|SAS/SPECTRAVIEW Software User's Guide|
|Data Set Requirements|
To use SAS/SPECTRAVIEW, your data must be stored in a SAS data set, which consists of variables and observations. A variable is a column in the data set, such as quantities or characteristics being measured, that has attributes such as a name and a type (character or numeric). An observation is the horizontal component of the data set, such as collections of values associated with a single entity; each observation contains one value for each variable in the data set.
The SAS data set that you use must have at least four variables:
You can specify an optional fifth variable, which can be character or numeric, as a BY variable. A BY variable allows you to animate an image so that you can see how response values change according to some grouping, like over time.
|How the Software Displays Data|
When you load a SAS data set into SAS/SPECTRAVIEW, the software does the following:
The variables that you specify for the axes frequently (but not always) represent dimensions of spatial data. For example, in a spatial diagram like the following cubic volume, the x,y,z coordinate 6,5,5 represents a location that is 6 ticks along the X axis, 5 ticks along the Y axis, and 5 ticks along the Z axis (counting from an origination point shown as 0 in this figure):
Coordinate 6,5,5 in Three-Dimensional Space
To illustrate the relationship among the axis values and the response values, consider the following spatial data example:
To determine the age of someone sitting in a specific seat in a stadium, you need to know the section, the row, and the seat number to locate that person. The three values are x,y,z coordinates that identify a specific location in space, which in this case is a stadium. Once located, the person can be asked his age; that number becomes the fourth value...the response value. You could collect the same information for everyone in the stadium. That is, you could attach an age response value to each location identified by section, row, and seat.
If you created a data set of the seating information and loaded it into SAS/SPECTRAVIEW, the following would occur:
You could then explore the data visually and determine, for example, whether age groupings occur in various locations in the stadium. You could also display any empty seats, which have no response value at that location.
Locations in three-dimensional space are similar to
stadium seat locations. For example, if you want to test the amount of sulphur
in the air at various locations, you would need three coordinates similar
to section, row, and seat. These might be 20 km east, 10 km north, and 200
meters up. The coordinates describe a specific location in space where a
sulphur sample can be taken and recorded. When you display the data, a color
is mapped to each response value, representing ranges of values, for example,
values between 0.0 and 0.5 could be red, values between 0.51 and 1.0 could
be yellow, and so on.
Since the axis variables represent different dimensions of data, you can use SAS/SPECTRAVIEW to explore non-spatial data as well.
For example, the sample data set MORTGAGE (which contains mortgage payments for various numbers of years, interest rates, and loan amounts) can be represented several ways. The axis variables could be principal amount, percentage rate, and term of loan; it does not matter which variable you assign to X, which to Y, and which to Z. This presumes that the PAYMENT variable is the response you want to explore. Or you could assign the variables so that AMOUNT is the response to explore, with axes of term, rate, and affordable payment range.
Assume the following variables for SAS/SPECTRAVIEW:
|RATE||as the X variable, which is the loan interest percentage rate.|
|AMOUNT||as the Y variable, which is the loan amount.|
|YEARS||as the Z variable, which is the number of years for the loan.|
|PAYMENT||as the response variable, which is the monthly payment amount.|
SAS/SPECTRAVIEW reads the data and generates the horizontal X axis (representing the loan interest rate), the vertical Y axis (representing the loan amounts), and the depth Z axis (representing the number of years for the loan). Each resulting data point is applied a color representing a response value, which is a monthly payment amount.
MORTGAGE Data Set Displayed as a Point Cloud examines the data using a point cloud (which is one of the SAS/SPECTRAVIEW visualization techniques), showing the relationship of the monthly payments to percentage rate, loan amount, and length of the loan. This point cloud displays a subset of the data points, showing only the higher loan payments (response values). With this point cloud, you can determine that most of the higher loan payments are for a shorter number of years.
MORTGAGE Data Set Displayed as a Point Cloud
|Summary of Software Tools|
the following tools that you use to create and analyze images.
Visualization techniques create images representing your data.
SAS/SPECTRAVIEW provides a variety of options that you can use with visualization techniques to aid in data exploration and analysis:
These options let you modify your view of the data:
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Copyright 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.