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>> help ksdensity

ksdensity Compute kernel density or distribution estimate

[F,XI]=ksdensity(X) computes a probability density estimate of the sample

in the vector X. ksdensity evaluates the density estimate at 100 points

covering the range of the data. F is the vector of density values and XI

is the set of 100 points. The estimate is based on a normal kernel

function, using a window parameter (bandwidth) that is a function of the

number of points in X.

F=ksdensity(X,XI) specifies the vector XI of values where the density

estimate is to be evaluated.

[F,XI,U]=ksdensity(...) also returns the bandwidth of the kernel smoothing

window.

ksdensity(...) without output arguments produces a plot of the results.

ksdensity(AX,...) plots into axes AX instead of GCA.

[...]=ksdensity(...,'PARAM1',val1,'PARAM2',val2,...) specifies parameter

name/value pairs to control the density estimation. Valid parameters

are the following:

Parameter Value

'censoring' A logical vector of the same length of X, indicating which

entries are censoring times (default is no censoring).

'kernel' The type of kernel smoother to use, chosen from among

'normal' (default), 'box', 'triangle', and

'epanechnikov'.

'npoints' The number of equally-spaced points in XI.

'support' Either 'unbounded' (default) if the density can extend

over the whole real line, or 'positive' to restrict it to

positive values, or a two-element vector giving finite

lower and upper limits for the support of the density.

'weights' Vector of the same length as X, giving the weight to

assign to each X value (default is equal weights).

'bandwidth' The bandwidth of the kernel smoothing window. The

default is optimal for estimating normal densities, but