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Octave supports various helpful statistical functions. Many are useful as initial steps to prepare a data set for further analysis. Others provide different measures from those of the basic descriptive statistics.
Center data by subtracting its mean.
If x is a vector, subtract its mean.
If x is a matrix, do the above for each column.
If the optional argument dim is given, operate along this dimension.
Programming Note: center has obvious application for normalizing
statistical data. It is also useful for improving the precision of general
numerical calculations. Whenever there is a large value that is common
to a batch of data, the mean can be subtracted off, the calculation
performed, and then the mean added back to obtain the final answer.
See also: zscore.
Compute the Z score of x
If x is a vector, subtract its mean and divide by its standard deviation. If the standard deviation is zero, divide by 1 instead.
The optional parameter opt determines the normalization to use when
computing the standard deviation and has the same definition as the
corresponding parameter for std.
If x is a matrix, calculate along the first non-singleton dimension. If the third optional argument dim is given, operate along this dimension.
The optional outputs mu and sigma contain the mean and standard deviation.
Compute histogram counts.
When x is a vector, the function counts the number of elements of
x that fall in the histogram bins defined by edges. This
must be a vector of monotonically increasing values that define the edges
of the histogram bins. n(k) contains the number of elements
in x for which edges(k) <= x < edges(k+1).
The final element of n contains the number of elements of x
exactly equal to the last element of edges.
When x is an N-dimensional array, the computation is carried out along dimension dim. If not specified dim defaults to the first non-singleton dimension.
When a second output argument is requested an index matrix is also returned. The idx matrix has the same size as x. Each element of idx contains the index of the histogram bin in which the corresponding element of x was counted.
See also: hist.
unique function documented at unique is often
useful for statistics.
Compute the binomial coefficient of n or list all possible combinations of a set of items.
If n is a scalar then calculate the binomial coefficient of n and k which is defined as
/ \ | n | n (n-1) (n-2) … (n-k+1) n! | | = ------------------------- = --------- | k | k! k! (n-k)! \ /
This is the number of combinations of n items taken in groups of size k.
If the first argument is a vector, set, then generate all
combinations of the elements of set, taken k at a time, with
one row per combination. The result c has k columns and
nchoosek (length (set), k) rows.
For example:
How many ways can three items be grouped into pairs?
nchoosek (3, 2) ⇒ 3
What are the possible pairs?
nchoosek (1:3, 2)
⇒ 1 2
1 3
2 3
Programming Note: When calculating the binomial coefficient nchoosek
works only for non-negative, integer arguments. Use bincoeff for
non-integer and negative scalar arguments, or for computing many binomial
coefficients at once with vector inputs for n or k.
Generate all permutations of v with one row per permutation.
The result has size factorial (n) * n, where n
is the length of v.
Example
perms ([1, 2, 3]) ⇒ 1 2 3 2 1 3 1 3 2 2 3 1 3 1 2 3 2 1
Programming Note: The maximum length of v should be less than or equal to 10 to limit memory consumption.
Return the ranks of x along the first non-singleton dimension adjusted for ties.
If the optional argument dim is given, operate along this dimension.
Count the upward runs along the first non-singleton dimension of x of length 1, 2, …, n-1 and greater than or equal to n.
If the optional argument dim is given then operate along this dimension.
See also: runlength.
Find the lengths of all sequences of common values.
count is a vector with the lengths of each repeated value.
The optional output value contains the value that was repeated in the sequence.
runlength ([2, 2, 0, 4, 4, 4, 0, 1, 1, 1, 1]) ⇒ [2, 1, 3, 1, 4]
See also: run_count.
Return the probit (the quantile of the standard normal distribution) for each element of p.
See also: logit.
Compute the logit for each value of p
The logit is defined as
logit (p) = log (p / (1-p))
See also: probit, logistic_cdf.
Return the complementary log-log function of x.
The complementary log-log function is defined as
cloglog (x) = - log (- log (x))
Create a contingency table t from data vectors.
The l_x and l_y vectors are the corresponding levels.
Currently, only 1- and 2-dimensional tables are supported.
Next: Statistical Plots, Previous: Descriptive Statistics, Up: Statistics [Contents][Index]