In ComtekAdvancedStructures/cmstatr: Statistical Methods for Composite Material Data
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
1. Introduction
This vignette is intended to contain the same validation that is included
in the test suite within the cmstatr package, but in a format that is
easier for a human to read. The intent is that this vignette will include
only those validations that are included in the test suite, but that the
test suite may include more tests than are shown in this vignette.
The following packages will be used in this validation. The version of
each package used is listed at the end of this vignette.
library(cmstatr)
library(dplyr)
library(purrr)
library(tidyr)
library(testthat)
Throughout this vignette, the testthat package will be used. Expressions
such as expect_equal are used to ensure that the two values are equal
(within some tolerance). If this expectation is not true, the vignette will
fail to build. The tolerance is a relative tolerance: a tolerance of 0.01
means that the two values must be within $1\%$ of each other.
As an example, the following expression checks that the value
10 is equal to 10.1 within a tolerance of 0.01. Such an expectation
should be satisfied.
expect_equal(10, 10.1, tolerance = 0.01)
The basis_... functions automatically perform certain diagnostic tests.
When those diagnostic tests are not relevant to the validation, the
diagnostic tests are overridden by passing the argument override = "all".
2. Validation Table
The following table provides a cross-reference between the various
functions of the cmstatr package and the tests shown within this
vignette. The sections in this vignette are organized by data set.
Not all checks are performed on all data sets.
Function | Tests
--------------------------------|---------------------------
ad_ksample() | Section 3.1, Section 4.1.2, Section 6.1
anderson_darling_normal() | Section 4.1.3, Section 5.1
anderson_darling_lognormal() | Section 4.1.3, Section 5.2
anderson_darling_weibull() | Section 4.1.3, Section 5.3
basis_normal() | Section 5.4
basis_lognormal() | Section 5.5
basis_weibull() | Section 5.6
basis_pooled_cv() | Section 4.2.3, Section 4.2.4,
basis_pooled_sd() | Section 4.2.1, Section 4.2.2
basis_hk_ext() | Section 4.1.6, Section 5.7, Section 5.8
basis_nonpara_large_sample() | Section 5.9
basis_anova() | Section 4.1.7
calc_cv_star() |
cv() |
equiv_change_mean() | Section 5.11
equiv_mean_extremum() | Section 5.10
hk_ext_z() | Section 7.3, Section 7.4
hk_ext_z_j_opt() | Section 7.5
k_equiv() | Section 7.8
k_factor_normal() | Section 7.1, Section 7.2
levene_test() | Section 4.1.4, Section 4.1.5
maximum_normed_residual() | Section 4.1.1
nonpara_binomial_rank() | Section 7.6, Section 7.7
normalize_group_mean() |
normalize_ply_thickness() |
transform_mod_cv_ad() |
transform_mod_cv() |
3. carbon.fabric Data Set
This data set is example data that is provided with cmstatr.
The first few rows of this data are shown below.
head(carbon.fabric)
3.1. Anderson--Darling k-Sample Test {#cf-ad}
This data was entered into ASAP 2008 [@ASAP2008] and the reported
Anderson--Darling k--Sample test statistics were recorded, as were
the conclusions.
The value of the test statistic reported by cmstatr and that reported
by ASAP 2008 differ by a factor of $k - 1$, as do the critical values
used. As such, the conclusion of the tests are identical. This is
described in more detail in the
Anderson--Darling k--Sample Vignette.
