similar to: Predict using SparseM.slm

I'm trying to use stepAIC on sparse matrices, and I need some help. The documentation for slm.fit suggests: slm.fit and slm.wfit call slm.fit.csr to do Cholesky decomposition and then backsolve to obtain the least squares estimated coefficients. These functions can be called directly if the user is willing to specify the design matrix in matrix.csr form. This is often advantageous in large

Hello Andy, Thank you for your offer to work together on implementing the your new scheduler on X86. I can start working on this right away. In case you were unaware, the new Silvermont micro-architecture is only out of order on the integer side. The SSE instructions are still in order, so the current postRA scheduler is very beneficial for code with lots of SSE instructions, such as the ISPC

Could someone advise me about how to react to the message: * checking R code for possible problems ... NOTE slm: no visible binding for global variable 'response' from R CMD check SparseM with * using R version 2.6.0 Under development (unstable) (2007-09-03 r42749) The offending code looks like this: "slm" <- function (formula, data, weights, na.action, method =

Hello, I came across a problem when building a randomForest model. Maybe someone can help me. I have a training- and a testdataset with a discrete response and ten predictors (numeric and factor variables). The two datasets are similar in terms of number of predictor, name of variables and datatype of variables (factor, numeric) except that only one predictor has got 20 levels in the training

iterations <- 100 nvars <- 4 combined <- rbind(scaleMiceTrain, scaleMiceTest) reducedSample <- combined reducedSample <- subset(reducedSample, select = -pID50) reducedSample <- subset(reducedSample, select = -id) for (i in 1:iterations) { miceSample <- sample(combined[,-c(1,2)],nvars, replace=FALSE) miceSample$pID50 <- combined$pID50 miceTestSample <-

Hi All, I created a two variable lm() model slm<-lm(y[1:3000,8]~y[1:3000,12]+y[1:3000,15]) I made two predictions predict(slm,newdata=y[201:3200,]) predict(slm,newdata=y[601:3600,]) there is no error message for either of these. the results are identical, and identical to slm$fitted as well. if this is not the right way to apply the model coefficients to a new set of inputs, what is

Dear R list, members of my course have encountered the following error message: > slm <- lm(price ~ engsize, autoframe) Error in get(x, envir, mode, inherits) : variable "FUN" was not found [more context is given in the fuller listing below]. Once the error is encountered it seems to persist; for example early in one session: > summary(blin.fit) Call: lm(formula = Response

Dear R gurus, I am looking for a way to fit a predictive model for a contingency table which has counts. I found that glm( family=poisson) is very good for figuring out which of several alternative models I should select. But once I select a model it is hard to present and interpret it, especially when it has interactions, because everything is done "relative to reference cell". This

Guys, Just to clarify that with the current fix in SLM there is no need to wait for other issues to be fixed (minor issue). So you can move on with your patch. From: Agabaria, Mohammed Sent: Wednesday, June 07, 2017 15:24 To: Zaks, Ayal <ayal.zaks at intel.com>; Chandler Carruth <chandlerc at gmail.com>; Flamedoge <code.kchoi at gmail.com>; Dehao Chen <dehao at google.com>

Could anybody help me out please? > cl<-as.factor(traindata[,13]) > knn(traindata[1:295,2], newdata[1:32,2], cl,k=2, prob=TRUE) Error in knn(traindata[1:295, 2], newdata[1:32, 2], cl, k = m, prob = TRUE) : Dims of test and train differ Both traindata and newdata have 13 elements. Only one of the first 12 elemnets is needed to predict the 13 element. What's the problem of

Johannes, I tried the last one and it gave me this: error: unknown target CPU 'XYZ' note: valid target CPU values are: nocona, core2, penryn, bonnell, atom, silvermont, slm, goldmont, goldmont-plus, tremont, nehalem, corei7, westmere, sandybridge, corei7-avx, ivybridge, core-avx-i, haswell, core-avx2, broadwell, skylake, skylake-avx512, skx, cascadelake,

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Hello, I am trying to load the package SparseM. It seems that I have successfully installed SparseM (version 0.78), but I did not succeed in loading the SparseM package into R 2.7. Does anybody know a trick for loading SparseM? Thanks in advance, Heike > library(SparseM,lib.loc=my.lib.loc) Error in packageDescription(pkg)$Version : $ operator is invalid for atomic vectors In addition:

I'm having trouble loading the package SparseM in R 1.8.1, OS = Windows 2000. Installing appeared to go well; I saw no error messages, html documentation was installed, and "installed.packages()" lists SparseM among the installed packages. When I try to load the library, however, I get the following: > library(SparseM) Error in slot(mlist, "argument") : Can't get

I have recently started using R and want to use the quantreg (and sparseM) packages. I downloaded the .tar files for each, and placed the subsequent folders into the library folder in the frameworks/R.framework/ resources/library folder with all the other packages. When I try to load either package from the package manager window I get: Loading required package: SparseM Error in

It is my actual target architecture ________________________________ From: Doerfert, Johannes <jdoerfert at anl.gov> Sent: Saturday, March 23, 2019 1:30 PM To: J S Cc: via llvm-dev Subject: Re: [llvm-dev] Generating object files more efficiently I copied "-march=XYZ" from your original email, you have to replace it with your actual target architecture or simply drop it.

Full_Name: David Pleydell Version: 2.2.1 OS: Debian Etch Submission from: (NULL) (193.55.70.206) There appears to be a conflict between the chol functions from the Matrix and the SparseM packages. chol() can only be applied to a matrix of class dspMatrix if SparseM is not in the path. with gratitude David > library(Matrix) > sm <- as(as(Matrix(diag(5) + 1), "dsyMatrix"),

Hello all, I am trying to use the "svm" method provided by e1071 (Version: 1.5-16) together with a matrix provided by the SparseM package (Version: 0.73) but it fails with this message: > model <- svm(lm, lv, scale = TRUE, type = 'C-classification', kernel = 'linear') Error in t.default(x) : argument is not a matrix although lm was created before with

read.matrix.csr does not close the connection: > library('SparseM') Package SparseM (0.96) loaded. > read.matrix.csr(foo) ... Warning message: closing unused connection 3 (foo) > -- Sam Steingold (http://sds.podval.org/) on Ubuntu 12.04 (precise) X 11.0.11103000 http://www.childpsy.net/ http://truepeace.org http://camera.org http://pmw.org.il http://think-israel.org

I have troubles installing the "SparseM" package on my Debian stable Linux system. Debian's version of R is: platform i386-pc-linux-gnu arch i386 os linux-gnu system i386, linux-gnu status major 1 minor 5.1 year 2002 month 06 day 17 language R This is the installation output: > R CMD INSTALL -l /usr/lib/R/ SparseM_0.36.tar.gz * Installing