isoreg {stats}  R Documentation 
Compute the isotonic (monotonely increasing nonparametric) least squares regression which is piecewise constant.
isoreg(x, y = NULL)
x, y 
in isoreg , coordinate vectors of the regression
points. Alternatively a single “plotting” structure can be
specified: see xy.coords .

The algorithm determines the convex minorant m(x) of the
cumulative data (i.e., cumsum(y)
) which is piecewise
linear and the result is m'(x), a step function with level
changes at locations where the convex m(x) touches the
cumulative data polygon and changes slope.
as.stepfun()
returns a stepfun
object which can be more parsimonious.
isoreg()
returns an object of class isoreg
which is
basically a list with components
x 
original (constructed) abscissa values x . 
y 
corresponding y values. 
yf 
fitted values corresponding to ordered x values. 
yc 
cumulative y values corresponding to ordered x values. 
iKnots 
integer vector giving indices where the fitted curve jumps, i.e., where the convex minorant has kinks. 
isOrd 
logical indicating if original x values were ordered increasingly already. 
ord 
if(!isOrd) : integer permutation order(x) of
original x . 
call 
the call to isoreg() used. 
The code should be improved to accept weights additionally and
solve the corresponding weighted least squares problem.
“Patches are welcome!”
Barlow, R. E., Bartholomew, D. J., Bremner, J. M., and Brunk, H. D. (1972) Statistical inference under order restrictions; Wiley, London.
Robertson, T., Wright,F. T. and Dykstra, R. L. (1988) Order Restricted Statistical Inference; Wiley, New York.
the plotting method plot.isoreg
with more examples;
isoMDS()
from the MASS package internally
uses isotonic regression.
(ir < isoreg(c(1,0,4,3,3,5,4,2,0))) plot(ir, plot.type = "row") (ir3 < isoreg(y3 < c(1,0,4,3,3,5,4,2, 3)))# last "3", not "0" (fi3 < as.stepfun(ir3)) (ir4 < isoreg(1:10, y4 < c(5, 9, 1:2, 5:8, 3, 8))) cat("R^2 =", formatC(sum(residuals(ir4)^2) / (9*var(y4)), dig=2),"\n")