search for: crawley

Hello, I have consciously avoided using step() for model simplification in favour of manually updating the model by removing non-significant terms one at a time. I'm using The R Book by M.J. Crawley as a guide. It comes as no surprise that my analysis does proceed as smoothly as does Crawley's and being a beginner, I'm struggling with what to do next. I have a model: lm(y~A * B * C) where A is a categorical variable with three levels and B and C are continuous covariates. Followi...

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 -- |> // | \\ [***********************************] |...

...s much of my work in involves chemical measurements from custom sensors using all sorts of transduction methods. I am looking for a reference that has sound statistical foundations with relevant R commands as well as multivariate support. I saw the new book, "The R Book", by Michael J. Crawley and wanted to know what R users thoughts of it. Thanks in advance, Matt [[alternative HTML version deleted]]

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...

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: model<-aov(Glycogen~Treatment/Ra...

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(heig...

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 get: Error in file(file, "rt") : cannot open the connection In addition: Warning mes...

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 c...

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, perspectives, or strengths? Should I just pick one at random and buy it? Thanks, --Paul

...atively 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 difficult! I have Crawley's 'The R book' which is proving a big help, but still does not provide me with all of the information I need. What I want to do:- I have a small dataset, with 'rate' as the dependent variable (y axis variable) and two groups ('cat' and 'box') which I have c...

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 a...

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 > REGISTER > SUBREGISTER > FIL...

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 occurring with multiple data sets loaded from ou...

...e 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 actual dataset is taken from Michael Crawley's "Statistics: An Introduction Using R" and is available as "pollute.txt" in http://www.bio.ic.ac.uk/research/crawley/statistics/data/zipped.zip.) Assume I have attached pollute. Then cor(pollute) will give me the correlation table for these seven variables. If I would pre...

...ct.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') * Michael J. Crawley, *The R Book*. (reviewed, rather negatively, in *R News * *7*:2) Is the list more or less arbitrary? Does it reflect some editorial judgment about the value of these books? If so, it might be more useful to include the books, but with critical reviews. It doesn't seem to be a matter of up-to...

I'm not sure if this is a bug, or if I'm doing something wrong. =20 =46rom the worms dataframe, which is at in a file called worms.txt at =20 http://www.imperial.ac.uk/bio/research/crawley/therbook <http://www.imperial.ac.uk/bio/research/mjcraw/therbook/index.htm>=20 =20 the idea is to extract a subset of the rows, sorted in declining order of worm density, with only the maximum worm density from each vegetation type: =20 worms<-read.table("c:\\temp\\worms.txt",h...

Dear all, I am fitting a glm to count data using poison errors with the log link. My goal is to test for the significance of model terms by calling the anova function on two nested models following the recommendation in Michael Crawley's guide to Statistical Computing. Without going into too much detail, essentially, I have a small overdispersion problem (errors do not fit the poisson assumption) so I am following Crawley's recommendation and setting family=quasipoisson and using an F test (rather than a chi-square test)...

...gt; 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] -0.317923 > kurtosis(loglen) [1] 1.860847 Using the example skew and kurtosis functions from M. J. Crawley's "Statistics: An introduction using R": pp 69 and 72: > mskew(loglen) [1] -0.3158337 > mkurtosis(loglen) [1] -1.155441 The kurtosis value here matches the SPSS calculation somewhat more closely, but is still not exactly the same. Looking at the functions, there is some diffe...

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 simil...

...sciences, I'd say. I'd like to help analyze our data using R. I found these titles that may teach me both stats and R: --Data Analysis and Graphics Using R by John Maindonald, John Braun --Introductory Statistics with R by Peter Dalgaard --Statistics: An Introduction using R by Michael J. Crawley --Using R for Introductory Statistics by John Verzani I recognize some of the authors by their postings here. Can anyone recommend any of these books over the others? I'm interested in a book that I can learn statistics by reading the chapters and working out the exercises and problems, there...