search for: mass4

Hi, I had a look at the MASS4 scripts in the MASS package, in Ch 11.3 Factor Analysis, there is a section of codes like: data(ability.cov) ability.FA <- factanal(covmat = ability.cov, factors = 1) ability.FA (ability.FA <- update(ability.FA, factors = 2)) #summary(ability.FA) round(loadings(ability.FA) %*% t...

Playing around with examples from MASS4, I found a font problem in the mosaicplot in R-2.3.0. It doesn't happen in other plots. Running this example from MASS4, page 326... library(MASS) caith1 <- as.matrix(caith) names(dimnames(caith1)) <- c("eyes", "hair") mosaicplot(caith1, color = TRUE) ...I get an ima...

...inger, 1999. ISBN 0-387-98825-4. 2. The Fourth Edition of the book from point 1. 3. `S Programming' by W. N. Venables and B. D. Ripley Springer. ISBN 0-387-98966-8, 2000. I can only by one of the above books. Q1. I have found the following info at the site http://www.stats.ox.ac.uk/pub/MASS4/ : ''The material on programming has been reduced since the first and second editions'' Is that also true that The material on programming has been reduced since the THIRD edition? Q2. Which of the books 1 or 2 do you suggest to buy? I mean which one is more complete? May be t...

Hello. I must fit a logistic regression to data in the form of proportions, but in which some of the proportions are zero. I therefore cannot use the glm function with a binomial link since the link function is not defined for p=0 or 1. What other solutions are available? Any references to this specific problem (i.e. regression using proportions, of which some are zero) would be welcome.

> It might clarify your thinking to note that a seasonal ARIMA model > is just an ``ordinary'' ARIMA model with some coefficients > constrained to be 0 in an efficient way. E.g. a seasonal AR(1) s = > 4 model is the same as an ordinary (nonseasonal) AR(4) model with > coefficients theta_1, theta_2, and theta_3 constrained to be 0. You > can get the same answer as from

Hi I have been asked to provide Weibull parameters from a paper using Kaplan Meir survival analysis. This is something I am not familiar with. The survival analysis in R works nicely and is the same as commercial software (only the graphs are superior in R). The Weibull does not and produces an error (see below). Any ideas why this error should occur? My approach may be spurious.

Hi all, Please, while using image() which is the graphical parameter which control the space between ylab and the y axis? I do need to write a number of relatively long y labels and I am not able the control, if possible, this space. See the effect I need to avoid... http://nvx.environmentalchange.net/@rrodriguez/images/overlapping.jpg Thanks for your help, Ricardo -- Ricardo Rodr?guez Your

I apologize for this post. I am new to R (two days) and I have tried and tried to calculated confidence intervals for medians. Can someone help me? Here is my data: institution1 0.21 0.16 0.32 0.69 1.15 0.9 0.87 0.87 0.73 The first four observations compose group 1 and observations 5 through 9 compose group 2. I would like to create a bootstrapped 90% confidence interval on the difference of

Hi R-help members, I have read a lot in the Archive about the "Type I" vs "Type III" sum of square. I think I have read confusing post so I want to have a clear idea of the problem. Here is an example. I have 3 groups of subjects of unequal sample size (G1 (n=7), G2 (n=7), G3 (n=4)). for Each subject I have 4 measures corresponding to the crossing of 2 factor (A & B)

I cannot find the glmmNQ function in the MASS package (or anywhere else I have tried) mentioned on page 296 of MASS4. I would appreciate directions. Thanks -- Ross Darnell

Hi! I'm trying to use lm(y~x) amongst others in an automated way; I've gone through the section on indexing in R-lang and I've looked MASS4. How do I find out more about the structure of the returned object? In perl I can look at object structure pretty-printed in the debugger - is there an R equivalent? I've used coef(lm(y~x))[[1]] and coef(lm(y~x))[[2]] to extract the intercept; but while summary(lm(y~x)) prints R-squared......

