similar to: Variance-covariance matrix for beta hat and b hat from lme

Hi, I would appreciate if someone could help me on track with this problem. I want to compute some parameters from a system of equations given a number of sample observations. The system looks like this: sum_i( A+b_i>0 & A+b_i>C+d_i) = x sum_i( C+d_i>0 & C+d_i>A+b_i) = y sum_i( exp(E+f_i) * ( A+b_i>0 & A+b_i>C+d_i) = z A, C, E are free variables while the other

Hi all, I am doing some experiment studies... It seems to me that with different combination of 5 parameters, the end results ultimately converged to two scalars. That's to say, some combinations of the 5 parameters lead to one end result and some other combinations of the 5 parameters lead to the other end result (scalar). I am thinking of this is sort of something like clustering or

I have data (each Y_i is a vector) in the form of Y_i = X_i \beta_i + Z_i b_i + epsilon_i Were it not for the measurement error (the epsilon_i) it's a very simple model --- nice and balanced, compound symmetry, and I'd just use lme(y ~ x1 + x2, random=~1|subj, ...) but the measurement error is throwing me off. Because the Y_i are actually derived from other data, I am able

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

In a classical meta analysis model y_i = X_i * beta_i + e_i, data {y_i} are assumed to be independent effect sizes. However, I'm encountering the following two scenarios: (1) Each source has multiple effect sizes, thus {y_i} are not fully independent with each other. (2) Each source has multiple effect sizes, each of the effect size from a source can be categorized as one of a factor levels

Dear UseRs: I have simulated data from a zero-inflated Poisson model, and would like to use a package like zicounts to test my code of fitting the model. My question is: can I use zicounts directly with the following simulated data? Create a sample of n=1000 observations from a ZIP model with no intercept and a single covariate x_{i} which is N(0,1). The logit part is logit(p_{i})=x_{i}*beta

Hello, I come from a non statistics background, but R is available to me, and I needed to test an implementation of smoothing spline that I have written in c++, so I would like to match the results with R (for my unit tests). I am following Smoothing Splines, D.G. Pollock (available online) where we have a list of points (xi, yi), the yi points are random such that: y_i = f(x_i) + e_i

Dear R-users Coming from a proc mixed (SAS) background I am trying to get into the use of (n)lme. In this connection, I have some (presumably stupid) questions which I am sure someone out there can answer: 1) With proc mixed it is easy to get a hold on the estimated variance parameters as they can be put out into a SAS data set. How do I do the same with lme-objects? For example, I can see the

Dear list, I'm trying to fit a multilevel (mixed-effects) model using the lme function (package nlme) in R 2.4.0. As a mixed-effects newbie I'm neither sure about the modeling nor the correct R syntax. My data is structured as follows: For each subject, a quantity Y is measured at a number (>= 2) of time points. Moreover, at time point 0 ("baseline"), a quantity X is

Hello, I come from a non statistics background, but R is available to me, and I needed to test an implementation of smoothing spline that I have written in c++, so I would like to match the results with R (for my unit tests) I am following http://www.nabble.com/file/p25569553/SPLINES.PDF SPLINES.PDF where we have a list of points (xi, yi), the yi points are random such that: y_i = f(x_i) +

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-

Hello R users! I've been experimenting with lmer to estimate a mixed model with a dichotomous dependent variable. The goal is to fit a hierarchical model in which we compare the effect of individual and city-level variables. I've run up against a conceptual problem that I expect one of you can clear up for me. The question is about random effects in the context of a model fit with a

I am interested in whether the slopes in a linear model are different from 0. I.e. I would like to obtain the slope estimates, and their standard errors, ``relative to 0'' for each group, rather than relative to some baseline. Explicitly I would like to write/represent the model as y = a_i + b_i*x + E i = 1, ..., K, where x is a continuous variate and i indexes groups (levels of a

I would like to code the following in R: a1(b1+b2+b3) + a2(b1+b3+b4) + a3(b1+b2+b4) + a4(b1+b2+b3) or in summation notation: sum_{i=1, j\neq i}^{4} a_i * b_i I realise this is the same as: sum_{i=1, j=1}^{4} a_i * b_i - sum_{i=j} a_i * b_i would appreciate some help. Thank you. -- View this message in context: http://r.789695.n4.nabble.com/summation-coding-tp4646678.html Sent from the R

Hi out there, imagine you have a dataset (x,y) with errors f, so that each y_i is y_i +- f_i. This is the normal case for almost all measurements, that one quantity y can only be measured with a certain accuracy. > x<-c(1,2,3) > y<-c(1.1,0.8,1.3) > f<-c(0.2,0.2,0.2) > plot(x,y) #whereas every y has the uncertainty of f If I now perform a nls-fit (and force the data

Hi out there, imagine you have a dataset (x,y) with errors f, so that each y_i is y_i +- f_i. This is the normal case for almost all measurements, since one quantity y can only be measured with a certain accuracy f. > x<-c(1,2,3) > y<-c(1.1,0.8,1.3) > f<-c(0.2,0.2,0.2) > plot(x,y) #whereas every y has the uncertainty of f If I now perform a nls-fit (and force the data

Hi: I've been reading "Computational Methods for Multilevel Modeling" by Pinheiro and Bates, the idea of embedding the technique in my own c-level code. The basic idea is to rewrite the joint density in a form to mimic a single least squares problem conditional upon the variance parameters. The paper is fairly clear except that some important level of detail is missing. For

Dear R, I have a simple question concerning with a special case of spare matrix multiplications. Say A is a 200-by-10000 dense matrix. B is a 10000-by-10000 block- diagonal matrix, and each diagonal block B_i is 100-by-100. The usual way I did A%*%B will take about 30 seconds which is to time consuming because I have to do this thousands of times. I also tried to partition A into 100 small blocks

Dear All, Suppose Z_i, i=1,...,m are marginally identically distributed as a two normal mixture p0*N(0,1) + (1-p0) *N( miu_i, 1) where miu_i are identically distributed according to a mixture and I have generated Z_i one by one . Now suppose these m random variables are jointly m-dimensional normal with correlation matrix M= (m_ij). How to proceed next or how to start correctly ? Question:

Hello, I have a simulated data structure in which students are nested within teachers, and with each student are associated two test scores. There are 20 classrooms and 25 students per classroom, for a total of 500 students and two scores per student. Here are the first 10 lines of my dataframe "d": studid tchid Y time 1 1 1 -1.0833222 0 2 1 1