similar to: Pearson's Chi-squared Test

Well, this question comes once and again. I have RC3 and Garf's RC2 tuned versions (160 and 350) in a nice graph. Nobody should be surprised that RC3 is better, but in some cases GRC2 shows better EAQUAL/bitrate results. There seems to be room for improvement in RC3 with some tweaking. See it by yourself at http://audio.sinderman.com/ Cheers, AGS.

Hello, I am hoping someone could shed some light on power calculations for me. I have two small data sets of unequal sample size after NA removal (m = 5, f = 7). m <- c(2.0863, 2.1340, 2.1008, 1.9565, 2.0413, NA, NA) f <- c(1.8938, 1.9709, 1.8613, 2.0836, 1.9485, 2.0630, 1.9143) In a R help message/reply from Sep 30, 2001, it was noted that the "power.t.test" function

Hello, I want to compute the pearson's correlation, but even for signals that are shifted. For example having two signals like: 1 1 2 1 1 and 1 2 1 1 1 the correlation is very low.. but if we shift them in the right we get much better correlation. I know that cross-correlation is used to find the best offset (where correlation will be bigger). Is there any metric that can do this job all

Hi! All, To find co-expressed genes from a expression matrix of dimension (9275 X 569), I used rcorr function from library(Hmisc) to calculate pearson correlation coefficient (PCC) and their corresponding p-values. From the correlation matrix (9275 X 9275) and pvalue matrix (9275 X 9275) obtained using rcorr function, I wanted to select those pairs whose PCC's are above 0.8 cut-off and then

Full_Name: Bin Nan Version: 1.3.0 OS: Win32 Submission from: (NULL) (141.211.15.110) The function resid(fit, "pearson") seems not giving the same Pearson's residuals for logistic regression as what Splus does. I found the problem when I fitted glm with family = binomial using Crowder's seed data.

How do I get non-squared correlation coefficient in some more sensible way than sqrt(summary(lm(y~x))$r.squared)? Thanks Matej -- Matej Cepl, matej at ceplovi.cz, Finger: 89EF 4BC6 288A BF43 1BAB 25C3 E09F EF25 D964 84AC 138 Highland Ave. #10, Somerville, Ma 02143, (617) 623-1488 The difference between death and taxes is death doesn't get worse every time Congress meets -- Will

Hi All, I have got matrix 'data' of dimension 22000x600. I want to make 50 independent samples of dimension 22000x300 from the original matrix 'data'. And then want to calculate pearsons CC for each of the obtained 50 matrices. It seems it is possible to do this using 'boot' function from library boot but I am not able to figure out how? I am really stuck. Please help!

I did a linear correlation of data using glm.fit and stored the output in the object "f": f <- glm.fit(x, y, w) I am intereseted in estimating the quality of the correlation. I am used to do it using pearson correlation coefficient "r" or "r^2". Can I extract this coefficient from the output of glm.fit? Is there another number in the output of glm.fit that

Hi all: There was a concern raised by reviewers of a manuscript of mine over the proper execution of a Pearson's correlation. In brief, this was undertaken in order to determine the relationship between the extent of wheel running (y axis) and ethanol intake (x axis) across three, separate 10 day periods in 7 animals. In the paper, the correlational plots for each 10 day-period had 70 data

Hi again List, Well this time I’m writing for a friend (really J). He needs to create a distance matrix based on an abundance matrix using the 1-Pearson’s R index. Well I told him to look at the proxy package, but there is only Pearson Index. He needs it to perform a clustering. Well, as soon as he told me there proxy only had the Pearson index I thought: “He could just do something like

Hello, I used the function cor to calculate the pearson correlation coefficient between variables. However, the resulting values do not correspond to the outcome of my excel-calculations, for which I used the formula Cor(x,y)=Cov(x,y)/(SD(x)*SD(y)) So my question is: How does the function "cor" compute the pearson correlation coefficient? Thank you in advance, Ake Nauta

Hello, I want to use pearson's correlation as distance between observations and then use any centroid based linkage distance (ex. Ward's distance) When linkage distances are formed as the Lance-Williams recursive formulation, they just require the initial distance between observations. See here: http://en.wikipedia.org/wiki/Ward%27s_method It is said that you have to use euclidean

Is Pearson's Chi-Square test for contingency tables asymptotically unbiased for large tables (large degrees of freedom) regardless of the expected values in each cell? The rule of thumb is that Pearson's Chi-square should not be used when large numbers of cells have expected values < 5. However, I compared the results on 4x4 contingency tables for R's chisq.test using chi-square

Hi, My name if Frank Tappen and I work for an SPC Software Company called DQS. A customer of ours has asked us to create Pearson Curves of their non-normal data sets and to calculate several statistics; one of them being CPK. I am searching for statistical routine which can do this for us and came across "R". My question is, does "R" have the capability of calculating CPK for

I have used the correlation analysis (pearson) in the agricolae package to analyse my data and got unexpectedly low p-values (therefore making many more highly significant correlations in my data than I had expected). I am wondering if the p-values given should be subtracted from 1 to give the real p-value, because for each variable compared against itself has a p-value of 1 and I thought it

Dear Sir, I am referring to your package "FAdist". I wish to know how to estimate the parameters of the distribution - "Log-Pearson Type III Distribution"? Will it be possible for you to guide me or inform the package in R, I can use to estimate the parameters. Regards Katherine [[alternative HTML version deleted]]

Hi everyone, I've been trying to find a function that outputs the Pearson and/or Spearman correlation coefficients for several variables with the associated statistics in one single table/matrix. For what I've been able to understand the Stats package is only able to compute these coeficients/statistics only in defined pairs. This becomes time consuming when we want to determine these

Dear Sirs What is the best aproximation to the standardized normal distribution: necessidade = c("sem necessidade","com necessidade") tipo =c("CE-1", "CE-2", "CE-3") dados=c(20,34,44,69,9,3) Tabela =cbind(expand.grid(list(Necessidade=necessidade, Tipo=tipo)), count=dados) Tabela.array=tapply(Tabela$count, Tabela[,1:2], sum) ni =

In running the following example of a loglinear model for the Titanic data, I was surprised to see NaN reported for the Pearson chisq > loglin(Titanic, margin=list(1:3, 4)) 2 iterations: deviation 2.273737e-13 $lrt [1] 671.9622 $pearson [1] NaN $df [1] 15 $margin $margin[[1]] [1] "Class" "Sex" "Age" $margin[[2]] [1] "Survived" Tracing it back,

Hi all, This is a very silly question or something escapes me: Let obj a simple gam poisson model. Let >obj<-gam(....,family=poisson) >obj1<-update(obj, family=quasi(link="log", var="mu")) >From summary.glm(obj1) the dispersion parameter is estimated 1.165; In fact it is: > (predict(obj1, se.fit=T)$se.fit[1:5]/predict(obj, se.fit=T)$se.fit[1:5])^2 4