search for: fiti

...the complete-data case. I still worry that it could get things wrong if there are NAs in the middle of the series, since say > x [1] 5 4 3 5 NA 5 4 6 4 3 > diff(x) [1] -1 -1 2 NA NA -1 2 -2 -1 > diff(x,,2) [1] 0 3 NA NA NA 3 -4 1 I suspect that what we really need is fitI <- lm(x ~ xreg - 1, na.action = na.omit) fit <- if(length(dx) > ncol(dxreg)) lm(dx ~ dxreg - 1, na.action = na.omit) else list(rank = 0L) if(fit$rank == 0L) { ## Degenerate model. Proceed anyway so as not to break old code fit &lt...

...## helpful to drop constant columns. xi <- qtx[select, , drop = FALSE] cols <- colSums(xi^2) > 1e-5 if(any(cols)) { xi <- xi[, cols, drop = FALSE] attr(xi, "assign") <- asgn.t[cols] fiti <- lm.fit(xi, qty[select,,drop=FALSE]) fiti$terms <- Terms } else { y <- qty[select,,drop=FALSE] fiti <- list(coefficients = numeric(0), residuals = y, fitted.values = 0 * y, weights = wts, rank = 0...

...nstant columns. > xi <- qtx[select, , drop = FALSE] > cols <- colSums(xi^2) > 1e-5 > if(any(cols)) { > xi <- xi[, cols, drop = FALSE] > attr(xi, "assign") <- asgn.t[cols] > fiti <- lm.fit(xi, qty[select,,drop=FALSE]) > fiti$terms <- Terms > } else { > y <- qty[select,,drop=FALSE] > fiti <- list(coefficients = numeric(0), residuals = y, > fitted.values = 0 * y...

Hey, I have stacked a couple of garchFit objects in a list with names $fit1, $fit2, ..., $fiti assigning objects names using a loop, i.e. after running the loop modelStack = list($fit1, $fit2,...,$fiti). Thus the following apply; a = modelStack$fit2, then a is the second garchFit object of formal class 'fGarch' with 11 slots, @call, @formula... etc. I then want to extract informati...

Dear all, I am using the nlsLM function to fit a Lorentzian function to my experimental data. The LM algorithm should allow to specify limits, but the upper limit appears not to work as expected in my code. The parameter 'w', which is peak width at half maximuim always hits the upper limit if the limit is specified. I would expect the value to be in-between the upper and lower limit with

...t;> on Wed, 27 May 2015 23:19:09 -0400 writes: {@PP, you forgot this part:} >>>>> peter dalgaard <pdalgd at gmail.com> >>>>> on Thu, 21 May 2015 14:36:03 +0200 writes: >> I suspect that what we really need is >> >> fitI <- lm(x ~ xreg - 1, na.action = na.omit) >> fit <- if(length(dx) > ncol(dxreg)) >> lm(dx ~ dxreg - 1, na.action = na.omit) >> else list(rank = 0L) >> if(fit$rank == 0L) { >> ## Degenerate model. Proceed anyway so as not to break old...

On 21 May 2015, at 10:35 , Martin Maechler <maechler at lynne.stat.math.ethz.ch> wrote: >> >> I noticed that the 3.2.1 release cycle is about to start. Is there any >> chance that this fix will make it into the next version of R? >> >> This bug is fairly serious: getting the wrong variance estimate leads to >> the wrong log-likelihood and the wrong

Dear all, When analyzing data from a climate change experiment using linear mixed-effects models, I recently came across a situation where: - the summary(model) showed a significant difference between the levels of a two-level factor, - while the anova(model) showed no significance for that factor (see below). My question now is: Is the anova.lme() approach correct for that model? And why does

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