similar to: Using cv.tree to assign cases to specific cv-groups

Greetings -- I'm programming a data mining system in R for protein structural data. As a seasoned Perl and Python and Ada and ML, et al., programmer, I am severely befuddled by the environment problem, where data is not found in a 3rd level nested function. I did peruse the parent frame not on the search path idea, and came up with a hack which kinda works, also below with the code which

I am using the tree package. One option is the cv.tree, which is supposed to run cross-validations on your tree models. Is there somewhere I can find some documentation on this function? I have the help file that comes with the library, but I need more, especially on what the output is. Thanks, Warren Schlechte HOH Fisheries Science Center 5103 Junction Hwy Mt. Home, TX 78058 Phone

Hi there, Does R has built-in codes for nonpara. bivariate regression so that I can estimate the joint distribution of two variables as a function of some covariates? Thanks a lot. --------------------------------------------------- Ximing Wu Department of Economics University of Guelph Guelph, Ontario, Canada, N1G 2W1 Tel: (519) 842-4120, ext 53014 Fax: (519) 763-8497 email: xiwu at

Dear Jesus, The difference is marginal when each code chunk does the same things. Your for loop does not yields the same output as the lapply. Here is the cleaned version of your code. n<-10000 set.seed(123) x<-rnorm(n) y<-x+rnorm(n) rand.data<-data.frame(x,y) k<-100 samples <- split(sample(n), rep(seq_len(k),length=n)) library(microbenchmark) microbenchmark( "for"

Dear R-users I use nnet for a classification (2 classes) problem. I use the code CVnn1, CVnn2 as described in V&R. The thing I changed to the code is: I define the (class) weight for each observation in each cv 'bag' and give the vector of weights as parameter of nnet(..weights = weight.vector...) Unfortunately I get an error during some (but not all!) inner-fold cv runs:

-------- Original Message -------- Subject: [AGDG-LIST:428] Summer Course in Guelph Date: Fri, 22 Dec 2006 09:12:24 -0500 From: Larry Schaeffer <lrs at uoguelph.ca> Reply-To: lrs at uoguelph.ca To: Animal Geneticist's Discussion <agdg-list at colostate.edu> The Centre for Genetic Improvement of Livestock at the University of Guelph is pleased to announce a one week summer course

The lapply loop and the for loop have very similar speed characteristics. Differences seen are almost always due to how you use memory in the body of the loop. This fact is not new. You may be under the incorrect assumption that using lapply is somehow equivalent to "vectorization", which it is not. -- Sent from my phone. Please excuse my brevity. On August 7, 2017 7:29:58 AM PDT,

Hi! I am doing a lapply and for comparaison and I get that for is faster than lapply. What I have done: n<-100000 set.seed(123) x<-rnorm(n) y<-x+rnorm(n) rand.data<-data.frame(x,y) k<-100 samples<-split(sample(1:n),rep(1:k,length=n)) res<-list() t<-Sys.time() for(i in 1:100){ modelo<-lm(y~x,rand.data[-samples[[i]]])

Hello seasons R users, Is it possible to store a complete regression result into an array? I've already been able to save individual regression coefficients, but would like to store the whole regression results into different arrays through a loop. So that in under different quantiles regressions, I would be able to create a loop and store the full regression result each time into a

Greetings I have been trying to use metaplot to create a forest plot of my meta-analysis results. My data is in the form of ORs, so I've been inputting the ORs as the point estimate (mn) and the SEs of the logORs as the SE (se). However, whilst the point estimates appear to be displayed properly, many of the confidence intervals go below zero (not possible for an OR, as far as I know). I

Georges Monette and I would like to announce a new package, cv, now on CRAN, which implements cross-validation of regression models. Some of the functions supplied by the package: - cv() is a generic function with a default method and computationally efficient "lm" and "glm" methods, along with a method for a list of competing models. There are also experimental

Georges Monette and I would like to announce a new package, cv, now on CRAN, which implements cross-validation of regression models. Some of the functions supplied by the package: - cv() is a generic function with a default method and computationally efficient "lm" and "glm" methods, along with a method for a list of competing models. There are also experimental

Hi I am using clang-6 to compile this C++ code and I see a strange temporary variable allocated at expression address 0x7ff1131536e8. If I change the ternary operator at line 483 to an if-else, the temporary is not allocated. Thanks Variables: ========= FFMPEGVideoCapture ffmpeg_video_capture_; cv::VideoCapture opencv_video_capture_; Function: ======== bool

Hi there, I am trying to use predict() with an object returned by cv.glmnet(), and get the following error: no applicable method for 'predict' applied to an object of class "cv.glmnet" What's wrong? my code: x=matrix(rnorm(100*20),100,20) y=rnorm(100) cv.fit=cv.glmnet(x,y) predict(cv.fit,newx=x[1:5,]) coef(cv.fit) Thanks so much, Asaf -- View this message in context:

Hi, everyone When I was using cv.lm(DAAG) , I found there might be something wrong with it. The problem is that we can't use it to deal with a linear model with more than one predictor variable. But the usage documentation hasn't informed us about this. You can find it by excuting the following code: xx=matrix(rnorm(20*3),ncol=3) bb=c(1,2,0) yy=xx%*%bb+rnorm(20,0,10)

Dear R-Helpers, I am trying to bootstrap the coefficient of variation on a suite of vectors, here I provide an example using one of the vectors in my study. When I ran this script with the vector x <-c(0.625, 0.071428571, 0.133333333, 0.125, 0), it returned CV(boot) [the second one], and stderr(boot) [the second one] without problem. However, when I ran it with the vector in the

Hi, perhaps you can help me to find out, how to find the best Lambda in a LASSO-model. I have a feature selection problem with 150 proteins potentially predicting Cancer or Noncancer. With a lasso model fit.glm <- glmpath(x=as.matrix(X), y=target, family="binomial") (target is 0, 1 <- Cancer non cancer, X the proteins, numerical in expression), I get following path (PICTURE

Hi, I've done some searching where others have had trouble with this error (or "warning" actually), but I'm unable to solve my problem. I have a data sheet with 13 columns and 36 rows. Each column has exactly the same number of rows. I've created glms and now want to do cross-validation on 2 of them. Please be gentle-- I'm new to R (and statistics, too, for that

Hello R users I recently learned how to use this command: lapply(datum,"[[","ColumnName") Unfortunately, I don't know how exactly it works, what it's called (in particular the "[[" part], and what other things you can do with it (retrieve multiple columns?). Given datum is a list of dataframes with the same column, but different number of rows,

Dear R-list, I'm not sure what I've found about a function in DAAG package is a bug. When I was using cv.lm(DAAG) , I found there might be something wrong with it. The problem is that we can't use it to deal with a linear model with more than one predictor variable. But the usage documentation hasn't informed us about this. The code illustrates my discovery: > library(DAAG)