similar to: Re: Testing for normality of residuals in a regression model

Hi, you can use this simple function: add.col<-function(df, new.col) {n.row<-dim(df)[1] length(new.col)<-n.row cbind(df, new.col) } see this example: > x<-cbind(c(1,2,3),c(4,5,6)) > x [,1] [,2] [1,] 1 4 [2,] 2 5 [3,] 3 6 > y<-c(7,8) > y [1] 7 8 > add.col<-function(df, new.col)

Hi, I'm dealing with time series. I usually use stl() to estimate trend, stagionality and residuals. I test for normality of residuals using shapiro.test(), but I can't test for autocorrelation and heteroskedasticity. Is there a way to perform Durbin-Watson test and Breusch-Pagan test (or other simalar tests) for time series? I find dwtest() and bptest() in the package lmtest, but it

Dear R-users, I'm finding for a R-package concerning graphs. Is there some kind of that package? I've a set of correlation coeffients between several variable and I wish to built a graph to link variables correlated. Many thanks. Best, Vito ===== Diventare costruttori di soluzioni "The business of the statistician is to catalyze the scientific learning process." George

I hope this example could help you best vito > x<-seq(-3.5,3.5,0.1) > x [1] -3.5 -3.4 -3.3 -3.2 -3.1 -3.0 -2.9 -2.8 -2.7 -2.6 -2.5 -2.4 -2.3 -2.2 -2.1 [16] -2.0 -1.9 -1.8 -1.7 -1.6 -1.5 -1.4 -1.3 -1.2 -1.1 -1.0 -0.9 -0.8 -0.7 -0.6 [31] -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 [46] 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3

Hi, I've a list containing parameters (intercepts & coefficients) of 12 regressions fitted > coeff [[1]] (Intercept) anno -427017.1740 217.0588 [[2]] (Intercept) anno -39625.82146 21.78025 ..... [[12]] (Intercept) anno 257605.0343 -129.7646 I want create a data frame with two columns (intercept and anno)using data in these list. Any help

Dear R-user, I wish to know if someone is using R as concern Datamining or KDD (Knowledge Discovery in Database) and if already exists a R package specialized in this kind of analysis. I found this contributes on the R web site: [20] Diego Kuonen. Introduction au data mining avec R : vers la reconqu??te du `knowledge discovery in databases' par les statisticiens. Bulletin of the Swiss

Hi, I'm dealing with a datamining analysis: I've a lot of categories of product sold per week (n. week =26, n. categories about 50. my dataframe is like this: Settimana ALIMENTI..ALTRI. ALIMENTI.APROTEICI 1 1 3 19 2 2 2 0 3 3 1 22 4 4 2

Dear all, in a previuos message was asked how get the mode of continous distribution. Now I'm asking if there an R function to obtain the mode in case of a discrete distribution or categorial data. The only way is to use table(): > x<-rep(1:5,100) > s<-sample(x,40) > t<-table(s) > t s 1 2 3 4 5 13 10 5 4 8 the mode is value=1 Thanks Cordially Vito =====

Hi Thomas, see these papers or books (some are available on the web): Diego Kuonen, Introduction au data mining avec R : vers la reconqu??te du `knowledge discovery in databases' par les statisticiens. Bulletin of the Swiss Statistical Society, 40:3-7, 2001. Consultabile all??indirizzo web: http://www.statoo.com/en/publications/2001.R.SSS.40/ Diego Kuonen and Reinhard Furrer, Data mining

Dear R-users, I've built these functions usefell for me to import/export data from/to Excel: importa.da.excel<-function(){read.delim2("clipboard", dec=",") ## questa funzione consente di importare dati da Excel in R ## selezionare in Excel le celle che contengono i dati, ## compresi in nomi delle colonne ## Autore: Vito Ricci email:vito_ricci at yahoo.com ## Data di

Hi, I'm dealing with time series with 1 observaton for day (data sampled daily). I will create a ts object using that time series and the function ts(). In ts() help is written: The value of argument 'frequency' is used when the series is sampled an integral number of times in each unit time interval. For example, one could use a value of '7' for 'frequency' when

