INF, NAN, and Null

Revision as of 22:04, 11 January 2016 by Bbecane (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 (or a number less than 1.796E308) -- e.g.

-1/0 → -Inf

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

Calculations using INF and NAN follow ANSI (Association of National Standards Institutes) standards, which follow the laws of mathematics as far as possible:

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

Expressions taking NAN as an operand or parameter give NAN as their result unless the expression has a well-defined logical or numerical value for any value of NAN:

True OR NAN → True
NaN AND False → False
IF True THEN 5 ELSE NAN → 5

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
1 AND NULL → NULL

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

Variable A :=
I ▶
1 2 3 4 5
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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