similar to: anovas ss typeI vs typeIII

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

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

# 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 R-Users, I have a trivial problem, but extensive googling and ??'ing did not solve it: I want to obtain the sums of squares from a summary.aov() object for later use. Example: > DV <- rnorm(100) > IV1 <- as.factor(rep(c("male", "female"), times = 50)) > IV2 <- as.factor(rep(c("young", "old"), times = 50)) > >

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

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 -

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

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

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

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

To avoid angry replies, let me first say that I know that the use of Type III sums of squares is controversial, and that some statisticians recommend instead that significance be judged using the non-marginal terms in the ANOVA. However, given that type III SS is also demanded by some… is there a function (equivalent to drop1 for lm) to obtain type III sums of squares for mixed models using the

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

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

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