similar to: output from multcomp and lm

Displaying 20 results from an estimated 400 matches similar to: "output from multcomp and lm"

2016 Dec 14
2
unexpected behaviour of search queries with mixed AND and OR
Hello, I found out an unexpected behaviour of search queries with mixed "AND" and "OR". With search query "\( condA OR condB condC \)" I get an error: Fatal: Use parenthesis when mixing ANDs and ORs if I switch left and right OR-part and use the query "\( condB condC OR condA \)" I get a result, but it is not the expected result of the query "\(
2001 Oct 08
3
testing diff for slopes and intercepts
I fit the model fit<-lm(thresh~cond*Ne) where thresh is the reponse cond is a factor with levels a, b, and c Ne is a continuous indep var I think of this full model as having three lines: thresh as a function of Ne for each condition. Thus we have slopea, slopeb, slopec, inta, intb, intc. lm output my params ------------------------- (Intercept) inta condb intb - inta condc
2016 Dec 14
0
unexpected behaviour of search queries with mixed AND and OR
On 14.12.2016 12:18, Juergen Raschke wrote: > Hello, > > > I found out an unexpected behaviour of search queries with mixed > "AND" and "OR". > > > With search query "\( condA OR condB condC \)" I get an error: > Fatal: Use parenthesis when mixing ANDs and ORs > > if I switch left and right OR-part and use the query > "\( condB
2007 Feb 25
1
Repeated measures logistic regression
Dear all, I'm struggling to find the best (set of?) function(s) to do repeated measures logistic regression on some data from a psychology experiment. An artificial version of the data I've got is as follows. Firstly, each participant filled in a questionnaire, the result of which is a score. > questionnaire ID Score 1 1 6 2 2 5 3 3 6 4 4 2 ...
2009 Oct 25
1
A naive question about permutation tests in the coin package
Dear R helpers, I am trying to understand how to use the independence_test function in the coin package. I think I suffer from a misunderstanding about what the package does. Either that or I do not understand how to use it properly. Specifically, I cannot understand if I can test independence of arbitrary statistics. Take the following example: set.seed(10) d <- data.frame(y = c(rnorm(10,
2006 Mar 09
1
bugs in simtest (PR#8670)
# R for Windows will not send your bug report automatically. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs at r-project.org # ###################################################### This report is joint from Richard Heiberger <rmh at temple.edu> and Burt Holland <bholland at temple.edu>. Burt Holland is the coauthor
2011 May 01
1
Simulation Questions
I have the following script for generating a dataset. It works like a champ except for a couple of things. 1. I need the variables "itbs" and "map" to be negatively correlated with the binomial variable "lunch" (around -0.21 and -0.24, respectively). The binomial variable "lunch" needs to remain unchanged. 2. While my generated variables do come out
2002 Jun 26
2
contrast matrix in package multcomp
Hi, I've got a problem building a contrast matrix for the Dunnet contrast in package multcopm. The following works fine: > summary(simtest(adiff ~ trial)) Simultaneous tests: Dunnett contrasts Data: adiff by trial Contrast matrix: trial1 trial2 trial3 trial4 trial5 trial2-trial1 -1 1 0 0 0 trial3-trial1 -1 0 1 0 0
2004 May 20
4
pmvt problem in multcomp
Hi, all: Two examples are shown below. I want to use the multiple comparison of Dunnett. It succeeded in upper case "example 1". However, the lower case "example 2" went wrong. In "example 2", the function pmvt return NaN, so I cannot show this simtest result. Is there any solution? (I changed the variable "maxpts" to a large number in front of the
2011 Apr 11
1
Help on calculating a variable using random numbers
I'm new to R, but I'm trying to write a program for a dissertation that generates a dataset as follows... subject=1:1000 treat=rbinom(1*1000,1,.13) gender=rbinom(1*1000,1,.5) eth=runif(1*1000, min=1, max=4) cogat=rnorm(1*1000, 100, 16) map=rnorm(1*1000, 200, 9) simtest=data.frame (subject=subject, treat=treat, gender=gender, eth=round(eth,digits=0),