When the RTD warp-tension data from this data set is entered into ASAP 2008,
it reports a test statistic of 0.456 and fails to reject the null hypothesis
that the batches are drawn from the same distribution. Adjusting for the
different definition of the test statistic, the results given by cmstatr
are very similar.
res <- carbon.fabric %>%
filter(test == "WT") %>%
filter(condition == "RTD") %>%
ad_ksample(strength, batch)
expect_equal(res$ad / (res$k - 1), 0.456, tolerance = 0.002)
expect_false(res$reject_same_dist)
res
When the ETW warp-tension data from this data set are entered into ASAP 2008,
the reported test statistic is 1.604 and it fails to reject the null
hypothesis that the batches are drawn from the same distribution. Adjusting
for the different definition of the test statistic, cmstatr gives nearly
identical results.
res <- carbon.fabric %>%
filter(test == "WT") %>%
filter(condition == "ETW") %>%
ad_ksample(strength, batch)
expect_equal(res$ad / (res$k - 1), 1.604, tolerance = 0.002)
expect_false(res$reject_same_dist)
res
4. Comparison with Examples from CMH-17-1G
4.1 Dataset From Section 8.3.11.1.1
CMH-17-1G [@CMH-17-1G] provides an example data set and results from
ASAP [@ASAP2008] and STAT17 [@STAT-17]. This example data set is duplicated
below:
dat_8_3_11_1_1 <- tribble(
~batch, ~strength, ~condition,
1, 118.3774604, "CTD", 1, 84.9581364, "RTD", 1, 83.7436035, "ETD",
1, 123.6035612, "CTD", 1, 92.4891822, "RTD", 1, 84.3831677, "ETD",
1, 115.2238092, "CTD", 1, 96.8212659, "RTD", 1, 94.8030433, "ETD",
1, 112.6379744, "CTD", 1, 109.030325, "RTD", 1, 94.3931537, "ETD",
1, 116.5564277, "CTD", 1, 97.8212659, "RTD", 1, 101.702222, "ETD",
1, 123.1649896, "CTD", 1, 100.921519, "RTD", 1, 86.5372121, "ETD",
2, 128.5589027, "CTD", 1, 103.699444, "RTD", 1, 92.3772684, "ETD",
2, 113.1462103, "CTD", 2, 93.7908212, "RTD", 2, 89.2084024, "ETD",
2, 121.4248107, "CTD", 2, 107.526709, "RTD", 2, 100.686001, "ETD",
2, 134.3241906, "CTD", 2, 94.5769704, "RTD", 2, 81.0444192, "ETD",
2, 129.6405117, "CTD", 2, 93.8831373, "RTD", 2, 91.3398070, "ETD",
2, 117.9818658, "CTD", 2, 98.2296605, "RTD", 2, 93.1441939, "ETD",
3, 115.4505226, "CTD", 2, 111.346590, "RTD", 2, 85.8204168, "ETD",
3, 120.0369467, "CTD", 2, 100.817538, "RTD", 3, 94.8966273, "ETD",
3, 117.1631088, "CTD", 3, 100.382203, "RTD", 3, 95.8068520, "ETD",
3, 112.9302797, "CTD", 3, 91.5037811, "RTD", 3, 86.7842252, "ETD",
3, 117.9114501, "CTD", 3, 100.083233, "RTD", 3, 94.4011973, "ETD",
3, 120.1900159, "CTD", 3, 95.6393615, "RTD", 3, 96.7231171, "ETD",
3, 110.7295966, "CTD", 3, 109.304779, "RTD", 3, 89.9010384, "ETD",
3, 100.078562, "RTD", 3, 99.1205847, "RTD", 3, 89.3672306, "ETD",
1, 106.357525, "ETW", 1, 99.0239966, "ETW2",
1, 105.898733, "ETW", 1, 103.341238, "ETW2",
1, 88.4640082, "ETW", 1, 100.302130, "ETW2",
1, 103.901744, "ETW", 1, 98.4634133, "ETW2",
1, 80.2058219, "ETW", 1, 92.2647280, "ETW2",
1, 109.199597, "ETW", 1, 103.487693, "ETW2",
1, 61.0139431, "ETW", 1, 113.734763, "ETW2",
2, 99.3207107, "ETW", 2, 108.172659, "ETW2",
2, 115.861770, "ETW", 2, 108.426732, "ETW2",
