
KERNEL DENSITY PLOTName:
where K is the kernel function and h is the smoothing parameter or window width. Currently, Dataplot uses a Gaussion kernel function. This downweights points smoothly as the distance from x increases. The width parameter can be set by the user (see Note: below), although Dataplot will provide a default width that should produce reasonable results for most data sets. A kernel density plot can be considered a refinement of a histogram or frequency plot.
where <y> is the variable of raw data values; and where the <SUBSET/EXCEPT/FOR qualification> is optional. Note that <y> can be either a variable or a matrix. If <y> is a matrix, a kernel density plot will be generated for all values in the matrix.
<SUBSET/EXCEPT/FOR qualification> where <y1> ... <yk> is a list of response variables; and where the <SUBSET/EXCEPT/FOR qualification> is optional. This syntax will overlay multiple kernel density plots on the same plot. Note that the response variables (<y1> ... <yk> can be either variables or matrices (or a mix of variables and matrices). For matrices, a kernel density plot will be generated for all values in the matrix.
<SUBSET/EXCEPT/FOR qualification> where <y> is the variable of raw data values; <x1> is a groupid variable; and where the <SUBSET/EXCEPT/FOR qualification> is optional. This syntax will generate a kernel density plot for each distinct value in the groupid variable. The kernel density plots will be generated on the same page.
<SUBSET/EXCEPT/FOR qualification> where <y> is the variable of raw data values; <x1> is the first groupid variable; <x2> is the second groupid variable; and where the <SUBSET/EXCEPT/FOR qualification> is optional. This syntax will cross tabulate the groupid variables and generate a kernel density plot for each unique combination of values for the <x1> and <x2> groupid variables. The kernel density plots will be generated on the same page.
KERNEL DENSITY PLOT Y SUBSET TAG = 2 KERNEL DENSITY PLOT Y FOR I = 1 1 800 MULTIPLE KERNEL DENSITY PLOT Y1 Y2 Y3 REPLICATED KERNEL DENSITY PLOT Y X1 X2
This algorithm is based on the Fast Fourier Transform (FFT). The use of the FFT results in much greater computational efficiency. The article that accompanies this algorithm provides the details of how the FFT is used and provides timing estimates of this implemenation relative to an algorithm based on the definition of the kernel function.
You can set the number of points for the density curve using the following command:
where <value> defines the number of points.
where s is the sample standard deviation and IQ is the sample interquartile range. Silverman provides justification for this choice. Basically, it should perform reasonably for a wide variety of distributions. Note that the optimal width depends on the underlying function, which is what we are trying to estimate. If the underlying data is in fact normally distributed, then Silverman (1986) shows that the optimal width is
where n is the number of points in the raw data and s is the sample standard deviation of the raw data. It may be worthwhile to generate the density curve using several different values for the width. Silverman also recommends trying to transform skewed data sets to be symmetric. The width can be set with the following command:
To plot the estimated cdf, enter
To plot the estimated ppf, enter
To reset the plotting of the pdf, enter
Given that we can estimate the ppf function, we can use this to generate random numbers based on the kernel density plot. If you would like to generate random numbers, enter the command
where <value> is a number between 1 and the maximum number of rows. If <value> is set to 0 or a negative number, no random numbers are generated. Specifically, the following procedure is used:
where YMINIMUM and YMAXIMUM are the minimum and maximum values of the raw data and H is the window width.
DENSITY PLOT DENSITY TRACE The word MULTIPLE is optional for the MULTIPLE KERNEL DENSITY PLOT command.
B. W. Silverman (1986), "Density Estimation for Statistics and Data Analysis," Chapman & Hall. David Scott (1992), "Multivariate Density Estimation," John Wiley.
2010/02: Support for the MULTIPLE and REPLICATION syntax 2010/02: Support for matrix arguments and the TO syntax 2018/07: Support for SET KERNEL DENSITY PROBABILITY FUNCTION 2018/07: Support for SET KERNEL DENSITY RANDOM NUMBERS MULTIPLOT SCALE FACTOR 2 MULTIPLOT 2 2 MULTIPLOT CORNER COORDINATES 0 0 100 100 . LET Y = NORMAL RANDOM NUMBERS FOR I = 1 1 1000 X3LABEL 1,000 NORMAL RANDOM NUMBERS KERNEL DENSITY PLOT Y . LET Y = LOGNORMAL RANDOM NUMBERS FOR I = 1 1 1000 X3LABEL 1,000 LOGNORMAL RANDOM NUMBERS KERNEL DENSITY PLOT Y . LET GAMMA = 2 LET Y = WEIBULL RANDOM NUMBERS FOR I = 1 1 1000 X3LABEL 1,000 WEIBULL RANDOM NUMBERS (GAMMA = 2) KERNEL DENSITY PLOT Y . LET Y = LOGISTIC RANDOM NUMBERS FOR I = 1 1 1000 X3LABEL 1,000 LOGISTIC RANDOM NUMBERS KERNEL DENSITY PLOT Y END OF MULTIPLOT Program 2: . Step 1: Read the data . skip 25 read zarr13.dat y skip 0 . . Step 2: Plot control features . multiplot corner coordinates 5 5 95 95 multiplot scale factor 2 title offset 2 title case asis label case asis case asis . . Step 3: Generate the kernel density plot . multiplot 2 2 . title PDF kernel density plot y . title CDF set kernel density probability function cdf kernel density plot y . title PPF set kernel density probability function ppf set kernel density random numbers 200 kernel density plot y . read dpst1f.dat yrand line color blue red title Blue: Original Data, Red: 200 Random Numbers set kernel density probability function pdf multiple kernel density plot y yrand . end of multiplot  
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Date created: 08/14/2001 