similar to: OLS Regression diagnostic measures check list - what to consider?

Hi, if you look at the first image (Image1) you see that there are 2 main clusters 7 and 8 I wanted to use pvclust to calculate a p-value whether these clusters are due to chance or statistically significant. Unfortunately pvclust does not provide a p-value for the first brunch (7 and 8). So I added a row to my matrix which is very different to the rest of the data to create an additional

Hello Prof. Harrell and dear R-help mailing list, I wish to perform a non-parametric repeated measures anova. If what I read online is true, this could be achieved using a mixed Ordinal Regression model (a.k.a: Proportional Odds Model). I found two packages that seems relevant, but couldn't find any vignette on the subject: http://cran.r-project.org/web/packages/repolr/

Dear all, When fitting an "ols.model", the confidence interval at 95% doesn't cover the plotted data points because it is very narrow. Does this mean that the model is 'overfitted' or is there a specific amount of serial correlation in the residuals? Which R functions can be used to evaluate (diagnostics) major model assumptions (residuals, independence, variance) when

Hello dear R members. I have been learning the Anova syntax in order to perform an SS type III Anova with repeated measures designs (thank you Prof. John Fox!) And another question came up: where/what are the (between/within) residuals for my model? ############ Play code: phase <- factor(rep(c("pretest", "posttest", "followup"), c(5, 5, 5)),

Hola Buenas noches   Alguien me podria indicar en que paquete puedo encontras modelos lineales y cuadratico para ajustar una serie de datos   Gracias [[alternative HTML version deleted]]

Hello dear R help group, I have the following setup to analyse: We have about 150 subjects, and for each subject we performed a pair of tests (under different conditions) 18 times. The 18 different conditions of the test are complementary, in such a way so that if we where to average over the tests (for each subject), we would get no correlation between the tests (between subjects). What we wish

Hello all, I would like to compare the similarity of two cluster solutions using a validation criteria (such as Hubert's gamma coefficient, the Dunn index the corrected rand index and so on) I see (from here:http://www.statmethods.net/advstats/cluster.html) that the function cluster.stats() in the fpc package provides a mechanism for comparing 2 cluster solutions - *BUT* - it requires me to

Hi, I am performing a repeated measures 2-way ANOVA to assess the influence of plant and leaf on aphid fecundity. Fecundity is measured for each aphid on a single leaf. Here is what I typed. wingless <- reshape(Wingless, varying =

I am just about working through Faraways excellent tutorial "practical regression and ANOVA using R" on page 24 he makes the x matrix: x <- cbind(1,gala[,-c(1,2)]) how can I understand this gala[,-c(1,2)])... I couldn't find an explanation of such "c-like" abbreviations anywhere. thanks for a hint. another problem: I couldn't load the faraway library, using the

Hi there, >library(faraway) >pima pregnant glucose diastolic triceps insulin bmi diabetes age test 1 6 148 72 35 0 33.6 0.627 50 1 2 1 85 66 29 0 26.6 0.351 31 0 >data(pima) >pima pregnant glucose diastolic triceps insulin bmi diabetes age test 1 6 148 72 35 0 33.6

Full_Name: José Otero Version: Version 1.5.0 (2002-04-29) OS: Redhat 7.3 Submission from: (NULL) (192.187.16.164) Hi: Installation of package faraway as root, from tarbal: R CMD INSTALL ./faraway.tar.gz ERROR: cannot extract package from './faraway.tar.gz' idem, from zipped package: R CMD INSTALL faraway.zip gzip: faraway.zip has more than one entry--rest ignored ERROR: cannot

Hi R community! I am analyzing the data set "motorins" in the package "faraway" by using the generalized additive model. it shows the following error. Can some one suggest me the right way? library(faraway) data(motorins) motori <- motorins[motorins$Zone==1,] library(mgcv) >amgam <- gam(log(Payment) ~ offset(log(Insured))+ s(as.numeric(Kilometres)) + s(Bonus) + Make +

Dear R- users and Helpers: I downloaded the package from www.stat.lsa.umich.edu/~faraway/book and installed it from local zip file. It looked fine. But when I input library(faraway) it showed " Error in library(faraway) : 'faraway' is not a valid package --- installed < 2.0.0? What I used is R 2.0.0 version now. What should I do? Thank you very much. Xin

Is this text publicized? I ran across it doing an internet search. I have never seen any reference to it and it is not on CRAN under documentation. http://www.stat.lsa.umich.edu/~faraway/book/ -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

Hi all Asterisk Gurus. I am really badly in need of help. Asterisk is very lovely software, but has one big disadvantage.. lack of documentation.But let's get to the point. 1. Is it normal that I get such a crappy quality with iax, some drops and clicks? Could anyone with some similar setup check my quality and say if this is what the people are so excited about? ( I used to work as a speech

Some attribute x from 17 individuals was recorded repeatedly on 6 time points using a Likert scale with 7 distractors. Which statistical test(s) can I apply to check whether the changes along the 6 time points were significant? set.seed( 123 ) x <- matrix( sample( 1:7, 17*6, repl=T ), nrow = 17, byrow = TRUE, dimnames = list(1:17, paste( 'T', 1:6, sep='' )) ) I found

influence.measures gives several measures of influence for each observation (Cook's Distance, etc) and actually flags observations that it determines are influential by any of the measures. Looks good! But how does it discriminate between the influential and non- influential observations by each of the measures? Like does it do a Bonferroni-corrected t on the residuals identified by

Hi, This is my first time so bear with me if I do anything silly. I'll learn! I'm a relatively new user of R as well so will appreciate any help. I have a data set in the form Trial.Group MeanHeart Rate Upper confidence level Lower confidence level 333subj: 0-5 M 80 60 120etcetc I want to plot a graph that has Trial.Group on the x axis(names, not just an

Faraway's book titled "Practical Regression and Anova using R", with full text available online at: http://cran.r-project.org/doc/contrib/Faraway-PRA.pdf refers to a data set, stat500, which compares midterm and final grades. It can be used to illustrate similar concepts. A google search for faraway.zip will locate the actual data. --- Date: Sun, 15 Feb 2004 10:37:08 -0800

I have got following error in drawing wavelet fitting. can some one help? > library(faraway) > data(lidar) > newlidar<-lidar[c(1:128),] > library(wavethresh) > wds <- wd(newlidar$logratio) > draw(wds) Error in plot.default(x = x, y = zwr, main = main, sub = sub, xlab = xlab, : formal argument "type" matched by multiple actual arguments [[alternative HTML