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...ihood estimates of theta = (a1, b1, a2, b2, P) which defines a mixture of a Poisson distribution and two gamma prior distributions (where the Poisson means have a gamma distribution, actually 2 gammas and P is the mixing factor). The likelihood function for theta is L(theta) = Pi,j{P f(Nij; a1, b1, Eij) + (1 ? P) f(Nij; a2, b2, Eij),} The maximum likelihood estimate of theta is the vector that maximizes the above equation (the values of N and E are given). The authors of the article I read say that the maximization involves an iterative search in the five dimensional parameter space, where each...

...inomial counts (determine the MLE). This maximisation involves a search in five-dimensional parameter space {?: ?1,?2, ?1, ?2, P} for the vector that maximises the likelihood as evidenced by the first and second derivatives of the function being zero. The likelihood is L(?) = ?ij {P f (Nij; ?1, ?1, Eij) + (1-P) f (Nij; ?2, ?2, Eij)} This involves millions of calculations. The computational procedures required for these calculations are based on the Newton-Raphson method. This is an old calculus-based technique that was devised to find the roots of an equation (e.g. the values of the independent v...

...og(abs(det(Q)))- (0.5/sigma2)* (t(y- (xM%*% Bt)) %*% invQ %*% (y-(xM %*% Bt))); sgm <- sigma; # gradients eq. (4.16) gr <- function(sgm) { gradVecs <- c(); # sgm <- c(sigma1, sigma2); sgm <- sgm*sgm; for (i in 1:length(sgm)) { Eij <- matrix(rep(0, length(sgm)^2), nrow=length(sgm), ncol=length(sgm)); Eij[i,i] <- 1.0; # trace term term1 <- -sum(diag((invQ %*% A) %*% outerM(IMatrix(totalTime-1),Eij) %*% t(A))); # very long term term2 <- (1/totalTime)*solve((t(y) %*%...

Hello. A quick question about incorporating variation due to study in the metafor package. I'm working with a particular data set for meta-analysis where some studies have multiple measurements. Others do not. So, let's say the effect I'm looking at is response to two different kinds of drug treatment - let's call their effect sizes T1 and T2. Some studies have multiple

Suppose I have a mixed-effects model where yij is the jth sample for the ith subject: yij= beta0 + beta1(age) + beta2(age^2) + beta3(age^3) + beta4(IQ) + beta5(IQ^2) + beta6(age*IQ) + beta7(age^2*IQ) + beta8(age^3 *IQ) +random intercepti + eij In R how can I get an F test against the null hypothesis of beta6=beta7=beta8=0? In SAS I can run something like contrast age*IQ 1, age^2*IQ 1, age^3 *IQ 1, but is there anything similar in R? Thanks in advance, Gang [[alternative HTML version deleted]]

...variable specified as: *** the equations, you can find in the attachment *** where the first equation is the conditional correlation among the two indices during the Subprime crisis, Dt is a dummy variable for the turmoil period, and the second equation (hij,t) is the conditional variance of eij,t The aim is, of course, to find the estimates of the regression model on structural shifts in the conditional correlations obtained in the DCC-GARCH model. I found an information that there is no function for AR(1)-GARCH(1,1) regression model. That's why it has to be done in two steps: 1) es...

...sis. The regression model is AR(1)-GARCH(1,1), using a dummy variable specified as where the first equation is the conditional correlation among the two indices during the Subprime crisis, Dt is a dummy variable for the turmoil period, and the second equation (hij,t) is the conditional variance of eij,t The aim is, of course, to find the estimates of the regression model on structural shifts in the conditional correlations obtained in the DCC-GARCH model. I found an information that there is no function for AR(1)-GARCH(1,1) regression model. That's why it has to be done in two steps: 1) es...

...LMM and their standard errors. Also, I am trying to estimate the variance components and their standard errors. Further, I am trying to use the likelihood ratio test to test H0: sigma^2_b = 0 (random effects variance component), and the t-test to test H0:mu=0 (intercept of the model Yij = mu + Bi + Eij). I am using the following program all.fix.coef.lme <- rep(NA,R) all.fix.coef.lm <- rep(NA,R) all.fix.coef.glm <- rep(NA,R) all.se.fix.coef <- rep(NA,R) all.fix.coef.glmm <- rep(NA,R) all.se.fix.coef.glmm <- rep(NA,R) all.varcomp.g <- rep(NA,R) all.se.varcomp.g2 <- rep...

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