INF, NAN, and Null

Revision as of 17:16, 9 July 2015 by Max (Talk | contribs)

These are special values that Analytica returns in particular conditions. You can also use them in expressions:

Inf means infinity -- e.g.,

1/0 → Inf

or a number larger than 1.796E308 (the largest number that your computer can represent explicitly) -- e.g.

1E307 * 100 → Inf

-Inf means negative infinity -- e.g.,

-1/0 → -Inf

or a number less than 1.796E308

NAN means "Not A Number" -- i.e. not a well-defined number nor infinity -- e.g.

0/0 → NAN
Sqrt(-1) → NAN

(If you enable Complex Numbers, Sqrt(-1) returns the valid imaginary number, 1j.)

Null means that there is no such value. For example, Slice and Subscript return Null if you try to get the nth slice over an Index with less than n values. For example:

Index Year := [2015, 2016, 2017]
Slice(Year, 4) → NULL
Variable X := Array(Year, [20, 23, 28])
X[Year = 2018] → NULL

More on INF and NAN

Analytica follows ANSI (Association of National Standards Institutes) standards for calculations using these special values:

1/Inf → 0
1/(-Inf) → 0
Inf + Inf → Inf
Inf - Inf → NAN

Expressions taking NAN as an operand or parameter may not generate NAN as the result if the expression has a well-defined logical or numerical value for any value of NAN:

True OR NAN → True
NaN AND False → False

More on NULL

When NULL appears in scalar operations, it generally produces a warning and evaluates to NULL, for example:

10 + NULL → NULL
NULL - 10 → NULL

Array-reducing functions ignore NULL. These examples demonstrate (assume A is indexed by I as indicated).

I: 1 2 3 4 5
A: 8 NULL 4 NULL 0
Sum(A, I) → 12
Average(A, I) → 4
JoinText(A, I, ', ') → "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.


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