Add additional statistics

src/fstats.f90

@@ -29,8 +29,10 @@ module fstats
     public :: variance
     public :: standard_deviation
     public :: median
+    public :: covariance
     public :: r_squared
     public :: adjusted_r_squared
+    public :: correlation
     public :: quantile
     public :: t_test_equal_variance
     public :: t_test_unequal_variance

src/fstats_descriptive_statistics.f90

@@ -11,6 +11,7 @@ module fstats_descriptive_statistics
     public :: median
     public :: quantile
     public :: trimmed_mean
+    public :: covariance
 
 contains
 ! ------------------------------------------------------------------------------
@@ -43,8 +44,8 @@ pure function mean(x) result(rst)
 pure function variance(x) result(rst)
     !! Computes the sample variance of the values in an array.
     !!
-    !! The variance computed is the sample variance such that the variance.
-    !! $$ s^2 = \frac{\Sigma \left( x_{i} - \bar{x} \right)^2}{n - 1} $$
+    !! The variance computed is the sample variance such that 
+    !! $$ s^2 = \frac{\Sigma \left( x_{i} - \bar{x} \right)^2}{n - 1} $$.
     real(real64), intent(in) :: x(:)
         !! The array of values to analyze.
     real(real64) :: rst
@@ -196,5 +197,45 @@ function trimmed_mean(x, p) result(rst)
     rst = mean(x(i1:i2))
 end function
 
+! ------------------------------------------------------------------------------
+pure function covariance(x, y) result(rst)
+    !! Computes the sample covariance of two data sets.
+    !!
+    !! The covariance computed is the sample covariance such that 
+    !! $$ q_{jk} = \frac{\Sigma \left( x_{i} - \bar{x} \right) 
+    !! \left( y_{i} - \bar{y} \right)}{n - 1} $$.
+    real(real64), intent(in), dimension(:) :: x
+        !! The first N-element data set.
+    real(real64), intent(in), dimension(size(x)) :: y
+        !! The second N-element data set.
+    real(real64) :: rst
+        !! The covariance.
+
+    ! Parameters
+    real(real64), parameter :: zero = 0.0d0
+    real(real64), parameter :: one = 1.0d0
+
+    ! Local Variables
+    integer(int32) :: i, n
+    real(real64) :: meanX, meanY
+
+    ! Process
+    n = size(x)
+    if (n <= 1) then
+        rst = zero
+    else
+        ! Compute the means
+        meanX = x(1)
+        meanY = y(1)
+        do i = 2, n
+            meanX = meanX + (x(i) - meanX) / i
+            meanY = meanY + (y(i) - meanY) / i
+        end do
+
+        ! Compute the covariance
+        rst = sum((x - meanX) * (y - meanY)) / (n - one)
+    end if
+end function
+
 ! ------------------------------------------------------------------------------
 end module

src/fstats_regression.f90

@@ -18,6 +18,7 @@ module fstats_regression
     public :: regression_statistics
     public :: r_squared
     public :: adjusted_r_squared
+    public :: correlation
     public :: coefficient_matrix
     public :: covariance_matrix
     public :: linear_least_squares
@@ -271,6 +272,26 @@ function adjusted_r_squared(p, x, xm, err) result(rst)
     rst = one - (one - r2) * (n - one) / (n - p - one)
 end function
 
+! ------------------------------------------------------------------------------
+pure function correlation(x, y) result(rst)
+    !! Computes the sample correlation coefficient (an estimate to the 
+    !! population Pearson correlation) as follows.
+    !!
+    !! $$ r_{xy} = \frac{cov(x, y)}{s_{x} s_{y}} $$.
+    !!
+    !! Where, $$ s_{x} $$ & $$ s_{y} $$ are the sample standard deviations of
+    !! x and y respectively.
+    real(real64), intent(in), dimension(:) :: x
+        !! The first N-element data set.
+    real(real64), intent(in), dimension(size(x)) :: y
+        !! The second N-element data set.
+    real(real64) :: rst
+        !! The correlation coefficient.
+
+    ! Process
+    rst = covariance(x, y) / (standard_deviation(x) * standard_deviation(y))
+end function
+
 ! ------------------------------------------------------------------------------
 subroutine coefficient_matrix(order, intercept, x, c, err)
     !! Computes the coefficient matrix \( X \) to the linear 

tests/fstats_statistics_tests.f90

@@ -848,5 +848,60 @@ function trimmed_mean_test_1() result(rst)
         end if
     end function
 
+! ------------------------------------------------------------------------------
+    function test_covariance_1() result(rst)
+        ! Arguments
+        logical :: rst
+
+        ! Variables
+        integer(int32), parameter :: n = 500
+        real(real64) :: x(n), y(n), cov, ans, avgX, avgY
+
+        ! Initialization
+        rst = .true.
+        call random_number(x)
+        call random_number(y)
+
+        avgX = mean(x)
+        avgY = mean(y)
+
+        ans = sum((x - avgX) * (y - avgY)) / (n - 1.0d0)
+
+        ! Test 1
+        cov = covariance(x, y)
+        if (.not.is_equal(cov, ans)) then
+            rst = .false.
+            print '(A)', "TEST FAILED: Covariance test 1"
+        end if
+    end function
+
+! ------------------------------------------------------------------------------
+    function test_correlation_1() result(rst)
+        ! Arguments
+        logical :: rst
+
+        ! Variables
+        integer(int32), parameter :: n = 500
+        real(real64) :: x(n), y(n), c, ans, avgX, avgY
+
+        ! Initialization
+        rst = .true.
+        call random_number(x)
+        call random_number(y)
+
+        avgX = mean(x)
+        avgY = mean(y)
+
+        ans = sum((x - avgX) * (y - avgY)) / &
+            sqrt(sum((x - avgX)**2) * sum((y - avgY)**2))
+
+        ! Test 1
+        c = correlation(x, y)
+        if (.not.is_equal(c, ans)) then
+            rst = .false.
+            print '(A)', "TEST FAILED: Correlation test 1"
+        end if
+    end function
+
 ! ------------------------------------------------------------------------------
 end module

tests/fstats_tests.f90

@@ -110,6 +110,14 @@ program tests
     local = trimmed_mean_test_1()
     if (.not.local) overall = .false.
 
+    ! Covariance Tests
+    local = test_covariance_1()
+    if (.not.local) overall = .false.
+
+    ! Correlation Tests
+    local = test_correlation_1()
+    if (.not.local) overall = .false.
+
     ! End
     if (.not.overall) then
         stop 1