search for: ltsreg

Displaying 10 results from an estimated 10 matches for "ltsreg".

2003 Jun 18
1
Ltsreg and nsamp="exact"
I'm trying to use least trimmed squares using ltsreg with nsamp="exact". When I use the following: rg <- ltsreg(x,y,nsamp="exact") I get: Error in lqs.default(x, y, nsamp = "exact", method = "lts") : NAs in foreign function call (arg 10) In addition: Warning message: NAs introduced by coercion In...
2001 Nov 29
0
ltsreg warnings (PR#1184)
Full_Name: Charles J. Geyer Version: 1.3.1 OS: linux-gnu-i686 Submission from: (NULL) (134.84.86.22) ltsreg gives incomprehensible (to me) warnings A homework problem for nonparametrics ########## start example ########## library(bootstrap) data(cell) names(cell) attach(cell) library(lqs) plot(V1, V2) fred <- ltsreg(V2 ~ V1 + I(V1^2)) curve(predict(fred, data.frame(V1 = x)), add = TRUE) coefficient...
2005 Sep 01
0
Robust Regression - LTS
Hi, I am using robust regression, i.e. model.robust<-ltsreg(MXD~ORR,data=DATA). My question:- is there any way to determine the Robust Multiple R-Squared (as returned in the summary output in splus)? I found an equivalent model in the rrcov package which included R-square, residuals etc in it's list of components, but when I used this package the only r...
1998 Apr 17
2
R-beta: lmsreg
Does R have a function like the S(plus) function, lmsreg, Least Median of Squares Regression? I am using R-0.61. Thank you, Mike Fleming mfleming at nass.usda.gov -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or
1998 Apr 17
2
R-beta: lmsreg
Does R have a function like the S(plus) function, lmsreg, Least Median of Squares Regression? I am using R-0.61. Thank you, Mike Fleming mfleming at nass.usda.gov -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or
2002 Jun 03
1
LTS
Hello I want to ask if the estimator method LTS (Least Trimmed Squares) is implemented in R. I've found the lqs(y~x,method = c("lts")) tool that implements LTS but minimazing the sum of the `quantile' smallest squared residuals. I don't know if this is the same as the clasical LTS, if it is, where do I set the trim (h value to trim the LS sum)? I'll be waiting
2004 May 21
2
Help with Plotting Function
Dear List: I cannot seem to find a way to plot my data correctly. I have a small data frame with 6 total variables (x_1 ... x_6). I am trying to plot x_1 against x_2 and x_3. I have tried plot(x_2, x_1) #obviously works fine plot(x_3, x_1, add=TRUE) # Does not work. I keep getting error messages. I would also like to add ablines to this plot. I have experimented with a number of other
1998 Aug 31
0
Packages aov, modreg, lqs, psplines
...tent of the loess functionality. lqs.tar.gz: ========== cov.rob Robust Estimation of Multivariate Location and Scatter lqs Resistant Regression by Least Trimmed and Least Quantile Sum of Squares lmsreg, ltsreg, cov.mve Compatibility functions This is much less complete (and the claimed mcd method is not yet operational). Comments please both on the design and from any experts out there on the methodology used. (BTW, as all the programs I have give different answers, it is very hard to establish the tr...
2012 Jan 23
1
R not giving significance tests for coefficients/estimates?
> 3x4 Error: unexpected symbol in "3x4" R has no idea that you equate "x" as multiplication.. use an astrix > 3*4 [1] 12 dominic wrote > > This is basically my code: > > library(MASS) > lmsreg(formula = b0 ~ b1 + b3 + b1xb2, data=mydata) > > b1xb2 is an interaction but it was the centered value for a continuous > variable times a
2003 Dec 03
5
add a point to regression line and cook's distance
Hi, This is more a statistics question rather than R question. But I thought people on this list may have some pointers. MY question is like the following: I would like to have a robust regression line. The data I have are mostly clustered around a small range. So the regression line tend to be influenced strongly by outlier points (with large cook's distance). From the application 's