I am trying to fit the chi-squared distribution to a set of data using the fitdistr function found in the MASS4 library, the data set is called ONES3, I have loaded it using the command ONES3<-read.table("ONES3.pdf",header=TRUE,na="NA") I print out the dataset ONES3 to the screen to make sure it has loaded Then I try to fit this data using the command fitdistr...

Hi all, I am using xyplot (lattice) to generate a figure like Figure 4.18 in MASS4, but I have the following two questions (1) how to change the font of x(y)lab? (2) how to plot the panels for each level of the conditional variable (a factor in my data set) in the order as occurred in the data.frame rather than in the order of the levels of the conditioning variable? Thanks...

Dear R helper, I hope that u can help me to sort out my problem because I sent an E-mail last night to R-list but I have not receive any help and at the same time I think this problem is not so hard. I have used the following functions before > K<-10 > prime<-c(2,3,5,7,11,13,17) > UN<-seq(1:K)%*%t(sqrt(prime)) > U1<-UN-as.integer(UN) > U<-matrix(qnorm(U1),K,7)

Hi, I'm doing a presentation on Neural Networks and Tree-Based Models in two weeks, at the moment I'm looking for a data set to use in the presentation. What I would like to use is a good old data, like the Iris data, that is already known by every statisticians. MASS4 uses the cpus data in Chapter 8.10 and the Cushing's syndrome in Chapter 12.4. These two data sets plus the Iris data I have mentioned make three possible candidate data sets. Does anyone has a good recommendation as to which data set is better? While I'm at it. Is it technically c...

Hello, I'm a newcomer to R so please forgive me if this is a silly question. It's that I have a linear regression: fm <- lm (x ~ y) and I want to test whether the slope of the regression is significantly less than 1. How can I do this in R? I'm also interested in comparing the slopes of two regressions: fm1 <- lm (x ~ y) fm2 <- lm (a ~ b) and asking if the slope of fm1 is

...ndard errors are close to those of the 4 other methods. Thanks to Douglas Bates, Saikat DebRoy for the revision, and to G?ran Brostr?m who run a simulation. In response to my first posting, Prof. B. Ripley wrote: ___ Although it has not been stated nor credited, this is very close to an example in MASS4 (there seems a difference in coding). Both the dataset and much of the alternative analyses are from the work of my student James McBroom (and other students have contributed). ____ Well, I thought having repeated "MASS" four times in the header of my submitted test programm was enough...

Dear all, Suppose I have a linear mixed-effects model (from the package nlme) with nested random effects (see below); how would I present the results from the random effects part in a publication? Specifically, I?d like to know: (1) What is the total variance of the random effects at each level? (2) How can I test the significance of the variance components? (3) Is there something like an

...e. However, > # it is weird for me that this is OK (if column y2 does not yet exist) > tmp["y2"] <- 2 > # but this is not > tmp[1:2, "y2"] <- 2 > > >>There is a lot of basic documentation on data manipulation in R/S, and a >>whole chapter in MASS4. Somehow most other people don't seem to find >>this a problem. > > > I just ordered MASS4 last week and I am eager to get it in my hands. In > meanwhile I read quite some documentation and what I more or less saw is > > tmp <- data.frame(y1=1:4, f1=factor(c(&quot...

...Kurt Hornik and Albrecht Gebhardt. Maintainer: Brian Ripley <ripley at stats.ox.ac.uk> BundleDescription: Functions and datasets to support Venables and Ripley, 'Modern Applied Statistics with S' (4th edition). License: GPL-2 | GPL-3 URL: http://www.stats.ox.ac.uk/pub/MASS4/ Packaged: 2009-07-31 13:56:57 UTC; ripley Repository: CRAN Date/Publication: 2009-08-05 11:20:53 Package: MASS Description: The main library and the datasets Title: Main Package of Venables and Ripley's MASS LazyLoad: yes LazyData: yes Built: R 2.9.2; i686-pc-linux-gnu; 2009-08-25 10:52:10 UTC...