similar to: update() ignores object

I am relatively new to R and object oriented programming. I have relied on SAS for most of my data analysis. I teach an introductory undergraduate forecasting course using the Diebold text and I am considering using R in addition to SAS and Eviews in the course. I work primarily with univariate or multivariate time series data. I am having a great deal of difficulty understanding and working with

Dear all I have a work-flow issue with lm(). When I use > lm(y1~x1, anscombe) Call: lm(formula = y1 ~ x1, data = anscombe) Coefficients: (Intercept) x1 3.0001 0.5001 I get as expected the formula, "y1 ~ x1", in the print()ed results or summary(). However, if I pass through a formula object > (form <- formula(y1~x1)) y1 ~ x1 > lm(form, anscombe) Call:

I have a question about the D'Agostino skewness test and the Anscombe-Glynn kurtosis test. agostino.test(x, alternative = c("two.sided", "less", "greater")) anscombe.test(x, alternative = c("two.sided", "less", "greater")) The option "alternative" in those two functions seems to be the null hypothesis. In the output, the

Another bad case is > moments::anscombe.test(rep(c(1,1.1),length=35)) Error in if (pval > 1) pval <- 2 - pval : missing value where TRUE/FALSE needed I haven't checked the formulas carefully, but I suspect the problem is from taking the cube root of a negative number in z <- (1 - 2/(9 * a) - ((1 - 2/a)/(1 + xx * sqrt(2/(a - 4))))^(1/3))/sqrt(2/(9 * a)) In R, the

moments::anscombe.test(x) does give errors when x has too few values or if all the values in x are the same > moments::anscombe.test(c(1,2,6)) Error in if (pval > 1) pval <- 2 - pval : missing value where TRUE/FALSE needed > moments::anscombe.test(c(2,2,2,2,2,2,2,2)) Error in if (pval > 1) pval <- 2 - pval : missing value where TRUE/FALSE needed You can use tryCatch() to

Hi Bill, Thanks for prompt reply and letting me know a way around it. I have more than 1200 observations and not all the values are the same. However, my data points are quite similar, for example, 0.079275, 0.078867, 0.070716 in millions and etc. I have run the data without converting it to millions and I still get the same error message. As I have kurtosis value, it should be fine for the

Dear useRs, I was searching CRAN for implementation of kurtosis and skewness tests, and found that there is some kind of lack on it. So, I have written three functions: 1. Anscombe-Glynn test for kurtosis 2. Bonett-Seier test based on Geary's kurtosis (which is not widely known, but I was inspired by original paper describing it, found coincidentally in Elsevier database) 3.

What is going on here? In the lines ending in #### the inputs and outputs are identical yet one gives a warning and the other does not. a1 <- `rownames<-`(anscombe[1:3, ], NULL) a2 <- anscombe[1:3, ] ix <- 5:8 # input arguments to #### are identical in both cases identical(stack(a1[ix]), stack(a2[ix])) ## [1] TRUE identical(a1[-ix], a2[-ix]) ## [1] TRUE res1 <-

They differ in whether the row names are "automatic": > .row_names_info(a1) [1] -3 > .row_names_info(a2) [1] 3 Best, -Deepayan On Tue, 14 Nov 2023 at 08:23, Gabor Grothendieck <ggrothendieck at gmail.com> wrote: > > What is going on here? In the lines ending in #### the inputs and outputs > are identical yet one gives a warning and the other does not. > >

Dear R-users, I am comparing differences in variance, skew, and kurtosis between two groups. For variance the comparison is easy: just var.test(group1, group2) I am using agostino.test() for skew, and anscombe.test() for kurtosis. However, I can't find an equivalent of the F.test or Mood.test for comparing kurtosis or skewness between two samples. Would the test just be a 1 df test on

In that case identical should be FALSE but it is TRUE identical(a1, a2) ## [1] TRUE On Tue, Nov 14, 2023 at 8:58?AM Deepayan Sarkar <deepayan.sarkar at gmail.com> wrote: > > They differ in whether the row names are "automatic": > > > .row_names_info(a1) > [1] -3 > > .row_names_info(a2) > [1] 3 > > Best, > -Deepayan > > On Tue, 14 Nov

Also why should that difference result in different behavior? On Tue, Nov 14, 2023 at 9:38?AM Gabor Grothendieck <ggrothendieck at gmail.com> wrote: > > In that case identical should be FALSE but it is TRUE > > identical(a1, a2) > ## [1] TRUE > > > On Tue, Nov 14, 2023 at 8:58?AM Deepayan Sarkar > <deepayan.sarkar at gmail.com> wrote: > > > >

On Tue, 14 Nov 2023 at 09:41, Gabor Grothendieck <ggrothendieck at gmail.com> wrote: > > Also why should that difference result in different behavior? That's justifiable, I think; consider: > d1 = data.frame(a = 1:4) > d2 = d3 = data.frame(b = 1:2) > row.names(d3) = c("a", "b") > data.frame(d1, d2) a b 1 1 1 2 2 2 3 3 1 4 4 2 > data.frame(d1,

Hi all, While running the anscombe.test in R, I'm getting an error of *Error in if (pval > 1) pval <- 2 - pval : missing value where TRUE/FALSE needed* for a few time series columns whereas for most of the series the function is working fine. I have checked for those specific columns for missing values. However, there is no NA/NAN value in the dataset. I have also run kurtosis for

Do you know of a reference that discusses alternative choices for plotting positions for a normal probability plot? The documentation for qqnorm says it calls ppoints, which returns qnorm((1:m-a)/(m+1-2*a)) with "a" = ifelse(n<=10, 3/8, 1/2)? The help pages for qqnorm and ppoints just refer to Becker, Chambers and Wilks (1988) The New S Language (Wadsworth & Brooks/Cole),

Thomas, that's a good point. I was thinking of anscombe[x1::y1] making it clear which one, but you would then want just x1::y1 to have unambiguous meaning on its own, which is impossible. As for x1:xN, it's unambiguous on its own. I thought one of the great advantages of R was that it could use different methods so that a new operator would not be needed. The colon operator would just

Wolfgang, It is common to handle relative risk problems using Poisson regression. In your example you have 8 events out of 508 tries, and 0/500 in the second data set. > tdata <- data.frame(y=c(8,0), n=c(508,500), group=1:0) > fit <- glm(y ~ group + offset(log(n)), data=tdata, family=poisson) Because of the zero, the standard beta/se(beta) confidence intervals don't work.

Dear list members, I've run into a problem with R CMD INSTALL under Windows Vista and R 2.8.0: --------- snip ----------- C:\Users\John Fox\workspace>c:\R\R-2.8.0\bin\R CMD INSTALL car installing to '' ---------- Making package car ------------ adding build stamp to DESCRIPTION installing NAMESPACE file and metadata installing R files installing inst files installing

can you recommend a good manual for R that starts with a data set and gives demonstrations on what can be done using R? I downloadedR Langauage definition and An introduction to R but haven't found them overly useful. I'd really like to be able to follow some tutorials using a dataset or many datasets. The datasets I have available on R are Data sets in package 'datasets':

Yesterday I had the opportunity to attend a seminar by George Box where he discussed some of the ideas that will be incorporated in the second edition of Box, Hunter, and Hunter "Statistics for Experimenters" due out in a few months. At the end of the presentation he distributed a list of quotes from the book and I felt that many of these would be appealing to members of this