2, 82.6133082, "ETW", 2, 116.260375, "ETW2",
2, 85.3690411, "ETW", 2, 121.049610, "ETW2",
2, 115.801622, "ETW", 2, 111.223082, "ETW2",
2, 44.3217741, "ETW", 2, 104.574843, "ETW2",
2, 117.328077, "ETW", 2, 103.222552, "ETW2",
2, 88.6782903, "ETW", 3, 99.3918538, "ETW2",
3, 107.676986, "ETW", 3, 87.3421658, "ETW2",
3, 108.960241, "ETW", 3, 102.730741, "ETW2",
3, 116.122640, "ETW", 3, 96.3694916, "ETW2",
3, 80.2334815, "ETW", 3, 99.5946088, "ETW2",
3, 106.145570, "ETW", 3, 97.0712407, "ETW2",
3, 104.667866, "ETW",
3, 104.234953, "ETW"
)
dat_8_3_11_1_1
4.1.1 Maximum Normed Residual Test {#c11-mnr}
CMH-17-1G Table 8.3.11.1.1(a) provides results of the MNR test from
ASAP for this data set. Batches 2 and 3 of the ETW data is considered
here and the results of cmstatr are compared with those published in
CMH-17-1G.
For Batch 2 of the ETW data, the results match those published in the
handbook within a small tolerance. The published test statistic is 2.008.
res <- dat_8_3_11_1_1 %>%
filter(condition == "ETW" & batch == 2) %>%
maximum_normed_residual(strength, alpha = 0.05)
expect_equal(res$mnr, 2.008, tolerance = 0.001)
expect_equal(res$crit, 2.127, tolerance = 0.001)
expect_equal(res$n_outliers, 0)
res
Similarly, for Batch 3 of the ETW data, the results of cmstatr match
the results published in the handbook within a small tolerance. The published
test statistic is 2.119
res <- dat_8_3_11_1_1 %>%
filter(condition == "ETW" & batch == 3) %>%
maximum_normed_residual(strength, alpha = 0.05)
expect_equal(res$mnr, 2.119, tolerance = 0.001)
expect_equal(res$crit, 2.020, tolerance = 0.001)
expect_equal(res$n_outliers, 1)
res
4.1.2 Anderson--Darling k--Sample Test {#c11-adk}
For the ETW condition, the ADK test statistic given in [@CMH-17-1G] is
$ADK = 0.793$ and the test concludes that the samples come from the
same distribution. Noting that cmstatr uses the definition of the
test statistic given in [@Stephens1987], so the test statistic given
by cmstatr differs from that given by ASAP by a factor of $k - 1$,
as described in the Anderson--Darling k--Sample Vignette.
res <- dat_8_3_11_1_1 %>%
filter(condition == "ETW") %>%
ad_ksample(strength, batch)
expect_equal(res$ad / (res$k - 1), 0.793, tolerance = 0.003)
expect_false(res$reject_same_dist)
res
Similarly, for the ETW2 condition, the test statistic given in [@CMH-17-1G]
is $ADK = 3.024$ and the test concludes that the samples come from different
distributions. This matches cmstatr
res <- dat_8_3_11_1_1 %>%
filter(condition == "ETW2") %>%
ad_ksample(strength, batch)
expect_equal(res$ad / (res$k - 1), 3.024, tolerance = 0.001)
expect_true(res$reject_same_dist)
res
4.1.3 Anderson--Darling Tests for Distribution {#c11-ad}
CMH-17-1G Section 8.3.11.2.1 contains results from STAT17 for
the "observed significance level" from the Anderson--Darling test
for various distributions. In this section, the ETW condition from the
present data set is used. The published results are given in the
following table. The results from cmstatr are below and are very
similar to those from STAT17.
