similar to: Use predict.lm

I have some time series data like 01/02/1990 0.531 0.479 01/03/1990 0.510 0.522 01/06/1990 0.602 0.604 there is no weekends and holidays. how do I graph them in a single plot that the x-axis is the dates and the y-axis is the time series? Thank you Regards, Jincai Jiang (Office) 212-761-3984 -------------------------------------------------------- This is not an offer (or solicitation

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

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

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

I found that read.table() does not work well on lines that is longer than 236 bytes. Is this know problem? Is it under fixing? Regards, Jincai Jiang (Office) 212-761-3984 -------------------------------------------------------- NOTICE: If received in error, please destroy and notify sender. Sender does not waive confidentiality or privilege, and use is prohibited. [[alternative HTML

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

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. > >

On Tue, May 30, 2017 at 1:40 AM Agabaria, Mohammed via llvm-dev < llvm-dev at lists.llvm.org> wrote: > We’re seeing nice improvements but also significant degradations on IA, > which we would like to investigate before the patch is committed. > > > > Major degradations we see: > > > > networking > > ip_pktcheckb1m -6.80 % > >

Quantile Regression for Longitudinal Data. Hi, I am trying to estimate a quantile regression using panel data. I am trying to use the model that is described in Dr. Koenker's article. So I use the code the that is posted in the following link: http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R I am trying to change the number quantiles being estimated. I change the codes about

I'd like to mention that there is a new quantile regression package "nprq" on CRAN for additive nonparametric quantile regression estimation. Models are structured similarly to the gss package of Gu and the mgcv package of Wood. Formulae like y ~ qss(z1) + qss(z2) + X are interpreted as a partially linear model in the covariates of X, with nonparametric components defined as

Oh yes. The "load package" under the "packages menu" in the Windows version does that. To check I typed "library(assist)" after starting R. Same behavior, ssr is found, but others like predict.ssr, and plot.ssr, give a "not found" message. Thanks for the suggestion. Mike