similar to: lattice plot axis scaling

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

Hello, I have a simple xyplot with rlm lines. I would like to add the a and b coefficients (y=ax+b) of the rlm calculation in each panel. I know I can do it 'outside' the xyplot command but I would like to do all at the same time. I found some posts with the same question, but no answer. Is it impossible ? Thanks in advance for your help. Ptit Bleu. x11(15,12) xyplot(df1$col2 ~

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)

Hi, I would appreciate of someone could explain how the residual standard error is computed for rlm models (MASS package). Usually, one would expect to get the residual standard error by > sqrt(sum((y-fitted(fm))^2)/(n-2)) where y is the response, fm a linear model with an intercept and slope for x and n the number of observations. This does not seem to work for rlm models and I am wondering

Dear List, Some independent variable were missing in calculation using lm and glm (gaussian). (X= Y1+Y2+…..+Y16, Independent number: 16 variable) However, those variables did work well in cor(X, Y) respectively. str(dataframe) was also run to ensure that the variables were all numbers. Moreover, the missing variables were different in lm and glm. In lm, 3 factors were not taken into

Dear list, I have encountered a special case for searching a linear regression where I'm not satisfied with the results obtained using the traditional least squares method (sometimes called OLS) for estimating/optimizing the residues to the regression line (see code below). Basically, a group of my x-y data are a bit off the diagonal line (in my case the diagonal represents the ideal or

Dear List, I tried to do cca based on species data and environmental variables (formula instead of community data). However, there was an error saying row sums must be >0. I searched the previous related messages but found few solutions. Please kindly help and thank you in advance. code This is vegan 1.17-3 Warning message: package 'vegan' was built under R version 2.10.1

How do I add to a trellis plot the best fit line from a robust fit? I can use panel.lm to add a least squares fit, but there is no panel.rlm function. -- Alan S Barnett <asb at mail.nih.gov> NIMH/CBDB

Hi - How can I add different notes in different panels? My data looks like ID Dose Visit Value 1 1 0 0.5 -6.5802e-02 2 1 0 1.0 2.4085e-01 3 1 0 1.5 -2.2907e-01 4 1 0 2.0 2.4074e-01 ... ... ... ... ... 270 45 30 3.0 -8.1316e-01 271 46 30 0.5 -2.0786e-01 272 46 30 1.0 -2.9336e-01 273 46 30 1.5 -5.5657e-01 274 46 30 2.0 -2.1659e-01

Hello, I am using package MuMIn to calculate AIC for a full model with 10 explanatory variables. Thanks in advance in sharing your experience. Q1 In the AIC list of all models, each model is differentiated by model number. Please kindly advise if it is possible to find the corresponding explanatory variable(s) for the model number. Q2 error message I tried to display sub-model with only

Hello, I'm trying to do a little rlm of some data that looks like this: UNIT COHORT perdo adjodds 1010 96 0.39890 1.06894 1010 97 0.48113 1.57500 1010 98 0.36328 1.21498 1010 99 0.44391 1.38608 It works fine like this: rlm(perdo ~ COHORT, psi=psisquare) But the problem is that I have about 100 UNITs, and I want to do a

On Friday 17 September 2004 13:52, RenE J.V. Bertin wrote: > Hello again, > > I am doing regressions (using panel.lmline() (and panel.abline( > rlm(...))) ) inside a panel method which I pass to bwplot(). > > What I would like to do is create a boxplot of categorised data > (binned on the independent variable), and superpose a regression line > which is calculated using the

On Thu, 16 Sep 2004, RenE J.V. Bertin wrote: > Dear all, > > A few quick questions about interpreting and evaluating the results of > linear regressions, to which I hope equally quick answers are possible. > > 1) The summary.lm method prints the R and R^2 correlation coefficients > (something reviewers like to see). It works on glm objects and (after > tweaking it to

I'm using pairs() to generate a scatterplot matrix; pairs(~ Fuzzy.gray.white.ratio+Fuzzy.gw.t.score+AgeWhenTested+signal_mean.noise, data=datam,subset=status=="control",main="Controls", labels=c("G/W","Peak Separation","Age","S/N")) How can I add regression lines to the plots?

Hi, I'm trying to save heatmaps as PDFs. However, the PDF version of the heatmaps (Heatmap_CAFvsTNF_run2.pdf) is blurred when compared to its counterpart, which was saved manually by using the software "Grab" (Heatmap_CAFvsTNF_run2.tiff). -----R code-------- sample_output <- "stroma_run2" filename <-

Among others, datam contains the columns: logconc, tm, dose, subj, bilirubin. None of these are factor variables. The following compartment models work (the first still has not converged after 100 interations): res1 <- nlme(logconc~p2+p3+log(dose/(exp(p1)-exp(p2))* (exp(-exp(p2)*tm)-exp(-exp(p1)*tm))),start=list(fixed=c(5,-2,-0.1)), fixed=list(p1+p2+p3~1),control=list(maxIter=100),

Dear List, I used rlm to calculate two regression models for two data sets (rlm due to two outlying values in one of the data sets). Now I want to compare the two regression slopes. I came across some R-code of Spencer Graves in reply to a similar problem: http://www.mail-archive.com/r-help at stat.math.ethz.ch/msg06666.html The code was: > df1 <- data.frame(x=1:10, y=1:10+rnorm(10))

In R, I'm imported a data frame of 2,321,123 by 4 called "dataF". I converted the data frame "dataF" to a matrix dataM <- as.matrix(dataF) Does R have an efficient routine to treat the special elements that contain "inf" in them. For example, can you separate the rows that have "inf" elements from the matrix into a separate matrix without