similar to: Running "all possible subsets" of a GLM (binomial) model

Hi, Fitting all possible models (GLM) with 10 predictors will result in loads of (2^10 - 1) models. I want to do that in order to get the importance of variables (having an unbalanced variable design) by summing the up the AIC-weights of models including the same variable, for every variable separately. It's time consuming and annoying to define all possible models by hand. Is there a

Hi, List , i have 6 predictor variables and i want to make possible combinations of these 6 predictors ,all the data is in matrix form , if i am having 6 predictors than possible combination of sets are 64 2 power 6, or 63 ,whatever it may be i want to store the result in another variable to each combination and that i want to put in some model , i want to put every combination in some model

Issue: After installing vms on vmware, I noticed a difference in behavior between centos 6 vs 7. On centos 6 I cannot ping the hostname and get a reply without first resolving in the /etc/hosts file by adding a new entry: 192.168.0.47 server1.example.com. But for centos 7 I am able to ping the hostname and get a reply even when I have not made any changes to the /etc/hosts file. Why is there

Hi! I've been browsing through the last months' archive and I can't find an answer to my question, so here it is (let's hope it's not too obvious): I'm working on extensions of an R library, and I would be very surprised if everyone developing R packages is doing the following, as I do: 1.- Write down a modification of an R file 2.- Exit the current R

Hi, I would like to have a script/function (or write one) that can calculate the linear models for all possible combinations of explaining variables. Eventually I would like to end up with a data base (or data frame) giving for each model the R2, R2adj, AIC etc. Currently I'm a bit stuck while writing my own script using the lm() function from the base package. Also, I haven't fund any

Dear R community, I've been writing simple functions for the past year and half and have come across a similar problem several times. The execution of a function within my own function produces NaN's or fails to execute as intended. My conundrum is that I can execute the function outside of my function without error, so it's difficult for me, as a novice functioneer, to figure out

Two posts in one day is not a good day...and this question seems like it should have an obvious answer: I have a matrix where rows are unique combinations of 1's and 0's: > combs=as.matrix(expand.grid(c(0,1),c(0,1))) > combs Var1 Var2 [1,] 0 0 [2,] 1 0 [3,] 0 1 [4,] 1 1 I want a single function that will give the row index containing an exact match

Dear R users, I want to check matrices when i change the order of the rows or/and the order of the columns or/and the combination of them i will give an example what i want  1  -1  1  1      1  1   1  1 -1  -1 -1 -1    -1 -1  -1  -1   1  1    1  1     1 -1    1   1 these 2 matrices are identical because i change the first row and make it third   1  -1  1  1      -1  1   1  1 -1  -1 -1

All, I am looking at an example in Aliaga's Interactive Statistics. Bag A has the following vouchers. BagA <- c(-1000,10,10,10,10,10,10, 10,20,20,20,20,20,20,30, 30,40,40,50,60) Bag B has the following vouchers. BagB <- c(10,20,30,30,40,40,50,50, 50,50,50,50,60,60,60,60, 60,60,60,1000) Two values are selected (from BagA or BagB) without

Hi, This has been asked before but not sufficiently answered from what I could find. How do you create combinations with repetitions (multisets) in R? If I have > set <- array(1:3) And I want to choose all combinations of picking 2 numbers, I want to get a print out like [,1] [,2] [1,] 1 1 [2,] 1 2 [3,] 1 3 [4,] 2 2 [5,] 2 3 [6,] 3 3 subsets(set,

I'm trying to replicate a time-series cross-sectional analysis (countries over years) with SEs clustered by country. ?The original analysis was done in Stata 10 with: xtreg [DV] [IVs] fe cluster(country). Using plm() in R (cran.r-project.org/web/packages/plm/index.html), I've replicated the coefficients. I sought to estimate country-clustered SEs with vcovHC(), and tried a variety of

I have one hundred and six independent variable that I would like to preform a correlation analysis on. Is there anyway to only get the values that are abolute value 0.6 or greater. thanks -- Stephen Sefick Research Scientist Southeastern Natural Sciences Academy Let's not spend our time and resources thinking about things that are so little or so large that all they really do for us is

Well, I''ve searched my brains out and I can''t seem to find a reason for this. I''m getting bad to medium performance with my new test storage device. I''ve got 24 1.5T disks with 2 SSDs configured as a zil log device. I''m using the Areca raid controller, the driver being arcmsr. Quad core AMD with 16 gig of RAM OpenSolaris upgraded to snv_134. The zpool

Hello, I am working with zero inflated models for a current project and I am getting wildly different results from R's zeroinfl(y ~ x, dist="negbin") command and Stata's zinb command. Does anyone know why this may be? I find it odd considering that zeroinfl(y ~ x, dist="poisson") gives identical to output to Stata's zip function. Thanks, --david [[alternative

Hi, I am tring to write a loop to compute this, ========================== x1=c( rep(-1,4), rep(1,4) ) x2=c( rep(c(-1,-1,1,1),2) ) x3=c( rep(c(-1,1),4) ) x1*x2 x1*x3 x2*x3 ======================== suppose i have x1,x2,x3 i want to compute their ' two factor interactions', x1x2,x1x3 and x2x3, I wrote ======================== for(i in 1:2){ for( j in i+1:3){ xij=c()

My two matrices are roughly the sizes of m1 and m2. I tried using two apply and cor.test to compute the correlation p.values. More than an hour, and the codes are still running. Please help to make it more efficient. m1 <- matrix(rnorm(100000), ncol=100) m2 <- matrix(rnorm(10000000), ncol=100) cor.pvalues <- apply(m1, 1, function(x) { apply(m2, 1, function(y) { cor.test(x,y)$p.value

Rodger Combs wrote: > I've run into some issues using Opus with source files in channel layouts > other than the default 8. For instance, 2.1 isn't supported, so I have to > either downconvert to 2.0 or upconvert to 5.1 (which usually involves adding > empty channels, which prevents the playback device from upconverting to the > native layout). > To address this,

Dear all, How can one fit just a fixed effects model in R? lme() seems to require a random component, whereas I am just looking for something like STATA's "xtreg, fe" function. Thanks, Pijus ---- W: 212-588-7897 F: 212-446-1955 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

I hope someone here knows the answer to this since it will save me from delving deep into documentation. Based on 22 pairs of vectors, I have noticed that tetrachoric correlation coefficients in stata are almost uniformly higher than those in R, sometimes dramatically so (TCC=.61 in stata, .51 in R; .51 in stata, .39 in R). Stata's estimate is higher than R's in 20 out of 22

General problem: I have 20 projects that can be invested in and I need to decide which combinations meet a certain set of standards. The total possible combinations comes out to 2^20. However I know for a fact that the number of projects must be greater than 5 and less than 13. So far the the code below is the best I can come up with for iteratively creating a set to check against my set of