similar to: linear regression and t-distribution

R-experts, I have fitted many different lines. The fast-tau estimator (yellow line) seems strange to me?because this yellow line is not at all in agreement with the other lines (reverse slope, I mean the yellow line has a positive slope and the other ones have negative slope). Is there something wrong in my R code ? Is it because the Y variable is 1 vector and should be a matrix ? Here is the

On 31/03/2018 11:57 AM, varin sacha via R-help wrote: > Dear R-experts, > > Here below my reproducible R code. I want to add many straight lines to a plot using "abline" > The last fit (fast Tau-estimator, color yellow) will not appear on the plot. What is going wrong ? > Many thanks for your reply. > It's not quite reproducible: you forgot the line to create

Dear R-experts, Here below my reproducible R code. I want to add many straight lines to a plot using "abline" The last fit (fast Tau-estimator, color yellow) will not appear on the plot. What is going wrong ? Many thanks for your reply. ########## Y=c(2,4,5,4,3,4,2,3,56,5,4,3,4,5,6,5,4,5,34,21,12,13,12,8,9,7,43,12,19,21)

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

Hi all, I have probably a basic question, but I can't seem to find the answer in the literature or in the R-archives. I would like to do a robust ANCOVA (using either rlm or lmRob of the MASS and robust packages) - my response variable deviates slightly from normal and I have some "outliers". The data consist of 2 factor variables and 3-5 covariates (fdepending on the model).

Dear All, When I run rlm to obtain robust standard errors, my output does not include p-values. Is there any reason p-values should not be used in this case? Is there an argument I could use in rlm so that the output does include p-values? Thanks in advance, Celso [[alternative HTML version deleted]]

Dear R users, I am trying to draw error bars in a bar plot, I use this code (tried many others which did not work): install.packages() library(gplots) y <-c(39.02, 46.42) se <- c(7.57,7.35) plot <- barplot(y, beside=TRUE, col=0, ylim=c(0,47), axis.lty=1, main="far-red", xlab="latitude", names.arg=c("56N", "68N")) superpose.eb(plot, y, se,

Hello I have a data set summarized like this: File name= Height Group Ind Age Trait 1 1 1 20 1 1 2 21 1 2 1 22 1 2 2 21 1 3 1 24 1 3 2 45 1 4 1 23 1 4 2 26 2 1 1 45 2 1 2 12 2 2 1 25 2 2 2 26 2 3 1 45 2 3 2 43 2 4 1 23 2 4 2 47 . . . I would like to plot Trait ~ Age but a different plot for each Group. I tried: > plot(Height$Trait ~ Height$Age | Group) But does not work. Any

Hello I am trying to draw a basic black and white map of two European countries. After searching some key words in google and reading many pages I arrived to the conclusion that persp() could be used to draw that map. I have prepared three small example files, which are supposed to be the files required for running that function. xvector is a vector with the longitudes yvector is a vector with

Thanks for your reply. I use mvrnorm from the *MASS* package and lmrob from the *robustbase* package. To further explain my data generating process, the idea is as follows. The explanatory variables are generated my a multivariate normal distribution where the covariance matrix of the variables is defined by Sigma in my code, with ones on the diagonal and rho = 0.15 on the non-diagonal. Then y

Hello I am trying to get out of an lm model the fstatistics, however after I run the model I write > names(Model) and the fstatistic does not appear only these. names(Model) [1] "coefficients" "residuals" "effects" "rank" "fitted.values" [6] "assign" "qr" "df.residual"

What is 'd'? What is 'n'? On Sun, Mar 4, 2018 at 12:14 PM, Christien Kerbert < christienkerbert at gmail.com> wrote: > Thanks for your reply. > > I use mvrnorm from the *MASS* package and lmrob from the *robustbase* > package. > > To further explain my data generating process, the idea is as follows. The > explanatory variables are generated my a

Hello I have a model with several hundred Y variables, and also several 1000 X variables. The model is linear lm(Y ~ X). My questions are: 1.- how to avoid writing all Xs variables? is list() the right function? 2.- about the multiple Ys with dependence among some of them, how to incorporate that information in the linear model? Thank you Rosario

Hello I have a annoying problem with R (which I am running in MAC). Every time I enter a function and I press enter to run it, I get many additional "<?s", it is really bothering me. I have tried to google to find out how to fix the problem but I did not succeed. Anyone with similar experience that could help me to change the set ups of the program or something. Thanks in advance

Hello I seem to find only two types of rotation for the factanal function in R, the Varimax and Promax, but is it possible to run a orthogonal and oblique rotations in R? Thanks in advance Rosario

This message also reports wrong estimates produced by lmRob.fit.compute() for nested factors when using the correct contrast matrix. And in these respects, I have found that S-Plus behaves the same way as R. Using the three available contrast types (sum, treatment, helmert) with lm() or lm.fit(), but just contr.sum with rlm() and lmRob(), and small examples, I generated contrast matrices for

Hello I have a problem on keeping the format when I export a matrix file with the write.table() function. When I import the data volcano from rgl package it looks like this in R: > data[1:5,] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [1,] 100 100 101 101 101 101 101 100 100 100 101 101 102 102 [2,] 101 101 102 102 102 102 102

What would be nice is to have something like a "robust" task view... Andy From: Berton Gunter > > Many thanks for this Martin. There now are several packages > with what appear to be overlapping functions (or at least > algorithms). Besides those you mentioned, "robust" and > "roblm" are at least two others. Any recommendations about > how or

Dear list members, I want to perform an MM-regression. This seems an easy task using the function lmrob(), however, this function provides me with NA coefficients. My data generating process is as follows: rho <- 0.15 # low interdependency Sigma <- matrix(rho, d, d); diag(Sigma) <- 1 x.clean <- mvrnorm(n, rep(0,d), Sigma) beta <- c(1.0, 2.0, 3.0, 4.0) error <- rnorm(n = n,

d is the number of observed variables (d = 3 in this example). n is the number of observations. 2018-03-04 11:30 GMT+01:00 Eric Berger <ericjberger at gmail.com>: > What is 'd'? What is 'n'? > > > On Sun, Mar 4, 2018 at 12:14 PM, Christien Kerbert < > christienkerbert at gmail.com> wrote: > >> Thanks for your reply. >> >> I use