search for: sigma_e

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 summary(a.fit). I have some problem in the lme4 package and hence use the nlme package. The example data also has some problem so I just...

Hi All, I'm trying to estimate some parameters in my model via GMM using the function gmm(), but I keep getting the message "The covariance matrix of the coefficients is singular". I've changed the moment conditions and the initial value of the parameters, and I still get this message. Are the results valid after receiving this message? Any ideas on how to get rid of it?

*Dear R-help: My name is Rodrigo and I have a question with nlme package in R to fit a mixed beta regression model. The details of the model are: Suppose that:* *j in {1, ..., J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~ Beta(mu_{ij} * phi_{ij}; (1 - mu_{ij}) * phi_{ij}) y_{ij} = mu_{ij} + w_{ij} * *with* *logit(mu_{ij}) = Beta_{0i} + Beta_{1i} * x1_{ij} + b2 * x2_{ij}

*Dear R-help: My name is Rodrigo and I have a question with nlme package in R to fit a mixed beta regression model. I'm so sorry. In the last email, I forgot to say that W is also a unknown parameter in the mixed beta regression model. In any case, here I send you the correct formulation. ** Suppose that:* *j in {1, ..., J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~

Dear R Help, I am trying to fit a nonlinear model for a mean function $\mu(Data_i, \beta)$ for a fixed covariance matrix where $\beta$ and $\mu$ are low- dimensional. More specifically, for fixed variance-covariance matrices $\Sigma_{z=0}$ and $\Sigma_{z=1}$ (according to a binary covariate $Z $), I am trying to minimize: $\sum_{i=1^n} (Y_i-\mu_(Data_i,\beta))' \Sigma_{z=z_i}^{-1} (Y_i-

dear R wizards: I would like to write a function that roughly places the equivalent of the following latex text into the current plot: \newcommand{ \placesigma }[4]{ \put(\#1,\#2){ \sigma_{A , #3} = #4 } I cannot figure out how to do this. I know I have to use a function that uses expressions in a text() invoke. But passing arguments and nesting strings and expressions has so far not

Hi, I am working with the lattice package and I want to label to groups in the xyplots with mathematical expressions. I short example for this library(lattice) Case<-factor(rep(1:2,each=300)) xx<-rnorm(600,0,1) yy<-rnorm(600,0,1) xyplot(yy~xx|Case) This results in two scatter plots with label "1" and "2". I would like to substitute this labels by math expression. I

Hello - Here's what I'm trying to do. I want to fit a time series y with ARMA(1,1) + GARCH(1,1), there are also an exogeneous variable x which I wish to include, so the whole equation looks like: y_t - \phi y_{t-1} = \sigma_t \epsilon_t + \theta \sigma_{t-1} \epsilon_{t-1} + c x_t where \epsilon_t are i.i.d. random variables \sigma_t^2 = omega + \alpha \sigma_{t-1}^2 + \beta

...0.197 -.0476592 .2315544 d87 | .1349289 .0813135 1.66 0.097 -.0244427 .2943005 _cons | .0235864 .1506683 0.16 0.876 -.271718 .3188907 -------------+---------------------------------------------------------------- sigma_u | .32460315 sigma_e | .35099001 rho | .46100216 (fraction of variance due to u_i) 2) can R do Fixed-effects (within) regression as Stata's xtreg? the followng example is from "Introductory Econometrics: A Modern Approach" by Jeffrey M. Wooldridge Chapter 14 - Advanced Panel Data Metho...

...ata matrix Z <- cbind(1, as.vector(data[,2])) y <- as.matrix(data[,1],nrow = 1, ncol = length(y)) # format Z into an array Zt <- array(NA,c(1,2,nrow(Z))) for(i in 1:nrow(Z)) { Zt[,,i] <- Z[i,] } # specify state-space form of the time-varying capm capm.ss <- function(alpha,beta,sigma_e,sigma_n1,sigma_n2) { Tt <- diag(1,2) Zt <- Zt ct <- matrix(0) dt <- matrix(0,nrow = 2, ncol = 1) GGt <- matrix(sigma_e^2) HHt <- diag(c(sigma_n1^2,sigma_n2^2),2) a0 <- c(0,0) P0 <- diag(10^6,2) return(list(a0 = a0, P0 = P0, ct = ct, dt = dt, Zt = Zt, Tt = Tt, GGt...

