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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

Dear List, I'm just teaching myself semi-parametric techniques. Apologies in advance for the long post. I've got observational data and a longitudinal, semi-parametric model that I want to fit in GAM (or potentially something equivalent), and I'm not sure how to do it. I'm posting this to ask whether it is possible to do what I want to do using "canned" commands

Dear all, I have a problem when using some classification functions (Kmeans, PAM, FANNY...) with a distance matrix, and i would to understand how it proceeds for the positioning of centroids after one execution step. In fact, in the classical formulation of the algorithm, after each step, to re-position the center, it calculates the distance between any elements of the old cluster and its

I found the article the "Y of R" in the latest R news to be very interesting. It is certainly challenging me to learn more about how R works "under the hood" as the author states. What is less clear to me is whether this approach is primarily for teaching purposes or has a real world application. What is meant by "fragility of reliance on the function name defined as a

Dear All: I need to have some data frame objects. First aa object: pH Formulation time Subject [1]1.2 F 0 1 [2]7.4 S 1 2 [3] MF 2 3 [4] 3 4 [5] n i Then, I need to produce 2*3(pH*formulation) different tables. This table includes column of (pH, Formulation, time S1 S2 S3 ?K..Si) and S1=

I am interested in comparing the fit of robust (i.e., S and MM) and non-robust (i.e., OLS) estimators when applied to a particular data set. The paper entitled "A comparison of robust versions of the AIC based on M, S and MM-estimators" (available at: http://ideas.repec.org/p/ner/leuven/urnhdl123456789-274771.html) presents formulas for robust Akaike information criteria (AIC) for the M,

Hi, I have a list of numbers. For each of the numbers, I take sum of squares of the numbers centered on the number chosen. If it is less than a certain constant, I will take the average of the numbers chosen. Anyone can give me a sample code. You help will be greatly appreciated. Peter [[alternative HTML version deleted]]

Good day fellow R users: I have routinely received the following message when attempting to estimate a GMM model for a somewhat square panel (N = 20, T = 9-27, Obs = 338) using the pgmm function in the plm package: Error in function (..., deparse.level = 1) : number of rows of matrices must match (see arg 2) So far, I am not wedded to a particular GMM model but what I have used thus far is

Dear all: I am a new R-user and I have 2 questions about it. 1) I have a dataframe. Based on ?formulation? and ?subject?, a dataframe is split into 4 dataframes. The example is as follows. Moreover, I want to calculate ?test? value for these 4 dataframes. My question is that the ?test? values not correct and I do not know where the problem is. 2) There are 12 ?test? (y) values from

You need to pay attention to the documentation more closely. If you don't know what something means, that is usually a signal that you need to study more... in this case about the difference between an input variable and a design (model) matrix. This is a concept from the standard linear algebra formulation for regression equations. (Note that I have never used RobPer, nor do I regularly

Dear All   I know that you do not have to help me (as this is not a pure R problem) but please do, i am new to R as a CPI compiler, i just need to do a sample to see which sampling method best works in different situations, therefore since this is for practice purposes nobody will finance a real project thats why i need you to help me direct me as to how simulate data (just direct me,not 100%

Dear All   I know that you do not have to help me (as this is not a pure R problem) but please do, i am new to R as a CPI compiler, i just need to do a sample to see which sampling method best works in different situations, therefore since this is for practice purposes nobody will finance a real project thats why i need you to help me direct me as to how simulate data (just direct me,not 100%

Hello, Does somebody could help me in the computation(formulation) of a hierarchical ANOVA using linear model in R? I'm working in a population biology study of an endangered species. My aim is to see if I have effects of "Density of individuals/m2" on several measured plants fitness traits. As independent variables I have: Humidity (continuous) Nutritive substance (continuous)

I have some data on a moving vehicle where, amongst other things, it looks as though it would be informative to fit a model with the following structure: Z = B.Y + errorz Y = C.X + errorz The X variables are observed predictor variables; 6 of the variables look promising (on the basis of what they mean). The Z variables are observed response variables; there are 4 of them. There is a

subject: trimmed mean in R seems to round the trimming fraction to r-help at stat.math.ethz.ch. Consider the following example of 10 numbers. 10% trimmed mean is correct but you can see that the result is the same for many trimming fractions till 0.20! For example 13% trimmed mean should use interpolation of second and eighth ordered observation. R does not seem to do this. The correct 13%

Users are often surprised and alarmed that the summary of a linear mixed model fit by lmer provides estimates of the fixed-effects parameters, standard errors for these parameters and a t-ratio but no p-values. Similarly the output from anova applied to a single lmer model provides the sequential sums of squares for the terms in the fixed-effects specification and the corresponding numerator

----- Original Message ----- From: "Richard A. O'Keefe" <ok at cs.otago.ac.nz> To: <paul at datavore.com> Sent: Thursday, September 04, 2003 2:56 AM Subject: Re: [R] Overlaying graphs > I do not know how to overlay the curve graphic on top of hist graphic. > > Do you know about the "add=TRUE" option for plot()? > > I am hoping to show visually

On 06/10/2010 08:16 PM, Chris Pearce wrote: > I have no plans to change the 'index' packet format further. I have proposed an alternative formulation of the index packet at http://github.com/bemasc/OggIndex/blob/master/Proposed-modified-spec.txt That repository also contains a working implementation of the alternative formulation. I have reviewed the details with Chris extensively.

On 11/06/2010 1:06 p.m., Benjamin M. Schwartz wrote: > On 06/10/2010 08:16 PM, Chris Pearce wrote: >> I have no plans to change the 'index' packet format further. > I have proposed an alternative formulation of the index packet at > > http://github.com/bemasc/OggIndex/blob/master/Proposed-modified-spec.txt > > That repository also contains a working implementation

I asked: > In this discussion of seq(), can anyone explain to > me _why_ seq(to=n) and seq(length=3) have different > types? Martin Maechler <maechler@stat.math.ethz.ch> replied: well, the explantion isn't hard: look at seq.default :-) That's the "efficient cause", I was after the "final cause". That is, I wasn't asking "what is it