Exactly right. I use the phrase to catch the unwary's attention. I
think the effect is properly placed on the y-axis.
IIRC, Ben Bolker (or was it Bert Gunter?) has also commented in the R-
help or r-devel pages this curious inversion of functional meaning.
--
David
On Feb 8, 2011, at 10:36 PM, Ravi Varadhan wrote:
David,
Please allow me to digress a lot here. You are one of the few
(inlcuding yours truly!) that uses the phrase "shallow learning
curve" to indicate difficulty of learning (I assume this is what you
meant). I always felt that "steep learning curve" was incorrect. If
you plotted the amount of learning on the Y-axis and time on the X-
axis, a steep learning curve means that one learns very quickly, but
this is just the opposite of what is actually meant.
Best,
Ravi.
____________________________________________________________________
Ravi Varadhan, Ph.D.
Assistant Professor,
Division of Geriatric Medicine and Gerontology
School of Medicine
Johns Hopkins University
Ph. (410) 502-2619
email: rvarad...@jhmi.edu
----- Original Message -----
From: David Winsemius <dwinsem...@comcast.net>
Date: Tuesday, February 8, 2011 10:09 pm
Subject: Re: [R] Removing Outliers Function
To: kirtau <kir...@live.com>
Cc: r-help@r-project.org
On Feb 8, 2011, at 9:11 PM, kirtau wrote:
I am working on a function that will remove outliers for regression
analysis.
I am stating that a data point is an outlier if its studentized
residual is
above or below 3 and -3, respectively. The code below is what i have
thus
far for the function
x = c(1:20)
y = c(1,3,4,2,5,6,18,8,10,8,11,13,14,14,15,85,17,19,19,20)
data1 = data.frame(x,y)
rm.outliers = function(dataset,dependent,independent){
dataset$predicted = predict(lm(dependent~independent))
dataset$stdres = rstudent(lm(dependent~independent))
m = 1
for(i in 1:length(dataset$stdres)){
dataset$outlier_counter[i] = if(dataset$stdres[i] >= 3 |
dataset$stdres[i] <= -3) {m} else{0}
}
j = length(which(dataset$outlier_counter >= 1))
while(j>=1){
print(dataset[which(dataset$outlier_counter >= 1),])
dataset = dataset[which(dataset$outlier_counter == 0),]
dataset$predicted = predict(lm(dependent~independent))
dataset$stdres = rstudent(lm(dependent~independent))
m = m+1
for(k in 1:length(dataset$stdres)){
dataset$outlier_counter[k] = if(dataset$stdres[k] >= 3 |
dataset$stdres[k] <= -3) {m} else{0}
}
j = length(which(dataset$outlier_counter >= 1))
}
return(dataset)
}
The problem that I run into is that i receive this error when i type
rm.outliers(data1,data1$y,data1$x)
" x y predicted stdres outlier_counter
16 16 85 22.98647 24.04862 1
Error in `$<-.data.frame`(`*tmp*`, "predicted", value =
c(0.114285714285714,
:
replacement has 20 rows, data has 19"
Note: the outlier_counter variable is used to state which "round" of
the
loop the datapoint was marked as an outlier.
This would be a HUGE help to me and a few buddies who run a lot of
different
regression tests.
The solution is about 3 or 4 lines of code to make the function, but
removing outliers like this is simply statistical malpractice. Maybe
it's a good thing that R has a shallow learning curve.
--
David Winsemius, MD
West Hartford, CT
______________________________________________
R-help@r-project.org mailing list
PLEASE do read the posting guide
and provide commented, minimal, self-contained, reproducible code.
David Winsemius, MD
West Hartford, CT
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
