View source for INF, NAN, and Null

[[category:Concepts]]
[[Category:Doc Status C]] <!-- For Lumina use, do not change -->
 
These are special values that Analytica returns in particular conditions. You can also use them in expressions:

'''Inf''' means infinity -- e.g., 
:1/0  &rarr; Inf
or a number larger than 1.796E308 (due to computer limitations) -- e.g. 
:1E307 * 100 &rarr; Inf
'''-Inf''' means negative infinity -- e.g., 
:-1/0  &rarr; -Inf
or a number less than 1.796E308
'''NAN''' means "Not A Number", where the result is numeric, but not a real number or infinity -- e.g.
:0/0 &rarr; NAN
:Sqrt(-1) &rarr; NAN
except if you enable [[Complex numbers]], in which case this will return a valid imaginary number, 1j.

'''Null''' means that there is no such value. For example, [[Slice]], [[Subscript]], [[SubIndex]], or [[MdTable]] return if you try to Slice out the nth slice over an Index with less than n values.

=== More on INF and NAN ===

Analytica follows ANSI (Association of National Standards Institutes) standards for computing with these special values, where applicable. 
:1/Inf &rarr; 0
:1/(-Inf) &rarr; 0
:Inf + Inf &rarr; Inf
:Inf - Inf &rarr; NAN

=== More on NULL ===

When NULL appears in scalar operations, it generally produces a warning and evaluates to NULL, for example:
:10 + NULL &rarr; NULL
:NULL - 10 &rarr; NULL
:1 AND NULL &rarr; NULL

Array-reducing functions ignore NULL.  These examples demonstrate (assume A is indexed by I as indicated).
:{|border="1"
! I: !! 1 !! 2 !! 3 !! 4 !! 5
|-
! A: || 8 || NULL || 4 || NULL || 0
|}

:[[Sum]](A, I) &rarr; 12
:[[Average]](A, I) &rarr; 4    
:[[JoinText]](A, I, ', ') &rarr; "8, 4, 0"

Graphs will simply ignore (not show) any point whose value is NULL.

Array-reducing functions include [[Sum]], [[Min]], [[Max]], [[ArgMin]], [[ArgMax]], [[Product]], [[Average]], [[JoinText]], [[Irr]], [[Npv]].  
Array functions [[Sum]], [[Min]] and [[Max]] also accept an optional parameter ''IgnoreNaN'' to ignore NaN values (which otherwise propagate, i.e. return NaN).

[[Regression]] also ignores any data points which have Y=Null, which is useful for missing data.