similar to: how to read the "Sum Sq" - column from summary.aov()

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

Hi all, I have data from an experiment with 3 independent variables, 2 are within and 1 is between. In addition to the dependent variable, I have a covariate that is a single measure per subject. Below I provide an example generated data set and my approach to implementing the ANCOVA. However the output confuses me; why does the covariate only appear in the first strata? Presumably it should

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

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

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'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",

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, 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

I have one other problem and then I'll stop and get back to writing. I want to plot a missing data matrix (the R matrix a la Little and Rubin) to graphically depict where the missing data lie in the matrix. Some statistical packages produce a graphical depiction of the missing data patterns by plotting the matrix, color-coding the plot with contrasting colors for either missing or

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

Dear List, I work with multilevel data from psychological group experiments and have frequently encountered a situation for which I haven't found an elegant solution: I need to assign the value of a specific group member to all members of the group. For example, I have a group leader (identified by a binary vector) and some attribute for all group members. I want to create a new

On 09/14/2017 10:31 PM, Daniel Neilson wrote: > > >> On Sep 14, 2017, at 9:30 PM, Hal Finkel <hfinkel at anl.gov >> <mailto:hfinkel at anl.gov>> wrote: >> >> >> On 09/14/2017 10:43 AM, Daniel Neilson wrote: >>> Thank you for your thoughts, Hal. More information below... >>> >>>> On Sep 13, 2017, at 5:43 PM, Hal Finkel

On Sep 14, 2017, at 9:30 PM, Hal Finkel <hfinkel at anl.gov<mailto:hfinkel at anl.gov>> wrote: On 09/14/2017 10:43 AM, Daniel Neilson wrote: Thank you for your thoughts, Hal. More information below... On Sep 13, 2017, at 5:43 PM, Hal Finkel <hfinkel at anl.gov<mailto:hfinkel at anl.gov>> wrote: On 09/13/2017 01:01 PM, Daniel Neilson via llvm-dev wrote: … snip For

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

Thank you for your thoughts, Hal. More information below... On Sep 13, 2017, at 5:43 PM, Hal Finkel <hfinkel at anl.gov<mailto:hfinkel at anl.gov>> wrote: On 09/13/2017 01:01 PM, Daniel Neilson via llvm-dev wrote: … snip For example, the following IR will produce different sets of IV users if either: i) The order of the PHI nodes in the %loop block are reordered; or ii) The

Hi all, I’ve most recently been grappling with a difficult to reproduce bug. I’ve traced the source of the difficulty in reproduction to the IVUsers analysis pass that is used by Loop Strength Reduction. Specifically, the IVUsers pass’s output is very sensitive to both the use list ordering of the instructions that it is looking at and the ordering of the Phi nodes in the header block of the loop