similar to: converting a t statistic to r2

Hi I spent hours looking over my formula. Somehow I cant find the reason why it gives me different answer. help appreciated. x = as.matrix(read.table("http://www.niehs.nih.gov/research/atniehs/core/microarrays/docs/heinloth.txt",1)) x = t(x) #now rows are subjects, cols are genes x = x[order(rownames(x)),] #order by treatment group oxygen, ultra-violet, gamma radiation y =

This has ABSOLUTELY nothing to do with R but I was hoping that someone might know because there are obviously a lot of very bright people on this list. Suppose I had a time series of data and at each point in time t, I was calculating x bar + plus minus sigma where x bar was based on a moving window of size n and so was sigma. So, if I was at time t , then x bar t plus minus sigma_t would be

Greetings, I have stumbled across some unexpected behavior (potential a bug) in, what I suspect to be R's (2.6.2 on Ubuntu Linux) t.test function; then again the problem may exist in my code. I have shutdown R and started it back up, re-run the code and re-experienced the error. I have searched on Google for the abnormal termination error message "(stderr < 10 * .Machine$double.eps *

I am doing a cluster analysis with hclust. I want to get hclust to output the Hotelling's T squared statistic for each cluster so I can evaluate is data points should be in a cluster or not. My research to answer this question has been unsuccessful. Does anyone know how to get hclust to output the Hotelling's T squared statistic for each cluster? Mike [[alternative HTML version

When I use xyplot (from package lattice) to produce a multi-series graph: xyplot(linear+quadratic+sqrt~x, data=df, main="complexity of different functions", ylab="y", col=c("red", "black", "orange"), type="b", lty=c(1,2,3), pch=c(1,2,3), auto.key=TRUE) where I changed the default colour, line type and data point type so that each series

Hi, Suppose I have a multidimensional array: tmp <- array(1:8, c(2,2,2)) is there a function out there that, given a one-dimensional array index, will return the separate indices for each array dimension? for instance, tmp[8] is equivalent to tmp[2,2,2]. I'd like to derive the vector (2,2,2) from the index 8. thanks, Brad Buchsbaum

I'm using R on Windows v2.0.1 with the nlme package (v3.1-53) and am finding some unexpected discrepancies in the output of intervals.lme and coef.lme. I've included a toy dataset at the end, but briefly, the data are longitudinal data from couples in marital therapy. Each spouse's relationship satisfaction is measured 4 times; I've fit both linear and quadratic models to the

This is a relatively recent article that is somewhat accessible. Jensen, D. R., and Solomon, Herbert (1994), "Approximations to joint distributions of definite quadratic forms", Journal of the American Statistical Association, 89 , 480-486 It has references to previous work. I also have an old paper that is so old I can't tell what journal it came out of:( Grad, Arthur and Solomon,

Hello. I am using R version 2.1.1 on Windows 2000. I am using a par(mfrow=c(2,2)) statement to produce 4 plots on one screen. I want a single horizontal legend to appear at the top of the four plots. My code is something like this: par(mfrow=c(2,2)) plot(x,y1) lines(x,y2) lines(x,y3) plot(x,z1) lines(x,z2) lines(x,z3) plot(x,t1) lines(x,t2) lines(x,t3) plot(x,w1) lines(x,w2) lines(x,w3)

Hi again! Following up my previous posting below (to which no response as yet), I have located a report which situates this type of question in a longitudinal modelling context. http://www4.stat.ncsu.edu/~dzhang2/paper/glm.ps Generalized Linear Models with Longitudinal Covariates Daowen Zhang & Xihong Lin (This work seems to originally date from around 1999). They consider an outcome Y,

Hi all, I've fit a mixed linear model to some longitudinal data. I'm interested in the differences in patterns of decrease in the dependent variable according to group status, and my hypothesis particularly predicts a difference between the groups in trajectory of change at between specific ages. The data shows a significant interaction between group and the linear and quadratic effects

Dear All, I am working with skewed-t copula in my research recently, so I needed to write an mle procedure instead of using a standard fit one; I stick to the sn package. On subsamples of the entire population that I deal with, everything is fine. However, on the total sample (difference in cross-sectional dimension: 30 vs 240) things go wrong - the objective function diverges to infinity. I

Hi everyone, I have a question. Now I am reading the resource code of the package "ssfcov". The resource code is as following. I cannot find the resource code of the function "myss2d" anywhere in the package. Can anyone give me a hint how to find it in the package. Thanks a lot!!bv > ssfcov function (time, x, subject, nbasis = 5, centered = FALSE, noDiag = TRUE) {

Buen día para todos los que ven este correo. En estos días estoy estudiando la potencia de una prueba en inferencia, pero no entiendo bien como correr un codigo que lo haga pertinentemente, el codigo que corro es el siguiente, la idea es que a medida que crece el tamaña muestral pues la potencia de la prueba aumente, pero no se como hacer para que, repita la prueba variando el tamaño de la

Hi all, I wrote a function that actually does what I want it to do, but it tends to be very slow for large amount of data. On my computer it takes 5.37 seconds for 16000 data points and 21.95 seconds for 32000 data points. As my real data consists of 18000000 data points it would take ages to use the function as it is now. Could someone help me to speed up the calculation? Thank you, Tonja

Hello. I have some basic question for you!. Has calculating R2 sense in mixed model? I think no! But i don't why! Thank in advance for your help Angelo

Dear R-help Members, I am wondering if anyone think of the optimal way of computing for several numeric variable the difference between 2 levels of a factor. To be clear let's generate a simple data frame with 2 numeric variables collected for different subjects (ID) and 2 levels of a TIME factor (time of evaluation)

Dear all: Subjects were measured two times (t1 and t2) on different variables (v1 ... vn). Between t1 and t2 there was an experimental manipulation. I computed two PCAs for time-points t1 and t2. Is it possible to combine both PCAs in order to get only one set of eigenvectors? Due to the experimental manipulation measurement values on time points t1 and t2 changed for each subject. Many

1) I have the following data situation: 96 plots 12 varieties 2 time points 2 technical treatments the experiment is arranged as follows: a single plot has two varieties tested on it. if variety A on plot #1 has treatment T1 applied to it, then variety B on plot #1 has treatment T2 applied to it. across the whole experiment variety A is exposed to treatment T1 the same number of times as

Hi All, I have a data frame: Time1 Time2 1 176:46:10 41:48:06 2 171:28:57 61:19:10 3 178:25:15 34:05:35 4 74:04:20 25:01:55 5 136:11:20 37:59:32 6 138:17:17 30:22:27 7 183:04:48 29:25:02 8 179:35:01 19:29:44 > str(df) 'data.frame': 8 obs. of 2 variables: $ Time1: Factor w/ 583 levels