search for: x1i

...d it to. I would gratefully appreciate any advice or help that anyone can give on this issue and I appreciate your time very much. library(MASS) library(splines) library(sm) library(quantreg) library(locfit) set.seed(20) Zi = rnorm(5000,0, 2) ei = mvrnorm(5000, c(.6,2), matrix(c(.7,0,0,.9),2,2)) X1i = exp(-.5*Zi + ei[,1]) X2i = exp(-.5*(Zi*log(T1i) -log(T1i) + Zi) + ei[,2]) X1i = log(X1i) boot.weight = rmultinom(1,5000, prob=rep(1/5000, 5000)) boot.subset = rep(1:5000, boot.weight) loc.model.1 = locfit(1*(X2i[boot.subset]< 10) ~ lp(X1i[boot.subset], Zi[boot.subset], style = c ? ? ?("...

Hi everybody, This has been causing me some trouble: I want to combine several lists of pairs into a single?vector but keep the pairs together. The lists are of the form: (1)?x1 x2 x3... N? (2) y1 y2 y3..? N I would like to keep it this way, simply appending one list to another, e.g. (1) x1i x2i... x1j x2j... N (2) etc. A?method like c(...) does not achieve this. It creates a single list, disregarding the pairs. Is there a more efficient way of doing it? Thanks! Oliver Marshall [[alternative HTML version deleted]]

Hi Dr. Viechtbauer, I'm doing meta-analysis using your package 'metafor'. I used the 'IRFT' to transform the incident rate. But when I tried to back-transform the summary estimates from function rma, I don't know what's the appropriate ti to feed in function transf.iirft. I searched and found your post about using harmonic mean for ni to back-transform the double

...approach does not yield the reported results. Is anyone familiar with the inner workings of this function and can either explain the calculation of the standard errors or provide code that explains the inner workings of this function. Thanks! Example of the model I am running: model1<- gls(Y~ X1I + X2 + X3 + X4, data=Dat1, correlation = corAR1(), method = "ML") My understanding of model errors: Y = b_0 + X1 b_1+ ...Xk b_k + Z Z_t =phi Z_{t-1) + e_t The residuals reported by GLS are the Z's, while the white noise terms are the e's. I cannot replicate the reported residual...

Dear all, I get an error using betrag on this data set :http://dl.dropbox.com/u/1866110/dump.csv. I run it like this regression f2.1=betareg(Y~X1+X2,data=dump) summary(f2.1) I get : Call: betareg(formula = Y ~ X1 + X2, data = dump) Standardized weighted residuals 2: Error in quantile.default(x$residuals) : missing values and NaN's not allowed if 'na.rm' is FALSE In addition:

Dear all, I have a couple of short noob questions for whoever can take them. I'm from a very non-stats background so sorry for offending anybody with stupid questions ! :-) I have been using logistic regression care of glm to analyse a binary dependent variable against a couple of independent variables. All has gone well so far. In my work I have to compare the accuracy of analysis to a C4.5

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...d it to. I would gratefully appreciate any advice or help that anyone can give on this issue and I appreciate your time very much. library(MASS) library(splines) library(sm) library(quantreg) library(locfit) set.seed(20) Zi = rnorm(5000,0, 2) ei = mvrnorm(5000, c(.6,2), matrix(c(.7,0,0,.9),2,2)) X1i = exp(-.5*Zi + ei[,1]) X2i = exp(-.5*(Zi*log(T1i) -log(T1i) + Zi) + ei[,2]) X1i = log(X1i) boot.weight = rmultinom(1,5000, prob=rep(1/5000, 5000)) boot.subset = rep(1:5000, boot.weight) loc.model.1 = locfit(1*(X2i[boot.subset]< 10) ~ lp(X1i[boot.subset], Zi[boot.subset], style = c ? ? ?("...

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