similar to: Some clarificatins of anova() and summary ()

Hi, I was hoping to clarify the exact behavior associated with this incantation: validate(fit.ols, method='cross', B=50) Output: index.orig training test optimism index.corrected n R-square 0.5612 0.5613 0.5171 0.0442 0.5170 50 MSE 1.3090 1.3086 1.3547 -0.0462 1.3552 50 Intercept 0.0000 0.0000 -0.0040 0.0040

would someone be kind enough to paste the code below into an R session ( ir you can paste it into a file and just source it ) and take a look at it ? I must be doing something wrong but i can't find it. I start out with a zoo object that has 100 elements in it. then, i only want to keep the rows in which the bid and the ask are both grester than zero. ( maybe my logic is wrong there ? ).

Dear all i use R only a few days and don't understand the difference between fivenum(x) und summary(x). > x [1] 20.77 22.56 22.71 22.99 26.39 27.08 27.32 27.33 27.57 27.81 28.69 29.36 [13] 30.25 31.89 32.88 33.23 33.28 33.40 33.52 33.83 33.95 34.82 > fivenum(x) [1] 20.770 27.080 29.025 33.280 34.820 > summary(x) Min. 1st Qu. Median Mean 3rd Qu. Max. 20.77 27.14

Hi all, I have a question about memory usage and IPC$ share. I'am auditing the swap usage of my server because I already have a problem with a full swap. I see that the smbd process take < 200Mo on swap : Example : PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 24429 lge 15 0 197m 39m 14m S 0.3 1.0 0:15.17 smbd but some process use more than 200Mo

Dear Rusers, I have met a difficult problem on explaining the differences of principal component analysis(PCA) between R,S-PLUS and SAS/STATA/SPSS, which wasn't met before. Althought they have got the same eigenvalues, their coeffiecients were different. First, I list my results from R,S-PLUS and SAS/STATA/SPSS, and then show the original dataset, hoping sb. to try and explain it.

I have a matrix which I wanted to convert to a data frame. As I could not succeed and resorted to export to csv and reimport it again. Why did I fail in the attempt and how can I achieve what I wanted without this roundabouts? The original matrix: > str(comb_model0) num [1:90, 1:4] 3.5938 0.0274 0.0342 0.0135 0.0207 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:90]

Hi, I would consider the calculation of r-squared in the following to be a bug, but then, I've been wrong before. It seems that R looks to see if the model contains an intercept term, and if it does not, computes r-squared in a way I don't understand. To my mind, the following are two alternative parametrizations of the same model, and should yield the same r-squared. Any insight much

Hi I went through some extensive search to find suitable method (package, function) to fit multiple peaks. The best I found is ALS package but it requires rather complicated input structure probably resulting from GC-MS experimental data and seems to be an overkill to my problem. I have basically simple two column data frame with columns time and sig dput(temp) structure(list(time = c(33, 34,

Hello everybody! I have an ARIMA model for a time series. This model was obtained through an auto.arima function. The resulting model is a ARIMA(2,1,4)(2,0,1)[12] with drift (my time series has monthly data). Then I perform a 12-step ahead forecast to the cited model... so far so good... but when I look the plot of my forecast I see that the result is really far from the behavior of my time

Dear All, I am a undergraduate using R for the first time. It seems like an excellent program and one that I look forward to using a lot over the next few years, but I have hit a very basic problem that I can't solve. I want to produce a standardised histogram, i.e. one where the area under the graph is equal to 1. I look at the manual for the histogram function and find this: freq:

Hi I compouted a multiple linear regression with repeated measures on one explanatory variable: BOLD peak (blood oxygenation) as dependent variable, and as independent variables I have: -age.group (binaray:young(0)/old(1)) -and task-difficulty measured by means of the reaction-time 'rt'. For 'rt' I have repeated measurements, since each subject did 12 different tasks. -> so

Hi Louis, On Wed, Apr 22, 2015 at 01:15:21PM +0200, L.P.H. van Belle wrote: > Or.. > get the sources and recompile agains 14.04 > > use the following commands. > enable the deb-src in /etc/apt/sources.list > and run : > > apt-get build-dep sernet-samba > apt-get source sernet-samba -b > > you wil end up with .deb files ;-) yep, thanks - I'll check it

Hi all, I can't find the error in the binomial GLM I have done. I want to use that because there are more than one explanatory variables (all categorical) and a binary response variable. This is how my data set looks like: > str(data) 'data.frame': 1004 obs. of 5 variables: $ site : int 0 0 0 0 0 0 0 0 0 0 ... $ sex : Factor w/ 2 levels "0","1": NA NA NA

Hi, I have a data set like this: Mutant Rep Time OD 02H02 1 0 0.029 02H02 2 0 0.029 02H02 3 0 0.023 02H02 1 8 0.655 02H02 2 8 0.615 02H02 3 8 0.557 02H02 1 12 1.776 02H02 2 12 1.859 02H02 3 12 1.668 02H02 1 16 3.379 02H02 2 16 3.726 02H02 3 16 3.367 306 1 0 0.033 306 2

I want to use something like printCoefmat() in a print.summary method to print a more nicely formatted version of the result from a summary method, but where the estimates may be cross-classified by one or more factors. However, printCoefmat() assumes that the labels for the parameters are the rownames of the object, and prints factors as integers. With one factor, I could just assign that as

Hello Ervin, The in-memory cache lookup could be added back into samba-4.1.6 by applying the attached patch, if it could be compiled with Louis how-to steps, perhaps got the chance to make U14.04 be stayed on samba-4. Here is my test, the test-bench is uploading 5000 files and each one is 1MB. The attached patch could improve 15% at case B). Case A) Original samba-4.1.6 will go through

Dear R users, I've written the following functions to implement Silverman's modality test ("Using kernel density estimates to investigate multimodality", J.R. Stat. Soc. B, 43, (1981), 97-99.), and I tested them on the Chondrite data set (Good & Gaskins, J. Amer. Stat. Ass., 75, (1980), 42-56). Values for the critical window width seem OK, which is not the case for the

When all else fails, read the help page... ?fivenum says to look at ?boxplot.stats, and the "Details" section of ?boxplot.stats has, well, details. Tukey had reasons to call those hinges rather than quartiles. Andy > -----Original Message----- > From: Knut M. Wittkowski [mailto:kmw at rockefeller.edu] > Sent: Thursday, May 29, 2003 5:58 PM > To: Matthias Kirschner >

Dear R users: When I create a table without Major Column headings, my *regular* column headings appear correct in the typeset latex file. The major row heading and row groups are as they should. w <- latex(mytab,title="",file="tab/my.tex",ctable=TRUE,caption="Descriptive statistics by

Hi Jones, many thanks again four your help and your time. Thanks for the patch too - I'll check it up. On my Ubuntu, there is a Samba 4.1.6. I'll install the samba source package, and will try to apply the patch, then - I hope - the package will be compiled as well. I'll notify to you about the result. (First, I need to upgrade that server.) Thanks again, Ervin On Thu, Apr