search for: e_i

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 indice. No...

Dear all, Given a LME model (following the notation of Pinheiro and Bates 2000) y_i = X_i*beta + Z_i*b_i + e_i, is it possible to extract the variance-covariance matrix for the estimated beta_i hat and b_i hat from the lme fitted object? The reason for needing this is because I want to have interval prediction on the predicted values (at level = 0:1). The "predict.lme" seems to provide point esti...

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 b...

...tive is to write the likelihood down and use optim or a similar function to get the MLE of the parameters of the model. I was also trying to compute Small Area Estimators using area level models, so that I have one observation per area. I tried to fit the following model: Yhat_i = X beta + u_i + e_i where Yhat is a direct estimator of the target variable, X, the area- level covariates, u_i the random effects independent and distributed as N(0, sigma2_u) and e_i the 'error' terms, which are distributed as N(0, sigma2_e/n_i), where n_i is the sample size in area i. This model should...

...the proper way to do a ''goodness of fit'' test using R. I know the steps I''d take if I were to do it ''manually'': grouping the numbers into classes, calculating the expected frequencies using ''ppois'', then calculating Chi_2_obs = Sum (e_i - o_i)^2/e_i) (where e_i represents the expected frequencies and o_i the observeds ones) and then finally calculating the p-value (using pchisq). I''ve read a lot of documentation, also tried googling for ''goodness of fit R'' but it was helpless, most of it is only about...

...e of weights argument did not had influence on results. I do not know if my specification of weights i.e. 1 / sd^2 is ok. Under least squares one minimizes sum(e^2_i) or sum(w_i * e^2_i) with weighted LS. If I consider that \sigma_i represents uncertainty in my "data" then e'_i = e_i / \sigma_i and we minimize sum(e'^2_i) = sum((e_i / \sigma_i)^2) = sum(e_i * \sigma^-2_i). Therefore weights i.e. w_i are equal to 1 / \sigma^2_i. Can anyone help me with this issue? Thank you very much! > library("R2WinBUGS") > data(schools) > schools > attach(sc...

hi! i want to change the "robustness weights" used by loess. these are described on page 316 of chambers and hastie's "statistical models in S" book as r_i = B(e_i,6m) where B is tukey's biweight function, e_i are the residulas, and m is the median average distance from 0 of the residuals. i want to change 6m to, say, 3m. is there a way to do this? i cant figure it out from the help files. thanks, rafael

Dear all, I have two highly correlated variables (y and x), and both of them depend on a third variable (A, for Area). Multiple regression (y=a+(b*x)+(c*A)) would have collinearity problems, so I decided to do a partial regression to predict y. I did it this way: - I regressed y to A, and calculated the residuals (e_y) (reg1) - I regressed x to A, and calculated the residuals (e_x) (reg2)

...lem 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 binomial family and logit link. Unlike an ordinary linear regression, there is no way to estimate an individual level random error in the linear predictor z_i = a + b*x_i + e_i because the variance of e_i is unidentified. The standard deviation of the logistic is pi*s/3, and we assume s=1, so the standard deviation is assumed to be pi/3 (just a bit bigger than 1, if you are comparing against the Standard Normal). The logistic fitting process sets the variance of the er...

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 am trying to fit a linear mixed model of the form y_ij = X_ij \beta + delta_i + e_ij where e_ij ~N(0,s^2_ij) with s_ij known and delta_i~N(0,tau^2) I looked at the ecme routine in package:pan, but this routine does not allow for different Vi (variance covariance matrix of the e_i vector) matrices for each cluster. Is there an easy way to fit this model in R or should I bite th...

...t 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 where e_i is normal with mean 0 and variance sigma_i^2 There is a smoothing parameter lambda between 0 and 1. .when lambda is 0, smoothness is all that matters, and the fitting function will be a straight line. .when lambda is 1, the result is the interpolating spline. In my case, this paramete...

Dear all, I fitted a non-parametric model using GAM function in R. i.e., gam(y~s(x1)+s(x2)) #where s() is the smooth function Then I obtained the coefficients(a and b) for the non-parametric terms. i.e., y=a*s(x1)+b*s(x2) Now if I want to use this estimated model to do optimization or sensitivity analysis, I am not sure how to incorporate the smooth function since s() may not

...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) + e_i where e_i is normal with mean 0 and variance sigma_i^2 There is a smoothing parameter lambda between 0 and 1. .when lambda is 0, smoothness is all that matters, and the fitting function will be a straight line. .when lambda is 1, the result is the interpolating spline. In my case, this paramet...

Dear All, I hope this is not too off topic. I am given a set of scatteplots (nothing too fancy; think about a normal x-y 2D plot). I do not deal with two time series (indeed I have no info about time). If I call A=(A1,A2,...) and B=(B1, B2, ...) the 2 variables (two vectors of numbers most of the case, but sometimes they can be categorical variables), I can plot one against the other and I

Hi! I'm learning mgcv, and reading Simon Wood's book on GAMs, as recommended to me earlier by some folks on this list. I've run into a question to which I can't find the answer in his book, so I'm hoping somebody here knows. My outcome variable is binary, so I'm doing a binomial fit with gam(). I have five independent variables, all continuous, all uniformly

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

Hello, I am interested in estimating this type of random effects panel: y_it = x'_it * beta + u_it + e_it u_it = rho * u_it-1 + d_it rho belongs to (-1, 1) where: u and e are independently normally zero-mean distributed. d is also independently normally zero-mean distributed. So, I want random effects for group i to be correlated in t, following an AR(1) process. Any idea of how to estima...

Hi I have trouble making a best fitted curve for a xy-plot. My data consist of two groups with four repititions for each x-value. plot(weight~gdd,data=weight,pch=as.numeric(species)) <http://r.789695.n4.nabble.com/file/n4647692/Weight.jpeg> Can you help? Cecilie -- View this message in context: http://r.789695.n4.nabble.com/Best-fitted-curve-tp4647692.html Sent from the R help

Dear R developers, I am a little disappointed that my bug report only made it to the wishlist, with the argument: Well, it does not say it has. Only relevant to prediction intervals. predict.lm does calculate prediction intervals for linear models from weighted regression, so they should be correct, right? As far as I can see they are bound to be wrong in almost all cases, if no weights