search for: epsilon_i

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 ab...

...rying to create a graphic where I calculate the residual from a regression and want to mark each residual with its observation number. So something like plot(0,0, type = "n", xlim = c(0,10)) for(i in 1:10){ text(i, 0, substitute(paste(epsilon[i]))) } except that i end up pasting \epsilon_i 10 times and not \epsilon_1, ..., \epsilon_10. i'd be much obliged if anyone can put me out of my misery and let me know how i am goofing up. many thanks _________________________________________________________________ [[alternative HTML version deleted]]

...iable, t-value= b_k/sigma_k With b_k is the coefficient for the k-th variable, and sigma_k =(t(x) x )^(-1) _kk is its standard deviation. I find sigma_k = sigma * n/(n*Sum x_{k,i}^2 -(sum x_{k,i}^2)) With sigma: the estimated standard deviation of the residuals, Sigma = sqrt(1/(N-K-1)*Sum epsilon_i^2) With: N: number of observations K: number of variables This formula comes from my old course of econometrics. For some reason it doesn't match the t-value produced by R (I am off by about 1%). I can match the other results produced by R (coefficients of the regression, r squared, et...

...*)", "", co), minlength=abbrev.length) } # Get and format DV m.y <- strsplit((as.character(object$call[2])), " ~ ")[[1]][1] # Write coefficent labels b.x <- paste("\\beta_{", co.n ,"}", sep="") # Write error term e <- "\\epsilon_i" # Format coefficint x variable terms m.x <- sub("}Int","}", paste(b.x, co.n, " + ", sep="", collapse="")) # If inline estimates convert coefficient labels to values if(estimates == "inline") { m.x <- sub("Int&quo...

I have a project due in my Linear Regression class re: regression on a data set & my professor gave us a hint that there were *exactly *2 sig interactions. The data set is attached. We have to find which predictors are significant, & which 2 interactions are sig. Also, I nedd some guidance for this & selecting the best model. I tried the `full' model, that being:

Dear R users I recently migrated from Statistica/SigmaPlot (Windows) to R (Linux), so please excuse if this may sound 'basic'. When running a nonlinear regression (V = Vmax * conc / (Ks + conc), i.e. Michaelis-Menten) on SigmaPlot, I get the output listed below: >>>Begin SigmaPlot Output<<< R = 0.94860969 Rsqr = 0.89986035 Adj Rsqr = 0.89458984 Standard Error of

Hello Everyone,   Learning about R and its wonderful array of functions. If it's not obvious, I usually try to find out what a function stands for. I think this helps me remember better.   One function that has me stumped is "sink." Can anyone tell me if this stands for something?   Thanks,   Paul         __________________________________________________ [[alternative HTML