similar to: rmvbin

I'm wondering if anyone would be able to help me out. I'm trying to use a type II ANOVA for a model with user-specified contrasts and covariates. I have two treatment groups and a control group. I'm comparing average treatment to controls and also between the two treatment groups, but I'm also looking to adjust for age and gender as covariates:

Dear People at R help, I am trying to figure out the syntax for a three-level logistic model with a single random effect (intercept): Data Collected My data consist of three levels: level 1 is four setting for each student (setting nested within student), and each student is registered in one of 14 universities (students nested within university). More detailed: A. 2,479 students who have a

Dear list, I would like to run an ANCOVA using car::Anova with repeated measures factors, but I can't figure out how to do it. My (between-subjects) covariate always interacts with my within-subject factors. As far as I understand ANCOVA, covariates usually do not interact with the effects of interest but are simply additive (or am I wrong here?). More specifically, I can add a covariate as

Dear all, I'm struggling with predicting "expected time until death" for a coxph and survreg model. I have two datasets. Dataset 1 includes a certain number of people for which I know a vector of covariates (age, gender, etc.) and their event times (i.e., I know whether they have died and when if death occurred prior to the end of the observation period). Dataset 2 includes another

Dear all, I would like to know how to run an analysis of covariance in R. For example, I have a data frame ("data") consisting of two second-degree categorical variables ("diagnosis" and "gender"), one continous independent variable ("age") and one continous dependent variable ("response"). I ran a simple anova to see the effects of diagnosis

Dear Experts, I am new to R and unfortunately cannot start with a simply statistical analysis: I manually determined the volume of the right and left hippocampus in a group of meditators and in a group of controls. My data-sheet looks as follows: observation subject group age gender hemisphere volume 1 am04 m 25 f left 3.637 2 am04 m 25 f right 3.713 3 ao08 m 47 m left 3.715 4 ao08 m 47

Hi Folks, I'm playing with glm.nb() in MASS. Reference: the negative binomial distribution P(y) = (Gamma(theta+y)/(Gamma(theta)*y!))*(p^theta)*(1-p)^y y = 0,1,2,... in the notation of the MASS book (section 7.4), where p = theta/(mu + theta) so (1-p) = mu/(mu + theta) where mu is the expected value of Y. It seems from ?glm.nb that an initial value of theta is either supplied, or

I have been asked to analyse some questionnaire data- which is not data I'm that used to dealing with. I'm hoping that I can make use of the nabble expertise (again). The questionnaire has a section which contains a particular issue and then questions which are related to this issue (and potentially to each other): 1) importance of the issue (7 ordinal categories from -3 to +3) 2) impact

I have been trying to use lavaan (version 0.4-7) for a simple path model, but the program seems to be computing far less degrees of freedom for my model then it should have. I have 7 variables, which should give (7)(8)/2 = 28 covariances, and hence 28 DF. The model seems to only think I have 13 DF. The code to reproduce the problem is below. Have I done something wrong, or is this something I

I apologize if this has been answered. I have researched this to the best of my ability, that's not to say the answer isn't in the archives just I am a new user and I don't know the proper terms to search under. I have an object: f <- mpr100 ~ time + nhb + hispanic + other + rural + hrural + factor(age) + factor(gender) +

Hi, I'm sure this is simple, but I haven't been able to find this in TFM, say I have some data in R like this (pasted here: http://pastebin.com/raw.php?i=sjS9Zkup): > head(df) gender age smokes disease Y 1 female 65 ever control 0.18 2 female 77 never control 0.12 3 male 40 state1 0.11 4 female 67 ever control 0.20 5 male 63 ever state1 0.16

Dear all, I would have some questions on the coxph function for survival analysis, which I use with frailty terms. My model is: mdcox<-coxph(Surv(time,censor)~ gender + age + frailty(area, dist='gauss'), data) I have a very large proportion of censored observations. - If I understand correctly, the function mdcox$frail will return the random effect estimated for each group on the

Hi. I was wondering if anyone might have some suggestions about how I can overcome a problem of "iteration limit reached without convergence" when fitting a mixed effects model. In this study: Outcome is a measure of heart action Age is continuous (in weeks) Gender is Male or Female (0 or 1) Genotype is Wild type or knockout (0 or 1) Animal is the Animal ID as a factor

Hi, I've got a microarray dataset (Illumina) coming from a blood assay with a case-control factor of interest. I also have several other covariates (gender, weight, etc...). I know that the experimental design is highly unbalanced with respect to Gender: female male control 12 7 case 7 17 Therefore, if there is a Gender effect, then it really

Dear R-user, I have a data like this below, age <- c("young","mid","old") married <- c("no","yes") income <- c("low","high","medium") gender <- c("female","male") I want to make some of combination data like these, age.income.dat <- expand.grid(age,

Hi, I am new to R, so I am unsure of the formula to set up this analysis. I would like to run a linear model with a continuous dependent variable (brain volume) and a continuous independent variable (age) while controlling for a categorical nuisance variable (gender). Age and brain volume are correlated. There are no gender differences in age but there are significant gender differences in brain

Hi, Late to the thread here, but I noted that your dependent variable 'know_fin' has 3 levels in the str() output below. Since you did not provide a full c&p of your glm() call, we can only presume that you did specify 'family = binomial' in the call. Is the dataset 'knowf3' the result of a subsetting operation, such that there are only two of the three levels of

Hello, I am trying to get the same values for the adjusted means and standard errors using R that are given in SAS for the following data. The model is Measurement ~ Age + Gender + Group. I can get the adusted means at the mean age by using predict. I do not know how to get the appropriate standard errors at the adjusted means for Gender using values from predict. So I attempted to get them

Michael, Thank you for the suggestion. I will take your advice and look more critically at the covariates. John On 7/27/2017 8:08 AM, Michael Friendly wrote: > Rather than go to a penalized GLM, you might be better off > investigating the sources of quasi-perfect separation and simplifying > the model to avoid or reduce it. In your data set you have several > factors with large

I'm studying the calibration function in the survey package in preparation for raking some survey data. Results from the rake function below agree with other sources. When I run calibrate, I get a warning message and the M and F weights seem to be reversed. Even allowing for that, the deviation between calibrated and raked weights is much more than I expected. I see that in the calibrate