similar to: Partial correlation function required

Hi, I am turning to you with a (hopefully simple?) stats question. I would like to test equality of two correlation coefficients in a setting with three variables X,Y,Z, i.e. equality of r(X,Y) and r(Z,Y). I have found a formula to transform the "2 dependent correlations difference" to t-distribution with N-3 df: t = (rxy - rzy)* SQRT[{(n - 3)(1 + rxz)}/ {2(1 - rxy^2 - rxz^2 - rzy^2 +

Hi, I have done different discriminant function analysis of multivariat data. With the CV=True option I was not able to perform the predict() call. What do I have to do? Or is there no possibility at all? You also need the predicted values to produce a plot of the analysis, as far as I know. Here my code: pcor.lda2<-lda(pcor~habarea+hcom+isol+flowcov+herbh+inclin+windprot+shrubcov+baregr,

Dear colleagues, I've been searching for information on the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy (MSA). This statistic is generated in SPSS and is often used to determine if a dataset is "appropriate" for factor analysis -- it's true utility seems quite low, but it seems to come up in stats classes a lot. It did in mine, and a glance through the R-help

Why do we get Partial correlation values greater than 1? I have used the default function pcor.mat :-- I have manipulated the default pcor.mat function a bit so ignore tha variables corr_type,element1_in_no,element2_in_no,P.Please ignore the ?pairwise? section and have a look at athe ?listwise ? part i.e else part. *pcor.mat <-

Hi All, How do i calculate KMO for a dataset? *Dataset:---------------------* m1 m2 m3 m4 m5 m6 m7 m8 1 2 20 20 2 1 4 14 12 2 9 16 3 5 2 5 5 15 3 18 18 18 13 17 9 2 4 4 7 7 2 12 2 11 11 11 5 7 8 5 19 5 2 20 18 6 7 4 7 4 7 9 3 3 7 5 5 5 12 5 13 13 12 8 6 6 4 3 5 17 17 16 9 12 12 4 2 4 4 14 14 10 5 14

I am trying to run the following function (a hierarchical bayes linear model) and receive the error in solve.default. The function was originally written for an older version of SPlus. Can anyone give me some insights into where the problem is? Thanks R 2.4.1 on MAC OSX 2mb ram Mark Grant markg at uic.edu > attach(Aspirin.frame) > hblm(Diff ~ 1, s = SE) Error in solve.default(R, rinv)

Hi, I would like to conduct a MANOVA. I know that there 's the manova() funciton and the summary.manova() function to get the appropriate summary of test statistics. I just don't manage to specify my model in the manova() call. How to specify a model with multiple responses and one explanatory factor? If I type:

Hej! I have the following problem: I would like to do partial correlations on non-parametric data. I checked "pcor" (Computes the partial correlation between two variables given a set of other variables) but I do not know how to change to a Spearman Rank Correlation method [pcor(c("BCDNA","ImProd","A365"),var(PCor))] Here''s a glimpse of

Hi All, This is my R-version information:--- > version _ platform i486-pc-linux-gnu arch i486 os linux-gnu system i486, linux-gnu status major 2 minor 7.1 year 2008 month 06 day 23 svn rev 45970 language R version.string R version 2.7.1 (2008-06-23) While calculating partial

Thank you Brian Ripley, Gardar Johannesson, and Marcel Wolbers for your prompt and friendly help! I will share any further learnings as I move through these suggestions. -Bob Abugov Brian Ripley wrote: I just use optim() on the sum of squares with non-negativity constraints. That did not exist in 1999. Gardar Johannesson wrote: You can always just use the quadratic programing library in R

> -----Original Message----- > From: llvmdev-bounces at cs.uiuc.edu [mailto:llvmdev-bounces at cs.uiuc.edu] > On Behalf Of Krzysztof Parzyszek > Sent: 03 March 2015 14:35 > To: llvmdev at cs.uiuc.edu > Subject: Re: [LLVMdev] Inline Assembly: Memory constraints with offsets > > On 3/3/2015 6:01 AM, Daniel Sanders wrote: > > Hi, > > > > I'm trying to

This is a question regarding the 'regTermTest' function in the 'survey' package. Imagine Z as a three level factor variable, and code ZB and ZC as the two corresponding dummy variables. X is a continuous variable. In a 'glm' of Y on Z and X, say, how do the two test specifications test.terms = c("ZB:X","ZC:X") # and test.terms = ~ ZB:X + ZC:X in

I'm trying to write code to calculate partial correlations (along with p-values). I'm new to R, and I don't know how to do this. I have searched and come across different functions, but I haven't been able to get any of them to work (for example, pcor and pcor.test from the ggm package). In the following example, I am trying to compute the correlation between x and y, while

Hello, after thorough searching of the R help files as well as S+-help, I'm coming to the list: Is there a possibility to compute partial correlation coefficients between multiple variables (correlation between two paired samples with the "effects of all other variables partialled out")? All I seem to find are the standard Pearson correlation coefficients (with cor()) and no clue

Hello, I'd like to convert the below time-series data with fft or wavelet related function and plot it. Could you let me know 1. How to convert xts data frame format to list format ? 2. How to plot fft or wavelet diagram ? Here is the data : &gt; class(zc) [1] "xts" "zoo" &gt; str(zc) An ‘xts’ object from (10/15/12 09:00:00) to (10/15/12 15:15:00)

Hi, I'm trying to implement the ZC inline assembly constraint for Mips. This constraint is a memory constraint that expands to an address with an offset (the range of the offset varies according to the subtarget), so the inline assembly in: int data[10]; void ZC(void) { asm volatile ("foo %0 %1" : : "ZC"(data[1]), "ZC"(data[2])); } Should expand to

Dear all, I am trying to get an estimate of the intercept for a linear model. In this case, I know the slope of the model, can anyone tell me how to constrain the formula in lm() so that it only estimates the intercept not the slope? Many thanks in advance, Sincerely, Liqing Zhang Dept. of Eco. Evol. Biol. Univ. of CA, Irvine email: lzhang at uci.edu >From VM Mon Apr 30 08:18:45 2001

Hi.. I'm not sure why polygon returns an area above the standard normal curve. z <- pretty(c(-3,3), 100) ht <- dnorm(z) data <- data.frame(z=z, ht=ht) zc <- 1.645 plot(data, type="l") lines(data) t <- subset(data, z>zc) polygon(t, col="red") Thanks, Lance [[alternative HTML version deleted]]

Dear all, I'm writing my manuscript to publish after analysis my final data with ANOVA, ANCOVA, MANCOVA. In a section of my result, I did correlation of my data (2 categirical factors with 2 levels: Quantity & Quality; 2 dependent var: Irid.area & Casa.PC1, and 1 co-var: SL). But as some traits (here Irid.area) are significantly influenced by the covariate (standard length, SL), I

Hi, the function makeARIMA(), designed to construct some state space representation of an ARIMA model, uses a C function called getQ0, which can be found at the end of arima.c in R source files (library stats). getQ0 takes two arguments, phi and theta, and returns the covariance matrix of the state prediction error at time zero. The reference for getQ0 (cited by help(arima)) is: