search for: beta_j

Hi all, I have some difficulties with the lme function and so this is my problem. Supoose i have the following model y_(ijk)=beta_j + e_i + epsilon_(ijk) where beta_j are fixed effects, e_i is a random effect and epsilon_(ijk) is the error. If i want to estimate a such model, i execute >lme(y~vec.J , random~1 |vec .I ) where y is the vector of my data, vec.J is a factor object and vec.I is the vector for the i indic...

...s the total number of individuals, $K$ indexes the total number of items, $p(x|\theta,\beta)$ is the data likelihood and $f(\theta)$ is a population distribution. For the rasch model, the data likelihood is: \begin{equation} p(x|\theta,\beta) = \prod^N_{i=1}\prod^K_{j=1} \Pr(x_{ij} = 1 | \theta_i, \beta_j)^{x_{ij}} \left[1 - \Pr(X_{ij} = 1 | \theta_i, \beta_j)\right]^{(1-x_{ij})} \end{equation} \begin{equation} \label{rasch} \Pr(x_{ij} = 1 | \theta_i, \beta_j) = \frac{1}{1 + e^{-(\theta_i-\beta_j)}} \quad i = (1, \ldots, K); j = (1, \ldots, N) \end{equation} \noindent where $\theta_i$ is the ability...

Dear All, Could anyone explain me how it is possible that one factor in a glmmPQL model is non-significant according to the anova() function, whereas it turns out to be significant (or at least some of its levels differ significantly from some other levels) according to the summary() function. What is the truth, which results shall I believe? And, is there any other way of testing for the

...= \int f''(t)^2 dt is the quadratic roughness functional use. Since J(f) is quadratic one can find a matrix \Sigma such that J(g) = c^T{\Sigma}c where c is the vector of spline coefficients. With J(f) defined as above the elements of \Sigma becomes \Sigma_{ij} = \int \beta_i''(t)\beta_j''(t) dt where \beta(t) is the vector of B-spline base functions. Finally, writing the matrix W as W := diag(\sqrt{w}) one can write L(f) as L(f) = (y - f)^T W^2 (y - f) + \lambda c^T{\Sigma}c which is the form used in help(smooth.spline). So back to my question, using smooth.spline(),...

...ults in Montgomery D.C (2001, chap 13, example 13-1). Briefly, there are three suppliers, four batches nested within suppliers and three determinations of purity (response variable) on each batch. It is a two stage nested design, where suppliers are fixed and batches are random. y_ijk=mu+tau_i+beta_j(nested in tau_i)+epsilon_ijk Here are the data, purity<-c(1,-2,-2,1, -1,-3, 0,4, 0,-4, 1, 0, 1,0,-1,0, -2,4,0,3, -3,2,-2,2, 2,-2,1,3, 4,0,-1,2, 0,2,2,1) suppli<-factor(c(rep(1,12),rep(2,12),rep(3,12))) b...

...ults in Montgomery D.C (2001, chap 13, example 13-1). Briefly, there are three suppliers, four batches nested within suppliers and three determinations of purity (response variable) on each batch. It is a two stage nested design, where suppliers are fixed and batches are random. y_ijk=mu+tau_i+beta_j(nested in tau_i)+epsilon_ijk Here are the data, purity<-c(1,-2,-2,1, -1,-3, 0,4, 0,-4, 1, 0, 1,0,-1,0, -2,4,0,3, -3,2,-2,2, 2,-2,1,3, 4,0,-1,2, 0,2,2,1) suppli<-factor(c(rep(1,12),rep(2,12),rep(3,12))) b...

I know this has been dealt with before on this list, but the previous messages lacked detail, and I haven't figured it out yet. The model is: \x_{ij} = \mu + \alpha_i + \beta_j \alpha is a random effect (subjects), and \beta is a fixed effect (condition). I have a link function: p_{ij} = .5 + .5( 1 / (1 + exp{ -x_{ij} } ) ) Which is simply a logistic transformed to be between .5 and 1. The data y_{ij} ~ Binomial( p_{ij}, N_{ij} ) I've generated data using this...

...any actual interest. This would go much further toward bringing the desciple to true enlightenment. Point 3 --- what hypothesis is being tested by SSA? Let factor A correspond to index i, and B to index j. Let the cell means be mu_ij. (In the overparameterized notation, mu_ij = mu + alpha_i + beta_j + gamma_ij.) The hypothesis being tested is H_0: mu_1.-bar = mu_2.-bar = ... = mu_a.-bar where factor A has a levels, and ``mu_i.-bar'' means the average (arithmetic mean) of mu_i1, mu_i2, ..., mu_ib. (Note --- factor B has b levels.) I.e. the hypothesis is that there is no difference...

I don't have a copy of Dobson (1990) from which the glm.D93 example is taken in example("glm"), but I'm strongly suspecting that these are made-up data rather than real data; the means of the responses within each treatment are _identical_ (equal to 16 2/3), so two of the parameters are estimated as being zero (within machine tolerance). (At this moment I don't understand

I would like to fit a hierarchical regression model from Witte et al. (1994; see reference below). It's a logistic regression of a health outcome on quntities of food intake; the linear predictor has the form, X*beta + W*gamma, where X is a matrix of consumption of 82 foods (i.e., the rows of X represent people in the study, the columns represent different foods, and X_ij is the amount of