search for: 1.84e

Hallo, I have this dataset with repeated measures. There are two within-subject factors, "formant" (2 levels: 1 and 2) and "f2 Ref" (25 levels: 670, 729, 788, 846, 905, 1080, 1100, 1120, 1140, 1170, 1480, 1470, 1450, 1440, 1430, 1890, 1840, 1790, 1740, 1690, 2290, 2210, 2120, 2040, 1950), and one between-subject factor, lang (2 levels:1 and 2). The response variable

Hi, I'm using geeglm function to account for the repeated measure. fit1<- geeglm( binary.outcome ~ age + race + gender + fever.yes.no, data=mydata, id=ID, family=binomial, corstr="exchangeable") summary(fit1)$coef gives too large estimates and standard deviation: Estimate Std.err Wald Pr(>|W|) (Intercept) 3.07e+16

Dear Friends, I am new to this mailing list. I am with the National University of Rosario, Argentina, and I am writing a book on software-based acoustical measurements, which includes a chapter on FLAC for archival and streaming purposes from an remote embedded system including a sensor. I would like to ask why the seekpoint information in the seek table metadata block reserves 64 bit for

Dear list members, I am trying to estimate parameters of the AR(1)-GARCH(1,1) model. I have one additional dummy variable for the AR(1) part. First I wanted to do it using garchFit function (everything would be then estimated in one step) however in the fGarch library I didn't find a way to include an additional variable. That would be the formula but, as said, I think it is impossible to add

Federico Miyara wrote: > I would like to ask why the seekpoint information in the seek table > metadata block reserves 64 bit for the number of first sample in > target frame and for the offset of the first byte of target frame. > > It seems to me a lot, since 2^64 = 1.84e+19, i.e., far more samples > and bytes than can be expected in any file... ever. Fast reverse to the year

Dear all, I would like to use the residuals in a general linear mixed effect model to diagnose model fit. I know that the resid function has been implemented for linear mixed models but not yet for general linear mixed effects. Is there a way to get them out of lmer fit objects? I tried searching the r-help archive and found nothing. I tried and failed to replicate what (I guessed would be

Here's the patch submit message for 4GB+ windows barrier. http://lists.xiph.org/pipermail//flac-dev/2013-March/003804.html On Wed, Jun 12, 2013 at 3:00 PM, <flac-dev-request at xiph.org> wrote: > Send flac-dev mailing list submissions to > flac-dev at xiph.org > > To subscribe or unsubscribe via the World Wide Web, visit >

After applying the "step" command to a long list of predictors I came up with the following showing there still are some non significant coefficients. I'd like to try the command "leap". However, I don't quite understand how it returns the spared predicting variables. Can please, someone help ? Tank you in advance. Mara EM > summary(stepmod) Call: lm(formula =

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Dear all: I converted the columns (Baci, Meti, Fungii, Protozoai) into integers (using excel) and then imported the data (.txt) into R. Interestingly, the other three variables were loaded as INT, but the 'Baci' one continued as Num. I imported the data using the following command line: X <- read.delim(file.choose(), header = TRUE, dec =

Gracias a todos por las recomendaciones. Ya me he puesto en contacto con el autor y le explicado donde esta el error. Es tan fácil como que han cambiado la estructura de los objetos tipos lmer desde la versión 2.15.3 con lo que su paquete dejo de funcionar. Por suerte la nueva estructura conserva por lo menos la parte necesaria para hacer sus funciones. Se localizar el fallo y como arreglarlo, lo

Javier, Creo que aquí aplica la ley de Linus que dice: "Dado un número suficientemente elevado de ojos, todos los errores se convierten en obvios". La persona que revisa y encuentra un error no necesariamente tiene que ser la misma que la que lo escribe. Una motivación muy importante al compartir un código es la de recibir los beneficios del control de calidad por parte de tus pares.

Hace unos meses os escribir para comunicaros que había un fallo en esta función. Como os prometí os comento la respuesta por si alguno está interesado en utilizar el paquete LMERconvenientsfucntions Dear Javier, The package has been updated and should work for you fine now. Note that function mcp.fnc does not return the fourth plot (dffits) anymore. We still have to figure out a way to compute