More clean up

examples/CMakeLists.txt

@@ -34,10 +34,6 @@ target_include_directories(allan_example PUBLIC ${fplot_INCLUDE_DIR})
 target_link_libraries(allan_example ${fortran-csv-module_LIBRARY})
 target_include_directories(allan_example PUBLIC ${fortran-csv-module_INCLUDE_DIR})
 
-# Bootstrap Regression Example
-add_executable(bootstrap_regression_example bootstrap_regression_example.f90)
-target_link_libraries(bootstrap_regression_example fstats)
-
 # Box-Muller Example
 add_executable(box_muller_example box_muller_example.f90)
 target_link_libraries(box_muller_example fstats ${fplot_LIBRARY})

examples/nl_regression_example.f90

@@ -30,7 +30,6 @@ program example
         ylmod(21), lresid(21), lparams(4)
     type(convergence_info) :: info
     type(regression_statistics) :: stats(4), lstats(4)
-    type(bootstrap_regression_statistics) :: bstats(4)
 
     ! Data to fit
     xp = [0.0d0, 0.1d0, 0.2d0, 0.3d0, 0.4d0, 0.5d0, 0.6d0, 0.7d0, 0.8d0, &
@@ -76,26 +75,6 @@ program example
     print 103, tab // "Converge on Parameter Change: ", &
         info%converge_on_residual_parameter, " (", info%residual_value, ")"
 
-    ! Compare via a bootstrap solution
-    params = 1.0d0  ! reset the initial guess
-    call bootstrap_nonlinear_least_squares(fun, xp, yp, params, ymod, resid, &
-        stats = bstats)
-
-    ! Display the Results
-    print '(A)', nl // "Bootstrap:"
-    print 100, (tab // "params(", i, "): ", params(i), i = 1, size(params))
-
-    print '(A)', "Statistics:"
-    print 104, ( &
-        "Coefficient ", i, ":" // nl // &
-        tab // "Standard Error: ", bstats(i)%standard_error, nl // &
-        tab // "Upper Confidence Limit: ", bstats(i)%upper_confidence_interval, nl // &
-        tab // "Lower Confidence Limit: ", bstats(i)%lower_confidence_interval, nl // &
-        tab // "T-Statistic: ", bstats(i)%t_statistic, nl // &
-        tab // "P-Value: ", bstats(i)%probability, &
-        i = 1, size(bstats) &
-    )
-
     ! As our model is simply a 3rd order polynomial, the linear_least_squares
     ! routine can also be used.  As a comparison, here's the 
     ! linear_least_squares solution

src/fstats.f90

@@ -72,9 +72,6 @@ module fstats
     public :: scaled_random_resample
     public :: bootstrap_statistics
     public :: bootstrap
-    public :: bootstrap_regression_statistics
-    public :: bootstrap_linear_least_squares
-    public :: bootstrap_nonlinear_least_squares
     public :: box_muller_sample
     public :: rejection_sample
     public :: lowess