Nothing
tests/testthat/test-subboafit.R
In Rsubbotools: Fast Estimation of Subbottin and AEP Distributions (Generalized Error Distribution)
##############################################################################
# Subboafit
# checks against original implementation of subbotools
# Depends on using the same sample to fit as the subbotools package
# since R changes the rng in each version, to work this test must be manually
# updated each time or use a constant sample
skip()
paste0("Subboafit")
test_that("SubLaplace:", {
# subboafit -V 1 < sublaplace.txt
#
#--- FINAL RESULT --------------------------------------------------
# | correlation matrix
# value std.err | bl br al ar m
# bl= 0.4988 0.0009042 | -- 0.0000 0.0000 0.0000 -0.0000
# br= 0.4999 0.0009073 | 0.6438 -- 0.0000 0.0000 0.0000
# al= 3.998 0.007684 | 0.2077 0.5035 -- 0.0000 0.0000
# ar= 3.992 0.007665 | 0.5044 0.2104 0.3945 -- -0.0000
# m = -0.0005344 -nan | -nan -nan -nan -nan --
#
# Upper triangle: covariances
# Lower triangle: correlation coefficients
#-------------------------------------------------------------------
#
# bl br al ar m log-like
# 4.9876e-01 4.9988e-01 3.9980e+00 3.9921e+00 -5.3440e-04 3.3857e+00
orig_value <-
generate_orig_dt(
coef =
c(4.9876e-01, 4.9988e-01, 3.9980e+00, 3.9921e+00, -5.3440e-04),
log_likelihood = 3.3857e+00,
std_error = c(0.0009042, 0.0009073, 0.007684, 0.007665, NaN)
# we pass the transposed matrix and the code corrects it
, matrix =
c(
NA, 0.0000, 0.0000, 0.0000, -0.0000,
0.6438, NA, 0.0000, 0.0000, 0.0000,
0.2077, 0.5035, NA, 0.0001, 0.0000,
0.5044, 0.2104, 0.3945, NA, -0.0000,
NaN, NaN, NaN, NaN, NA
),
distribution = "subboafit"
)
check_fits(orig_value, .5, subboafit)
# Generate tests for the covariance matrices
data_test <- generate_datasets(.5)
subbo_test <- subboafit(data_test$x, verb = 3)
})
test_that("Laplace:", {
# subboafit -V 1 < laplace.txt
#
#--- FINAL RESULT --------------------------------------------------
# | correlation matrix
# value std.err | bl br al ar m
# bl= 1.003 0.002636 | -- -0.0000 0.0000 -0.0000 0.0000
# br= 1.001 0.002634 | -0.0021 -- -0.0000 0.0000 -0.0000
# al= 1.002 0.001803 | 0.6199 -0.0034 -- -0.0000 0.0000
# ar= 0.9977 0.001798 | -0.0025 0.6182 -0.0039 -- -0.0000
# m = 0.003416 0.002343 | 0.5632 -0.5641 0.5881 -0.5877 --
#
# Upper triangle: covariances
# Lower triangle: correlation coefficients
#-------------------------------------------------------------------
#
# bl br al ar m log-like
# 1.0027e+00 1.0007e+00 1.0020e+00 9.9769e-01 3.4161e-03 1.6923e+00
orig_value <-
generate_orig_dt(
coef = c(1.0027e+00, 1.0007e+00, 1.0020e+00, 9.9769e-01, 3.4161e-03),
log_likelihood = 1.6923e+00,
std_error = c(0.002636, 0.002634, 0.001803, 0.001798, 0.002343),
matrix =
c(
NA, -0.0000, 0.0000, -0.0000, 0.0000,
-0.0021, NA, -0.0000, 0.0000, -0.0000,
0.6199, -0.0034, NA, -0.0000, 0.0000,
-0.0025, 0.6182, -0.0039, NA, -0.0000,
0.5632, -0.5641, 0.5881, -0.5877, NA
),
distribution = "subboafit"
)
check_fits(orig_value, 1, subboafit)
})
test_that("Subnormal:", {
# subboafit -V 1 < subnormal.txt
#
#--- FINAL RESULT --------------------------------------------------
