similar to: Error in get(x, envir, mode, inherits)

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 <-

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 =

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

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

Hi, I am trying out the SparseM package and had the a question. The following piece of code works fine: ... fit = slm(model, data = trainData, weights = weight) ... But how do I use the fit object to predict the values on say a reserved testDataSet? In the regular lm function I would do something like this: predict.lm(fit,testDataSet) Thanks -Bala

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>

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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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.

gday gurus, could someone please help me with this one? Can't find it in the doco. summary of my lm object gives "Pr(>|t|)" but coef doesn't. How do I get hold of these probabilities within an object? There doesn't seem to be an appropriate attribute within the object - I was hoping for something like "my.lm$probabilies". I was hoping to grab the best t

I would like to use the "row number" information returned from performing a subset command on a dataframe. For example, I would like to automatically delete some rows from a dataframe if they match a criteria. Here is my example below. data(airquality) names(airquality) subset(airquality, airquality$Month == 6) Now how do I delete the row numbers returned automatically? I

Is there a place that shows how to create two plots that are stacked on top of each other where they share a common x-axis scale, but have differnt y-axis scale? Say have the following data: airquality Stack plot(airquality$Day, airquality$Wind) on top of plot(airquality$Day, airquality$Temp). I am interested in stacking the two on top of each other with no seam, or plotting the two lines with

Hi All, After I run the lm function, where (which variable) do I find my R-squared for further calculations? Thanks, Tom -- View this message in context: http://n4.nabble.com/R2-in-lm-function-tp963958p963958.html Sent from the R help mailing list archive at Nabble.com.

-march for clang and -march for llc do different things unfortunately. -march for clang at least on x86 is the same as -mcpu in llc. Which is an artifact of gcc compatibility. ~Craig On Sat, Mar 23, 2019 at 1:40 PM Doerfert, Johannes via llvm-dev < llvm-dev at lists.llvm.org> wrote: > Oh, my bad. > > > Idk why llc seems to know that architecture but clang does not. > >

I would like to add a column to the airquality dataset that contains the date 1950-01-01 in each row. This method does not appear to work: > attach(airquality) > data1 <- transform(airquality,Date=as.Date("1950-01-01")) Error in data.frame(list(Ozone = c(41L, 36L, 12L, 18L, NA, 28L, 23L, 19L, : arguments imply differing number of rows: 153, 1 I can't decipher what

Is there any reason why 'attach' is not generic in R? I notice that it is in another system, for example, and I can see some applications if it were so in R. Bill Venables. Bill Venables, CMIS, CSIRO Laboratories, PO Box 120, Cleveland, Qld. 4163 AUSTRALIA

My apologies in advance for my thickness but I can't seem to solve the following, seemingly simple, data manipulation problem: I have a data frame that contains multiple factors and multiple continuous response variables, but duplicates of some factor combinations. The duplicates contain bad data, so I would like to eliminate the duplicates. I would like to retain the entire rows

Dear list, I get the following message when I use rcorr in library "Hmisc" ------------------------------------------------------ > rcorr(lskPox0t30, type=c("spearman")) Error in "[<-.data.frame"(*tmp*, is.na(x), value = 1e+30) : matrix subscripts not allowed in replacement ------------------------------------------------------ I do not understand