similar to: Significance test

Mathew Brown Institute of Bioclimatology University of G?ttingen B?sgenweg 2 37077 G?ttingen, Germany t: +49 551 39 9359 mathew.brown at forst.uni-goettingen.de On 9/24/2011 6:00 PM, r-help-request at r-project.org wrote: > Send R-help mailing list submissions to > r-help at r-project.org > > To subscribe or unsubscribe via the World Wide Web, visit >

This adds a binding for 'blockdev --setra', allowing you to adjust the readahead parameter for filesystems and devices. --- daemon/blockdev.c | 30 ++++++++++++++++++++---------- generator/actions.ml | 14 ++++++++++++++ 2 files changed, 34 insertions(+), 10 deletions(-) diff --git a/daemon/blockdev.c b/daemon/blockdev.c index 8a7b1a8..6e8821d 100644 --- a/daemon/blockdev.c +++

Thank you very much for your reply. Your code work well with this example. I modified a little to fit my real data, I got an error massage. Error in split.default(x = seq_len(nrow(x)), f = f, drop = drop, ...) : Group length is 0 but data length > 0 On Thu, Jan 31, 2013 at 12:21 PM, arun kirshna [via R] < ml-node+s789695n4657196h87@n4.nabble.com> wrote: > Hi, > Try this: >

hi, at first; thanks for the help on getting confidence intervals in R. now I have a pure statistical question. I hope you don't mind if I ask ... I have an expectation of how large my beta-weight in a regression should be - so I have an "ideal" or expected regression line. Now the real beta-weight is less then the expected and when I draw the confidence interval lines

Hi everyone, I'm trying to determine the significance of a trendline. From my internet search months ago, I came across the following post. I modified tim and dat for simiplicity. tim <- 1:10 dat <- c(0.17, 1.09 ,0.11, 0.82, 0.23, 0.38 ,2.47 ,0.41 ,0.75, 1.44) fstat <- summary(lm(dat~tim))$fstatistic p.val <-

Hello All, I'm looking for an R package that will run the "Brien's Test" (see Brien et al., 1984, Biometrika) on a correlation matrix. Or if anyone can help with the equations used to calculated the chi-squared statistic for the grand mean, main effects, interactions, and equal correlation that would also be great. Thanks in advance, B Wiltse -- View this message in

Hi there, I tried this code from homepage: http://myowelt.blogspot.de/2008/04/beautiful-correlation-tables-in-r.html <http://myowelt.blogspot.de/2008/04/beautiful-correlation-tables-in-r.html> corstarsl <- function(x){ require(Hmisc) x <- as.matrix(x) R <- rcorr(x)$r p <- rcorr(x)$P ## define notions for significance levels; spacing is important. mystars <- ifelse(p <

FWIW, I wrote a little function to extract variable importance as defined in the CART book a while ago. It's rather limited: Only works for regression problem, and you need to set maxsurrogate=0 and maxcompete=0. It may (or may not) help you: varimp.rpart <- function(x) { dev <- x$frame[, c("var", "dev")] dev <- dev[dev$var != "<leaf>",

Hello, I have two classifications. How can I compare the overall-accuracy of these classifications to each other? Is there a possibility within R to test if the achieved overall-accuracy for the classifications are differing significantly? Additionaly, are the McNemar-Test and Broker-Test implented in a package of R? Thank in advance for your help, Markus

I'd like to create a plot similar to this one from Kochanska et al. (Development and Psychopathology, 2011): <http://pages.videotron.com/do999/r-help/Kochanska_graph_ros.jpg> http://pages.videotron.com/do999/r-help/Kochanska_graph_ros.jpg I am new to ggplot2 and can't see any obvious way to create distinct coloured regions for the background, nor can I find any relevant

I am using the non-linear least squares routine in "R" -- nls. I have a dataset where the nls routine outputs tight confidence intervals on the 2 parameters I am solving for. As a check on my results, I used the Python SciPy leastsq module on the same data set and it yields the same answer as "R" for the coefficients. However, what was somewhat surprising was the the

In the summary of the gam object produced by gamm, the "Approximate significance of smooth terms" appears to be a test of the improvement in fit over a linear model, rather than a test of the significance of the overall effect of x on y: test.gamm<-gamm(y~te(x, bs="cr"), random=list(grp=~1)) summary(test.gamm$gam) . . . Approximate significance of smooth terms:

Greetings, I have the following problem: I want to compare a "parameter trajectory", i.e. a series of real numbers (representing equidistant samples of a time-varying parameter) produced by some "model", to a reference trajectory, measured from the real world, in order to get a rating of how good the model that produced the first trajectory is. Ok, so I use the RMS of the

Does anybody know how to perform a step function by significance of p instead of AIC? I want to perform a forward-backward stepwise logistic regression and want to compare the results obtained by steps of significance and by steps of AIC. Thank you -- View this message in context: http://n4.nabble.com/Step-by-significance-tp1754237p1754237.html Sent from the R help mailing list archive at

Hello all, I have a set of microarray timeseries (approx. 18000 genes and 7 time points per gene) data that I have correlated with a template (a vector of length 7). I have also correlated random data from a normal distribution with the same mean and standard deviation as my data set with the template, as well as, correlating a random permutation of my data set with the template. So I now

I would like to be able to extract the significance levels (or standard errors) of the variance-covariance parameters in nlme objects (i.e. from the varClasses and/or corClasses). Is there a way to do it, or do I have to reverse the intervals function calculations ? Thanks. Rob _________________________________________________________________ [[alternative HTML version deleted]]

Hi, there: I am wondering if there is a package in R (doing decison trees) which can provide some methods to evaluate the significance of attributes. I remembered randomForest gives some output like that. Unfortunately my current computing env. cannot handle my datasets if I use randomForest. So, I am thinking if other packages can do this job or not. Thanks, Ed

Hello everyone, I am employing the waba method from the multilevel package for obtaining a within-group correlation (Description: http://bg9.imslab.co.jp/Rhelp/R-2.4.0/src/library/multilevel/man/waba.html). Does anybody know a way or a calculation for obtaining a significance value for that correlation? And another question: Does anybody know whether it is possible to save individual

Hi, I have a bunch of data points x from two classes A & B, and I'm creating a classifier. So I have a function f(x) which estimates the probability that x is in class A. (I have an equal number of examples of each, so p(class) = 0.5.) One way of seeing how well this does is to compute the error rate on the test set, i.e. if f(x)>0.5 call it A, and see how many times I

Hi everyone, When the CCF between two series of observations is plotted in R, a line indicating (presumably) the significance threshold appears across the plot. Does anyone know how this threshold is determined (it is different for each set of series) and how its value can be extracted from R? I've tried saving the CCF into an object and unclassing the object, but there's nothing there to