similar to: variables as arguments in formulae in aov?

Hi all, I've done some anovas, found some significance and proceeded to do the post hoc tests to determine the source of the significance. I used the multcmp package as suggested by Andy Liaw (thank you). I am, however, unsure how to interpret the results of the call to simint. Could someone help me with this? I've attached the code and the results it produced. There are 3 groups

Hi everybody, I'm having problems with the barchart command in the lattice package. I'm creating barcharts from matrices with with anything from 20 to 71 columns. When I leave the column names alone, that is they are set in the read.table command (and inherited by subsequent commands) the correct number barcharts is created by the "print(barchart(...))" command.

Hi everybody, I'm having trouble getting an eval-parse combination to work with lme in a for loop. Consider the code below. The call to lme outside the loop works. The call to aov inside the loop works, but the call to to lme inside the loop does not. The latter fails with Error in model.frame.default(formula = ~meanCol + Group + c1 + c3 + Subject, : variable lengths differ (found for

Hi, I'm currently working on porting some SAS scripts to R, and hence need to do the same calculation (and get the same results) as SAS in order to make the transition easier for users of the script. In the script, I'm dealing with a two-factorial repeated-measures anova. I'll try to give you a short overview of the setup: - two between-cell factors: facBetweenROI (numbering

Greetings all, I've done some ANOVAs and have found significant effects across my groups, so now I have to do some post-hoc tests to ascertain which of the groups is driving the significant effect. Usually one would do something like a Newman-Keuls or Scheffe test to get at this but based on googling and browsing through the r-help archives R doesn't support these and they seem to be

Hi, I'm still trying to figure out that GLM procedure in SAS. Let's start with the simple example: PROC GLM; MODEL col1 col3 col5 col7 col9 col11 col13 col15 col17 col19 col21 col23 =/nouni; repeated roi 6, ord 2/nom mean; TITLE 'ABDERUS lat ACC 300-500'; That's the same setup that I had in my last email. I have three factors: facSubj,facCond and facRoi. I had this pretty

How do we specify a contrast interaction matrix for an ANOVA model? We have a two-factor, repeated measures design, with Cue Direction (2) x Brain Hemisphere(2) Each of these has 2 levels, 'left' and 'right', so it's a simple 2x2 design matrix. We have 8 subjects in each cell (a balanced design) and we want to specify the interaction contrast so that: CueLeft>CueRght

Hi everybody, I'm using the gmodels package to convert human readable contrasts into the format required by R and would be grateful if someone could confirm for me whether I've got the contrasts right in the sample code below. I'm working on the assumption that the contrasts are index according to the way that levels reports them for a factor. In my case

Hi, I am using nsd as my authority-only dns servers and find it fast, stable, reliable - all of the design goals you set out for, great. I have a zone that is intended only to be visible to people on my network. In bind I would enforce this with an 'allow-query' configuration directive. I checked the latest 'nsd-for-bind-users' document, and 'allow-query' is not

Hello, I use the simplest of examples that somebody can think of in order to generate a matrix of random numbers from within C, calling appropriate R functions. The concrete example is below: --- file pico.c #include <stdio.h> #include <stdlib.h> #include <math.h> #include <R.h> #include <Rmath.h> #define COLM( i, j, m ) ( m*j + i) void pico ( double *y, int n,

Hello- I have a script which steps through a series of subjects, and for the subjects I remove outlying values. After removing these outliers, I specify a cutoff, keeping only values over a certain value (e.g., 1.96). I want to populate a matrix with a statistic of the values that make the cutoff (for example, the mean). However, in some subjects, after outliers and the cutoff are specified,

Hi all- I am stumped. The code in A. returns errors at lines 14 and 15 and fails to load series1 and series2. However, in B., if temp1 and temp2 are called again (which returns a "Table exists" error; see lines 14-17 in B.) series1 and series2 load correctly. Any ideas? Also-I am open to any suggestions to improve the code as I am a horrific programmer. Thanks A. 1 >

Hallo! I have run a EEG experiment and got the following data group: 1 2 1 2 1 2 1 2 ... as factor, 2 levels between subjects fixed effect (patient vs control) subj: 1 2 ... 14 1 2 ... 14 as factor 7 patients 7 control random effect condition: 1 1 ... 2 2 ... 1 1 ... 2 2 as factor, 2 levels within subjects, ie every subject worked on every cond fixed effect roi: 1 ... 2 ... 3 ... 4 ... as factor,

Hello, I've used this command to analyse changes in brain volume: mod1<-aov(Volume~Sex*Lobe*Tissue+Error(Subject/(Lobe*Tissue)),data.vslt) I'm comparing males/females. For every subject I have 8 volume measurements (4 different brain lobes and 2 different tissues (grey/white matter)). As aov() provides only type I anovas, I would like to use lmer() with type II, however, I have

Hi everybody, I'm trying to construct contrasts for an ANOVA to determine if there is a significant trend in the means of my groups. In the following example, based on the type of 2x3 ANOVA I'm trying to perform, does the linear polynomial contrast generated by contr.poly allow me to test for a linear trend across groups? doi=data.frame( Group=c( rep(1, 5), rep(2, 5), rep(3, 5),

Dear Community, I have a problem I am trying to code in R. Suppose there are 7 products and each have a ROI value attached to it. There are 400 representatives who calls 150,000 customers with these product detailing and achieve sales through the calls. There is a cost per call and revenue earned from the call. Constraint is revenue should be greater than the cost. Now there can be at most 7

I have a plot created with strat.plot() from package rioja. When the plot is created with scale.percent=FALSE, each x axes is labeled at 0 and its maximum. However, when scale.percent=TRUE, the x axes are not labeled. I need to use scale.percent=TRUE and I need labels for the x axes. I have been able to add labels to the x axes with mtext but it is very tedious to find the correct

Hi, I know this question has been asked before but I have not seen an answer pertaining to repeated measures anovas. I got a simple data set with two factors: block (3 levels) and prime (2 levels). The dataset (expData) are stuctured so one column (block) states which block a trial is in and one column (prime) states which prime was used in the trial, another column (RT) states reation time for

Hi list! I have a data frame called fix and a list of index vectors called rois: > head(rois, 3) [[1]] [1] 2 1 [[2]] [1] 3 [[3]] [1] 6 7 28 26 27 24 25 The part that's causing the issue is the following line: lapply(rois, function(roi) fix$x[roi] <- 100) So for every index vector I'd like to set the respective entries in the data frame (fix) to 100. I

Hi All, Went to Dags page and did not see an entry or Centos for yum configuration. Is Dag ok to use for Centos? If so, how would I enter the info in my dag repo file? Phil