similar to: F values from a Repeated Measures aov

I am executing a Repeated Measures Analysis of Variance with 1 DV (LOCOMOTOR RESPONSE), 2 Within-Subjects Factors (AGE, ACOUSTIC CONDITION), and 1 Between-Subjects Factor (SEX). Does anyone know whether the between-subjects factor (SEX) belongs in the Error Term of the aov or not? And if it does belong, where in the Error Term does it go? The 3 possible scenarios are listed below: e.g., 1.

I have executed a Repeated Measures ANOVA with one DV (latency) and one within subject factor (acoustic condtion: 3 levels) by bootstrapping my sampling distribution of F from the empirical sample distribution. I chose to resample because the sample distribution deviates from normality a lot. The overall F is significant and now I wish to decompose this with contrasts to ask if latencies to

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

I am attempting to model data with the following variables: timepoint - n=48, monthly over 4 years hospital - n=3 opsn1 - no of outcomes total.patients skillmixpc - skill mix percentage nurse.hours.per.day Aims To determine if skillmix affects rate (i.e. no.of.outcomes/total.patients). To determine if nurse.hours.per.day affects rate. To determine if rates vary between

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

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

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 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, 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

library(ggplot2) a<- read.table("data", header=T) b = na.omit(a) ggplot(data=b) + geom_line(aes(x=timepoint, y=value,group=sample, colour= factor(sample))) +? geom_point(aes(x=timepoint, y=value, group=s ample)) + facet_wrap(~bio, scales = "free",ncol = 5) +theme_bw() + opts(legend.direction = "horizontal",??? legend.position = "top",????

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

What is the best way to report the standard error when publishing Kaplan-Meier plots? In my field (Vascular Surgery), practitioners loosely refer to the "10% error" cutoff as the point at which to stop drawing the KM curve. I am interpreting this as the *standard error of the cumulative hazard*, although I'm having a difficult time finding some guidelines about this (perhaps I am

System: R 2.3.1 on a Windows XP computer. I am validating several cancer prognostic models that have been published with a large independent dataset. Some of the models report a probability of survival at a specified timepoint, usually at 5 and 10 years. Others report only the linear predictor of the Cox model. I have used Harrell's c index for censored data (rcorr.cens) as a measure of

I want to extra the part of the formula not including the response variable from an lm object. For example if the lm object ABx.lm was created by the call ABx.lm <- lm( y ~ A + B + x, ...) Then ACx.lm is saved as part of a workspace. I wish to extract "~ A + B + x". Later in my code I will fit another linear model of the form z ~ A + B + x for some other response variable z. I

1) I have the following data situation: 96 plots 12 varieties 2 time points 2 technical treatments the experiment is arranged as follows: a single plot has two varieties tested on it. if variety A on plot #1 has treatment T1 applied to it, then variety B on plot #1 has treatment T2 applied to it. across the whole experiment variety A is exposed to treatment T1 the same number of times as

Hi all. I have a script which I call from cron. It basically does some stopping of a few services, rsyncs all files to a remote server and then starts the services again. However, rsync segfaults: /share/bin/cron.root.backup.sh: line 28: 18453 Segmentation fault rsync -acx --delete ${_backup_dirs} backup-server::backup-client If I run rsync from the command-line everything works as

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 )

I get the error undefined symbol: Rf_rownamesgets when I try to load my package, which include C++ code that calls that function. This is particularly strange since the code also calls Rf_classgets, and it loaded OK with just that. Can anyone tell me what's going on? For the record, I worked around this with the general purpose attribute setting commands and R_RowNamesSymbol. I

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 =

> >I fixed it in svn. Could you check that? > > Now all platforms match again. Note that the measured SNR for this test > sample is lower than with the broken code (10.87 vs 11.10), but of course > this is no way to judge the real quality. SNR, especially on a single sample, can be very misleading. Yet, could you just check that the DSP results match what you get on a PC?