Hi, in which R-package I could find skewness and kurtosis measures for a distribution? I built some functions: gamma1<-function(x) { m=mean(x) n=length(x) s=sqrt(var(x)) m3=sum((x-m)^3)/n g1=m3/(s^3) return(g1) } skewness<-function(x) { m=mean(x) me=median(x) s=sqrt(var(x)) sk=(m-me)/s return(sk) } bowley<-function(x) { q<-as.vector(quantile(x,prob=c(.25,.50,.75)))

Hi, I found this document, but it concerns S+. If it could interest you'll see: http://faculty.washington.edu/ezivot/book/QuanCopula.pdf Cordially Vito You wrote: Dear R: Is there a function or a reference to simulate Gumbel copulas, please? Thanks in advance! Sincerely, Erin Hodgess mailto: hodgess at gator.uhd.edu R version 2.0.1 windows ===== Diventare costruttori di soluzioni

Thanking John for his suggestion I build this function which get the mode of both categorial and discrete data. Mode<-function(x){t<-table(x) if (is.numeric(x)) as.numeric(names(t)[t == max(t)]) else (names(t)[t == max(t)]) } Any other improvement and suggestion will welcome. Best Vito > s [1] 1 1 6 1 1 7 6 5 6 2 1 4 5 6 6 7 3 5 4 1 7 3 7 3 3 7 7 2 1 4 4 2 7 7 6 6 1 2 [39] 5 1 7 7

Hi, see ?cor in base package to get correlation matrix for your data. Maybe it could be usefull getting principal components (give a look to: ? princomp (base)) to reduce the number of variables. Hoping I helped you. Best regards, Vito You wrote: Hi all: I have a question on how to go about creating a correlation matrix. I have a huge amount of data....21 variables for 3471 times. I want

Dear all, in last weeks you discussed about R vs SAS. I want to ask your opinion about a comparison between R and SPSS. I don't know this software, but some weeks ago I went to a presentation of this product. I found it really user-friendly with GUI (even if I'd prefer command line) and very usefull and simple to use in creation and managing tables, OLAP tecniques, pivot table. What you

Dear All, I wish to inform, especially Italian speaking R-users, that on CRAN web site is now available a contribute (in Italian language) about using R in ts analysis. Any comments would be appreciated. Best regards, Vito ===== Diventare costruttori di soluzioni Became solutions' constructors "The business of the statistician is to catalyze the scientific learning process."

Hi, did you see: as.data.frame() as.matrix() as.vector() matrix() > x a b c 1 1 2 3 2 1 2 3 3 2 3 4 4 3 4 5 > is.data.frame(x) [1] TRUE > as.matrix(x) a b c 1 1 2 3 2 1 2 3 3 2 3 4 4 3 4 5 > y<-as.matrix(x) > is.matrix(y) [1] TRUE > as.vector(y) [1] 1 1 2 3 2 2 3 4 3 3 4 5 > z<-as.vector(y) > m<-matrix(z,ncol=3) > m [,1] [,2] [,3] [1,] 1 2

Hi, see these links: http://www.liacc.up.pt/~ltorgo/DataMiningWithR/ http://sawww.epfl.ch/SIC/SA/publications/FI01/fi-sp-1/sp-1-page45.html Brian D. Ripley, Datamining: Large Databases and Methods, in Proceedings of "useR! 2004 - The R User Conference", may 2004 http://www.ci.tuwien.ac.at/Conferences/useR-2004/Keynotes/Ripley.pdf looking for a book I suggest: Trevor Hastie , Robert

Hi, Attention chi-squared distribution, unlike F distribution, has only df1 as parameter, not df1 and df2. So correct into: outer(1:3, 1:3, function(df1, df2) qchisq(0.95, df1, df2)) outer(1:3, 1:3, function(df1, df2) qchisq(0.95, df1)) ^^^^^^^^^^^^^^^^^^^^ Regards, Vito you wrote: Dear Rs: outer(1:3, 1:3, function(df1, df2) qf(0.95, df1, df2)) I compare this F