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Dear R users I have a big matrix like 6021 1188 790 290 1174 1015 1990 6613 6288 100714 6021 1 0.658 0.688 0.474 0.262 0.163 0.137 0.32 0.252 0.206 1188 0.658 1 0.917 0.245 0.331 0.122 0.148 0.194 0.168 0.171 790 0.688 0.917 1 0.243 0.31 0.122 0.15 0.19 0.171 0.174 290 0.474

Full_Name: Adrian Custer Version: 0.90.0 OS: Linux on Thinkpad (pentium) and desktop (K6) Submission from: (NULL) (128.32.251.234) When I create a boxplot, and then try to overlay a lowess fit or just the points, the points do not appear in the highest level and the lowess curve does not reach the highest level. However, if I add one to each of the models, the problem is solved. I tried this

I've created a short program to print a table of learning curve factors. However, I cannot figure out how to format the table to: 1) Get rid of the [1]s in the first column and replace it with the values of N. 2) Line up the first row with the factors (decimal fractions). Thanks for any help. The complete program and output is as follows: > Lc<-seq(0.70,0.95,0.05) #Specify learning

Hi, I am getting a warning message when I am fitting a generalized linear mixed model (m1.2 below). CHOLMOD warning: matrix not positive definite Error in objective(.par, ...) : Cholmod error `matrix not positive definite' at file:../Supernodal/t_cholmod_super_numeric.c, line 614 Any idea? Thanks for your help, Reza > sessionInfo() R version 2.4.1 (2006-12-18) i386-pc-mingw32

I have a data frame, lets call it dat, with 3 columns ( mc, yr, ret) which represent market cap, year, and return. mc is a factor, mc, and ret are real numbers. I want to add a column to the data calculated as follows. For each year, I want to split the data by mc decile, then calculate the mean ret within that mc decile, and finally subtract that year's decile mean from the raw return. Then

Hi all I am trying to generate a normal unbalanced data to estimate the coefficients of LM, LMM, GLM, and GLMM and their standard errors. Also, I am trying to estimate the variance components and their standard errors. Further, I am trying to use the likelihood ratio test to test H0: sigma^2_b = 0 (random effects variance component), and the t-test to test H0:mu=0 (intercept of the model Yij = mu

# I have a question about how factanal is calculating the regression factor # scores based on an oblique rotation (promax) of the factors. # # As is explained in the help file, regression factor scores are # obtained as # # hat f = Lambda' Sigma^-1 x # # However, according to Harman's "Modern Factor Analysis" (e.g. second # edition, pp. 351-352) the formula is # # hat f = Phi

I am a total newbie at R but experienced with computers. If this is not the right forum for this question, please let me know one that is. I searched in the R manuals in the Help section, the Nabble help and Rseek but no luck. I am trying to install the package gcl on my recently installed R 2.8.1 I searched on cran for the windows version and found a zip file for the package In RGui, I ran

Dear R-users I analysed the same data set by two different ways; analysis of covariance by using lm and anova functions and multiple comparison by using simtest function in the multcomp library. The output from the analysis of covariance is; > y<-lm(D~Cond+Q1,data=x) > anova(y) Analysis of Variance Table Response: D Df Sum Sq Mean Sq F value Pr(>F) Cond 2

Hello all, I am working with a 1000x1000 matrix, and I would like to return a 1000x1000 matrix that tells me which value in the matrix is greater than a theshold value (1 or 0 indicator). i have tried mat2<-as.matrix(as.numeric(mat1>0.25)) but that returns a 1:100000 matrix. I have also tried for loops, but they are grossly inefficient. THanks for all your help in advance. Lanre

Dear all, When analyzing data from a climate change experiment using linear mixed-effects models, I recently came across a situation where: - the summary(model) showed a significant difference between the levels of a two-level factor, - while the anova(model) showed no significance for that factor (see below). My question now is: Is the anova.lme() approach correct for that model? And why does

Dear all, I am attempting to explain patterns of arthropod family richness (count data) using a regression model. It seems to be able to do a pretty good job as an explanatory model (i.e. demonstrating relationships between dependent and independent variables), but it has systematic problems as a predictive model: It is biased high at low observed values of family richness and biased low at

Is there a way to set up a regression in R that forces two coefficients to be equal but opposite in sign? I'm trying to setup a model where a subject appears in a pair of environments where a measurement X is made. There are a total of 5 environments, one of which is a baseline. But each observation is for a subject in only two of them, and not all subjects will appear in each

Hello, this is probably more a statistical question than an R-specific problem, but I'll risk it. I've fitted a Cox Proportional hazard model with one factor Treatment (seven levels) as a predictor variable. The general Null hypothesis (all groups show the same survival behaviour) is clearly rejected. Now, is there any (statistically sensible) way of doing pairwise comparisons and/or

Hi, I have both linux and solaris NIS client against a Solaris NIS Server, but my linux box are shamefully slow compare to solaris some sample: on Solaris 5.8: # ypwhich transporter02.domain.com # time id userid uid=36923(userid) gid=36923(u_036923) real 0.0 user 0.0 sys 0.0 on Linux: [root at linux]# ypwhich transporter02.domain.com [root at amsdc2-n-s04taw root]# time

How do you get the design matrix R used when calculating ANOVA? -- View this message in context: http://www.nabble.com/how-to-get-design-matrix--tp23464638p23464638.html Sent from the R help mailing list archive at Nabble.com.

Greetings, I am a very novice user with R, and in the course of running a linking procedure : P.old<- function (a, c, b, xi){ #a contains a parameters #c contains c parameters #b contains b parameters #xi is a one column vector containing quadrature points for xi(=theta-gamma)for one item tmp <- a*(xi-b)

Dear all. Let say I have a group of codes which will be used in many places in my overall R-code files. These group of codes will be used within a for-loop (with a big length, like 10000 times) and also many other places outside of that for loop. As this group of codes are being used in many places, I thought to put them within a user-defined function. Here my question is, is there any speed

https://bugzilla.samba.org/show_bug.cgi?id=3116 Summary: large tar files: 1 gig size: retransmitted: rsync_rsh Product: rsync Version: 2.6.6 Platform: x86 OS/Version: FreeBSD Status: NEW Severity: normal Priority: P3 Component: core AssignedTo: wayned@samba.org ReportedBy:

Thank you, Greg, and also to Scott Ellison, who replied privately. I am in the process of trying out both suggestions. After I sent my initial message, I came across the Systemfit package, which allows specification of constraints on parameters. In theory, this should solve my problem perfectly. However, I was not able to get it to work with my data, as every attempt yielded the following