Please see in-line below.
On 02/08/2013 05:20 AM, Giuseppe Amatulli wrote:
Hi Rolf,
sorry for this late answer and thanks for your kind explanation and
relative R code. I really appreciate.
In reality the concept that I'm trying to address is a bit more complex.
I'm fitting a model y vs 6 predictors with MARS / RandomForest /
Multiple Linear Regression Models having 140 observations.
I have the prediction of each model and would like to delineate the
prediction ellipses for 3 models, for the 95% probability, and
plotting them together with the observation vs prediction.
I think that the prediction-ellipses code that you provide to me is
valid also for predictions derived by not-linear model (such as MARS
and RF).
Is it correct?
Probably not. The degrees of freedom will very likely be
wrong. You need to consult a suitable book on multivariate
analysis --- quite possibly Seber's book "Multivariate
Observations"
would help.
or should i use an alternative solution ?
At the very least some adjustment will be required I think.
Moreover, I was expecting that the abline (lm(b,a)) would be
correspond to the main axis of the prediction ellipse,
Why do you expect that?
but is not this
the case.
why?
No idea. What are "b" and "a"? In general if a and b are
jointly Gaussian you can
(i) regress b on a and plot the fitted regression line, which
is what abline(lm(b~a)) will do, or
(ii) form the prediction ellipse for (a,b) .
The major axis of this prediction ellipse will NOT be the
regression
line. I have a vague recollection that this major axis is the
line which
minimizes the sum of squares of the *orthogonal* distances of the
points to the line (whereas the regression line minimizes the
sum of
squares of the *vertical* distances).
But none of this seems to me to have much if anything to do with
what you are trying to accomplish.
At any rate this discussion has nothing to do with R. You
should ask
about it on some statistics discussion forum, or consult with
an expert
on multivariate statistics.
cheers,
Rolf Turner
Thanks in advance
Giuseppe
On 28 January 2013 19:04, Rolf Turner <rolf.tur...@xtra.co.nz> wrote:
I believe that the value of "radius" that you are using is incorrect. If you
have a data
matrix X whose columns are jointly distributed N(mu,Sigma) then a
confidence
ellipse for mu is determined by
n * (x - Xbar)' S^{-1}(x - Xbar) ~ T^2
where Xbar is the mean vector for X and S is the sample covariance matrix,
and where "T^2" means Hotelling's T-squared distribution, which is equal to
(n-1)*2/(n-2) * F_{2,n-2}
the latter representing the F distribution on 2 and n-2 degrees of freedom.
Thus (I think) your radius should be
radius <- sqrt(2 * (npts-1) * qf(0.95, 2, npts-2)/(npts*(npts-2)))
where npts <- length(a). Note that it is qf(0.95,2,npts-2) and *NOT*
qf(0.95,2,npts-1).
To get the corresponding *prediction* ellipse simply multiply the foregoing
radius by sqrt(npts+1). By "prediction ellipse" I mean an ellipse such that
the probability that a new independent observation from the same population
will fall in that ellipse is the given probability (e.g. 0.95). Note that
this does
not mean that 95% of the data will fall in the calculated ellipse (basically
because
of the *dependence* between S and the individual observations).
These confidence and prediction ellipses are (I'm pretty sure) valid under
the assumption that the data are (two dimensional, independent) Gaussian,
and that you use the sample covariance and sample mean as "shape" and
"centre". I don't know what impact your robustification procedure of using
cov.trob() will/would have on the properties of these ellipses.
A script which does the ellipses for your toy data, using the sample
covariance
and sample mean (rather than output from cov.trob()) is as follows:
#
# Script scr.amatulli
#
require(car)
a <- c(12,12,4,5,63,63,23)
b <- c(13,15,7,10,73,83,43)
npts <- length(a)
shape <- var(cbind(a, b))
center <- c(mean(a),mean(b))
rconf <- sqrt(2 * (npts-1) * qf(0.95, 2, npts-2)/(npts*(npts-2)))
rpred <- sqrt(npts+1)*rconf
conf.elip <- ellipse(center, shape, rconf,draw = FALSE)
pred.elip <- ellipse(center, shape, rpred,draw = FALSE)
plot(pred.elip, type='l')
points(a,b)
lines(conf.elip,col="red")
cheers,
Rolf Turner
On 01/27/2013 10:12 AM, Giuseppe Amatulli wrote:
Hi,
I'm using the R library(car) to draw confidence/prediction ellipses in a
scatterplot.
>From what i understood the ellipse() function return an ellipse based
parameters: shape, center, radius .
If i read dataEllipse() function i can see how these parameters are
calculated for a confidence ellipse.
ibrary(car)
a=c(12,12,4,5,63,63,23)
b=c(13,15,7,10,73,83,43)
v <- cov.trob(cbind(a, b))
shape <- v$cov
center <- v$center
radius <- sqrt(2 * qf(0.95, 2, length(a) - 1)) # radius <- sqrt(dfn *
qf(level, dfn, dfd))
conf.elip = ellipse(center, shape, radius,draw = F)
plot(conf.elip, type='l')
points(a,b)
My question is how I can calculate shape, center and radius to obtain a
prediction ellipses rather than a confidence ellipse?
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