Sometimes the number of observations in your data is different from the number of observations in an output data set created by a procedure. This eliminates the possibility of reading the output into the DataObject that contains the original data.
For example, you can use the SCORE procedure to evaluate a linear model on a separate set of values of the explanatory variables. Or you can use the SCORE statement of the LOESS, GAM, or TPSPLINE procedure to evaluate a nonparametric model. In each of these cases, you can read variables from the output data set by using the SAS/IML USE and READ statements and then use the DrawLine method to add the relevant curve to a plot.
As an example of this approach, suppose you want to add a kernel density estimate to a histogram of the min_pressure
variable. You can do this by calling the KDE procedure. Add the following statements at the bottom of the program window,
and select → from the main menu.
submit; proc kde data=Hurr; univar min_pressure / out=KDEOut plots=none; run; endsubmit;
The KDE procedure creates the data set Work.KDEout
on the SAS server with 401 observations. The data set contains a variable Value
that consists of evenly spaced points between the minimum and maximum values of min_pressure
. A variable named Density
contains the kernel density estimate evaluated at the points of Value
.
Note: You do not need to use the SORT procedure to sort this data set by Value
, because it was created in sorted order. However, for unsorted data, you need to sort by the independent variable.
A straightforward approach is to create a histogram and add the kernel density estimate by reading in the Work.KDEout
data set. Add the following statements at the bottom of the program window, and select → from the main menu.
use KDEOut; read all var {value density}; close KDEOut; declare Histogram hist; hist = Histogram.Create( dobj, "min_pressure" ); hist.ShowDensity(); /* show density instead of frequency */ hist.DrawUseDataCoordinates(); hist.DrawLine( value, density );
Figure 6.4 displays the resulting histogram and the overlaid kernel density estimate.
Figure 6.4: A Histogram Overlaid with a Kernel Density Estimate