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Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

Dear all, I'm quite new in R and I have a problem with the function which. When I use it to select a subset of a dataframe it works well but somewhere R takes trace of the past dataframe and this creates problems with following operations. For example: sentences <- read.xls("frasi.tot.march.3.xls", header=TRUE) head(sentences) fam subjID Cond Code reg total first

Hello everyone, Thanks to all who offered suggestions after my initial query to the list. I've been busy trying to absorb the docs as fast as possible, but this little thing has me stumped for the moment. I'm creating a random sample of 100 item and saving it to a vector named 'iq'. I'd like to create a second vector (iq_prime, say) by adding 20 to each element of the

Hello everyone, Thanks to all who offered suggestions after my initial query to the list. I've been busy trying to absorb the docs as fast as possible, but this little thing has me stumped for the moment. I'm creating a random sample of 100 item and saving it to a vector named 'iq'. I'd like to create a second vector (iq_prime, say) by adding 20 to each element of the

Hi R users, I need some help in the followings: I'm doing factor analysis and I need to extract the loading values and the Proportion Var and Cumulative Var values one by one. Here is what I am doing: > fact <- factanal(na.omit(gnome_freq_r2),factors=5); > fact$loadings Loadings: Factor1 Factor2 Factor3 Factor4 Factor5 b1freqr2 0.246 0.486 0.145

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Hi folks, I just signed up klibc, and started playing with it. Building it was just a matter of some minutes. Next step was to copy all the built shared libs and executables into an empty directory tree (/bin and /lib) and to do a chroot into it. I've got a prompt, ash seems to be working, 'zcat -h' works, but others (e.g. sleep, cat)

I have a situation with a large dataset (3000+ observations), where I'm doing lags as regressors, where I get: Call: lm(formula = rj ~ rM + rM.1 + rM.2 + rM.3 + rM.4) Residuals: 1990-06-04 1994-11-14 1998-08-21 2002-03-13 2005-09-15 -5.64672 -0.59596 -0.04143 0.55412 8.18229 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.003297 0.017603

# 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

Hi, I'm estimating a loglogistic aft (accelerated failure time) model, just a simple plain vanilla one (without time dependent covariates), I'm comparing the results that I obtain between aftreg (eha package) and survreg(surv package). If I don't use any covariate the results are identical , if I add covariates all the coefficients are the same until a precision of 10^4 or 10^-5 except

Hi, I am trying to run a simple logistic regression using lrm() to calculate a odds ratio. I found a confusing output when I use summary() on the fit object which gave some OR that is totally different from simply taking exp(coefficient), see below: > dat<-read.table("dat.txt",sep='\t',header=T,row.names=NULL) > d<-datadist(dat) > options(datadist='d')

Hello, Based on yesterday's R-help thread (help: program efficiency), and following Bill's suggestions, it appeared that sequence: > sequence function (nvec) unlist(lapply(nvec, seq_len)) <environment: namespace:base> could benefit from being written in C to avoid unnecessary memory allocations. I made this version using inline: require( inline ) sequence_c <- local( {

Dear all: "predict.glm" provides an example to perform logistic regression when the response variable is a tow-columned matrix. I find some paradox about the degree of freedom . > summary(budworm.lg) Call: glm(formula = SF ~ sex * ldose, family = binomial) Deviance Residuals: Min 1Q Median 3Q Max -1.39849 -0.32094 -0.07592 0.38220 1.10375

I have the following function f <- function { ... model <- lm(rttx[,1] ~ rttx[,2] + 0); summary(model); ... } while summary(model) shows the summary if I execute the function line by line in the Command Line Interface, if I call f() summary is silent how to solve it? or is there workaround? thanks in advance Ulisses Debian GNU/Linux: a dream come true

Hi All, I have run many regression analyses (14000 +) and want to collect the coefficients and pvalues into an excel file. I can get the statements below to work up to step 4. I can printout the regressionresults (sample output below). So my hope is to run something like step 5 and 6 and put the pvalues (and then coefficients) into an excel file. Can anyone suggest what I am doing wrong or a

Hi, I have a Community (COM) composed of 6 species: A, B, C, D, E & F. The density of my Community is thus (Eq.1): dCOM = dA + dB + dC + dE + dF I would like to calculate and plot a linear regression between the density of each of my species and the density of the whole community (illustrating how the density of each species varies with variations of the whole community). For example, I would

Hi, Is anybody know the purpose of this method (xen_evtchn_do_upcall)? When I run a user level application involved in TCP receiving and the SoftIRQ for eth0 on the same CPU core, everything is OK. But if I run them on 2 different cores, there will be xen_evtchn_do_upcall() existing (maybe when the local_bh_disable<http://www.cs.fsu.edu/~baker/devices/lxr/http/ident?i=local_bh_disable>() or

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

Dear all,I am a new user of WinBUGS and need your help. After running the following code, I got parameters of beta0 through beta4 (stats, density), but I don't know how to get the prediction of the last value of h, the variable I set to NA and want to model it using the following code.Does anyone can given me a hint? Any advice would be greatly appreciated.Best

Hi folks, Warning: I don't know if the result I am getting makes sense, so this may be a statistics question. The fitted values from my binomial lmer mixed model seem to consistently overestimate the cell means, and I don't know why. I assume I am doing something stupid. Below I include code, and a binary image of the data is available at this link:

Hello all- I am a relatively new user of R and am working through a graduate course in Statistics that uses Minitab, SAS and some Matlab. I like using R but am having some trouble lining up the output from lda() to that of the other programs' results. The dataset below is a modified set of wine data from the Pinot Noir data set as an illustration of the 2 group LDA scenario. Mo Ba