search for: sigma_b

...cts model fit by REML Data: barrag Log-restricted-likelihood: -70.92739 Fixed: fza_tension ~ 1 (Intercept) 90.86667 Random effects: Formula: ~1 | molde (Intercept) Residual StdDev: 2.610052 2.412176 Number of Observations: 30 Number of Groups: 3 I want to obtain \rho = \sigma_b^2 / (\sigma_b^2 + \sigma^2) I know that I obtain \sigma_b^2 and \sigma^2 with > VarCorr(modeloMx1) molde = pdLogChol(1) Variance StdDev (Intercept) 6.812374 2.610052 Residual 5.818593 2.412176 But, I want to know if I can obtain \rho = 6.8123/(6.8123 + 5.8185) = 0.53934 str...

...calar numbers. One can then analytically calculate the maximum-likelihood results for various quantities, getting the standard-looking results that the ML estimate of the within-group random error is sigma^2 = sum(e_ij^2)/N Eq. g.1 and the ML estimate of the between-group random error is: sigma_b^2 = sum(b.hat_i^2)/N_groups Eq. g.2 (where b.hat_i are the fitted estimates of b_i, and e_ij are the residuals after fitting). Note that these formulas are slightly different than the standard unbiased least-squares results, which divide by the number of degrees of freedom corrected for the...

...C logLik 149.2183 156.169 -70.60916 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 2.06847 0.7800331 [...etc...] I can extract the estimate of the variance component \sigma (0.7800331) via sigma <- fm1OrthF$sigma How do I extract the other component \sigma_b (2.06847) ? Cheers, Murray Jorgensen -- Dr Murray Jorgensen http://www.stats.waikato.ac.nz/Staff/maj.html Department of Statistics, University of Waikato, Hamilton, New Zealand Email: maj at waikato.ac.nz Fax 7 838 4155 Phone +64 7 838 4773 wk Home +64 7 8...

I would like to fit the following model: logit(p_{ij}) = \mu + a_i + b_j where a_i ~ N(0, \sigma_a^2) , b_j ~ N(0, \sigma_b^2) and \sigma_a = \sigma_b. Is it possible to fit a model with such a constraint on the variance components in glmer? -- View this message in context: http://r.789695.n4.nabble.com/glmer-question-tp4351829p4351829.html Sent from the R help mailing list archive at Nabble.com.

...------------------------------------------------------ I have practiced using the command from 'Mixed -Effects models in S and S-plus' as follow fm1logit.lme <- lme(logitp~1, data=logit, random = ~1|factor(Tr)) It can be written in this model Y_ik = Mew + B_i + E_ik where M_i ~ N(0,sigma_b^2) E_ik ~ N(0,sigma^2) but it is not the same what my model is. Could somebody please point me in the right direction ? Sorry if this turns out to be an extreamly simple question, I'm a new user to R. Thank you very much, Ae

...quot;contr.poly")) > aov(Conc ~ Lab + Error(Lab / Bat), data=coop, subset = Spc=="S1") However, as shown in V&R, raov also equated the expected and observed mean squares, to solve for and display the variance components associated with the random factors, \sigma_\epsilon^2, \sigma_B^2, and \sigma_L^2 in a column labeled "Est. Var.". Given the analytical forms of the expected mean squares for each stratum, I can obviously do this manually. But is there way to get R to do it automatically, a la raov? This would be particularly useful for mixed cases in which the an...

Greetings, 1) Is there a nice way of extracting the variance estimates from an lme fit? They don't seem to be part of the lme object. 2) In a series of simulations, I am finding that with ML fitting one of my random effect variances is sometimes being estimated as essentially zero with massive CI instead of the finite value it should have, whilst using REML I get the expected value. I guess

...ckages including the quasi-bayesian mle, I have to write my own function. Unfortunately, I have absolutely no experience in doing this. If you're not familiar with the QB-MLE, I attached the formula as pdf. The idea is to extend the usual MLE with prior beliefs about the values sigma_n and sigma_b. My priors are already included in the code below. I intend to try a mixture of two normal distributions with same mean, and variances 1 and 5 as starting values. This is what I've done so far: > R <-read.table("C:\\...\\rendite.txt", header=F) > qbmle <- function(p,...

HI, I would like to extract the variance components estimation in lme function like a.fit<-lme(distance~age, data=aaa, random=~day/subject) There should be three variances \sigma_day, \sigma_{day %in% subject } and \sigma_e. I can extract the \sigma_e using something like a.fit$var. However, I cannot manage to extract the first two variance components. I can only see the results in