2004 Aug 13
5
simtest for Dunnett's test
Hi! I use simtest fonction of multcomp package to compile a Dunnett's test. I have 10 treatments and one control group, so i create a matrix with: m<-matrix(0,10,11) m[1,1]<--1 m[1,2]<-1 m[2,1]<--1 m[2,3]<-1 m[3,1]<--1 m[3,4]<-1 m[4,1]<--1 m[4,5]<-1 m[5,1]<--1 m[5,6]<-1 m[6,1]<--1 m[6,7]<-1 m[7,1]<--1 m[7,8]<-1 m[8,1]<--1 m[8,9]<-1
2005 May 07
1
Test on mu with multivariate normal distribution
Dear WizaRds, I am sorry to bother you with a newbie question, but although I tried to solve my problem using the various .pdf files (Introduction, help pages etc.), I have come to a complete stop. Please be so kind as to guide me a little bit along my way of exploring multivariate analysis in R. I want to test wether the means-vector mu1 of X, consisting of the means per column of that matrix
2008 Oct 29
1
problem with "simtest"
Hello all I am working with the package multcomp but I have problems with the function simtest; the program say that can not find this function, nevertheless I doesn't have any problem with the function glht that it is in the same package. Someone knows what could be the problem? Thank you [[alternative HTML version deleted]]
2005 May 15
3
adjusted p-values with TukeyHSD?
hi list, i have to ask you again, having tried and searched for several days... i want to do a TukeyHSD after an Anova, and want to get the adjusted p-values after the Tukey Correction. i found the p.adjust function, but it can only correct for "holm", "hochberg", bonferroni", but not "Tukey". Is it not possbile to get adjusted p-values after
2007 Aug 11
1
Connecting to database on statup
Hello, Q/ Is it possible to create a DBMS connection automatically on startup of R? (Making sure of course that the db server has been started...) I am running MySQL on Mac OS X 10.4.2 with R2.4.1. I have tried to write a function using the RMySQL commands (below) and place them in .First of .RProfile: drv <- dbDriver("MySQL") dbcon <- dbConnect(drv, {other parameters present in
2004 Jan 18
1
multcomp, simint, simtest and computation duration
Dear R-listers, I am trying to compute simultaneous confidence intervals with simint from the package multcomp. 230 measures (abundance) have been taken in 23 sites (factor) of a data.frame (donnees: a file can be sent on request, saved with save(donnees,file="donnees")). I would like to get all pairwise comparisons with : mc<- simint(ren~ID,type="Tukey",data=donnees) I
2009 Oct 03
3
else if statement error
Hello, I am doing a simple if else statement in R. But it always comes out error such as 'unexpected error' There are two variables. ini and b. when ini=1, a=3; when ini>1 and b>2, a=5; all other situations, a=6. I don't know where it is wrong. Here is my code ini=3 b=4 if (ini==1) { a=3 } else if (ini>1 and b>2 ) { a=5 } else {a=6} Thanks a
2005 Jun 06
1
multiple comparison test
hello, after an anova I use pairwise.t.test(), it gives only p.value and I want the t.stat. I try to get these by computing the Welch approximation of the degree of freedom and using the qt(p.value,df) function but when I test this method with t.test results (the function gives p.value and t.test), I doesn't find the same t.stat. I also use the simtest(x~y,type="Tukey") function
2006 Feb 16
0
(m)simtest ?
Hi,. We have 2 values (first formant F1, second formant F2) for a given phoneme for six languages. We want to see whether the languages are significantly different one from another for this given phoneme. We have done a manova on our data and it works well, but we doesn't allow us to see which pair of languages are different. If we have only one formant for the phoneme, we would use
2005 Dec 02
1
Ancova and lme use
Dear R-users, We expect to develop statistic procedures and environnement for the computational analysis of our experimental datas. To provide a proof of concept, we plan to implement a test for a given experiment. Its design split data into 10 groups (including a control one) with 2 mesures for each (ref at t0 and response at t1). We aim to compare each group response with control response