5 2 9 10 2 13 14 2 19 22 2 24 32 Within each trial I'd like to use linear interpolation between each successive time sample to fill in

I'm working on a rather interesting consulting problem with a client. A number of physical variables are measured on a number of cricket bowlers in the performance of a delivery. An example variable might be a directional component of angular momentum for a particular joint measured at a large number (101) of equally spaced timepoints. Each bowler generates a (fairly smooth) curve for

I have the following dataset: 96 plots 12 varieties 2 time points The experiment is arranged as follows: A single plot has two varieties tested on it. With respect to time points, plots come in 3 kinds: (1) varietyA, timepoint#1 vs. variety B, timepoint#1 (2) varietyA timepoint #2 vs. varietyB timepoint #2 (3) varietyA timepoint #1 vs. variety A timepoint#2 - there are 36 of each kind