help kdensity dialog: kdensity
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Title
[R] kdensity -- Univariate kernel density estimation
Syntax
kdensity varname [if] [in] [weight] [, options]
options description
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Main
kernel(kernel) specify kernel function; default is
kernel(epanechnikov)
bwidth(#) half-width of kernel
generate(newvar_x newvar_d) store the estimation points in newvar_x
and the density estimate in newvar_d
n(#) estimate density using # points; default
is min(N, 50)
at(var_x) estimate density using the values
specified by var_x
nograph suppress graph
Kernel plot
cline_options affect rendition of the plotted kernel
density estimate
Density plots
normal add normal density to the graph
normopts(cline_options) affect rendition of normal density
student(#) add Student's t density with # degrees of
freedom to the graph
stopts(cline_options) affect rendition of the Student's t
density
Add plots
addplot(plot) add other plots to the generated graph
Y axis, X axis, Titles, Legend, Overall
twoway_options any options other than by() documented in
[G] twoway_options
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kernel description
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epanechnikov Epanechnikov kernel function; the default
epan2 alternative Epanechnikov kernel function
biweight biweight kernel function
cosine cosine trace kernel function
gaussian Gaussian kernel function
parzen Parzen kernel function
rectangle rectangle kernel function
triangle triangle kernel function
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fweights, aweights, and iweights are allowed; see weight.
Menu
Statistics > Nonparametric analysis > Kernel density estimation
Description
kdensity produces kernel density estimates and graphs the result.
Options
+------+
----+ Main +-------------------------------------------------------------
kernel(kernel) specifies the kernel function for use in calculating the
kernel density estimate. The default kernel is the Epanechnikov
kernel (epanechnikov).
bwidth(#) specifies the half-width of the kernel, the width of the
density window around each point. If bwidth() is not specified, the
"optimal" width is calculated and used; see [R] kdensity. The
optimal width is the width that would minimize the mean integrated
squared error if the data were Gaussian and a Gaussian kernel were
used, so it is not optimal in any global sense. In fact, for
multimodal and highly skewed densities, this width is usually too
wide and oversmooths the density.
generate(newvar_x newvar_d) stores the results of the estimation.
newvar_x will contain the points at which the density is estimated.
newvar_d will contain the density estimate.
n(#) specifies the number of points at which the density estimate is to
be evaluated. The default is min(N,50), where N is the number of
observations in memory.
at(var_x) specifies a variable that contains the values at which the
density should be estimated. This option allows you to more easily
obtain density estimates for different variables or different
subsamples of a variable and then overlay the estimated densities for
comparison.
nograph suppresses the graph. This option is often used with the
generate() option.
+-------------+
----+ Kernel plot +------------------------------------------------------
cline_options affect the rendition of the plotted kernel density
estimate. See [G] cline_options.
+---------------+
----+ Density plots +----------------------------------------------------
normal requests that a normal density be overlaid on the density estimate
for comparison.
normopts(cline_options) specifies details about the rendition of the
normal curve, such as the color and style of line used. See [G]
cline_options.
student(#) specifies that a Student's t density with # degrees of freedom
be overlaid on the density estimate for comparison.
stopts(cline_options) affects the rendition of the Student's t density.
See [G] cline_options.
+-----------+
----+ Add plots +--------------------------------------------------------
addplot(plot) provides a way to add other plots to the generated graph.
See [G] addplot_option.
+-----------------------------------------+
----+ Y axis, X axis, Titles, Legend, Overall +--------------------------
twoway_options are any of the options documented in [G] twoway_options,
excluding by(). These include options for titling the graph (see [G]
title_options) and for saving the graph to disk (see [G]
saving_option).
Examples
Setup
. sysuse auto
Graph kernel density estimates for length
. kdensity length
Same as above, but use 20 for the half-width of the kernel
. kdensity length, bw(20)
Obtain kernel density estimates for weight using the Parzen kernel
function, store these results in x2, and suppress the graph
. kdensity weight, kernel(parzen) gen(x2 parzen) nograph
Saved results
kdensity saves the following in r():
Scalars
r(bwidth) kernel bandwidth
r(n) number of points at which the estimate was evaluated
r(scale) density bin width
Macros
r(kernel) name of kernel
Also see
Manual: [R] kdensity
Help: [R] histogram
