Hello Forum,
I have data for two groups of subjects for a ratio variable (cortisol
level) assessed three times during the day (at 0600, 0900 and 2100 hours).
So I have three means for each group. I have been asked to do a slope
analysis to compare the two groups.
According to my
answers. But
aov(score~group, data=mydata)
also produces an ANOVA table, with incorrect entries. My question is: what
exactly is R doing when I did not specify that group was a factor?
Ravi Kulkarni
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I do not know if this is what you want, but take a look at the External
links at the bottom of the Wikipedia page:
http://en.wikipedia.org/wiki/Theil_index
Ravi
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Hello,
I have data for an ANOVA where the between-subjects factor has three
levels. How do I run a test of normality (using shapiro.test) on each
of the levels of the factor for the dependent variable separately
without creating extra datasets?
Thanks,
Ravi
Thanks! Exactly what I wanted.
Ravi
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Hello,
I notice that when I do Levene's test to test equality of variances across
levels of a factor, I get different answers in R and SPSS 16.
e.g.: For the chickwts data, in R, levene.test(weight, feed) gives
F=0.7493, p=0.5896.
SPSS 16 gives F=0.987, p=0.432
Why this difference? Which
I think I have an answer: SPSS uses absolute deviations from the _mean_ in
Levene's test.
(See calculation in
http://www.uvm.edu/~dhowell/gradstat/psych340/Lectures/Anova/anova2.html)
R uses absolute deviations from the _median_ (R help).
So the difference.
Ravi
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I forgot to reply to Peter Ehler's question: I am using Levene's test in the
car package.
Ravi
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Giving different colors to plots of different levels of a factor can
be pleasing. For a boxplot, for example, the following produces a nice
plot:
boxplot(len~supp*dose, data=ToothGrowth, col=c(“orange”,”gray”))
If I try to specify (using the lattice package)
histogram(~len|supp*factor(dose),
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
Apologies. I should have checked the archives first... I did not know that it
was so widely discussed...
Ravi
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of ANOVA correspond to? I mean that I
think of my ANOVA as a regression model (a General Linear Model) and the
various ways of entering predictors as the various ANOVA Types.
Hope that makes sense...
Ravi Kulkarni
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Hello,
Since I initiated this discussion some days ago, I discovered a paper that
may be of interest:
ANOVA for unbalanced data: Use Type II instead of Type III sums
of squares
by ØYVIND LANGSRUD
Statistics and Computing 13: 163–167, 2003
Ravi
It's probably binary data - which implies that you can only read it with
the application that created it. What is the filename extension?
Ravi
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I have tried looking for the source code for the pt() function in
https://svn.r-project.org/R/trunk/src/library/stats/
and am unable to find it there. Can someone please tell me where to find it?
Thanks,
Ravi Kulkarni
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for explicit examples of ANOVA in R:
http://www.personality-project.org/R/r.anova.html
Ravi Kulkarni
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for the
categorical variables as required by some statistical software packages,
with some sort of numeric dummy-variable coding?
I am using:
glm(binvar~x+y+z, family=binomial(link=logit))
Thanks,
Ravi Kulkarni
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How do I use the extra information that two of my predictors are ordinal?
(I did not know I could do that.)
Thanks,
Ravi
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in the model? Is that what is causing the difference?
Thanks,
Ravi Kulkarni
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.
Thanks,
Ravi Kulkarni
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https
Use R Commander to do this. R Commander is a R package that offers a GUI for
R. It can be downloaded like any other R package.
If you use R Commander, there is a menu option where you specify that you
want to read an Excel file (you can also read text, SPSS, Minitab, Stata,
Access... files). It
The ROCR package has methods to compute AUC and related methods. You might
want to check it out.
Ravi
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Yes, there does not seem to be a method for comparing two ROC curves in ROCR.
But I found a discussion in the R-archives which may be useful:
http://tolstoy.newcastle.edu.au/R/help/06/03/23667.html
You should also see the replies to that post.
I am also interested in
I know this is a very elementary question... I could not find a solution
looking at old posts.
I am unable to access a variable outside the scope of a for loop, even when
the variable was defined before the loop:
haar - function() {
a = c(1.4560773, 2.3752412, 0.9798882, 3.0909252, 2.3986487,
Of course! Works fine now.
Thanks, Martyn and Jim...
Ravi
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I am trying to reproduce the output of a time series example in Koop's book
Analysis of Financial Data. Koop does the example in Excel and I used the
ts function followed by the lm function.
I am unable to get the exact coefficients that Koop gives - my coefficients
are slightly different.
After
I am trying to work out a bootstrapped Tobit regression model. I get the
coefficients all right, but they all have standard error zero. And I am
unable to figure out why. I know the coefficients are correct because that's
what I get when do a Tobit (without bootstrapping). Here's my code:
#
If you look at the documentation for ar, you will find the following
description of aic:
aic The differences in AIC between each model and the best-fitting
model
So this means that your model order is 5 (look at the pacf).
If you want the actual AIC of your model: (I call your
Can anyone give me links to reviews/comparisons of R with Amos for SEM? I
have found some but they are a little old (2009).
Ravi
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This is in version 2.10.1
sqrt(3)
[1] 1.732051
6/7
[1] 0.8571429
options(digits=3)
sqrt(3)
[1] 1.73
6/7
[1] 0.857
sqrt(7)
[1] 2.65
7/9
[1] 0.778
Ravi
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I know this is not really an R question - it is a query about a recent book
on R (R In Action) by Robert Kabacoff, (Manning Publications 2011).
There are many references to interesting topics in R in the book, BUT, I do
not find a bibliography/list of references in the book!
Does anybody know if
Hello,
I am trying to run the code for quadratic discriminant analysis with the
Spambase data set following the code example in the book R in a Nutshell
by Joseph Adler (page 443).
The line of code:
spam.qda - qda(formula=is_spam~.,
data=spambase.training)
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