StatVector
Related Docs:
object StatVector
| package stat
Construct a StatVector from a vector of type VectorI (vector of Ints).
Construct a StatVector from a vector of type VectorI (vector of Ints).
the vector used to initialize StatVector
Construct a StatVector from a vector of type VectorD (vector of Doubles).
Construct a StatVector from a vector of type VectorD (vector of Doubles).
the vector used to initialize StatVector
the dimension/size of the vector
whether the estimators are restricted to be unbiased.
Multiply this 'row' vector by matrix m.
Multiply this vector by scalar s.
Multiply this vector by vector b.
Multiply in-place this vector and scalar s.
Multiply in-place this vector and vector b.
Add this vector and scalar s._1 only at position s._2.
Add this vector and scalar s._1 only at position s._2.
the (scalar, position) to add
Add this vector and scalar s.
Add this vector and vector b.
Concatenate this vector and scalar b.
Concatenate this vector and vector b.
Add in-place this vector and scalar s.
Add in-place this vector and vector b.
From this vector subtract scalar s._1 only at position s._2.
From this vector subtract scalar s._1 only at position s._2.
the (scalar, position) to subtract
From this vector subtract scalar s.
From this vector subtract vector b.
From this vector subtract in-place scalar s.
From this vector subtract in-place vector b.
Divide this vector by scalar s.
Divide this vector by vector b (element-by-element).
Divide this vector by vector b (element-by-element).
the vector to divide by
Divide in-place this vector and scalar s.
Divide in-place this vector and vector b.
Create a vector of the form (0, ...
Create a vector of the form (0, ... -1, ... 0) where the -1 is at position j.
the position to place the -1
the size of the vector (upper bound = size - 1)
Return the vector that is the element-wise absolute value of this matrix.
Return the vector that is the element-wise absolute value of this matrix.
Compute the 'k'-lag auto-correlation of 'this' vector.
Compute the 'k'-lag auto-correlation of 'this' vector.
the lag parameter
Compute the k-lag auto-covariance of 'this' vector.
Compute the k-lag auto-covariance of 'this' vector.
the lag parameter
Compute the averaged median, which is the median when dim is odd and the average of the median and the next'k'-median when dim is even.
Get this vector's entire array.
Get this vector's entire array.
Get this vector's elements within the given range (vector slicing).
Get this vector's elements within the given range (vector slicing).
the given range
Get this vector's element at the i-th index position.
Find the argument maximum of this vector (index of maximum element).
Find the argument maximum of this vector (index of maximum element).
the ending index (exclusive) for the search
Return the argument maximum of this vector (-1 if its not positive).
Return the argument maximum of this vector (-1 if its not positive).
the ending index (exclusive) for the search
Find the argument minimum of this vector (index of minimum element).
Find the argument minimum of this vector (index of minimum element).
the ending index (exclusive) for the search
Return the argument minimum of this vector (-1 if its not negative).
Return the argument minimum of this vector (-1 if its not negative).
the ending index (exclusive) for the search
Determine whether x is contained in this vector.
Determine whether x is contained in this vector.
the element to be checked
Compute Pearson's correlation of 'this' vector with vector 'y'.
Compute Pearson's correlation of 'this' vector with vector 'y'.
the other vector
Count the number of strictly negative entries in this vector.
Count the number of strictly negative entries in this vector.
Count the number of strictly positive entries in this vector.
Count the number of strictly positive entries in this vector.
Compute the covariance of 'this' vector with vector 'y'.
Compute the covariance of 'this' vector with vector 'y'.
the other vector
Cumulate the values of this vector from left to right (e.g., create a cdf from a pmf).
Cumulate the values of this vector from left to right (e.g., create a cdf from a pmf). Example: (4, 2, 3, 1) --> (4, 6, 9, 10)
the dimension/size of the vector
the dimension/size of the vector
Compute the dot product (or inner product) of this vector with vector b.
Compute the dot product (or inner product) of this vector with vector b.
the other vector
Override equals to determine whether vector this equals vector b.
Override equals to determine whether vector this equals vector b.
the vector to compare with this
Expand the size (dim) of this stat vector by 'more' elements.
Expand the size (dim) of this stat vector by 'more' elements.
the number of new elements to add
Return the index of the first negative element in this vector (-1 otherwise).
Return the index of the first negative element in this vector (-1 otherwise).
the ending index (exclusive) for the search
Return the index of the first positive element in this vector (-1 otherwise).
Return the index of the first positive element in this vector (-1 otherwise).
the ending index (exclusive) for the search
Show the flaw by printing the error message.
Show the flaw by printing the error message.
the method where the error occurred
the error message
Iterate over this vector element by element.
Iterate over this vector element by element.
the function to apply
Must also override hashCode to be be compatible with equals.
Must also override hashCode to be be compatible with equals.
Produce the range of all indices (0 to one less than dim).
Produce the range of all indices (0 to one less than dim).
Compute the confidence interval half-width for the given confidence level.
Compute the confidence interval half-width for the given confidence level. The Confidence Interval (CI) is on the mean, i.e., CI = [mean +/- interval].
the confidence level
Check whether this vector is nonnegative (has no negative elements).
Check whether this vector is nonnegative (has no negative elements).
Find the element with the greatest magnitude in this vector.
Find the element with the greatest magnitude in this vector.
Map the elements of this vector by applying the mapping function f.
Map the elements of this vector by applying the mapping function f.
the function to apply
Take the maximum of this vector with vector b (element-by element).
Take the maximum of this vector with vector b (element-by element).
the other vector
Find the maximum element in this vector.