# | correlation matrix
# value std.err | bl br al ar m
# bl= 1.505 0.005966 | -- -0.0000 0.0000 -0.0000 0.0000
# br= 1.503 0.005967 | -0.4643 -- -0.0000 0.0000 -0.0000
# al= 0.765 0.002499 | 0.8585 -0.6045 -- -0.0000 0.0000
# ar= 0.7625 0.002495 | -0.6040 0.8585 -0.7549 -- -0.0000
# m = 0.001722 0.003918 | 0.7780 -0.7786 0.9149 -0.9150 --
#
# Upper triangle: covariances
# Lower triangle: correlation coefficients
#-------------------------------------------------------------------
#
# bl br al ar m log-like
# 1.5053e+00 1.5029e+00 7.6504e-01 7.6245e-01 1.7217e-03 1.2572e+00
orig_value <-
generate_orig_dt(
coef = c(1.5053e+00, 1.5029e+00, 7.6504e-01, 7.6245e-01, 1.7217e-03),
log_likelihood = 1.2572e+00,
std_error = c(0.005966, 0.005967, 0.002499, 0.002495, 0.003918),
matrix =
c(
NA, -0.0000, 0.0000, -0.0000, 0.0000,
-0.4643, NA, -0.0000, 0.0000, -0.0000,
0.8585, -0.6045, NA, -0.0000, 0.0000,
-0.6040, 0.8585, -0.7549, NA, -0.0000,
0.7780, -0.7786, 0.9149, -0.9150, NA
),
distribution = "subboafit"
)
check_fits(orig_value, 1.5, subboafit)
})
test_that("Normal:", {
# subboafit -V 1 < normal.txt
#
#--- FINAL RESULT --------------------------------------------------
# | correlation matrix
# value std.err | bl br al ar m
# bl= 2.012 0.01143 | -- -0.0001 0.0000 -0.0000 0.0001
# br= 1.997 0.01138 | -0.6925 -- -0.0000 0.0000 -0.0001
# al= 0.7107 0.003887 | 0.9248 -0.8091 -- -0.0000 0.0000
# ar= 0.7049 0.00387 | -0.8080 0.9251 -0.9227 -- -0.0000
# m = 0.004351 0.005779 | 0.8724 -0.8735 0.9729 -0.9730 --
#
# Upper triangle: covariances
# Lower triangle: correlation coefficients
#-------------------------------------------------------------------
#
# bl br al ar m log-like
# 2.0121e+00 1.9968e+00 7.1068e-01 7.0494e-01 4.3510e-03 1.0725e+00
orig_value <-
generate_orig_dt(
coef = c(2.0121e+00, 1.9968e+00, 7.1068e-01, 7.0494e-01, 4.3510e-03),
log_likelihood = 1.0725e+00,
std_error = c(0.01143, 0.01138, 0.003887, 0.00387, 0.005779),
matrix =
c(
NA, -0.0001, 0.0000, -0.0000, 0.0001,
-0.6925, NA, -0.0000, 0.0000, -0.0001,
0.9248, -0.8091, NA, -0.0000, 0.0000,
-0.8079, 0.9251, -0.9227, NA, -0.0000,
0.8724, -0.8735, 0.9729, -0.9730, NA
),
distribution = "subboafit"
)
check_fits(orig_value, 2, subboafit)
})
test_that("SuperNormal:", {
# subboafit -V 1 < supernormal.txt
#
#--- FINAL RESULT --------------------------------------------------
# | correlation matrix
# value std.err | bl br al ar m
# bl= 2.523 0.01928 | -- -0.0003 0.0001 -0.0001 0.0001
# br= 2.474 0.01919 | -0.8063 -- -0.0001 0.0001 -0.0001
# al= 0.7015 0.005593 | 0.9520 -0.8903 -- -0.0000 0.0000
# ar= 0.6842 0.005548 | -0.8879 0.9531 -0.9668 -- -0.0000
# m = 0.01212 0.007865 | 0.9178 -0.9203 0.9879 -0.9882 --
#
# Upper triangle: covariances
# Lower triangle: correlation coefficients
#-------------------------------------------------------------------
#
# bl br al ar m log-like
# 2.5234e+00 2.4742e+00 7.0150e-01 6.8424e-01 1.2117e-02 9.7328e-01
orig_value <-
generate_orig_dt(
coef = c(2.5234e+00, 2.4742e+00, 7.0150e-01, 6.8424e-01, 1.2117e-02),
log_likelihood = 9.7328e-01,
std_error = c(0.01928, 0.01919, 0.005593, 0.005548, 0.007865),
matrix =
c(
NA, -0.0003, 0.0001, -0.0001, 0.0001,
-0.8063, NA, -0.0001, 0.0001, -0.0001,
0.9520, -0.8903, NA, -0.0000, 0.0000,
-0.8879, 0.9531, -0.9668, NA, -0.0000,
0.9178, -0.9203, 0.9879, -0.9882, NA
),
distribution = "subboafit"
)
check_fits(orig_value, 2.5, subboafit)
})
##############################################################################
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Rsubbotools documentation built on April 16, 2025, 5:10 p.m.