Distribution | OSL
-------------|------------
Normal | 0.006051
Lognormal | 0.000307
Weibull | 0.219
res <- dat_8_3_11_1_1 %>%
filter(condition == "ETW") %>%
anderson_darling_normal(strength)
expect_equal(res$osl, 0.006051, tolerance = 0.001)
res
res <- dat_8_3_11_1_1 %>%
filter(condition == "ETW") %>%
anderson_darling_lognormal(strength)
expect_equal(res$osl, 0.000307, tolerance = 0.001)
res
res <- dat_8_3_11_1_1 %>%
filter(condition == "ETW") %>%
anderson_darling_weibull(strength)
expect_equal(res$osl, 0.0219, tolerance = 0.002)
res
4.1.4 Levene's Test (Between Conditions) {#c11-lbc}
CMH-17-1G Section 8.3.11.1.1 provides results from ASAP for Levene's test
for equality of variance between conditions after the ETW and ETW2 conditions
are removed. The handbook shows an F statistic of 0.58, however if this
data is entered into ASAP directly, ASAP gives an F statistic of 0.058,
which matches the result of cmstatr.
res <- dat_8_3_11_1_1 %>%
filter(condition != "ETW" & condition != "ETW2") %>%
levene_test(strength, condition)
expect_equal(res$f, 0.058, tolerance = 0.01)
res
4.1.5 Levene's Test (Between Batches) {#c11-lbb}
CMH-17-1G Section 8.3.11.2.2 provides output from STAT17. The
ETW2 condition from the present data set was analyzed by STAT17
and that software reported an F statistic of 0.123 from Levene's
test when comparing the variance of the batches within this condition.
The result from cmstatr is similar.
res <- dat_8_3_11_1_1 %>%
filter(condition == "ETW2") %>%
levene_test(strength, batch)
expect_equal(res$f, 0.123, tolerance = 0.005)
res
Similarly, the published value of the F statistic for the CTD condition is
$3.850$. cmstatr produces very similar results.
res <- dat_8_3_11_1_1 %>%
filter(condition == "CTD") %>%
levene_test(strength, batch)
expect_equal(res$f, 3.850, tolerance = 0.005)
res
4.1.6 Nonparametric Basis Values {#c11-hk-wf}
CMH-17-1G Section 8.3.11.2.1 provides STAT17 outputs for the ETW
condition of the present data set. The nonparametric Basis values are listed.
In this case, the Hanson--Koopmans method is used. The published A-Basis
value is 13.0 and the B-Basis is 37.9.
res <- dat_8_3_11_1_1 %>%
filter(condition == "ETW") %>%
basis_hk_ext(strength, method = "woodward-frawley", p = 0.99, conf = 0.95,
override = "all")
expect_equal(res$basis, 13.0, tolerance = 0.001)
res
res <- dat_8_3_11_1_1 %>%
filter(condition == "ETW") %>%
basis_hk_ext(strength, method = "optimum-order", p = 0.90, conf = 0.95,
override = "all")
expect_equal(res$basis, 37.9, tolerance = 0.001)
res
4.1.7 Single-Point ANOVA Basis Value {#c11-anova}
CMH-17-1G Section 8.3.11.2.2 provides output from STAT17 for
the ETW2 condition from the present data set. STAT17 reports
A- and B-Basis values based on the ANOVA method of 34.6 and
63.2, respectively. The results from cmstatr are similar.
