similar to: Bug on Mac version of lm()?

Hello, I'm doing some linear regression: >lm<-lm(osas~alp,data) >summary(lm) However, the Rsquared in the output of summary() is not the same as the "standard" Rsquared calculated by spreadsheets, and outlined in statistical guidebooks, being SSR/SSTO. The output says "multiple Rsquared", but it is no multiple regression... What's the difference? Thanks,

Hello, Is there any simple command to write the data.frame to the file in the format of command line input? For example, I read a table as > data <- read.table() and want to write out the 'data' object into my R file as data <- data.frame( some.column1=c( 1, 2 ), some.column2=c( 1, 2 ) row.names=c("row name1",

How can I extract the adjusted R^2 value from an ols object (using rms package)? library(rms) x <- rnorm(10) y <- x + rnorm(10) ols1 <- ols(y ~ x) Typing "ols1" displays adjusted R^2 among other things, but how can I assign it to a variable? I tried str(ols1) but couldn't see where to go from there. Thanks, Mark Seeto

Dear R-help, In the rms package, I have fitted an ols model with a variable represented as a restricted cubic spline, with the knot locations specified as a previously defined vector. When I save the model object and open it in another workspace which does not contain the vector of knot locations, I get an error message if I try to predict with that model. This also happens if only one workspace

Using hccm() I got a heteroscedasticity correction factor on the diagonal of the return matrix, but I don't know how to incorporate this into my linear model: METHOD 1: > OLS1 <- lm(formula=uer92~uer+low2+mlo+spec+degree+hit) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0623377 0.0323461 -1.927 0.057217 . uer 0.2274742 0.0758720

Dear friends i am trying to fit an Ornstein-Uhlenbeck process by MAXIMUM LIKELYHOOD method. i found these formulas on http://www.sitmo.com/article/calibrating-the-ornstein-uhlenbeck-model/ this is the mean-reverting process http://r.789695.n4.nabble.com/file/n4637271/process.txt process.txt and this is the script that i am using....... ouFit.ML=function(spread) { n=length(spread)

Hello ALL! I have a problem to plot factor (lets say gender) as a line, or at least both line and point, from ols model: ols1 <- ols(Y ~ gender, data=dat, x=T, y=T) plot(ols1, gender=NA, xlab="gender", ylab="Y", ylim=c(5,30), conf.int=FALSE) If I convert gender into discrete numeric predictor, and use forceLines=TRUE, plot is not nice and true, since it shows values

Dear all: I'm trying to fit the optimal Box-Cox transformation related to nls (see the code below) for the demand of money data in Green (3th Edition) but in the last step R gives the next error message. Error en `[.data.frame`(eval(object$data), , as.character(formula(object)[[2]])[2]) : undefined columns selected. ?Any idea to solve the problem? Thanks in advance,

I get this error while computing partial correlation. *Error in solve.default(Szz) : system is computationally singular: reciprocal condition number = 4.90109e-18* Why is it?Can anyone give me some idea ,how do i get rid it it? This is the function i use for calculating partial correlation. pcor.mat <- function(x,y,z,method="p",na.rm=T){ x <- c(x) y <- c(y)

Hi All, This is my R-version information:--- > version _ platform i486-pc-linux-gnu arch i486 os linux-gnu system i486, linux-gnu status major 2 minor 7.1 year 2008 month 06 day 23 svn rev 45970 language R version.string R version 2.7.1 (2008-06-23) While calculating partial

Hi I just upgraded to http://svn.xiph.org/trunk/speex r10357 and got this build warning: alfredh@io:$ make -s mdf.o libspeex/mdf.c: In function 'speex_echo_cancel': libspeex/mdf.c:321: warning: statement with no effect libspeex/mdf.c:317: warning: `adapt_rate' might be used uninitialized in this function Is this intentional? In any case here is a simple fix: Index:

----- Forwarded Message ----- From: vramaiah at neo.tamu.edu To: "bernhard pfaff" <bernhard.pfaff at pfaffikus.de> Sent: Friday, November 11, 2011 9:03:11 AM GMT -06:00 US/Canada Central Subject: Use of R for VECM Hello Fellow R'ers I am a new user of R and I am applying it for solving Bi-Variate (Consumption and Output) VECM with Co-Integration (I(1)) with three lags on

Below is a version of [.data.frame that is faster for subscripting rows of large data frames; it avoids calling duplicated(rows) if there is no need to check for duplicate row names, when: i is logical attr(x, "dup.row.names") is not NULL (S+ compatibility) i is numeric and negative i is strictly increasing "[.data.frame" <- function (x, i, j,

I've been reading the R mail archives and I've found a lot of messages with this same kind of problem, but I can't understand the answers. Can one of you try to explain this to me? Here's my example. Given a regression model and a variable, I want to use unstack() on the vector of residuals and make some magic with the result. But unstack hates me. PCSE <- function

Hello, I wanted to set my own time intervals for shutdown. {poweroff,return,paused.return}, so I had to change sources, where it has been set in a hard way. poweroff: Sxx\r return: Zxx\r paused.return: SxxRyyyy\r Solution A: Why not adding an info about these commands in a generic way and issuing shutdown.{poweroff.xx,return.xx,paused.return.xxyyyy} Solution B: Why not creating a special

Full_Name: Marek Ancukiewicz Version: 2.01 OS: Linux Submission from: (NULL) (132.183.12.87) It seems that the the standard error of prediction of the linear regression, caclulated with predict.lm is incorrect. Consider the following example where the standard error is first calculated with predict.lm, then using delta method. and finally, using the formula rms*sqrt(1+1/n+(xp-x0)^2/Sxx). Marek

Why do we get Partial correlation values greater than 1? I have used the default function pcor.mat :-- I have manipulated the default pcor.mat function a bit so ignore tha variables corr_type,element1_in_no,element2_in_no,P.Please ignore the ?pairwise? section and have a look at athe ?listwise ? part i.e else part. *pcor.mat <-

(I'm sending to r-bugs because rpart is one of the recommended packages and is always installed. I'm also sending it directly to Dr. Ripley, as the maintainer.) rpart working as a classifier does not work (produces no splits) when the class indicator has no instances of one of the factor levels, as long as the factor level is not the final level. I have at least a partial fix, which I

Hi, anybody have any ideas on this ? I have two sequences of proportions. Due to conventions in my field, I need to produce a linear correlation between the two. Each sequence of proportions are based upon differing numbers of observations (although within a sequence the number of observations may not fluctuate too much) so it may be necessary/advisable to variance stabilize. Is their a best

On Wed, 13 Dec 2023 09:04:18 +0100 Hilmar Berger via R-devel <r-devel at r-project.org> wrote: > Still, I feel that default partial matching cripples the functionality > of data.frame for larger tables. Changing the default now would require a long deprecation cycle to give everyone who uses `[.data.frame` and relies on partial matching (whether they know it or not) enough time to