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##############################################################################
# Subboafit
# checks against original implementation of subbotools
# Depends on using the same sample to fit as the subbotools package
# since R changes the rng in each version, to work this test must be manually
# updated each time or use a constant sample
skip()
paste0("Subboafit")
test_that("SubLaplace:", {
# subboafit -V 1 < sublaplace.txt
#
#--- FINAL RESULT --------------------------------------------------
# | correlation matrix
# value std.err | bl br al ar m
# bl= 0.4988 0.0009042 | -- 0.0000 0.0000 0.0000 -0.0000
# br= 0.4999 0.0009073 | 0.6438 -- 0.0000 0.0000 0.0000
# al= 3.998 0.007684 | 0.2077 0.5035 -- 0.0000 0.0000
# ar= 3.992 0.007665 | 0.5044 0.2104 0.3945 -- -0.0000
# m = -0.0005344 -nan | -nan -nan -nan -nan --
#
# Upper triangle: covariances
# Lower triangle: correlation coefficients
#-------------------------------------------------------------------
#
# bl br al ar m log-like
# 4.9876e-01 4.9988e-01 3.9980e+00 3.9921e+00 -5.3440e-04 3.3857e+00
orig_value <-
generate_orig_dt(
coef =
c(4.9876e-01, 4.9988e-01, 3.9980e+00, 3.9921e+00, -5.3440e-04),
log_likelihood = 3.3857e+00,
std_error = c(0.0009042, 0.0009073, 0.007684, 0.007665, NaN)
# we pass the transposed matrix and the code corrects it
, matrix =
c(
NA, 0.0000, 0.0000, 0.0000, -0.0000,
0.6438, NA, 0.0000, 0.0000, 0.0000,
0.2077, 0.5035, NA, 0.0001, 0.0000,
0.5044, 0.2104, 0.3945, NA, -0.0000,
NaN, NaN, NaN, NaN, NA
),
distribution = "subboafit"
)
check_fits(orig_value, .5, subboafit)
# Generate tests for the covariance matrices
data_test <- generate_datasets(.5)
subbo_test <- subboafit(data_test$x, verb = 3)
})
test_that("Laplace:", {
# subboafit -V 1 < laplace.txt
#
#--- FINAL RESULT --------------------------------------------------
# | correlation matrix
# value std.err | bl br al ar m
# bl= 1.003 0.002636 | -- -0.0000 0.0000 -0.0000 0.0000
# br= 1.001 0.002634 | -0.0021 -- -0.0000 0.0000 -0.0000
# al= 1.002 0.001803 | 0.6199 -0.0034 -- -0.0000 0.0000
# ar= 0.9977 0.001798 | -0.0025 0.6182 -0.0039 -- -0.0000
# m = 0.003416 0.002343 | 0.5632 -0.5641 0.5881 -0.5877 --
#
# Upper triangle: covariances
# Lower triangle: correlation coefficients
#-------------------------------------------------------------------
#
# bl br al ar m log-like
# 1.0027e+00 1.0007e+00 1.0020e+00 9.9769e-01 3.4161e-03 1.6923e+00
orig_value <-
generate_orig_dt(
coef = c(1.0027e+00, 1.0007e+00, 1.0020e+00, 9.9769e-01, 3.4161e-03),
log_likelihood = 1.6923e+00,
std_error = c(0.002636, 0.002634, 0.001803, 0.001798, 0.002343),
matrix =
c(
NA, -0.0000, 0.0000, -0.0000, 0.0000,
-0.0021, NA, -0.0000, 0.0000, -0.0000,
0.6199, -0.0034, NA, -0.0000, 0.0000,
-0.0025, 0.6182, -0.0039, NA, -0.0000,
0.5632, -0.5641, 0.5881, -0.5877, NA
),
distribution = "subboafit"
)
check_fits(orig_value, 1, subboafit)
})
test_that("Subnormal:", {
# subboafit -V 1 < subnormal.txt
#
#--- FINAL RESULT --------------------------------------------------
# | correlation matrix
# value std.err | bl br al ar m
# bl= 1.505 0.005966 | -- -0.0000 0.0000 -0.0000 0.0000
# br= 1.503 0.005967 | -0.4643 -- -0.0000 0.0000 -0.0000
# al= 0.765 0.002499 | 0.8585 -0.6045 -- -0.0000 0.0000
# ar= 0.7625 0.002495 | -0.6040 0.8585 -0.7549 -- -0.0000
# m = 0.001722 0.003918 | 0.7780 -0.7786 0.9149 -0.9150 --
#
# Upper triangle: covariances
# Lower triangle: correlation coefficients
#-------------------------------------------------------------------
