dadrivr wrote:
The variables have the same length, but with different numbers of missing
values (NA). As a result, the residuals calculations (xres & yres) have
different lengths, and I cannot compute the correlation between the two
(error of incompatible dimensions - see example below). Is there a way,
when calculating residuals, to leave the NAs in the residual calculation and
output? Thanks!
x <- c(1,20,14,NA,9)
y <- c(5,6,7,9,10)
z <- c(13,NA,16,14,NA)
xres <- residuals(lm(x ~ z))
yres <- residuals(lm(y ~ z))
cor(xres, yres)
ct <- cor.test(xres, yres)
ct$estimate
ct$p.value
Well, your example above just uses two points for the x on z
regression and that gives zero-residuals. Let's use
something a bit more realistic:
set.seed(123)
x <- rnorm(5)
y <- rnorm(5)
z <- rnorm(5)
x[sample(5,1)] <- NA
z[sample(5,1)] <- NA
fmx <- lm(x ~ z, na.action = na.exclude)
fmy <- lm(y ~ z, na.action = na.exclude)
yres <- resid(fmy)
xres <- resid(fmx)
cor(xres, yres, use = "p") # -0.95978
ct <- cor.test(xres, yres)
ct$estimate # -0.95978
ct$p.value # 0.04021994
Check out the 'na.exclude' action in lm(): it preserves
the length of the residual vector. Then the 'use='
argument to cor() uses pairwise complete observations.
cor.test() will do that automatically.
-Peter Ehlers
Ista Zahn wrote:
1) Think about what you did wrong. It doesn't make sense to do
correlation/regression with variables of different lengths. You can
have missing values in one or more variables, if that's what you mean.
Just code them NA.
2) Just add in the predictors, e.g.
residuals(lm(y ~ z1 + z2))
-Ista
On Wed, Nov 11, 2009 at 10:34 PM, dadrivr <dadr...@gmail.com> wrote:
Awesome, that's what I was looking for. Â I have two additional questions:
(1)
What can I do if the variables are of different lengths? (2) How do I
update
the formula if I want to control for more than one variable.
Let's take the following example:
x <- c(1,20,14,7,9)
y <- c(5,6,7,9,10,11)
z <- c(13,27,16,5,4,17,20)
a <- c(4,6,7,1)
xres <- residuals(lm(x ~ z))
yres <- residuals(lm(y ~ z))
cor(xres, yres)
ct <- cor.test(xres, yres)
ct$estimate
ct$p.value
How do I update the above formula to:
(1) take into account that the variables are of different lengths? Â I get
an
error when calculating the residuals.
(2) control for z and a (i.e., more than one variable)?
Thanks so much for your help.
Peter Ehlers wrote:
dadrivr wrote:
I'm trying to write code to calculate partial correlations (along with
p-values). Â I'm new to R, and I don't know how to do this. Â I have
searched
and come across different functions, but I haven't been able to get any
of
them to work (for example, pcor and pcor.test from the ggm package).
In the following example, I am trying to compute the correlation
between
x
and y, while controlling for z (partial correlation):
x <- c(1,20,14,7,9)
y <- c(5,6,7,9,10)
z <- c(13,27,16,5,4)
What function can I append to this to find this partial correlation?
Many
thanks!
I'm not sure what you need, but does this give you what
you want:
xres <- residuals(lm(x ~ z))
yres <- residuals(lm(y ~ z))
cor(xres, yres)
# [1] 0.9778857
or
ct <- cor.test(xres, yres)
ct$estimate  # 0.9978857
ct$p.value  # 0.003934582
 -Peter Ehlers
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______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
--
Ista Zahn
Graduate student
University of Rochester
Department of Clinical and Social Psychology
http://yourpsyche.org
______________________________________________
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.
______________________________________________
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.