res <- dat_8_3_11_1_1 %>%
filter(condition == "ETW2") %>%
basis_anova(strength, batch, override = "number_of_groups",
p = 0.99, conf = 0.95)
expect_equal(res$basis, 34.6, tolerance = 0.001)
res
res <- dat_8_3_11_1_1 %>%
filter(condition == "ETW2") %>%
basis_anova(strength, batch, override = "number_of_groups")
expect_equal(res$basis, 63.2, tolerance = 0.001)
res
4.2 Dataset From Section 8.3.11.1.2
[@CMH-17-1G] provides an example data set and results from
ASAP [@ASAP2008]. This example data set is duplicated
below:
dat_8_3_11_1_2 <- tribble(
~batch, ~strength, ~condition,
1, 79.04517, "CTD", 1, 103.2006, "RTD", 1, 63.22764, "ETW", 1, 54.09806, "ETW2",
1, 102.6014, "CTD", 1, 105.1034, "RTD", 1, 70.84454, "ETW", 1, 58.87615, "ETW2",
1, 97.79372, "CTD", 1, 105.1893, "RTD", 1, 66.43223, "ETW", 1, 61.60167, "ETW2",
1, 92.86423, "CTD", 1, 100.4189, "RTD", 1, 75.37771, "ETW", 1, 60.23973, "ETW2",
1, 117.218, "CTD", 2, 85.32319, "RTD", 1, 72.43773, "ETW", 1, 61.4808, "ETW2",
1, 108.7168, "CTD", 2, 92.69923, "RTD", 1, 68.43073, "ETW", 1, 64.55832, "ETW2",
1, 112.2773, "CTD", 2, 98.45242, "RTD", 1, 69.72524, "ETW", 2, 57.76131, "ETW2",
1, 114.0129, "CTD", 2, 104.1014, "RTD", 2, 66.20343, "ETW", 2, 49.91463, "ETW2",
2, 106.8452, "CTD", 2, 91.51841, "RTD", 2, 60.51251, "ETW", 2, 61.49271, "ETW2",
2, 112.3911, "CTD", 2, 101.3746, "RTD", 2, 65.69334, "ETW", 2, 57.7281, "ETW2",
2, 115.5658, "CTD", 2, 101.5828, "RTD", 2, 62.73595, "ETW", 2, 62.11653, "ETW2",
2, 87.40657, "CTD", 2, 99.57384, "RTD", 2, 59.00798, "ETW", 2, 62.69353, "ETW2",
2, 102.2785, "CTD", 2, 88.84826, "RTD", 2, 62.37761, "ETW", 3, 61.38523, "ETW2",
2, 110.6073, "CTD", 3, 92.18703, "RTD", 3, 64.3947, "ETW", 3, 60.39053, "ETW2",
3, 105.2762, "CTD", 3, 101.8234, "RTD", 3, 72.8491, "ETW", 3, 59.17616, "ETW2",
3, 110.8924, "CTD", 3, 97.68909, "RTD", 3, 66.56226, "ETW", 3, 60.17616, "ETW2",
3, 108.7638, "CTD", 3, 101.5172, "RTD", 3, 66.56779, "ETW", 3, 46.47396, "ETW2",
3, 110.9833, "CTD", 3, 100.0481, "RTD", 3, 66.00123, "ETW", 3, 51.16616, "ETW2",
3, 101.3417, "CTD", 3, 102.0544, "RTD", 3, 59.62108, "ETW",
3, 100.0251, "CTD", 3, 60.61167, "ETW",
3, 57.65487, "ETW",
3, 66.51241, "ETW",
3, 64.89347, "ETW",
3, 57.73054, "ETW",
3, 68.94086, "ETW",
3, 61.63177, "ETW"
)
4.2.1 Pooled SD A- and B-Basis {#c12-psd}
CMH-17-1G Table 8.3.11.2(k) provides outputs from ASAP for the data set
above. ASAP uses the pooled SD method. ASAP produces the following
results, which are quite similar to those produced by cmstatr.
