similar to: functions to calculate t-stats, etc. for lm.fit objects?

Hello, I was wondering if someone knows the formula used by the function lm to compute the t-values. I am trying to implement a linear regression myself. Assuming that I have K variables, and N observations, the formula I am using is: For the k-th variable, t-value= b_k/sigma_k With b_k is the coefficient for the k-th variable, and sigma_k =(t(x) x )^(-1) _kk is its standard deviation.

Hi, While exploring how summary.lm generated its output I came across a section that left me puzzled. at around line 57 R <- chol2inv(Qr$qr[p1, p1, drop = FALSE]) se <- sqrt(diag(R) * resvar) I'm hoping somebody could explain the logic of these to steps or alternatively point me in the direction of a text that will explain these steps. In particular I'm puzzled

Hi - I am doing a monte carlo experiment that requires to do a linear regression of a matrix of vectors of dependent variables on a fixed set of covariates (one regression per vector). I am wondering if anyone has any idea of how to speed up the computations in R. The code follows: #regression function #Linear regression code qreg <- function(y,x) { X=cbind(1,x) m<-lm.fit(y=y,x=X)

Hi, I am trying to set up a Fedora Directory server for centralised authentication. I configure the directory server, add a user called (via the Java GUI) test and then, using system-config-authentication, enable LDAP on both tabs. I then try to log-in using the test account I set up on the directory, but I get an error message in /var/log/messages: May 30 16:28:27 ds1 sshd(pam_unix)[4445]:

Hello, I want to have a title that will look something like: "Results for \theta=2.1", given that I have a variable theta=2.1, and \theta should show on the screen like the greek letter. I've tried a lot of things: theta <- 2.1 plot(1:10, main=expression(paste("Results for", theta, "=", eval(theta)))) or using bquote plot(1:10, main=paste("Results for

Full_Name: Juan Gea Version: R version 2.6.2 OS: Fedora Core 6 Submission from: (NULL) (79.153.48.49) Abort: objeS <- matrix("AAA",1000000) class(objeS) outTxt <- textConnection("vaClob", open = "w", local = FALSE) dput(objeS,outTxt) close(outTxt) R version 2.6.2 (2008-02-08) Copyright (C) 2008 The R Foundation for Statistical Computing ISBN

If you found my subject heading to be confusing then I'm sure you'll enjoy the example I've included below. I find the apply type functions to be wonderful for avoiding loops but when I use them with existing functions, I end up using loops anyway to extract the vectors I want. I would appreciate it if someone could show me how to avoid these loops. Thanks. EXAMPLE:

Curious to know if this warning is expected behavior, and if so, what is the recommended way instead: > dput(letters, file = stderr()) c("a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p",

Hello everyone, I'm trying to estimate the parameters of the returns series attached using the GARCH code below, but I get the following error message: Error in solve.default(Hessian) : system is computationally singular: reciprocal condition number = 0 Error in diag(solve(Hessian)) : error in evaluating the argument 'x' in selecting a method for function 'diag' Can

Hello all, I'm trying to create a heatmap using 2 matrices I have: z and v. Both matrices represent different correlations for the same independent variables. The problem I have is that I wish to have the values from matrix z to be represented by color intensity while having the values from matrix v to be represented by circle size. I currently have the following in front of me and an unsure

I am working with some survival data with missing values. I am using the mice package to do multiple imputation. I have found code in this thread which handles pooling of the MI results: https://stat.ethz.ch/pipermail/r-help/2007-May/132180.html Now I would like to plot a survival curve using the pooled results. Here is a reproducible example: require(survival) require(mice) set.seed(2) dt

R developers, I have a first attempt to make an aov function. Eventually I want to build in Error() structure, but first I am trying to get this presentable for balanced data with only a single stratum, just using residual error. I am following R. M. Heiberger's Computation for the Analysis of Designed Experiments, Wiley (1989) I a using a wrapper (aov.bal) to call the

This patch introduces a handful of new options, I mentioned earlier in: http://www.mail-archive.com/nut-upsdev at lists.alioth.debian.org/msg02088.html See the large commit message in the follow-up for the details and rationale. I realize it's a bit larger diff - so if it's required I can split it into few smaller ones. Michal Soltys (1): APC smart driver update and new features.

I define the following function to convert a t-value with degrees of freedom DF to another t-value with different degrees of freedom fullDF: tConvert <- function(tval, DF, fullDF) ifelse(DF>=1, qt(pt(tval, DF), fullDF), 0) It works as expected with the following case: > tConvert(c(2,3), c(10,12), 12) [1] 1.961905 3.000000 However, it gives me warning for the example below although the

I really can't figure out how to get and set array indices from the c interface. so to get an element I'm calling tindex = *fn\SymbolTable(*index\name) index = LLVMBuildLoad(builder,tindex,"index") arr = *fn\SymbolTable(*array\name) arrptr = LLVMBuildLoad(Builder,arr,"arrayptr") tmp = LLVMBuildGEP(Builder,arrptr,index,0,"ptr") ptr =

Sorry for a bit longer delay than I anticipated, I was stuffed with the work. This is the next iteration of the patch adding some functionality to apcsmart driver, and relying on 'ignorelb' recently added. Follow up from previous thread: http://www.mail-archive.com/nut-upsdev at lists.alioth.debian.org/msg02331.html Main differences is that V3 is split into many small patches, so the

I have tried the following code to plot and save many box plots in files. The code works by itself, but does not run in a loop. The error indicates that there is no device. The error is printed below the code. Code: for(i in 1:len) { A = as.numeric(delta[Delta[i,1]]) B = as.numeric(delta[Delta[i,2]]) C = as.numeric(delta[Delta[i,3]]) D = as.numeric(delta[Delta[i,4]]) data

This is 2nd version of the earlier patch featuring a few new features and fixes to the apcsmart driver, following the remarks in: http://www.mail-archive.com/nut-upsdev at lists.alioth.debian.org/msg02294.html Major changes from v1: - handle battery.charge and battery.runtime checks at main.c level - handle "immutable but writable" conflict gracefully at driver level -

When I do t.test on two distributions (see example below), it outputs numerous data about the t.test. What I'd like to do is individually capture some of this data and assign it to other variables. However, I am unable to find anything in the help section. In the example below, the t value is -4.0441 and the p-value is 0.006771 How can I assign these values to two variables, let's

For block effects with small variance, lmer will sometimes estimate the variance as being very close to zero and issue a warning. I don't have a problem with this -- I've explored things a bit with some simulations (see below) and conclude that this is probably inevitable when trying to incorporate random effects with not very much data (the means and medians of estimates are plausibly