search for: cerebral

Sirs, No doubt many of the cerebral and erudite correspondents on the R-list have memorized all the functions in all the CRAN packages. But for the benefit of those less competent, such as I, could posters explicitly quote the source of non-base functions? Lately, intervals() and chron() were discussed on the list, without cle...

...y sampled pairs of data from the same source, and my reading (and my statistician) tells me that GEE is an appropriate method for that type of data set. I’m using the geeglm program from the geepack download. My data is correlated data from two estimates of vascular permeability in a rat model of cerebral tumor. One estimate, the MRI estimate, can be conducted non-invasively and repeatedly. The other estimate, the QAR estimate, can be done just once since it involves sacrificing the animal. The MRI estimate needs to be characterized as to its correlation with the QAR estimate, and that is what th...

...ce Computing with R". http://tinyurl.com/csqmlq?linked to a chart of R color names, while http://tinyurl.com/darcho?linked to a discussion about RColorBrewer, a better way to create ranges of colors for charts. http://tinyurl.com/c7v8nm?linked to a useful and amusing list of R resources from Cerebral Mastication. http://tinyurl.com/cn64bf?linked to solutions in R for some of the Project Euler programming puzzles. http://tinyurl.com/c9qqq4?showed how to speed up backtesting in R with parallel computing from ParallelR 2.0. http://tinyurl.com/cvtbmu?discussed the animations package for creating...

...here). A small example dataset and R scripts are available via ftp://ftp.mrsc.ucsf.edu/pub/dweber/cortical_timeSeries.tar These example data are from one brain region of interest (roi), called the middle frontal gyrus (MFG). We have estimated activity in this brain region for the left and right cerebral hemisphere (this is one factor in the analysis). These data are for a short period prior to the cue (-300 ms) and a longer period after the cue (1400 ms; the S appeared at 1000 ms). There are 8 subjects in this dataset. Each subject has an ERF ANALYSIS to DATE: For each time bin of about 20 ms...

Hi, I have here a problem I''ve been working at for a little while but I can''t seem to get the error messages to appear correctly. My form looks something like this for "ModelA": [errors_for "modelA"] [form for "modelA"] [text "modelA" "fieldname1"] [text "modelB" "fieldname2"] [submit] [/form] The reason

The title is "Microsoft cerebrates fifteen years of poor security". But it's about more than Microsoft's security. It talks about buffer overflows, memory separation, OpenBSD's data "canary", etc. I think it's a good read for anyone interested on this kind of stuff. http://www.theinquirer.net/?article=11108 Santos

...ambdas <- apply(dd,2 , BoxCoxLambda) > lambdas [1] 0.1666667 0.1666667 The identical lambdas do not seem right to me; nor do I understand why boxcox.lm apparently throws the error while boxcox.formula does not (it also calls NextMethod()) So I would welcome clarification to clear my clogged (cerebral) sinuses. :-) Best, Bert On Sat, Jul 8, 2023 at 11:25?AM John Fox <jfox at mcmaster.ca> wrote: > > Dear Ron and Bert, > > First (and without considering why one would want to do this, e.g., > adding a start of 1 to the data), the following works for me: > > ------ sni...

...t; dd[, 2] <- dd[, 2]^3 > apply(dd, 2, BoxCoxLambda, simplify = TRUE) [1] 0.2 0.1 Best, John > nor do I understand why > boxcox.lm apparently throws the error while boxcox.formula does not > (it also calls NextMethod()) So I would welcome clarification to clear > my clogged (cerebral) sinuses. :-) > > > Best, > Bert > > > On Sat, Jul 8, 2023 at 11:25?AM John Fox <jfox at mcmaster.ca> wrote: >> >> Dear Ron and Bert, >> >> First (and without considering why one would want to do this, e.g., >> adding a start of 1 to the...

...ment and the data analysis uses an aov model that looks like this: roiAOV <- aov( roi ~ (Cue*Hemisphere) + Error(Subject/ (Cue*Hemisphere)), data=roiDataframe) where Cue is 2 levels (an arrow on a screen that points left or right) and Hemisphere is 2 levels (brain activity in the left or right cerebral hemisphere). There are 8 subjects and they all have one observation for all levels of Cue * Hemisphere (ie, a within-subjects design). I need some functions for calculation of Box's M and the Greenhouse- Geisser correction factor. Below is some code example that I have for the latter, but I...

...ay be distributed freely provided that no fee is charged for this distribution and the authors are given credit. All the product names mentioned herein are trademarks of their respective owners. $Id: SSH1-deattack-advisory.txt,v 1.9 2001/02/08 22:46:53 iarce Exp $ --- "Understanding. A cerebral secretion that enables one having it to know a house from a horse by the roof on the house, Its nature and laws have been exhaustively expounded by Locke, who rode a house, and Kant, who lived in a horse." - Ambrose Bierce ==================[ CORE Seguridad de la Informacion S.A. ]=======...

Dear Ron and Bert, First (and without considering why one would want to do this, e.g., adding a start of 1 to the data), the following works for me: ------ snip ------ > library(MASS) > BoxCoxLambda <- function(z){ + b <- boxcox(z + 1 ~ 1, + lambda = seq(-5, 5, length.out = 101), + plotit = FALSE) + b$x[which.max(b$y)] + } > mrow <- 500

If anyone wants to see a prime example of how inefficient it is to program in SAS, take a look at the SAS programs provided by the US Agency for Healthcare Research and Quality for risk adjusting and reporting for hospital outcomes at http://www.qualityindicators.ahrq.gov/software.htm . The PSSASP3.SAS program is a prime example. Look at how you do a vector product in the SAS macro

I am trying to create a frequency distribution and I am a bit confused. Here are the commands I have entered: > data <- read.csv(file="40609_sortedfinal.csv",head=TRUE,sep=",") > NumberOfActionsByStatus = data$STATUS > NumberOfActionsByUser = data$ETS_LOGIN > NumberOfBidOffer = data$BID_OFFER > NumberOfActionsByUser.freq = table(NumberOfActionsByUser) >