similar to: Need help for interpreting the simulation results...

I am studying on statistical shape analysis, I wonder is there any way or package available that I can perform Euclidean Distance Matrix Analysis (EDMA I or EDMA II) in R... thanks Gokhan -- View this message in context: http://r.789695.n4.nabble.com/Euclidean-Distance-Matrix-Analysis-EDMA-in-R-tp2266797p2266797.html Sent from the R help mailing list archive at Nabble.com.

Hi, Can anybody point me to an online resource that will assist with interpreting Asterisk log files? I note that a similar question was asked in this forum some time ago (http://lists.digium.com/pipermail/asterisk-users/2007-June/189793.html), which doesn't appear to have received any responses. On that occasion, the OP was seeking a log parser - I'm looking for more of a general

Hi I have a couple of functions that work on the object created by another R command and then print out or summarise the results of this work. The main function is defined as: hotelling.t <- function(obj) { #internal commands } I then have print.hotelling.t() that takes the list returned by hotelling.t and prints it with some extra significance calculations, formatting, etc. I want to

Hi, I have a function for doing permutation tests for Manovas, written with the help of folks here on the list. It seems to work ok, and I've found that there is indeed a significant difference among groups in my analysis. I want to follow up on this by testing for which pairs of groups are significantly different. Reasoning that since Wilks Lambda is a generalization of the Hotelling T test,

I want to calculate the probability that a group will include a particular point using the squared Mahalanobis distance to the centroid. I understand that the squared Mahalanobis distance is distributed as chi-squared but that for a small number of random samples from a multivariate normal population the Hotellings T2 (T squared) distribution should be used. I cannot find a function for

Hello, I originally posted this on the stats stack exchange site, but given its focus on R software, it was removed -- so I figured I'd post here. I'm having trouble interpreting a change in effect direction and significance when I add an interaction term to my glmer() model. *Part 1* I ran an experiment in which participants made categorical decisions (out of two categories) in one of

Hello. Douglas Bates has explained in a previous posting to R why he does not output residual degrees of freedom, F values and probabilities in the mixed model (lmer) function: because the usual degrees of freedom (obs - fixed df -1) are not exact and are really only upper bounds. I am interpreting what he said but I am not a professional statistician, so I might be getting this wrong... Does

Hi, I have a simple question on interpreting output results from your msm package in R when using a categorical model with hcovariates i.e. covariates on the parameters of the emission model. The interpretation is straightforward for normal distributions, etc. but when using categorical distributions I get results like these: prob P(1)

Dear All,   wondering if you know of a good resource on-line to read on interpreting the parameter estimate result "statistics' for the output of a nonlinear regression model. I am specifically having a hard time finding information on the interpretation  of t value and Pr > /t/ in layman terms.   any direction or help would be greatly apreciated,   thanks,   Andras [[alternative

HI Guys, I know that this forum is not for homework but I am trying to interpret R output code. I was just wondering if someone might be able to help. I have been given the following. For (X1,X2) distributed bivariate normal with parameters mu1 = 5.8 mu2 = 5.3 sd1 = sd2 = 0.2 and p = 0.6 The r-code and inpit/output are as follows input m <- 5.3 + 0.6*(6.3 - 5.8) s <-

Hi, I'm wondering if you can help me, this is a really simple query but I keep getting confused. I have run a GLM to see how boldness varies over time following a particular treatment. The results are as follows... Call: glm(formula = boldtwentyfour ~ treatment + boldcontrol) Deviance Residuals: Min 1Q Median 3Q Max -1.7577 -0.5469 0.0456 0.5515 1.5327

Dear list member, My question is related to input file format to an Anova from car package. Here is an example of what I did: My file format is like this (and I dislike the idea that I will need to recode it): Hormone day Block Treatment Plant Diameter High N.Leaves SH 23 1 1 1 3.19 25.3 2 SH 23 1 1 2 3.42 5.5 1 SH 23 1 2 1 2.19 5.2 2 SH 23 1 2 2 2.17 7.6 2 CH 23 1 1 1 3.64 6.5 2 CH 23 1 1 2

Hi, I am trying to do a repeated measures ANOVA to determine if there is a significant difference between two sets of timecourse data. Each individual was given a single treatment and then measured for one variable for 10 days. Here is made-up example of what my data would look like:

Dear Friends, I had performed anova test on certain data frame (Health Care Management) and got results [summary(aov)]. I am new to R and also some extent to Statistics. Can somebody help me how should I interpret these figures. I feel difficulty in interpreting values and respective rows and columns. The following is the result to which I request interpretation: > anova.stress$effects

Dear R users; Is there any function to plot the confidence ellipse (Hotelling's T^2) in an score plot from a PCA? I have the scores off course , say scores for PC1 and PC2, and the value of the Hotelling's T^2 statistic. Thanks for any hint PM

By accident, I left out the lines defining n1 and n2. Here it is as a function. Peter B. hotelling <- function(d1,d2){ k <- ncol(d1) n1 <- nrow(d1) n2 <- nrow(d2) xbar1 <- apply(d1,2,mean) xbar2 <- apply(d2,2,mean) dbar <- xbar2-xbar1 v <- ((n1-1)*var(d1)+(n2-1)*var(d2))/(n1+n2-2) t2 <- n1*n2*dbar%*%solve(v)%*%dbar/(n1+n2) f <-

Good morning I've made an PCA and I'd like to plot a confidence region based on Hotelling T2? Does anyone know how to compute it? Thank you -- View this message in context: http://r.789695.n4.nabble.com/hotelling-and-confidence-region-tp3524204p3524204.html Sent from the R help mailing list archive at Nabble.com.

Version 0.1-0 of a new package `ffmanova' is now available on CRAN. Comments, suggestions, etc. are welcome. Please use the email address ffmanova (at) mevik.net. The package implements 50-50 MANOVA (Langsrud, 2002) with p-value adjustment based on rotation testing (Langsrud, 2005). The 50-50 MANOVA method is a modified variant of classical MANOVA made to handle several highly correlated

Version 0.1-0 of a new package `ffmanova' is now available on CRAN. Comments, suggestions, etc. are welcome. Please use the email address ffmanova (at) mevik.net. The package implements 50-50 MANOVA (Langsrud, 2002) with p-value adjustment based on rotation testing (Langsrud, 2005). The 50-50 MANOVA method is a modified variant of classical MANOVA made to handle several highly correlated

Dear friends, Being very new to this, I was wondering if I could get some pointers and guidance to interpreting the results of performing a linear regression with ordinal predictors in R. Here is a simple, toy example: y <- c(-0.11, -0.49, -1.10, 0.08, 0.31, -1.21, -0.05, -0.40, -0.01, -0.12, 0.55, 1.34, 1.00, -0.31, -0.73, -1.68, 0.38, 1.22, -1.11, -0.20) x <-