similar to: R memory and CPU requirements

I have a simple linear model (fitted with lm()) with 2 independant variables : one categorical and one integer. When I run summary.lm() on this model, I get a standard linear regression summary (in which one categorical variable has to be converted into many indicator variables) which looks like : Estimate Std. Error t value Pr(>|t|) (Intercept) -3595.3 2767.1 -1.299

It is unclear to me how aov() handles non-categorical variables. I mean it works and produces results that I would expect, but I was under impression that ANOVA is only defined for categorical variables. In addition, help(aov) says that it "call to 'lm' for each stratum", which I presume means that it calls to lm() for every group of the categorical variable, however I

Hi I have data from 12 subjects. The measurement is log(expression) of a particular gene and can be assumed to be normally distributed. The 12 subjects are divided into the following groups: Infected, Vaccinated, Lesions - 3 measurements Infected, Vaccintaed, No Lesions - 2 measurements Infected, Not Vaccinated, Lesions - 4 measurements Uninfected, Not Vaccinated, No Lesions - 3 measurements

The R movement is picking up steam in the center of America. People that ignored my R-enthusiasm 10 years ago are now calling me up asking for presentations. I need to make a 2 hour presentation to a collection of faculty and grad students who might like to use R. I don't want to make it seem too complicated (as I often do), but I don't want to mislead them to think it will be easy. I

All, I'm trying to create an augPred plot in the nlme library, similar to the plot on p.43 of Pinheiro & Bates (Mixed Effects Models in S and S-Plus) for their Pixel data. My data structure is the same as the example but I still get the error msg below. > comp.adj.UKV <- groupedData(adj.UKV ~ Time | Patient_no/Lisinopril, data = comp.adj.UKV.frm, order.groups = F) >

Hi, Many of the complaints about lattice stem from the default settings (a.k.a. theme) on screen devices, which has a grey background. It's easy enough to change this, even pseudo-permanently through a startup script, so this is not a serious problem. However, there's one situation where this has an unfortunate effect: on Windows, someone might 1. create a plot (on screen) 2. right click

Dear R list members, I'd like to produce rug plots at the bottom of panels in a trellis display (using the lattice package), but par("usr") doesn't return user coordinates for panels, and consequently rug fails, as the following example (suggested to me by Georges Monette) illustrates: > x <- rnorm(50) > y <- rnorm(50) > f <-

It was recently pointed out on the lists that the S-PLUS Trellis suite has a function called reorder.factor that's useful in getting useful ordering of factors for graphs. I happily went ahead and implemented it, but it turns out that R (not S-PLUS) has a generic called reorder (with a method for "dendrogram"). Naturally, this causes R to think I'm defining a method for

Hi, I ran a small data set from a factorial experiment through R, Minitab and JMP... the result from R is significantly different from what Minitab or JMP give... The data set is at the following link: http://www.personal.psu.edu/nug107/Uploads/2x3_16repsANOVA.txt The first 5 columns are the factors and the next three are responses. In particular, for the response beta11MSE, two of the

Hello, I believe the aov() function in R uses a "Type-I sum-of-squares" by default as against "Type-III". This is relevant for me because I am trying to understand ANOVA in R using my knowledge of ANOVA in SPSS. I can only reproduce the results of an ANOVA done using R through SPSS if I specify that SPSS uses a Type-I sum-of-squares. (And yes, I know that when the sample

Hi, I am trying a two way anova with unequal sample sizes but results are not as expected: I take the example from Applied Linear Statistical Models (Neter et al. pp889-897, 1996) growth rate gender bone development 1.4 1 1 2.4 1 1 2.2 1 1 2.4 1 2 2.1 2 1 1.7 2 1 2.5 2 2 1.8 2 2 2 2 2 0.7 3 1 1.1 3 1 0.5 3 2 0.9 3 2 1.3 3 2 expected results are

I was wondering, whether there is a way to have statistical significance test for cluster agreement. I know that I can use classAgreement() function to get Rand index, which will give me some indication whether the clusters agree or not, but it would be interesting to have a formal test. Thanks.

Good Morning, I am using xyplot to show two variables for a large number of subjects. Currently I am using xyplot(y~x|as.factor(ID), layout=c(7,9)) to give me a little plot of the x and y factors for each ID. The purpose of the plot is to let the PI quickly look through the data for irregularities. The good news is that they like the layout of the xyplot, but they don't want to have to

I have a three-way unbalanced ANOVA that I need to calculate (fixed effects plus interactions, no random effects). But word has it that aov() is good only for balanced designs. I have seen a number of different recommendations for working with unbalanced designs, but they seem to differ widely (car, nlme, lme4, etc.). So I would like to know what is the best or most usual way to go about working

I am using the package nlme to fit a simple random effects (variance components model) with 3 parameters: overall mean (fixed effect), between subject variance (random) and within subject variance (random). I have 16 subjects with 1-4 obs per subject. I need a 3x3 variance-covariance matrix that includes all 3 parameters in order to compute the variance of a specific linear

>>>>> Martin Maechler <maechler at stat.math.ethz.ch> >>>>> on Thu, 14 Sep 2017 10:13:02 +0200 writes: >>>>> Fox, John <jfox at mcmaster.ca> >>>>> on Wed, 13 Sep 2017 22:45:07 +0000 writes: >> Dear Terry, >> Even the behaviour of lm() and glm() isn't entirely consistent. In both cases,

Hello R gurus, I am a beginner with R. I am doing an ANCOVA analysis using 'aov,' and need some help understanding how 'aov' works. I have a dataset (taken from http://faculty.vassar.edu/lowry/ch17pt2.html) looking at hypnotic induction. The variable 'X' is a measure of how susceptible the subject is to being hypnotized, the variable 'Y' is how well the

This is still not repaired in R version 3.6.0 Patched (2019-05-17 r76528) > library(latticeExtra) > a <- xyplot(1 ~ 1) > c(a,a) Warning message: In formals(fun) : argument is not a function Can we have it in R-3.6.1 that Peter just announced? Rich On Mon, Apr 2, 2018 at 4:08 AM Deepayan Sarkar <deepayan.sarkar at gmail.com> wrote: > > On Fri, Mar 23, 2018 at 7:58 AM,

Hi all I'm trying to complete a textbook example originally designed for SPSS in R, and I therefore need to find out how to compute an unbalanced ANOVA in R. I did a search on the mailinglist archives an found a post by Prof. Ripley saying one should use the lme function for (among other things) unbalanced ANOVAs, but I have not been able to use this object. My code gives me an error.. Why

No, there are lots of situations where that doesn't make sense. You don't want to have to define local copies of the functions from every package you use, for example. I think the takeaway is to learn how R scoping works, and keep things simple. That's one reason I tend to avoid "tidyverse" packages. There are a lot of really good ideas in those packages, but