Condition | CTD | RTD | ETW | ETW2
----------|-------|-------|-------|------
B-Basis | 93.64 | 87.30 | 54.33 | 47.12
A-Basis | 89.19 | 79.86 | 46.84 | 39.69
res <- basis_pooled_sd(dat_8_3_11_1_2, strength, condition,
override = "all")
expect_equal(res$basis$value[res$basis$group == "CTD"],
93.64, tolerance = 0.001)
expect_equal(res$basis$value[res$basis$group == "RTD"],
87.30, tolerance = 0.001)
expect_equal(res$basis$value[res$basis$group == "ETW"],
54.33, tolerance = 0.001)
expect_equal(res$basis$value[res$basis$group == "ETW2"],
47.12, tolerance = 0.001)
res
res <- basis_pooled_sd(dat_8_3_11_1_2, strength, condition,
p = 0.99, conf = 0.95,
override = "all")
expect_equal(res$basis$value[res$basis$group == "CTD"],
86.19, tolerance = 0.001)
expect_equal(res$basis$value[res$basis$group == "RTD"],
79.86, tolerance = 0.001)
expect_equal(res$basis$value[res$basis$group == "ETW"],
46.84, tolerance = 0.001)
expect_equal(res$basis$value[res$basis$group == "ETW2"],
39.69, tolerance = 0.001)
res
4.2.2 Pooled SD A- and B-Basis (Mod CV) {#c12-psdmcv}
After removal of the ETW2 condition, CMH17-STATS reports the pooled A- and
B-Basis (mod CV) shown in the following table.
cmstatr computes very similar values.
Condition | CTD | RTD | ETW
----------|-------|-------|------
B-Basis | 92.25 | 85.91 | 52.97
A-Basis | 83.81 | 77.48 | 44.47
res <- dat_8_3_11_1_2 %>%
filter(condition != "ETW2") %>%
basis_pooled_sd(strength, condition, modcv = TRUE, override = "all")
expect_equal(res$basis$value[res$basis$group == "CTD"],
92.25, tolerance = 0.001)
expect_equal(res$basis$value[res$basis$group == "RTD"],
85.91, tolerance = 0.001)
expect_equal(res$basis$value[res$basis$group == "ETW"],
52.97, tolerance = 0.001)
res
res <- dat_8_3_11_1_2 %>%
filter(condition != "ETW2") %>%
basis_pooled_sd(strength, condition,
p = 0.99, conf = 0.95, modcv = TRUE, override = "all")
expect_equal(res$basis$value[res$basis$group == "CTD"],
83.81, tolerance = 0.001)
expect_equal(res$basis$value[res$basis$group == "RTD"],
77.48, tolerance = 0.001)
expect_equal(res$basis$value[res$basis$group == "ETW"],
44.47, tolerance = 0.001)
res
4.2.3 Pooled CV A- and B-Basis {#c12-pcv}
This data set was input into CMH17-STATS and the Pooled CV method was selected.
The results from CMH17-STATS were as follows. cmstatr produces very similar
results.
Condition | CTD | RTD | ETW | ETW2
----------|-------|-------|-------|------
B-Basis | 90.89 | 85.37 | 56.79 | 50.55
A-Basis | 81.62 | 76.67 | 50.98 | 45.40
res <- basis_pooled_cv(dat_8_3_11_1_2, strength, condition, override = "all")
expect_equal(res$basis$value[res$basis$group == "CTD"],
90.89, tolerance = 0.001)
expect_equal(res$basis$value[res$basis$group == "RTD"],
85.37, tolerance = 0.001)
expect_equal(res$basis$value[res$basis$group == "ETW"],
56.79, tolerance = 0.001)
expect_equal(res$basis$value[res$basis$group == "ETW2"],
50.55, tolerance = 0.001)
res
res <- basis_pooled_cv(dat_8_3_11_1_2, strength, condition,
p = 0.99, conf = 0.95, override = "all")
expect_equal(res$basis$value[res$basis$group == "CTD"],
81.62, tolerance = 0.001)
expect_equal(res$basis$value[res$basis$group == "RTD"],
76.67, tolerance = 0.001)
expect_equal(res$basis$value[res$basis$group == "ETW"],
50.98, tolerance = 0.001)
expect_equal(res$basis$value[res$basis$group == "ETW2"],
45.40, tolerance = 0.001)
res
4.2.4 Pooled CV A- and B-Basis (Mod CV) {#c12-pcvmcv}
This data set was input into CMH17-STATS and the Pooled CV method was selected
with the modified CV transform. Additionally, the ETW2 condition was removed.