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

Hi, Does anybody have experience to solve an quadratic programming problem with quadratic constraints in R? It seems that the package "quadprog" only handles the quadratic programming with linear constraint. My probelm is to maximze x^T\Sigma_{xy} y, subject to x^Tx=1, y^T\Sigma_{yy} y=1, and sum(y)<t, or sum(y)=t, where x and y are the variable, and the Sigma's and t are

Hi, I was trying to repeat the estimation of threshold GARCH models from the book "Analysis of Financial Time Series" by Ruey S. Tsay, and I was succesfull, but I had to use "for" loop, which is quite slow. The loop is necessary, since you need to calculate recursive sequence. Is there a faster way to do this in R, without using loops? The model is such: r_t = \mu + \alpha_2

Hello, I would like to add to axis labels special caracters. Instead of writing :plot(simul, xlab="beta", ylab="sigma11") It would be great if I could write something as in LaTeX :plot(simul, xlab="\beta", ylab="\sigma_{11}") Is there a way to do that ? Thank you _________________________________________________________________ ues clics pour retrouver tout

I use smooth.spline(x, y) in package modreg and I would like to get value of penalized log likelihood and preferable also its two parts. To make clear what I am asking for (and make sure that I am asking for the right thing) I clarify my problem trying to use the same notation as in help(smooth.spline): I want to find the natural cubic spline f(x) such that L(f) = \sum_{k=1}{n} w[k](y[k] -

Please pardon an extremely naive question. I see related earlier posts, but no responses which answer my particular question. In general, I'm very confused about how to do variance decomposition with random and mixed effects. Pointers to good tutorials or texts would be greatly appreciated. To give a specific example, page 193 of V&R, 3d Edition, illustrates using raov assuming pure

...ew references I've been able to track down (Arvesen, Biometrcs, 1970 is one), seem to say that I should just sample with replacement the i units. Plus they seem to indicate that a log transform is needed. The Arvesen reference used something like using log(v_a/v_e) as an estimator for sigma_a^2/sigma_e^2 and getting an interval and then transforming to get to an interval for the ICC (although it's not clear to me how to get the other ICC in a two-level nested design). Any insights would be appreciated. Rick B.

Hello all I'm trying to learn how to fit a nested model in R. I found a toy example on internet where a dataset that have?3 areas and 4 sites within these areas. When I use Minitab to fit a nested model to this data, this is the ANOVA table that I got: Nested ANOVA: y versus areas, sites Analysis of Variance for y Source DF SS MS F P areas 2 4.5000 2.2500

...0 -.034508 -.0290287 _Iyear_1999 | -.0647261 .0027674 -23.39 0.000 -.0701501 -.0593021 _cons | 1.802112 .009304 193.69 0.000 1.783876 1.820348 -------------+---------------------------------------------------------------- sigma_u | .38142386 sigma_e | .2173114 rho | .75494455 (fraction of variance due to u_i) ------------------------------------------------------------------------------ F test that all u_i=0: F(48854, 97301) = 3.30 Prob > F = 0.0000 3. Compute marginal effect of eco at sample mean...

...-.034508 -.0290287 _Iyear_1999 | -.0647261 .0027674 -23.39 0.000 -.0701501 -.0593021 _cons | 1.802112 .009304 193.69 0.000 1.783876 1.820348 -------------+---------------------------------------------------------------- sigma_u | .38142386 sigma_e | .2173114 rho | .75494455 (fraction of variance due to u_i) ------------------------------------------------------------------------------ F test that all u_i=0: F(48854, 97301) = 3.30 Prob > F = 0.0000 3. Compute marginal effect of eco at sample...