search for: e_ij

I am using the package nlme to fit a simple random effects (variance components model) with 3 parameters: overall mean (fixed effect), between subject variance (random) and within subject variance (random). I have 16 subjects with 1-4 obs per subject. I need a 3x3 variance-covariance matrix that includes all 3 parameters in order to compute the variance of a specific linear

I am trying to fit a linear mixed model of the form y_ij = X_ij \beta + delta_i + e_ij where e_ij ~N(0,s^2_ij) with s_ij known and delta_i~N(0,tau^2) I looked at the ecme routine in package:pan, but this routine does not allow for different Vi (variance covariance matrix of the e_i vector) matrices for each cluster. Is there an easy way to fit this model in R or should I bite the...

Dear list, I am currently writing up some of my R models in a more formal sense for a paper, and I am having trouble with the notation. Although this isn't really an 'R' question, it should help me to understand a bit better what I am actually doing when fitting my models! Using the analysis of co-variance example from MASS (fourth edition, p 142), what is the correct notation for the

...Details: I''ve read relevant parts of Pinheiro and Bates''s book and various articles. To test my understanding of various definitions, I considered the simplest limit of fitting a 1-parameter model (i.e., just the mean or intercept) with 1 level of grouping: y_ij = beta + b_i + e_ij where beta is the population mean, b_i is the random effect for the i''th group, and e_ij is the random error of the j''th observation from the i''th group. I''m using the simple "Rail" dataset of N=18 observations, consisting of 3 measurements of ultraso...

suppose something like probability(passing test) is driven by 1. fixed effects -- sex 2. district effects - district funding 3. school effects - neighborhood income, racial composition, % two parent families, ... 4. class effects - teacher quality measurement, 5. individual random effects - IQ. how would such a model be setup in lmer? I can't find much discussion on the

Dear useRs (called Frank Harrell, most likely), after having preached for years to my medical colleagues to be cautious with stepwise selection procedures, they chanted back asking for an alternative when using mixed models. There is a half dozen laXXX packages around for all types of linear models, but as far I see there is none for mixed models such as lme. Even boot.stepAIC (which I

Hi everyone, I want to fit the following mixed effect model Y_ij = b_0i + b_1i * (t_ij*grp_ij == 1) + b_2i * (t_ij*grp_ij == 2) + v_0i + v_1i*t_ij + e_ij with a different covariance matrix of random effects for each group. (Y is the response t is time grp is the group indicator b 's are fixed effects v 's are random effects) I know that this is possible in SAS (I am no expert in SAS, I just looked up some notes) as * Fit...

Hello, can anybody here explain what a Jonckheere-Terpstra test is and whether it is implemented in R? I just know it's a non-parametric test, otherwise I've no clue about it ;-( . Are there alternatives to this test? thanks for help, Arne

...etrisation of the model which by default in R is formed using the so-called "treatment" contrasts. To wander from R into statistics (sorry Bert) the problem arises because the "usual" parametrisation of the model is the "over-parametrised" form: Y_ij = mu + beta_i + E_ij (i = 1, ..., I, j = 1, ..., J_i) where Y_ij is the j-th observation corresponding to the i-th "treatment" or group. (Things get a bit more complicated in "multi-way" models; let's not go there.) The parameter "mu" is the "grand mean" and the "be...

Below you will find the output from a failed multi-level model run. I am trying to estimate the following model: Pr(PLFP=1)= logistic regression -> B1_j * bm + B2_j * wm + B3_j bf + B4_j wf + B5 yrsed+ B6 age+ B7 age^2+e_ij B1_j = G01 + G11 bmxd + d1 B2_j = G02 + G12 wmxd + d2 B3_j = G03 + G13 bfxd + d3 B4_j = G04 + G14 wfxd + d4 d1-d4 freely correlated Note that there is no intercept, so the B1-B4 are race/sex specific intercepts (bm=black male, wm=white male, bf=black female, wf=white female). This model all...

This is really a statistical issue. What do you think the Intercept term represents? See ?contrasts. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Thu, Feb 15, 2018 at 5:27 PM, Marc Girondot via R-help < r-help at

Dear R-er, I try to get the standard error of fitted parameters for factors with a glm, even the reference one: a <- runif(100) b <- sample(x=c("0", "1", "2"), size=100, replace = TRUE) df <- data.frame(A=a, B=b, stringsAsFactors = FALSE) g <- glm(a ~ b, data=df) summary(g)$coefficients # I don't get SE for the reference factor, here 0:

Hi R-help. I am trying to run a linear mixed model with nested factors with either lme or lmer and I am having no luck obtaining the same results as Minitab. Here is Minitab's code: MTB > GLM 'count' = site year replicate(site year) site*year; SUBC> Random 'year' 'replicate'; Can you tell me how to code this in R? The settings are typeII, Tukey,

hi, is there a way of calculating of measuring dependence between two categorical variables. i tried using the chi square test to test for independence but i got error saying that the lengths of the two vectors don't match. Suppose X and Y are two factors. X has 5 levels and Y has 7 levels. This is what i tried doing >temp<-chisq.test(x,y) but got error "the lengths of the two