search for: 1.93e

I'm using stepAIC to fit a model. Then I'm trying to use that model to predict future happenings. My first few variables are labeled as their column. (Is this a problem?) The dataframe that I use to build the model is the same as the data I'm using to predict with. Here is a portion of what is happening.. This is the value it is predicting = > [1] 9.482975 Summary of the

When regressing by month, how do I get the coefficients out into a new data set? I'm looking for [ month, a, b, c ] from the Pastor-Stambaugh model I'm using which is: r[i+1] = a + b * r[i] + c * v[i] + e the model I'm using wants to create a new dataseries based on the coefficient in each month. I'm doing a simple linear regression on DataSet, and >

Hi everyone, I'm sorry if this turns out to be more a statistical question than one specifically about R - but would greatly appreciate your advice anyway. I've been using a logistic regression model to look at the relationship between a binary outcome (say, the odds of picking n white balls from a bag containing m balls in total) and a variety of other binary parameters:

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

https://bugs.freedesktop.org/show_bug.cgi?id=28095 --- Comment #15 from Brent <bzipitidoo at hotmail.com> --- Yes, someone else said that hardware was unusual. But Nouveau did work with it, at elast somewhat, in the later 2.6 kernels. If it helps at all, the Diamond Viper V550 video card reports a version of 1.93E during POST. Tried older versions of Puppy Linux, and ran into other

Dear R-helpers, I have performed a PLS regression with the mvr function from the pls.pcr package an I have 2 questions : 1- do you know if mvr automatically centers the data ? It seems to me that it does so... 2- why in the situation below does the output say that the optimal number of latent variables is 4 ? In my humble opinion, it is 2 because the RMS increases and the R2 decreases when 3 LVs

Hi, I've a list of list. I want to extract an element by the rownames. I can extract it by: data[[1]][[1]][[4]][1] But I want to exctract it by a command like this: data[[1]][["B0"]][["smac"]][["cont"]][1] It's possible? Thanks, Alfredo > str(data) List of 1 $ :List of 4 ..$ :List of 4 .. ..$ : num [1, 1:3] 0.4 0.458 0.5 .. ..$ : num

This conversion should be feature compatible with the standalone nbd code. Note that the use of libnbd makes the binary for this particular plugin fall under an LGPLv2+ license rather than BSD; but the source code in nbd.c remains BSD. A lot of code simply disappears, now that I'm no longer directly utilizing the NBD protocol files but relying on libnbd. Coordination between threads from

libnbd-0.1.4-1 is now available in Fedora 29/30 updates testing. Diffs since v2 - rebase to master, bump from libnbd 0.1.2 to 0.1.3+, add tests to TLS usage which flushed out the need to turn relative pathnames into absolute, doc tweaks Now that the testsuite covers TLS and libnbd has been fixed to provide the things I found lacking when developing v2, I'm leaning towards pushing this on

libnbd-0.1.2-1 is now available in Fedora 29/30 updates-testing, although it was not compiled against libxml2 so it lacks uri support (I ended up testing patch 4 with a self-built libnbd). Diffs since v1 - rebase to master, bump from libnbd 0.1 to 0.1.2, add URI support, better timing results Still not done - patch 5 needs associated tests Eric Blake (5): nbd: Check for libnbd nbd:

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