similar to: hierarchical clustering with pearson's coefficient

Hi, I am new to R. I have a matrix that I have assigned to the object ?colon?. > colon<-read.table("c:\\alon.txt",header=T,row.names=1) attach(colon) names(colon) The dimenstions are 2000 62. Each of the 62 columns (titled norm1, norm2, norm3, etc) has 2000 different numbers (?continuous? values) within it. I have also assigned a name for each of the 2000 rows of the

Hello, I have two problems in automating multiple glm(s) operations. The data file is tab delimited file with headers and two columns. like "ABC" "EFG" 1 2 2 3 3 4 dat <- read.table("FILENAME", header=TRUE, sep="\t", na.strings="NA", dec=".", strip.white=TRUE) dataf <- read.table("FILENAME", header=FALSE,

Hi, See below I reply your message for <https://stat.ethz.ch/pipermail/r-help/2008-April/160966.html>[R] predict.glm & newdata posted on Fri Apr 4 21:02:24 CEST 2008 You say it ##works fine but it does not: if you look at the length of yhat2, you will find 100 and not 200 as expected. In fact predict(reg1, data=x2) gives the same results as predict(reg1). So I am still looking for

Hello All: This series is an update version of multiqueue virtio-net driver based on Krishna Kumar's work to let virtio-net use multiple rx/tx queues to do the packets reception and transmission. Please review and comments. Test Environment: - Intel(R) Xeon(R) CPU E5620 @ 2.40GHz, 8 cores 2 numa nodes - Two directed connected 82599 Test Summary: - Highlights: huge improvements on TCP_RR

Hello All: This series is an update version of multiqueue virtio-net driver based on Krishna Kumar's work to let virtio-net use multiple rx/tx queues to do the packets reception and transmission. Please review and comments. Test Environment: - Intel(R) Xeon(R) CPU E5620 @ 2.40GHz, 8 cores 2 numa nodes - Two directed connected 82599 Test Summary: - Highlights: huge improvements on TCP_RR

Hi all - I'm stumped by the following mdl <- glm(resp ~ . , data = df, family=binomial, offset = ofst) WORKS yhat <- predict(mdl) WORKS yhat <- predict(mdl,newdata = df) FAILS Error in drop(X[, piv, drop = FALSE] %*% beta[piv]) : subscript out of bounds I've tried without offset, quoting binomial. The offset variable ofst IS in df. Previous postings indicate possible

Hi, I'm trying to melt() some data for subsequent cast()ing and am encoutering errors. The overall process requires a couple of casts()s and melt()s. ########Start Session 1########## ## I have the data in a (fully) melted format and can cast it fine... > norm1[1:10,] Pool SNP Sample.Name variable value 1 1 rs1045485 CA0092 Height.1 0.003488853 2 1 rs1045485

Hi, I'm having a bit of trouble with the axes in my plots. I don't like the way R does not have them cross in the origin. Is there another plot/axis function? i tried using abline as suggested by someone from this list, but in my case this gives no satisfactory result, as the abline does sometimes lie on top of the y axis and sometimes not, depending on how i scale the image... #bild16

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.

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

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

How i can perform a Pearson's Chi-squared Test in this data set: | Outcome -----------------+-----------+----------------------------------+ Treatment | Sex | None |Some | Marked | Total -----------------+------------+--------+--------+-------------+ Active | Female | 6 | 5 | 16 | 27

Hello, I am trying to collect several global measures or statistics for time-series as well as packages of R that can compute them. I have found several of them in papers and books, but the literature is so big i am sure i am missing several of them. skewness kurtosis min max mean SD trend seasonality periodicity chaos (Lyapunov Exponent) / Largest Lyapunov Exponent (i think is the same

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 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

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

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

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