#
# bl br al ar m log-like
# 1.5053e+00 1.5029e+00 7.6504e-01 7.6245e-01 1.7217e-03 1.2572e+00
orig_value <-
generate_orig_dt(
coef = c(1.5053e+00, 1.5029e+00, 7.6504e-01, 7.6245e-01, 1.7217e-03),
log_likelihood = 1.2572e+00,
std_error = c(0.005966, 0.005967, 0.002499, 0.002495, 0.003918),
matrix =
c(
NA, -0.0000, 0.0000, -0.0000, 0.0000,
-0.4643, NA, -0.0000, 0.0000, -0.0000,
0.8585, -0.6045, NA, -0.0000, 0.0000,
-0.6040, 0.8585, -0.7549, NA, -0.0000,
0.7780, -0.7786, 0.9149, -0.9150, NA
),
distribution = "subboafit"
)
check_fits(orig_value, 1.5, subboafit)
})
test_that("Normal:", {
# subboafit -V 1 < normal.txt
#
#--- FINAL RESULT --------------------------------------------------
# | correlation matrix
# value std.err | bl br al ar m
# bl= 2.012 0.01143 | -- -0.0001 0.0000 -0.0000 0.0001
# br= 1.997 0.01138 | -0.6925 -- -0.0000 0.0000 -0.0001
# al= 0.7107 0.003887 | 0.9248 -0.8091 -- -0.0000 0.0000
# ar= 0.7049 0.00387 | -0.8080 0.9251 -0.9227 -- -0.0000
# m = 0.004351 0.005779 | 0.8724 -0.8735 0.9729 -0.9730 --
#
# Upper triangle: covariances
# Lower triangle: correlation coefficients
#-------------------------------------------------------------------
#
# bl br al ar m log-like
# 2.0121e+00 1.9968e+00 7.1068e-01 7.0494e-01 4.3510e-03 1.0725e+00
orig_value <-
generate_orig_dt(
coef = c(2.0121e+00, 1.9968e+00, 7.1068e-01, 7.0494e-01, 4.3510e-03),
log_likelihood = 1.0725e+00,
std_error = c(0.01143, 0.01138, 0.003887, 0.00387, 0.005779),
matrix =
c(
NA, -0.0001, 0.0000, -0.0000, 0.0001,
-0.6925, NA, -0.0000, 0.0000, -0.0001,
0.9248, -0.8091, NA, -0.0000, 0.0000,
-0.8079, 0.9251, -0.9227, NA, -0.0000,
0.8724, -0.8735, 0.9729, -0.9730, NA
),
distribution = "subboafit"
)
check_fits(orig_value, 2, subboafit)
})
test_that("SuperNormal:", {
# subboafit -V 1 < supernormal.txt
#
#--- FINAL RESULT --------------------------------------------------
# | correlation matrix
# value std.err | bl br al ar m
# bl= 2.523 0.01928 | -- -0.0003 0.0001 -0.0001 0.0001
# br= 2.474 0.01919 | -0.8063 -- -0.0001 0.0001 -0.0001
# al= 0.7015 0.005593 | 0.9520 -0.8903 -- -0.0000 0.0000
# ar= 0.6842 0.005548 | -0.8879 0.9531 -0.9668 -- -0.0000
# m = 0.01212 0.007865 | 0.9178 -0.9203 0.9879 -0.9882 --
#
# Upper triangle: covariances
# Lower triangle: correlation coefficients
#-------------------------------------------------------------------
#
# bl br al ar m log-like
# 2.5234e+00 2.4742e+00 7.0150e-01 6.8424e-01 1.2117e-02 9.7328e-01
orig_value <-
generate_orig_dt(
coef = c(2.5234e+00, 2.4742e+00, 7.0150e-01, 6.8424e-01, 1.2117e-02),
log_likelihood = 9.7328e-01,
std_error = c(0.01928, 0.01919, 0.005593, 0.005548, 0.007865),
matrix =
c(
NA, -0.0003, 0.0001, -0.0001, 0.0001,
-0.8063, NA, -0.0001, 0.0001, -0.0001,
0.9520, -0.8903, NA, -0.0000, 0.0000,
-0.8879, 0.9531, -0.9668, NA, -0.0000,
0.9178, -0.9203, 0.9879, -0.9882, NA
),
distribution = "subboafit"
)
check_fits(orig_value, 2.5, subboafit)
})
##############################################################################
Try the Rsubbotools package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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