The results from CMH17-STATS were as follows. cmstatr produces very similar
results.
Condition | CTD | RTD | ETW
----------|-------|-------|-------
B-Basis | 90.31 | 84.83 | 56.43
A-Basis | 80.57 | 75.69 | 50.33
res <- dat_8_3_11_1_2 %>%
filter(condition != "ETW2") %>%
basis_pooled_cv(strength, condition, modcv = TRUE, override = "all")
expect_equal(res$basis$value[res$basis$group == "CTD"],
90.31, tolerance = 0.001)
expect_equal(res$basis$value[res$basis$group == "RTD"],
84.83, tolerance = 0.001)
expect_equal(res$basis$value[res$basis$group == "ETW"],
56.43, tolerance = 0.001)
res
res <- dat_8_3_11_1_2 %>%
filter(condition != "ETW2") %>%
basis_pooled_cv(strength, condition, modcv = TRUE,
p = 0.99, conf = 0.95, override = "all")
expect_equal(res$basis$value[res$basis$group == "CTD"],
80.57, tolerance = 0.001)
expect_equal(res$basis$value[res$basis$group == "RTD"],
75.69, tolerance = 0.001)
expect_equal(res$basis$value[res$basis$group == "ETW"],
50.33, tolerance = 0.001)
res
5. Other Data Sets
This section contains various small data sets. In most cases, these data sets
were generated randomly for the purpose of comparing cmstatr to other
software.
5.1 Anderson--Darling Test (Normal) {#ods-adn}
The following data set was randomly generated. When this is
entered into STAT17 [@STAT-17], that software gives the value
$OSL = 0.465$, which matches the result of cmstatr
within a small margin.
dat <- data.frame(
strength = c(
137.4438, 139.5395, 150.89, 141.4474, 141.8203, 151.8821, 143.9245,
132.9732, 136.6419, 138.1723, 148.7668, 143.283, 143.5429,
141.7023, 137.4732, 152.338, 144.1589, 128.5218
)
)
res <- anderson_darling_normal(dat, strength)
expect_equal(res$osl, 0.465, tolerance = 0.001)
res
5.2 Anderson--Darling Test (Lognormal) {#ods-adl}
The following data set was randomly generated. When this is
entered into STAT17 [@STAT-17], that software gives the value
$OSL = 0.480$, which matches the result of cmstatr
within a small margin.
dat <- data.frame(
strength = c(
137.4438, 139.5395, 150.89, 141.4474, 141.8203, 151.8821, 143.9245,
132.9732, 136.6419, 138.1723, 148.7668, 143.283, 143.5429,
141.7023, 137.4732, 152.338, 144.1589, 128.5218
)
)
res <- anderson_darling_lognormal(dat, strength)
expect_equal(res$osl, 0.480, tolerance = 0.001)
res
5.3 Anderson--Darling Test (Weibull) {#ods-adw}
The following data set was randomly generated. When this is
entered into STAT17 [@STAT-17], that software gives the value
$OSL = 0.179$, which matches the result of cmstatr
within a small margin.
dat <- data.frame(
strength = c(
137.4438, 139.5395, 150.89, 141.4474, 141.8203, 151.8821, 143.9245,
132.9732, 136.6419, 138.1723, 148.7668, 143.283, 143.5429,
141.7023, 137.4732, 152.338, 144.1589, 128.5218
)
)
res <- anderson_darling_weibull(dat, strength)
expect_equal(res$osl, 0.179, tolerance = 0.002)
res
5.4 Normal A- and B-Basis {#ods-nb}
The following data was input into STAT17 and the A- and B-Basis values
were computed assuming normally distributed data. The results were 120.336
and 129.287, respectively. cmstatr reports very similar values.
