Dear Rolf (and List),
Thank you for your help on error bars.
I fear that neither of the suggestions quite answer my immediate need.
1. Notches will not work because I have more than 2 levels.
2. The errbar function will useful once I know the error bars to put
in.
I thing I have figured it out but I would greatly appreciate
feedback (positive or negative) from the list:
For 2 or more levels with ordinary ANOVA the least significant
error bars are given by
(qt(0.975,degrees_of_freedom)sqrt((s1^2+s2^2)/sqrt(n))/2
Am I correct that for 3 levels the error bars are given by
(qt(0.975,degrees_of_freedom)sqrt((s1^2+s2^2+s3^2)/sqrt(n))/2
where the argument of the first square root is the standard error
of the sample mean?
If I am correct, then an analogous express would seem to hold
where the normal approximation is a good approximation to the
binomial distribution.
for 2 samples
z(..475)sqrt(theta1(1-theta1)+theta2(1-theta2))/2
and for 3 samples
z(..475)sqrt(theta1(1-theta1)+theta2(1-theta2)+theta3(1-theta3))/2
Does this sound right?
Thanks and best wishes,
Rich
Date: Tue, 24 May 2011 12:03:16 +1200
From: Rolf Turner <rolf.tur...@xtra.co.nz>
To: Richard Friedman <fried...@cancercenter.columbia.edu>
Cc: r-help@r-project.org
Subject: Re: [R] Analog of least significant difference error bars for
proportions
Message-ID: <4ddaf5c4.3080...@xtra.co.nz>
Content-Type: text/plain
On 24/05/11 11:23, Richard Friedman wrote:
Dear R-list,
In the R-book, p.464, Michael Crawley recommends that error
bars for bar plots of normally distributed continuous response
variables with categorical explanatory variables be given by
1/2 of the least significant difference, where the least significant
difference is defines as
qt(0.975,degrees_of_freedom)*standard_error_of_the_difference.
The idea is that the above quantity visually conveys whether or not
the means are different more realistically than do standard errors.
I have analyzed proportions with categorical variables using
the glm function with a binomial error model. I wish to plot a bar
graph with the height of the bars the proportions. Is there a way
to define error bars analogous to the least significant difference
bars
described above that can convey the overlap of proportions?
The experimentalists with whom I work just love error bars. I would
like to
make them as meaningful as possible.
(1) The errbar() function in the Hmisc package will allow you to set any
``spread'' that you wish on your error bars.
(2) In respect of maximal meaningfulness: The naive viewer tends to
interpret error bars by concluding that if the ranges of two pairs of
error bars do not overlap then the two quantities being estimated are
``significantly different''. Hence it strikes me that you might want to
imitate what is done for the notches in boxplots, which are designed
to make such an interpretation roughly correct.
From the help on boxplot.stats():
The notches (if requested) extend to |+/-1.58 IQR/sqrt(n)|. This seems
to be based on the same calculations as the formula with 1.57 in
Chambers /et al./ (1983, p. 62), given in McGill /et al./ (1978, p.
16). They are based on asymptotic normality of the median and roughly
equal sample sizes for the two medians being compared, and are said to
be rather insensitive to the underlying distributions of the samples.
The idea appears to be to give roughly a 95% confidence interval for
the difference in two medians.
cheers,
Rolf Turner
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