Find the maximum element in this vector.
the ending index (exclusive) for the search
Compute mean the elements of this vector.
Compute mean the elements of this vector.
Compute the 'k'-median ('k'-th smallest value) of 'this' vector.
Compute the 'k'-median ('k'-th smallest value) of 'this' vector. Setting 'k = (dim+1)/2' gives the regular median.
the type of median (k-th smallest value)
Take the minimum of this vector with vector b (element-by element).
Take the minimum of this vector with vector b (element-by element).
the other vector
Find the minimum element in this vector.
Find the minimum element in this vector.
the ending index (exclusive) for the search
Compute the mean square (ms) of 'this' vector.
Compute the Euclidean norm (2-norm) of this vector.
Compute the Euclidean norm (2-norm) of this vector.
Compute the Manhattan norm (1-norm) of this vector.
Compute the Manhattan norm (1-norm) of this vector.
Compute the Euclidean norm (2-norm) squared of this vector.
Compute the Euclidean norm (2-norm) squared of this vector.
Normalize this vector so that it sums to one (like a probability vector).
Normalize this vector so that it sums to one (like a probability vector).
Normalize this vector to have a maximum of one.
Normalize this vector to have a maximum of one.
Normalize this vector so its length is one (unit vector).
Normalize this vector so its length is one (unit vector).
Get the number of samples.
Create a vector of the form (0, ...
Create a vector of the form (0, ... 1, ... 0) where the 1 is at position j.
the position to place the 1
the size of the vector (upper bound = size - 1)
Determine if the Confidence Interval (CI) on the mean is tight enough.
Determine if the Confidence Interval (CI) on the mean is tight enough.
the cut-off value for CI to be considered tight
the confidence level
Compute the relative precision, i.e., the ratio of the confidence interval half-width and the mean.
Compute the relative precision, i.e., the ratio of the confidence interval half-width and the mean.
the confidence level
Establish the rank order of the elements in 'this' vector, e.g., (8.0, 2.0, 4.0, 6.0) is (3, 0, 1, 2).
Compute the root mean square (rms) of 'this' vector.
Check whether the other vector is at least as long as this vector.
Check whether the other vector is at least as long as this vector.
the other vector
(Changed in version 2.9.0) The behavior of scanRight has changed. The previous behavior can be reproduced with scanRight.reverse.
Compute Spearman's rank correlation of 'this' vector with vector 'y'.
Compute Spearman's rank correlation of 'this' vector with vector 'y'.
the other vector
http://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient
Select a subset of elements of this vector corresponding to a basis.
Select a subset of elements of this vector corresponding to a basis.
the set of index positions (e.g., 0, 2, 5)
Set each value in this vector to x.
Set the values in this vector to the values in array u.
Set the values in this vector to the values in array u.
the array of values to be assigned
Set the format to the newFormat (e.g., "%.6g,\t" or "%12.6g,\t").
Set the format to the newFormat (e.g., "%.6g,\t" or "%12.6g,\t").
the new format String
Compute the skewness of 'this' vector.
Compute the skewness of 'this' vector. Negative skewness indicates the distribution is elongated on the left, zero skewness indicates it is symmetric, and positive skewness indicates it is elongated on the right.
http://www.mathworks.com/help/stats/skewness.html
Slice this vector from to end.
Slice this vector from to end.
the start of the slice (included)
the end of the slice (excluded)
Sort this vector in-place in ascending (non-decreasing) order.
Sort this vector in-place in ascending (non-decreasing) order.
Sort this vector in-place in descending (non-increasing) order.
Sort this vector in-place in descending (non-increasing) order.
Return the vector containing the square each element of this vector.
Return the vector containing the square each element of this vector.
Compute the standard deviation of 'this' vector.
Sum the elements of this vector.
Sum the elements of this vector.
Sum the elements of this vector skipping the i-th element.
Sum the elements of this vector skipping the i-th element.
the index of the element to skip
Sum the positive (> 0) elements of this vector.
Sum the positive (> 0) elements of this vector.
Swap elements 'i' and 'j' in this vector.
Swap elements 'i' and 'j' in this vector.
the first element in the swap
the second element in the swap
Compute the product of critical value from the t-distribution and the standard deviation.
Compute the product of critical value from the t-distribution and the standard deviation.
the confidence level
Convert a VectorD into a VectorI.
Convert a VectorD into a VectorI.
Convert this vector to a String.
Convert this vector to a String.
Toggle/flip the bias flag for the estimators.
(Changed in version 2.9.0) transpose throws an IllegalArgumentException if collections are not uniformly sized.
Compare this vector with vector b.
Return the negative of this vector (unary minus).
Return the negative of this vector (unary minus).
Set this vector's elements over the given range (vector slicing).
Set this vector's elements over the given range (vector slicing).
the given range
the vector to assign
Set this vector's elements over the given range (vector slicing).
Set this vector's elements over the given range (vector slicing).
the given range
the value to assign
Set this vector's element at the i-th index position.
Set this vector's element at the i-th index position.
the given index
the value to assign
the 1D array used to store vector elements
the 1D array used to store vector elements
Compute the variance of 'this' vector.
Return the vector containing each element of this vector raised to the s-th power.
Return the vector containing each element of this vector raised to the s-th power.
the scalar exponent
Raise each element of this vector to the s-th power.
The
StatVectorclass provides methods for computing common statistics on a data vector. Both maximum likelihood (the default) and unbiased estimators are supported. Unbiased should only be used on sample (not population) data. Ex: It can be used to support the Method of Independent Replications (MIR).