similar to: regression and lmer

# I've tried to make this easy to paste into R, though it's probably so simple you won't need to. # I have some data (there are many more variables, but this is a reasonable approximation of it) # here's a fabricated data frame that is similar in form to mine: my.df <- data.frame(replicate(10, round(rnorm(100, mean=3.5, sd=1)))) var.list <- c("dv1",

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 -

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

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.

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? >

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 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

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 &

Hello! I have data containing a large number of probabilities (about 60) of nonzero coefficients to predict 10 different independent variables (in 10 different BMA models). i've arranged these probabilities in a matrix like so: (IV1) (IV2) (IV3) ... p(b0) p(b0) p(b0) p(b1) p(b1) p(b1) p(b2) p(b2) p(b2) ... where p(b1) for independent variable 1 is p(b1 !=

While rototilling packet.c, I did some looking at cipher_encrypt in cipher.c. It ends up that for SSH_CIPHER_NONE in cipher_encrypt, it uses memcpy. However, it also appears that dest and src can be equal in cipher_encrypt. On most sane libc implementations, memcpy == memmove. However, ANSI C makes no such guarantee, and some implementations out there are bound to try to optimize memcpy

Hi, I would like to simulate data with a binary outcome and a set of predictors that are correlated. I want to be able to fix the number of event (Y=1) vs. non-event (Y=0). Thus, I fix this and then simulate the predictors. I have 2 questions: 1. When the predictors are continuous, I can use mvrnorm(). However, if I have continuous, ordinal and binary predictors, I'm not sure how to simulate

Greetings; I've been reading the OpenSSH source code and have a question about the des_ssh1_setiv function in cipher.c. (cut-n-pasted here from cipher.c v1.47) : static void des_ssh1_setiv(CipherContext *cc, const u_char *iv, u_int ivlen) { memset(cc->u.des.iv, 0, sizeof(cc->u.des.iv)); } This doesn't use the *iv parameter. Compare with: static void

Greetings, Apologies for such a simple question, but I have been trying to figure this out for a few days now (I'm quite new to R), have read manuals, list posts, etc., and not finding precisely what I need. I am accustomed to working in Stata, but I am currently working through the multilevel package right now. In Stata, I would include the statement "if model1 == 1" at the end

Hi Steve, Do you primarily find this to help for nested loops? If so, that could be because LSR explicitly bails out of processing them: // Skip nested loops until we can model them better with formulae. if (!L->empty()) { DEBUG(dbgs() << "LSR skipping outer loop " << *L << "n"); return; } I don't know how much time you're

Hello, In order to experiment with OpenCA, I have built an RPM for redhat 7.2 of a recent OpenSSL snapshot (the binary rpm on the OpenCA was built with the different target directories and libraries. Unfortunately these recent OpenSSL snapshots seems to break all OpenSSH tarballs and RPMs that I have been able to find. None of them seem to compile successfully, even the snapshots at

On Wed, Sep 23, 2015 at 12:00 PM, Hal Finkel <hfinkel at anl.gov> wrote: >> >> Should we try the patch in it's current location, namely after LSR? > > Sure; post the patch as you have it so we can look at what's going on. > http://reviews.llvm.org/D12494 One particular point: The algorithm checks that SCEV's are equal when their raw pointers are equal. Is

I have data from a design in which items are completely nested within subjects. Subject is the only second-level factor, but I have multiple first-level factors (IVs). Say there are 2 such independent variables that I am interested in. What is the proper syntax to fit a mixed-effects model with a by-subject random intercept, and by-subject random slopes for both the 2 IVs? I can

Hello everyone, I have tried to look for this everywhere and so far have no luck. I have a plotMeans(DV, IV1, IV2, error.bars="se") graph that plots my data (DV-continuous, IVs are factors, IV1 - two levels, IV2-four levels). I am trying to increase a scale of my y-axis (to be consistent with my other graphs), but unfortunately nothing works with "plotMeans" function, which

Hi all, I have been struggling with ANOVAs on R. I am new to R, so I created a simple data frame, and I do some analyses on R just to learn R and then check them on SPSS to make sure that I am doing fine. Here is the problem that I've run into: when we use the aov function, it uses SS Type I as default (on SPSS it is Type III). Then I used the Anova function under cars package using the

I''m running brglm with binomial loguistic regression. The perhaps multicollinearity-related feature(s) are: (1) the k IVs are all binary categorical, coded as 0 or 1; (2) each row of the IVs contains exactly C (< k) 1''s; (3) k IVs, there are n * k unique rows; (4) when brglm is run, at least 1 IV is reported as involving a singularity. I''ve tried recoding the n