similar to: working with sparse matrix

Dear all, I would like to ask your help concerning an error message I get. I have the following struct str(CRagentInTime[[1]]) List of 2 $ timelag: int 0 $ CRagent:List of 50 ..$ :List of 3 .. ..$ CRmap: num [1:256, 1:256] NA NA NA NA NA NA NA NA NA NA ... .. ..$ xy : num [1:2] 10 177 .. ..$ sr : num [1:49] -94.9 -92.8 -79.5 -97.6 -78.4 ... and I wanted to select all the sr fields

Hi All,  In order to compare two different logistic regressions, I think I need to compare them based on their BIC values, but I am not sure if the smaller BIC would mean a better model or the reverse is true? Thanks a lot,Andra [[alternative HTML version deleted]]

Hi, all: Does anybody know how to avoid the intercept term in cv.glmnet coefficient? When I say "avoid", it does not mean using coef()[-1] to omit the printout of intercept, it means no intercept at all when doing the analysis. Thanks. [[alternative HTML version deleted]]

Hi My data looks like this startDate="2008-06-01" dateRange =c( "2008-10-01","2008-12-01") Is there any method to find the week number from the startDate range ----- Thanks in Advance Arun -- View this message in context: http://r.789695.n4.nabble.com/Week-number-from-a-date-tp4410223p4410223.html Sent from the R help mailing list archive at Nabble.com.

Hi all, I am using the glmnet R package to run LASSO with binary logistic regression. I have over 290 samples with outcome data (0 for alive, 1 for dead) and over 230 predictor variables. I currently using LASSO to reduce the number of predictor variables. I am using the cv.glmnet function to do 10-fold cross validation on a sequence of lambda values which I let glmnet determine. I then take

Yes, that will halve the number of multiplications. If you?re looking for such optimisations then you can also consider ifelse(G=='male', 65L, 58L). That will definitely use less time & memory if WC is integer, but the trade-offs are more complicated if WC is floating point. Regards, Jorgen Harmse. From: avi.e.gross at gmail.com <avi.e.gross at gmail.com> Date: Friday,

Just a minor point in the suggested solution: df$LAP <- with(df, ifelse(G=='male', (WC-65)*TG, (WC-58)*TG)) since WC and TG are not conditional, would this be a slight improvement? df$LAP <- with(df, TG*(WC - ifelse(G=='male', 65, 58))) -----Original Message----- From: R-help <r-help-bounces at r-project.org> On Behalf Of Jorgen Harmse via R-help Sent: Friday,

Hi R users I apologise in advance for this question as I suspect it is simple and perhaps others have had this problem. I am struggling to sort out how to fix the x axes so that the labels don't overlap. I have put the following example together to show my problem. library(lattice) titre <- as.factor(rep(c(10999,20999,30999,40999,50999,60999,

Hello, I appreciate this is likely to be an easy question. I am trying to obtain the residuals from a linear regression where the line is forced to have a 1:1 relationship. An example of the data: A<-c(0.9803922, 1.3850416, 0.8241758, 0.0000000, 0.4672897, 1.1904762, 0.0000000, 0.9456265, 1.5151515) B<-c(1.3229572, 1.9471488, 1.3182674, 0.7007708, 1.0185740, 1.0268562, 0.8695652,

Hello all, I've been working on a package to do various things related to the Conway-Maxwell-Poisson distribution and wanted to be able to make fast random draws from the distribution. My R code was running quite slow so I decided to give Rcpp a bash. I had used it before but only for extremely basic stuff and always using inline. This time I decided to give making a proper package a go.

Dear, I would like know the way to calculate the variance explained of a regression tree. I use the function "ctree" from library "party" many thanks [[alternative HTML version deleted]]

Dear subscribers, I am looking for a function which would allow me to model the dependent variable as the number of successes in a series of Bernoulli trials. My data looks like this ID TRIALS SUCCESSESS INDEP1 INDEP2 INDEP3 1 4444 0 0.273 0.055 0.156 2 98170 74 0.123 0.456 0.789 3 145486 30 0.124

Hi, I must be doing something silly here, because I can't get the studentised and standardised residuals from r output of a linear model to agree with what I think they should be from equation form. Thanks in advance, Jennifer x = seq(1,10) y = x + rnorm(10) mod = lm(y~x) rstandard(mod) residuals(mod)/(summary(mod)$sigma) rstudent(mod)

Hi All,  I am trying to run cv.glmnet(x,y,family="multinomial", nfolds =4) and I only have 8 observations and the number of features I have is 1000, so my x matrix is 8 by 1000 and when I run the following, I get this error, I am not sure what is causing this problem.  Error in predmat[which, , seq(nlami)] = preds :   number of items to replace is not a multiple of replacement length Can

Hi,   Could any one please help how I can transform data based on Box-Cox Transformations in R.   Any helps will be much appreciated.   thanks, Kagba [[alternative HTML version deleted]]

Dear members of R forum, I'm trying to perform a simply dynamic programming model in R, following the reccomendations of Soetart & Herman (A practical guide to ecological modeling). However, I've obtained a number of problems, that I'm unable to solve (even thoughI've tried during at least 2 hours). Can anyone help me? Many thanks > ccrit <- 0 > cmax <- 5 >

Hi users I'm student who is struggling with basic R programming. Would you please help me with this problem. "My english is bad" I hope that my question is clear: I have a matrix in wich there are two colmns( yp, yt) Yp: predicted values from my model. yt: true values ( my dependante variable y is a categorical;3 modalities (0,1,2) I don't know how to procede to calculate the

Hi everyone, I have a zero vector of length N and I would like to randomly allocate the value 1 to one of the values of this vector. I presume I have to use the uniform distribution but could someone tell me how I should process? Thanks in advance, Boris -- View this message in context: http://r.789695.n4.nabble.com/generate-randomly-a-value-of-a-vector-tp3798190p3798190.html Sent from the R

This mean First, I am no expert but I am analyzing some marketing data. I have information on two versions of the same site, and I have data on the number of times people filled out a form on each version of the site. Sample data: Site 1 Site 2 Filled out form 10 35 Did not fill out form 50

Hi all, I'm trying to write a MH algorithm in R for a standard normal distribution, I've been trying for a good week or so now with multiple attempts and have finally given up trying to do it on my own as I'm beginning to run out of time for this, would somebody please tell me what is wrong with my latest attempt: n=100 mu=0 sigma=1 lik<-function(theta) exp(((theta-mu)^2)/2*sigma)