similar to: model simplification using Crawley as a guide

Hello all- I would appreciate any guidance that can be provided. I am new to R and am using it exclusively in a statistics program I am undertaking that mainly references Minitab. My focus is on data modeling and further more multivariate data analysis as much of my work in involves chemical measurements from custom sensors using all sorts of transduction methods. I am looking for a

Hi, anybody have the dataset used in Gamma Errors chapter of the Crawley's books (An Introduction to Data Analysis using S-Plus). specifically the functionalresponse and the Density datasets. Thanks Ronaldo -- For every problem there is one solution which is simple, neat, and wrong. -- H. L. Mencken -- |> // | \\ [***********************************] | ( ? ? ) [Ronaldo Reis

Hi, I have got to my hands an excellent book by Michael J. Crawley ``Statistical Computing: An Introduction to Data Analysis using S-Plus'' (John Wiley & Sons, Ltd, ISBN 0-471-56040-5). Its beauty for me is in the fact, that it is more of ``An Introduction to Data Analysis'' than ``using S-Plus'', but I guess that it may be of interest for many others. Most of the

Hi All, I would like to ask a question on fixed and random effecti in lme. I am fiddlying around Mick Crawley dataset "rats" : http://www.bio.ic.ac.uk/research/mjcraw/statcomp/data/ The advantage is that most work is already done in Crawley's book (page 361 onwards) so I can check what I am doing. I am tryg to reproduce the nested analysis on page 368:

Hi, I ran the example on pp. 799-800 from Machael Crawley's "The R Book" using package survival v. 2.36-5, R 2.13.0 and RStudio 0.94.83. The model is a Cox's Proportional Hazards model. The result was quite different compared to the R Book. I have compared my code to the code in the book but can not find any differences in the function call. My results are attached as well as a

Hi, I try to run the example of Crawley's Book on the page 661, but it fail, look > repmeasures <- read.table("../Packages/Crawley/data/repmeasures.txt",header=T) > attach(repmeasures) > rep <- as.factor(rep) > library(nlme) > model <- lme(height~seed,random=~time|rep/seed) Erro em lme.formula(height ~ seed, random = ~time | rep/seed) : iteration limit

Apologies if this has been asked before. I am having trouble understanding the R mailing list never mind R! I am relatively new to R having migrated from Minitab and SPSS. I have managed to do some more complicated statistics such as hierarchical partitioning of variance on an 80,000 record dataset but have to admit that drawing a simple bar plot I could do by hand is proving extremely

I'm looking for a book for someone completely ignorant of statistics who wishes to learn both statistics and R. I've found three possibilities, one by Verzani ("Using R for Introductory Statistics"), one by Crawley ("Statistics: An Introduction using R"), and one by Dalgaard ("Introductory Statistics with R"). Do these books have different emphases,

Hi, I'm trying to teach myself R, and by the way, re-learning statistics using Crawley's "Statistics: an introduction using R". I've reached the regression chapter, and when it deals with non-linear regresion using the nls library I face the following problem: I follow the steps--- >deer<-read.table("c:\\temp\\jaws.txt",header=T) ---data available at

Hi all I have a hopefully not too stupid question about multi-level / mixed-effects modeling. I was trying to test a strategy from Crawley's 2013 R Book on a data set with the following structure: - dependent variable: CONSTRUCTION (a factor with 2 levels) - independent fixed effect: LENGTH (an integer in the interval [1, 61]) - random effects with the following hierarchical structure: MODE

I apologize for how basic a question this is. I am a Stata user who has begun using R, and the syntax differences still trip me up. The most basic questions, involving as they do general terms, can be the hardest to find solutions for through search. Assume for the moment that I have a dataset that contains seven variables: Pollution, Temp, Industry, Population, Wind, Rain and Wet.days. (This

Dear friends, I am testing glm as at page 514/515 of THE R BOOK by M.Crawley, that is on proportion data. I use glm(y~x1+,family=binomial) y is a proportion in (0,1), and x is a real number. I get the error: In eval(expr, envir, enclos) : non-integer #successes in a binomial glm! But that is exactly what was suggested in the book, where there is no mention of a similar warning. Where am I

Hello all, I've become confused by the output produced by a call to anova(model1,model2). First a brief background. My model used to predict final tree height is summarised here: Df Sum Sq Mean Sq F value Pr(>F) Treatment 2 748.35 374.17 21.3096 7.123e-06 *** HeightInitial 1 0.31 0.31 0.0178 0.89519

Hi, I'm a newcomer to R, having previously used SPSS. One problem I have run into is computing kurtosis. A test dataset is here: http://www.whinlatter.ukfsn.org/2401.dat > library(moments) > data <- read.table("2401.dat", header=T) > attach(data) > loglen <- log10(Length) With SPSS, I get Skewness -0.320 Kurtosis -1.138 With R: > skewness(loglen) [1]

I have a feeling that this is an embarassingly simple fix, but I've been at it for most of the morning and can't get things figured out. I'm trying to work through some examples in Crawley's "The R Book". I have installed packages and libraries as described in the book, but when I try, for example: data<-read.table("c:\\temp\\daphnia.txt", header=T)

I'm trying to learn statistics and R at the same time. I have an undergraduate science degree and one year of calculus (30 years ago), but never took a stats course. I hope to take some stats courses in the next year, but thought I would start to see how much I could teach myself. I work for an organization that analyses behavior change communication programs regarding HIV/AIDS and

New installation seems to have behavior I cannot figure out. Here is illustrative sequence where I load a small data set (test) from Crawley's files and try to run a simple linear model and get an error message. Oddly, R reports that the variable 'test$ozone' is numeric while, after attaching test, the variable ozone is not numeric. Can someone please help? This behavior is

R-wizards I have successfully run with S+ a statistical power calculation for non-normal distributions as presented in M. Crawley?s new book.? When I tried the newest version of R on the same code, the $ parse statement doesn't seem to retrieve the appropriate number from a table. Note that some of the cosmetic differences in the two tables have to be dealt with by the parser. Any ideas

I've been poking around with GLMs (on which I am *not* an expert) on behalf of a student, particularly binomial (standard logit link) nested models with overdispersion. I have one possible bug to report (but I'm not confident enough to be *sure* it's a bug); one comment on the general inconsistency that seems to afflict the various functions for dealing with overdispersion in GLMs

I was wondering what the criteria were for including books on the Books Related to R page <http://www.r-project.org/doc/bib/R-books.html>. (There is no maintainer listed on this page.) In particular, I was wondering why the following two books are not listed: * Andrew Gelman, Jennifer Hill, *Data Analysis Using Regression and Multilevel/Hierarchical Models*. (CRAN package 'arm') *