similar to: boyplots nearly identical but still highly significant effect?

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 have a quick and (hopefully) straightforward question regarding the plot-function after running aov. if i plot an equation like this: plot(dataSubjects~factorA, data=mydata) R gives me the boxplots for this particular factor A. my model, however contains several factors. is there a straightforward way to plot barplots for a specific factor with the constraint that those values

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

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 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

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, is there any function in R with which i can compute subject-wise means for a single factor level? for instance, i wanna have the mean for each subject for the first level of factor A, how can i get that? is there a straightforward way to solve this problem? kind regards jake [[alternative HTML version deleted]]

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.

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

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:

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

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, I have problem with this Windows program called P5Szamla - Invoicing program. I filled a bug. http://bugs.winehq.org/show_bug.cgi?id=18469 " When the one record a new invoice the entered dates is not same those appear in print preview. Please check the attached picture's date "D?tum" fileds. It is different in print previes in the program but all the same in the print

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 =

Hello, helpeRs, I am attempting to reshape (either base R or package reshape) multiple .csv spreadsheets from a very unfortunate wide format to long format. ?Each spreadsheet looks something like this, after being read in to R: toy <- data.frame(year = rep(2007:2008,each = 20), month = rep(1:5,each = 4, length = 40), day = rep(1:2,each = 2,length = 40), hhmm = rep(1100:1101,length = 40),