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...sing a test statistic is generally not a good > > idea. > > > > -----Original Message----- > > From: r-help-bounces at stat.math.ethz.ch > > [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Liaw, Andy > > Sent: Friday, May 25, 2007 12:04 PM > > To: gatemaze at gmail.com; Frank E Harrell Jr > > Cc: r-help > > Subject: Re: [R] normality tests [Broadcast] > > > > From: gatemaze at gmail.com > > > > > > On 25/05/07, Frank E Harrell Jr <f.harrell at vanderbilt.edu> wrote: > > > > gatemaze at gmai...

Hello, on a simple linear model the values produced from the fitted(model) function are difference from manually calculating on calc. Will anyone have a clue... or any insights on how fitted function calculates the values? Thank you. -- -- Yianni [[alternative HTML version deleted]]

Hi, among the many (5) methods that I found in the list to do partial correlation in the following two that I had a look I am getting different t-values. Does anyone have any clues on why is that? The source code is below. Thanks. pcor3 <- function (x, test = T, p = 0.05) { nvar <- ncol(x) ndata <- nrow(x) conc <- solve(cor(x)) resid.sd <- 1/sqrt(diag(conc)) pcc <-

Hi, is this list only related to R issues or it has a broader context regarding questions and discussions about statistics. Is there any other email list or forum for that? For example, I have a question regarding variance. It is defined as: variance = sum(sq(Xi-mean)) / (N-1) and I never understood why not define it as variance = sum(absolute(Xi-mean) / (N-1) I read somewhere that this cannot

Hi, I am looking for documentation, reference guides, etc. that explain the output of functions... For example using cor.test(...., method="pearson") with Pearson's corr coeff the output is: Pearson's product-moment correlation data: a and b t = 0.2878, df = 14, p-value = 0.7777 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval:

Yes, I already had a look on previous posts but nothing is really helpful to me. The code is: postscript(filename, horizontal=FALSE, onefile=FALSE, paper="special", bg="white", family="ComputerModern", pointsize=10); par(mar=c(5, 4, 0, 0) + 0.1); plot(x.nor, y.nor, xlim=c(3,6), ylim=c(20,90), pch=normal.mark); gives error Error in plot.new() : figure margins too

Hi, on a > summary(lm(y~x)) are the computed t-values for two.sided or one.sided. By looking on some tables they seem like they are for two.sided. Is it possible to have them for one.sided? If this makes sense... Thanks. [[alternative HTML version deleted]]

Hi all, apologies for seeking advice on a general stats question. I ve run normality tests using 8 different methods: - Lilliefors - Shapiro-Wilk - Robust Jarque Bera - Jarque Bera - Anderson-Darling - Pearson chi-square - Cramer-von Mises - Shapiro-Francia All show that the null hypothesis that the data come from a normal distro cannot be rejected. Great. However, I don't think it looks

Hi, after reading the archives I found some methods... adopted and modified one of them to the following. I think it is correct after checking and comparing the results with other software... but if possible someone could have a look and spot any mistakes I would be grateful. Thanks pcor3 <- function (x, test = T, p = 0.05, alternative="two.sided") { nvar <- ncol(x) ndata