similar to: Input file format to Anova from car package

Dear R experts, beginners and everyone else, I'm calculating "cumulative periodogram" using the command "cpgram" [1] from the MASS library. Here is a short example with the "lh" (hormone level) dataset: library(MASS) plot(lh,type="l",ylab="value",xlab="time", main="Hormone Levels (lh)") spectrum(lh,

Often, when exploring a dataset, I'd like to plot several very different Y variables against the same X variable, in panels stacked one over the other. Is there an easy way to do this? I'd like to achieve an elegant look similar to the look achieved by lattice in conditioned plots--for instance no space between panels. But unlike in straightforward conditioned plot, each panel may be on a

Hi, This is not an R question, but I've seen opinions given on non R topics, so I wanted to give it a try. :) How would one treat a variable that was measured once, but is known to fluctuate a lot? For example, I want to include a hormone in my regression as an explanatory variable. However, this hormone varies in its levels throughout a day. Nevertheless, its levels differ substantially

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I'm attempting the heruclean task of teaching myself Introductory Statistics and R at the same time. I'm working through Peter Dalgaard's Introductory Statistics with R, but don't understand why the answer to one of the exercises works. I'm hoping someone will have the patience to explain the answer to me, both in the statistics and R areas. Exercise 6.1 says: The zelazo data

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Dear all, I am trying to analyze data from an experiment like this: Factors: Hormone - Levels: SH, CH (S = without; C=with; H=Hormone) Time - Levels: 19/08/09, 04/09/09, 18/09/09, 08/10/09, 20/10/09 (DD/MM/YY) Nutrition - Levels: Completa, Sem (without) Macronutrition - Levels: Ca, K, Mg, P, Sem (without) Time is the measures day. It reflect the days after germination. Blocks : 4 plants per

Hi, I have a experiment with block, plots, sub-plots, and sub-sub-plots with repeated measures and 3 factors (factorial design) when we have been observed diameter (mm), high (cm) and leaves number (count). However, we don't have one treatment in one factor, so, my design is unbalanced. On a previous message here, a friend tell me that "It appears to me that your design is a split-split

Some time ago I was allowed to discuss "Diamond Graphs", and whether they would be useful in R, in this mailing list. The August 2003 issue of The American Statistician has finally arrived here and I have been able to read the article. A number of points of interest arise. 1. The article is "A Diamond-Shaped Equiponderant Graphical Display of the Effects of Two

Dear all, I'm confused by the time serie parameters in data(lh) : sueoka:~ lobry$ R --vanilla --quiet > tsp(lh) [1] 1 48 1 because documentation says: QUOTE A regular time series giving the luteinizing hormone in blood samples at 10 mins intervals from a human female, 48 samples. UNQUOTE So that I would expect the time serie to end at 480 minutes or 8 hours. Shouldn't we have

Full_Name: José Otero Version: Version 1.5.0 (2002-04-29) OS: Redhat 7.3 Submission from: (NULL) (192.187.16.164) Hi: Installation of package faraway as root, from tarbal: R CMD INSTALL ./faraway.tar.gz ERROR: cannot extract package from './faraway.tar.gz' idem, from zipped package: R CMD INSTALL faraway.zip gzip: faraway.zip has more than one entry--rest ignored ERROR: cannot

can you recommend a good manual for R that starts with a data set and gives demonstrations on what can be done using R? I downloadedR Langauage definition and An introduction to R but haven't found them overly useful. I'd really like to be able to follow some tutorials using a dataset or many datasets. The datasets I have available on R are Data sets in package 'datasets':

We have a problem inverting a matrix which has the following eigenvalues: > eigen(tcross, only.values=TRUE) $values [1] 7.917775e+20 2.130980e+16 7.961620e+13 8.241041e+12 2.258325e+12 [6] 3.869428e+11 6.791041e+10 2.485352e+09 9.863098e+08 9.819373e+05 [11] 3.263408e+05 2.929853e+05 2.920419e+05 2.714355e+05 8.733435e+04 [16] 8.127136e+04 6.543883e+04 5.335074e+04

Research Assistant/Staff Scientist: Technology in Environmental Health (Silent Spring Institute; Boston, MA) Silent Spring Institute is a non-profit scientific research group studying the environment and health. We are a multi-disciplinary team and international leader in research on everyday exposures to hormone disruptors, tests for breast carcinogens, and environmental links to breast cancer.

There are three different types of HGH products. The confusion is that all three are advertised as if they were the same. The three types are: 1) --- Homeopathic HGH 2) --- Pre-cursor HGH 3) --- Real or synthetic HGH (delivered by injection or, by an oral spray method). Do you know differences? Call us and we'll explain them to you. Our toll free number is

There are three different types of HGH products. The confusion is that all three are advertised as if they were the same. The three types are: 1) --- Homeopathic HGH 2) --- Pre-cursor HGH 3) --- Real or synthetic HGH (delivered by injection or, by an oral spray method). Do you know differences? Call us and we'll explain them to you. Our toll free number is

Hi I am working on corpora of automatically recognized utterances, looking for features that predict error in the hypothesis the recognizer is proposing. I am using the glm functions to do logistic regression. I do this type of thing: * logistic.model = glm(formula = similarity ~., family = binomial, data = data) and end up with a model: > summary(logistic.model) Call:

Hi everyone, Although this question is more related to ChIP and ChIP-seq, it could be probably anchored in a more general statistical context. The question is : what method is better to assess the significance of the change in a signal (the signal can be DNA binding, for instance) given the background and 2 conditions. <. condition1 (eg no treatment) : background = 1;

Dear all, is there anyone who can help me extracting p-values from an Anova object from the car library? I can't seem to locate the p-values using str(result) or str(summary(result)) in the example below > A <- factor( rep(1:2,each=3) ) > B <- factor( rep(1:3,times=2) ) > idata <- data.frame(A,B) > fit <- lm( cbind(a1_b1,a1_b2,a1_b3,a2_b1,a2_b2,a2_b3) ? sex,

There is now a preliminary version of a time series package in the R-devel snapshots, and we would welcome feedback on it. It is based in part on the packages bats (Martyn Plummer) and tseries (Adrian Trapletti) and in part on code I had or have written. (Thanks for the contributions, Martyn and Adrian!) Some of the existing ts code has been changed, for example to plot multiple time series, so