similar to: two newbie questions

Dear all,  I hope this is the right place to ask this question. I am reviewing a research where the analyst(s) are using a linear regression model. The dependent variable (DV) is a continuous measure. The independent variables (IVs) are a mixture of linear and categorical variables. The author investigates whether performance (DV - continuous linear) is a function of age (continuous IV1 -

Hello all, Is there an r function that exists that will perform repeated measures MANOVAs? For example, let's say I have 3 DVs, one between-subjects IV, and one within-subjects IV. Based on the documentation for the manova command, a function like that below is not appropriate because it cannot take Error arguments. manova(cbind(DV1,DV2,DV3) ~ BetweenSubjectsIV * WithinSubjectsIV +

Hi, Sorry to bother you again. I would like to estimate the effect of several categorical factors (two between subjects and one within subjects) on two continuous dependent variables that probably covary, with subjects as a random effect. *I want to control for the covariance between those two DVs when estimating the effects of the categorical predictors** on those two DVs*. The thing is, i

Hello everyone! I am currently working with a time series panel data set measuring six dependent variables: 4 of which are binary and 2 of which are count data. I am interested in constructing a model to measure if the dependent variables influence one another. For example: DV1~ DV2 + IV1+IV2+ Controls and DV2~ DV1 + IV1+ IV2+ Controls (where IV stands for independent variable, not

Hi, I have been using R to do multiple analyses of variance with two covariates, but recently found that the results in SPSS were very different. I have check several books and web resources and I think that both methods are correct, but I am less familiar with R, so I was hoping someone could offer some suggestions. Oddly simple ANOVA is the same in SPSS and R. Including covariates improves the

Does anyone know if there is a way to run multivariate linear regression in R? I tried using the lm function (e.g., lm(dv1, dv2~iv1+iv2+iv3), but got error messages. Is my syntax wrong, or do I need a particular package? Thanks, Jeff-- ________________________________________________________ Jeffrey J. Lusk, Ph.D. Postdoctoral Research Associate Department of Forestry &

Dear all,  I hope this is the right place to ask this question. I am reviewing a research where the analyst(s) are using a linear regression model. The dependent variable (DV) is a continuous measure. The independent variables (IVs) are a mixture of linear and categorical variables. The author investigates whether performance (DV - continuous linear) is a function of age (continuous IV1 -

On Wed, Sep 2, 2015 at 5:36 AM, James Molloy <james at jamesmolloy.co.uk> wrote: > Hi, > > Coremark really isn't a good enough test - have you run the LLVM test suite > with this patch, and what were the performance differences? For the test suite single source benches, the 235 tests improved performance, 2 regressed and 705 were unchanged. That seems very optimistic.

On Mon, Aug 31, 2015 at 5:52 PM, Jake VanAdrighem <jvanadrighem at gmail.com> wrote: > Do you have some specific performance measurements? Averaging 4 runs of 10000 iterations each of Coremark on my X86_64 desktop showed: -O2 performance: +2.9% faster with the L.E.V. pass -Os size: 1.5% smaller with the L.E.V. pass In the case of Coremark, the benefit comes mainly from the matrix

Dear R users,    my question concerns my interest in comparing the beta coefficients between two identical regression models in two (actually 3) groups. Disclaimer: I am quite new to R, so I might be missing some terminology that I have not come across.   I am trying to find out if I can easily implement a Wald test in R for this, but the only relevant thing that I came across is this link

Dear everyone: I am a new user of R. I have a dataset with a dependent variable (DV) censored at different values. The dataset looks like, conditions .....IDV1 IDV2 DV 1 2 4 89 1 6 6 75 1 4 5 0 ( DV<=70) ...... 2 3 5 15 2 5 5 0

Hello, I would like to change the background color in only -one- of the strips in a multipanel lattice xyplot, from the default yellow-brown color. Until now, I only managed to change the background strip color in all of the strips using the par.settings, but I do not get it to work for only 1 strip. Below is the current code I am using. The resulting plot is here:

hi ,I am writing a soft package based on R. But when I try to read a configure file showed as below. How can I read the parameter in this text file. How can I read the parameter into each variable in this file ? configinfo<-scan(file(configfile),ok=TRUE,n=-1) scan seems need every line have same column ? configinfo <- readLines(configfile,ok=TRUE,n=-1) methodnum <-

Dear all, I try to conduct a SEM / two stage least squares regression with the following equations: First: X ~ IV1 + IV2 * Y Second: Y ~ a + b X therein, IV1 and IV2 are the two instruments I would like to use. the structure I would like to maintain as the model is derived from economic theory. My problem here is that I have trouble solving the equations to get the reduced form so I can run

On 07/10/2014 06:43 PM, Peter De Schrijver wrote: > On Thu, Jul 10, 2014 at 09:34:34AM +0200, Alexandre Courbot wrote: >> This series adds support for reclocking on GK20A. The first two patches touch >> the clock subsystem to allow GK20A to operate, by making the presence of the >> thermal and voltage devices optional, and allowing pstates to be provided >> directly

Hello! I have a data frame A (below) with a grouping factor (group). I take my DV and create the new, lagged DV by applying the function lag.it (below). It works fine. A <- data.frame(year=rep(c(1980:1984),3), group= factor(sort(rep(1:3,5))), DV=c(rnorm(15))) lag.it <- function(x) { DV <- ts(x$DV, start = x$year[1]) idx <- seq(length = length(DV)) DVs <- cbind(DV, lag(DV,

Which cases does this pass handle which aren't otherwise optimized out by passes like GlobalValueNumbering or DeadCodeElimination? Thanks, Jake VanAdrighem On Thu, Sep 10, 2015 at 2:35 PM, Steve King <steve at metrokings.com> wrote: > Hello LLVM, > It seems this thread has gone cold. Is there some low risk way for > the community to take the new pass for a test drive? >

This is a simple problem that has stumped me: I'm trying to loop through a few dozen variable names in graphs. I've tried various approaches like this: attach(mydata) ivs <- c("oneiv", "anotheriv", "yetanotheriv") dvs <- c("onedv", "anotherdv", "yetanotherdv") for (iv in ivs) { for (dv in dvs) { graphname <- paste(iv,

Are there any implicit assumptions about where alloca instructions can appear. I've got a failing test where the only difference between a passing test and a failing test is one application of this code in instcombine: // Convert: malloc Ty, C - where C is a constant != 1 into: malloc [C x Ty], 1 Seems pretty harmless to me. Later on the instcombine code does this: // Scan to the end of

This way it is possible to change where the matching start, instead of always assuming it is the beginning. --- common/mlpcre/PCRE.ml | 2 +- common/mlpcre/PCRE.mli | 5 ++++- common/mlpcre/pcre-c.c | 16 +++++++++++++--- common/mlpcre/pcre_tests.ml | 11 ++++++++--- 4 files changed, 26 insertions(+), 8 deletions(-) diff --git a/common/mlpcre/PCRE.ml b/common/mlpcre/PCRE.ml