similar to: Predict(); Warning rank deficient matrix

dear R users, I have fit the lm() on a mtrix of responses. i.e M1 = lm(cbind(R1,R2)~ X+Y+0). When i use summary(M1), it shows details for R1 and R2 separately. Now i want to use stepAIC on these models. But when i use stepAIC(M1) an error message comes saying that dropterm.mlm is not implemented. What is the way out to use stepAIC in such cases. regards,

Hi, I have encountered this error when attempting a One-way Repeated-measure ANOVA with my data. I have read the "Anova in car: SSPE apparently deficient rank" thread by I'm not sure the within-subject interaction has more degrees of freedom than subjects in my case. I have prepared the following testing script: rm(list = ls())

Dear: I want to construct a gee model in R. When I ran the program, there was a warning in the output. The warning is "Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27 gee(cbind(hyper, nohyper) ~ I(Ethnic) + I(Gender) + I(drink) + : rank-deficient model matrix" What is the rank-deficient model matrix? What should I do to

Take the following example: a <- rnorm(100) b <- trunc(3*runif(100)) g <- factor(trunc(4*runif(100)),labels=c('A','B','C','D')) y <- rnorm(100) + a + (b+1) * (unclass(g)+2) m <- lm(y~a+b*g) summary(m) Here b is discrete but not treated as a factor. I am interested in computing the effect of b within groups defined by the

I have design with two repeated-measures factor, and no grouping factor. I can analyze the dataset successfully in other software, including my legacy DOS version BMDP, and R's 'aov' function. I would like to use 'Anova' in 'car' in order to obtain the sphericity tests and the H-F corrected p-values. I do not believe the data are truly deficient in rank. I

Hi everyone. I am a new user of R so thanks for your help! I'm running a regression and receive an error that states that the "residuals have rank 45 < 169." What does this mean? Thanks! -- View this message in context: http://n4.nabble.com/deficient-rank-question-tp1562644p1562644.html Sent from the R help mailing list archive at Nabble.com.

Dear R-devel, It seems that lsfit incorrectly reports coefficients when the input matrix 'x' is rank-deficient, see the example below: ## here values of 'b' and 'c' are incorrectly swapped > x <- cbind(a=rnorm(100), b=0, c=rnorm(100)); y <- rnorm(100); lsfit(x, y)$coef Intercept a b c -0.0227787 0.1042860 -0.1729261 0.0000000 Warning

Hello all, I am getting the following error: Error in linear.hypothesis.mlm(mod, hyp.matrix.1, SSPE = SSPE, V = V, : The error SSP matrix is apparently of deficient rank = 7 < 11 After running: mod.ok <- lm(as.matrix(dat[,-1]) ~ DC, data=dat) (av.ok <- Anova(mod.ok, idata=idata, idesign=~week)) Although if I jitter the data in "dat", the function seems to work. What

Dear all, I would like to fit an lm in which a subset of the explanatory variables are linearly dependent. Thus I would like to include the restriction that all betas of these variables sum to 1. Is there a way to this in R? Or is this what happens automatically if I set singular.ok= T (which is the default, I believe)? Thanks for your help. Lorenz --

Dera R users, I have written a function which computes variance, sd, r^2, R^2adj etc. But i am not able to return all of them in return statement. So how to return more than one variable from function. In C i used to return by pointers etc. is there any way like that. Thanks in advance.

Dear list, When I use the predict.glm method on a glm fitted object, I get the following warning message: In addition: Warning message: > In predict.lm(object, x) : > prediction from a rank-deficient fit may be misleading As the documentation says this happens if "the fit is rank-deficient, some of the columns of the design matrix will have been dropped". It is my

I am interested in Neural network models in R. Is there any reference material/tutorial which i can use. Regards, ____________________________________________________________________________________ TV dinner still cooling? Check out "Tonight's Picks" on Yahoo! TV.

Author: cth Repository: /hg/zfs-crypto/gate Revision: 8c3216f8ae4cd2c699db490d2a79406f3c420d36 Log message: 6308380 e_ddi_majorinstance_to_path implementation deficient for /devices/pseudo paths Files: update: usr/src/uts/common/os/sunddi.c

I want to plot variation of more than one variable in single plot with different point types and points sizes . Can someone help me to do that, Thanks in advance. vinod ____________________________________________________________________________________ Now that's room service! Choose from over 150,000 hotels

Hello all, I've run into a problem where I can't run predict.lme on an object simplified via a stepAIC. A similar post has been recorded on this list: https://stat.ethz.ch/pipermail/r-help/2008-May/162047.html but in my case, I'm going to great lengths to not repeat that poster's error and still coming up short. Any advice would be much appreciated. It would seem that, after

A recent article in the earth science literature cited below and available at http://geography.uoregon.edu/datagraphics/EOS/ points out that rainbow color schemes and mixtures of green and yellow can be troublesome for people with color-deficient vision. The authors propose alternative schemes that can be viewed and downloaded in RGB, HSV, CMYK, and RGB256 formats from

Dear R-users; Previously I posted a question about the problem of rank deficiency in summary.manova. As somebody suggested, I'm attaching a small part of the data set. #*************************************************** "test" <- structure(.Data = list(structure(.Data = c(rep(1,3),rep(2,18),rep(3,10)), levels = c("1", "2", "3"), class =

I'm using stepAIC to fit a model. Then I'm trying to use that model to predict future happenings. My first few variables are labeled as their column. (Is this a problem?) The dataframe that I use to build the model is the same as the data I'm using to predict with. Here is a portion of what is happening.. This is the value it is predicting = > [1] 9.482975 Summary of the

Hi, stepAIC generic plot function creates useful graphics for the diagnosis of multiple regressions. To create predicted versus observed plots, I use to look for the coefficients, copy them by hand, calculate R?, then plot. Is there a more automated way to plot predicted versus observed with its associated R? output using stepAIC, or another function? Kind regards, S.-?. Parent Universit?

Hi all, I have received the following error from summary.manova: Error in summary.manova(manova.test, test = "Pillai") : residuals have rank 36 < 64 The data is simulated data for 64 variables. The design is a 2*2 factorial with 10 replicates per treatment. Looking at the code for summary.manova, the error involves a problem with qr(). Does anyone have a suggestion as to how to