On Jul 11, 2011, at 5:16 PM, Sam Steingold wrote:
* David Winsemius <qjvafrz...@pbzpnfg.arg> [2011-07-11 15:50:04
-0400]:
On Jul 11, 2011, at 3:36 PM, Sam Steingold wrote:
* David Winsemius <qjvafrz...@pbzpnfg.arg> [2011-07-11 15:32:26
-0400]:
On Jul 11, 2011, at 3:18 PM, Sam Steingold wrote:
I need this plot:
given: x,y - numerical vectors of length N
plot xi vs mean(yj such that |xj - xi|<epsilon)
(running mean?)
alternatively, discretize X as if for histogram plotting and plot
mean
y
over the center of the histogram group.
It sounds as though you asking for smoothing splines with an
adjustable
band width ... something that is very easy in R.
Unlikely. I do not need smoothing. I have far too many points far
too densely for splines to make much sense.
That in turn does not make sense (to me anyway.) Just narrow the
bandwidth.
wdym?
this is not (really) a time series
But they are ordered on the x dimension. Try:
> x <- rnorm(1000)
> y <- 3 + x*0.5 + rnorm(1000)
> plot(x,y)
> lines( lowess(x,y))
> lines( lowess(x,y, 0.1), col="red") # narrowing the bandwidth from
the default.
I just need the mean (with confidence bounds, if possible) over many
small intervals.
Parametric or non-parametric CI's? It sounds as though you may want
quantile regression.
Also sounds easy to implement. Post an example. At the moment it is
not
clear if your intervals are disjoint or overlapping.
the intervals should partition the domain, like the result of hist:
What is the point of offering this code? It seems to be doing what you
want (but is also plotting a bunch of other stuff). Are you trying to
use someone else's code (who by the way appears to have been a former
SAS programmer judging by the totally unnecessary semi-colons) but
just narrow down the plotting task?
This would appear to be doing what you wanted:
dev.new();
# Data Prep
hi <- hist(x,breaks=breaks,main="histogram of time of day");
br <- length(hi$mids);
cat("breaks=",breaks," br=",br,"\n");
hi$breaks1 <- hi$breaks[2:(br+1)];
hi$means <- vector(mode="numeric", length = br);
hi$stdevs <- vector(mode="numeric", length = br);
for (i in 1:br) {
good <- hi$breaks[i] <= x & x < hi$breaks1[i];
tmp <- y[good];
hi$means[i] <- mean(tmp);
hi$total[i] <- sum(tmp);
hi$stdevs[i] <- sd(tmp);
}
hi$good <- hi$counts >= 5;
# Plotting call
plot3(hi$mids,hi$means,hi$stdevs,hi$good);
xlab="time of day";
title(main=paste(name,"mean pnl vs time of day"),xlab,ylab="mean
pnl");
plot3 <- function(x,m,s,good) {
x <- x[good];
m <- m[good];
s <- s[good];
t <- m+s;
b <- m-s;
r <- c(min(b),max(t));
plot(x,m,ylim=r,xlab="",ylab="");
points(x,b,col="red");
points(x,t,col="blue");
}
plot.mean <- function (x,y, scale = 2, breaks = exp(log(length(x))/
scale),
name="all") {
dev.new();
hi <- hist(x,breaks=breaks,main="histogram of time of day");
br <- length(hi$mids);
cat("breaks=",breaks," br=",br,"\n");
hi$breaks1 <- hi$breaks[2:(br+1)];
hi$means <- vector(mode="numeric", length = br);
hi$stdevs <- vector(mode="numeric", length = br);
for (i in 1:br) {
good <- hi$breaks[i] <= x & x < hi$breaks1[i];
tmp <- y[good];
hi$means[i] <- mean(tmp);
hi$total[i] <- sum(tmp);
hi$stdevs[i] <- sd(tmp);
}
hi$good <- hi$counts >= 5;
str(hi);
dev.new();
plot3(hi$mids,hi$means,hi$stdevs,hi$good);
xlab="time of day";
title(main=paste(name,"mean pnl vs time of day"),xlab,ylab="mean
pnl");
dev.new();
plot(hi$mids,hi$total,main=paste(name,"total pnl vs time of day"),
xlab=xlab,ylab="total pnl");
dev.new();
plot(hi$mids,cumsum(hi$total),xlab=xlab,ylab="cumulative pnl",
main=paste(name,"cumulative pnl vs time of day"));
hi
}
--
Sam Steingold (http://sds.podval.org/) on CentOS release 5.6 (Final)
X 11.0.60900031
http://jihadwatch.org http://camera.org http://ffii.org
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David Winsemius, MD
West Hartford, CT
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