similar to: once more: methods on missing data

Hello everybody! I have 2 >issues< concerning methods applied to missing data. I think they're bugs, but who knows. 1. var(NA) returns Error in var(NA) : missing observations in cov/cor instead of NA. I expanded the summary-function to my.summary including SDev, in order to use it with tapply, which crashes in case of groups with no valid data. 2. is a similar problem. I use

Hello, eveRybody, I've been trying to find the origin for the following formatting-"inconsistency": E. g., look at the number of digits in summary.defaults()'s output when NAs occur: in my example below the number of NA's is displayed as an integer, the rest as numeric (floating point numbers): > summary.default( c( 1:2, NA)) Min. 1st Qu. Median Mean 3rd Qu.

Hi all, Based upon an exchange with G?ran Brostr?m on R-Help today: https://stat.ethz.ch/pipermail/r-help/2016-February/435992.html there appears to be a bug in summary.data.frame() in the case where a data frame contains Date class columns that contain NA's and other columns, if present, do not. Example, modified from R-Help: x <- c(18000000, 18810924, 19091227, 19027233, 19310526,

(summary.default): show the vector length in addition to quantiles diff -u -i -p -F '^(def' -b -w -B /home/sds/src/R-3.0.1/src/library/base/R/summary.R.old /home/sds/src/R-3.0.1/src/library/base/R/summary.R --- /home/sds/src/R-3.0.1/src/library/base/R/summary.R.old 2013-03-05 18:02:33.000000000 -0500 +++ /home/sds/src/R-3.0.1/src/library/base/R/summary.R 2013-09-10 10:19:02.682946339

Here's a bug one of my students noticed. When you call plot() on a glm object, plot.lm gets called. The second plot it shows is supposed to give a normal QQ plot of the standard deviance residuals, but it doesn't. The glm object created by glm.fit returns something (the IRLS weights?) in fit$weights which plot.lm takes as observation weights, so you get strange residuals in the QQ

Stock returns and other financial data have often found to be heavy-tailed. Even Cauchy distributions (without even a first absolute moment) have been entertained as models. Your qq function subtracts numbers on the scale of a normal (0,1) distribution from the input data. When the input data are scaled so that they are insignificant compared to 1, say, then you get essentially the

Hello All, Using the standard "summary" function in 'R', I ran across some odd behavior that I cannot understand. Easy to reproduce: Typing: summary(c(6,207936)) Yields:: Min. *1st Qu. Median Mean 3rd Qu. Max.* 6 *51990 104000 104000 156000 207900* None of these values are correct except for the minimum. If I perform "quantile(c(6,

Dear R-help folks: Here is my platform: > version platform sparc-sun-solaris2.7 arch sparc os solaris2.7 system sparc, solaris2.7 status major 1 minor 5.0 year 2002 month 04 day 29 language R I have a

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

Dear list, I reduced my data to the following: x <- c(1,4,2,6,8,3,4,2,4,5,1,3) y <- as.factor(c(2,2,1,1,1,2,2,1,1,2,1,2)) z <- as.factor(c(1,2,2,1,1,2,2,3,3,3,3,3)) I can produce the statistical summary just fine. s1 <- tapply(x, y, summary) d1 <- tapply(x, y, sd) s2 <- tapply(x, z, summary) d2 <- tapply(x, z, sd) First thing: I have 100 plus factors to analyse. Theirs

Full_Name: Long Qu Version: 2.3.1 OS: Windows XP Submission from: (NULL) (64.113.93.235) The QQ-plot of two versions of simulating noncentral F-distributed random numbers has quite different scales: > qqplot(rf(1000,2,15,3),qf(runif(1000),2,15,3)) The rf() function reads: > rf function (n, df1, df2, ncp = 0) { if (ncp == 0) .Internal(rf(n, df1, df2)) else rchisq(n, df1,

I would like to build a forest of regression trees to see how well some covariates predict a response variable and to examine the importance of the covariates. I have a small number of covariates (8) and large number of records (27368). The response and all of the covariates are continuous variables. A cursory examination of the covariates does not suggest they are correlated in a simple fashion

Has anyone seen this issue before on solaris 2.7. I keep getting this error for libshout when trying to compile the example. root#[/usr/local/src/libshout-1.0.9/example]#gcc -lshout -o test example.c Undefined first referenced symbol in file socket /usr/local/lib/gcc-lib/sparc-sun-solaris2.7/3.2/../../../libshout.so recv

Dear R users I get different F-statistic results for a "within" model, when using "time" or "twoways" effects in plm() [1] and when manually specifying the time control dummies [2]. [1] vignette("plm") [2] http://cran.r-project.org/doc/contrib/Farnsworth-EconometricsInR.pdf Two examples below: library("AER") data("Grunfeld", package =

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 >

I have written a function to emulate minitab's QQ plotting output (with SW test and AD test results on the graph): mtab.norm<-function(x) { library(nortest) library(lattice) x<-as.numeric(x) x<-as.vector(x) plot.ht<-4.6 plot.wd<-4.6 pt.ht=plot.ht/5 txt.sz<-(plot.ht/7.5) X11(width=plot.wd, height=plot.ht, bg='gray96') qqplot(x, pch=16, cex=pt.ht,

https://bugzilla.samba.org/show_bug.cgi?id=4856 Summary: Filenames are displayed before successful transfer in verbose mode Product: rsync Version: 2.5.7 Platform: All OS/Version: Linux Status: NEW Severity: normal Priority: P3 Component: core AssignedTo: wayned@samba.org

Hi List I'm having difficulty understanding how plm should work with dynamic formulas. See the commands and output below on a standard data set. Notice that the first summary(plm(...)) call returns the same result as the second (it shouldn't if it actually uses the lagged variable requested). The third call results in error (trying to use diff'ed variable in regression) Other info:

Hi, I have a query about the maximum length of vector that can be plotted in one go in a postscript driver. Try the following code (in 1.7.0; version details below): t <- seq(from=0, to=4*pi, length=200000) y <- sin(t) postscript(file="o.ps") plot(t, y, type="l") dev.off() If I view the postscript file o.ps in "gv", it takes many seconds before eventually

Dear all, I have a question about the 'sdev' value returned by the princomp function (which does principal components analysis). On the help page for princomp it says 'sdev' is 'the standard deviations of the principal components'. However, when I calculate the principal components for the USArrests data set, I don't find this to be the case: Here is how I