similar to: data format of x in x <- sqlQuery()

Hello, Attached is a mail regarding question how to insert Date field using ROracle package. I am stuck with this problem and appreciate receiving help from gurus on this list. Code used mainly is: library(ROracle) ### --- Version 0.53 drv <- dbDriver("Oracle") con <- dbConnect( drv, "user/passwd") d <- data.frame(CDATE = "2004-03-10 10:12:00") ps

I am trying to compute the periodicity of a time series. I would like to know which function in R does it. Also, how do I plot a Enright / Chi-square periodogram using R ? ( Enright, J.T., 1965, Journal of Theoret. Biol. 8,426-468) Greatly appreciate your help. Thanks in advance, Sukhaswami Malladi *************************************************************************** The contents

Dear List, What is the way in R to plot a graph having date time (e.g. format 12/31/2002 13:12) in the x axis and numerical value (e.g. temperature) on the Y-axis ? I could not get 'plot()' to do this as X axis values are non-numeric. 'bwplot()' sorts the date values and thereby changes the order of the dates and of Y. The date/time values have to be shown on the X-axis as tick

R-help list, I ported some R code from MS Windows PC to sun-solaris. Both do queries on the same MySQL database. PC version of R is 1.6.1 and solaris version is 1.6.0. I use RMySQL_0.5-0 (on solaris) and RODBC 1.0-1 on PC to connect to MySQL. The PC version took about 65 mins. to run wheras the Solaris (SunOS 5.7) version took 375 mins. (> 6 hrs). R and RMySQL are resident on the same host

Full_Name: Jerome Asselin Version: 1.7.1 OS: Red Hat Linux 7.2 Submission from: (NULL) (24.77.125.119) Setting the parameter na.action=na.omit should remove incomplete records in princomp. However this does not seem to work as expected. See example below. Sincerely, Jerome Asselin data(USArrests) princomp(USArrests, cor = TRUE) #THIS WORKS USArrests[1,3] <- NA princomp(USArrests, cor =

Peter, As I understand your Q. You probably have data that is similar to each other like stock Prices for all RHS variable. In that case the difference between corr and cov is not significant; however, if your RHS contains totally dissimilar variables it matters a great deal. If x1 income, x2 job type, x3 Education level, etc..., then taking cov of these variables would not be desireable

Dear R-helpers, Using the package Lattice, I performed a PCA. For example pca.summary <- summary(pc.cr <- princomp(USArrests, cor = TRUE)) The Output of "pca.summary" looks as follows: Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Standard deviation 1.5748783 0.9948694 0.5971291 0.41644938 Proportion of Variance 0.6200604

Hi, I run the example for princomp for R211 I got the following error for biplot > ## The variances of the variables in the > ## USArrests data vary by orders of magnitude, so scaling is appropriate > (pc.cr <http://pc.cr> <- princomp(USArrests)) # inappropriate Erreur dans cov.wt(z) : 'x' must contain finite values only > princomp(USArrests, cor = TRUE) # =^=

Hi All, would appreciate an answer on this if you have a moment; Is there a function (before I try and write it !) that allows the input of a covariance or correlation matrix to calculate PCA, rather than the actual data as in princomp() Regards Glenn [[alternative HTML version deleted]]

I'm attempting to modify how biplot draws its red vectors (among other things). This is how I've started: Biplot <- function(xx, comps = c(1, 2), cex = c(.6, .4)) { ## Purpose: Makes a biplot with princomp() object to not show arrows ## ---------------------------------------------------------------------- ## Arguments: xx is an object made using princomp() ##

Dear list, I want to perform a biplot, using customized titels for the x and y axis. Setting xlab="" and ylab="" resulted in an error, e.g.: > data(USArrests) > biplot(princomp(USArrests),xlab="",ylab="") Error in biplot.default(t(t(scores[, choices])/lam), t(t(x$loadings[, : length of dimnames[1] not equal to array extent > How do I

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

Dear R users, When I tried to use princomp() from stats packages to do Principal Components Analysis, I am not very clear what is the "scale". And the scores are different from "PROC PRINCOMP" procedure from SAS. Using the example data from this package: restpc <- princomp(USArrests, cor = TRUE) > restpc$scale Murder Assault UrbanPop Rape 4.311735 82.500075

Hi ! I've used the example given in the documentation for the prcomp function both in R and SPAD to compare the results obtained. Surprisingly, I do not obtain the same results for the coordinates of the principal composantes with these two softwares. using USArrests data I obtain with R : > summary(prcomp(USArrests)) Importance of components: PC1 PC2

Full_Name: Hinrich Göhlmann Version: 1.4.1 OS: Windows 2000 Submission from: (NULL) (148.177.130.121) Hi all, I've searched the known bugs and hopefully I did not oversee this one... When I have done a normal zz<-princomp(na.omit(raw.data)) and I then draw a biplot with biplot(zz,cex=c(.1,.7),col=c(5,1),choice=c(1,2),xlim=c(-2,2),ylim=c(-5,2)) the x axis gets set properly, but the y

Which is the parameter that is used to decrease the size of ylabs plotted in biplot? I tried playing with cex and cex.lab I am not getting it right pc <- princomp(USArrests) biplot(pc, xlabs = rep("", nrow(USArrests)),ylabs=(colnames(USArrests))) Thanks../Murli [[alternative HTML version deleted]]

In comparing the results of princomp and prcomp I find: 1. The reported standard deviations are similar but about 1% from each other, which seems well above round-off error. 2. princomp returns what I understand are variances and cumulative variances accounted for by each principal component which are all equal. "SS loadings" is always 1. 3. Same happens

Dear listserv, I am trying to perform a principal components analysis and create an output table of the eigenvalues for the dependent variables. What I want is to see which variables are driving each principal components axis, so I can make statements like, "PC1 mostly refers to seed size" or something like that. For instance, if I try the example from ?prcomp > prcomp(USArrests,

Hi all, I am using the vegan package to run a prcincipal components analysis on forest structural variables (tree density, basal area, average height, regeneration density) in R. However, I could not find out how to extract factor loadings (correlations of each variable with each pca axis), as is straightforwar in princomp. Do anyone know how to do that? Moreover, do anyone knows

Hello fellow Rers, I have a no-doubt simple question which is turning into a headache so would be grateful for any help. I want to do a principal components analysis directly on a correlation matrix object rather than inputting the raw data (and specifying cor = TRUE or the like). The reason behind this is I need to use polychoric correlation coefficients calculated with John Fox's