dat <- c(
137.4438, 139.5395, 150.8900, 141.4474, 141.8203, 151.8821, 143.9245,
132.9732, 136.6419, 138.1723, 148.7668, 143.2830, 143.5429, 141.7023,
137.4732, 152.3380, 144.1589, 128.5218
)
res <- basis_normal(x = dat, p = 0.99, conf = 0.95, override = "all")
expect_equal(res$basis, 120.336, tolerance = 0.0005)
res
res <- basis_normal(x = dat, p = 0.9, conf = 0.95, override = "all")
expect_equal(res$basis, 129.287, tolerance = 0.0005)
res
5.5 Lognormal A- and B-Basis {#ods-lb}
The following data was input into STAT17 and the A- and B-Basis values
were computed assuming distributed according to a lognormal distribution.
The results were 121.710
and 129.664, respectively. cmstatr reports very similar values.
dat <- c(
137.4438, 139.5395, 150.8900, 141.4474, 141.8203, 151.8821, 143.9245,
132.9732, 136.6419, 138.1723, 148.7668, 143.2830, 143.5429, 141.7023,
137.4732, 152.3380, 144.1589, 128.5218
)
res <- basis_lognormal(x = dat, p = 0.99, conf = 0.95, override = "all")
expect_equal(res$basis, 121.710, tolerance = 0.0005)
res
res <- basis_lognormal(x = dat, p = 0.9, conf = 0.95, override = "all")
expect_equal(res$basis, 129.664, tolerance = 0.0005)
res
5.6 Weibull A- and B-Basis {#ods-wb}
The following data was input into STAT17 and the A- and B-Basis values
were computed assuming data following the Weibull distribution.
The results were 109.150
and 125.441, respectively. cmstatr reports very similar values.
dat <- c(
137.4438, 139.5395, 150.8900, 141.4474, 141.8203, 151.8821, 143.9245,
132.9732, 136.6419, 138.1723, 148.7668, 143.2830, 143.5429, 141.7023,
137.4732, 152.3380, 144.1589, 128.5218
)
res <- basis_weibull(x = dat, p = 0.99, conf = 0.95, override = "all")
expect_equal(res$basis, 109.150, tolerance = 0.005)
res
res <- basis_weibull(x = dat, p = 0.9, conf = 0.95, override = "all")
expect_equal(res$basis, 125.441, tolerance = 0.005)
res
5.7 Extended Hanson--Koopmans A- and B-Basis {#ods-hkb}
The following data was input into STAT17 and the A- and B-Basis values
were computed using the nonparametric (small sample) method.
The results were 99.651
and 124.156, respectively. cmstatr reports very similar values.
dat <- c(
137.4438, 139.5395, 150.8900, 141.4474, 141.8203, 151.8821, 143.9245,
132.9732, 136.6419, 138.1723, 148.7668, 143.2830, 143.5429, 141.7023,
137.4732, 152.3380, 144.1589, 128.5218
)
res <- basis_hk_ext(x = dat, p = 0.99, conf = 0.95,
method = "woodward-frawley", override = "all")
expect_equal(res$basis, 99.651, tolerance = 0.005)
res
res <- basis_hk_ext(x = dat, p = 0.9, conf = 0.95,
method = "optimum-order", override = "all")
expect_equal(res$basis, 124.156, tolerance = 0.005)
res
5.8 Extended Hanson--Koopmans B-Basis {#ods-hkb2}
The following random numbers were generated.
dat <- c(
139.6734, 143.0032, 130.4757, 144.8327, 138.7818, 136.7693, 148.636,
131.0095, 131.4933, 142.8856, 158.0198, 145.2271, 137.5991, 139.8298,
140.8557, 137.6148, 131.3614, 152.7795, 145.8792, 152.9207, 160.0989,
145.1920, 128.6383, 141.5992, 122.5297, 159.8209, 151.6720, 159.0156
)
All of the numbers above were input into STAT17 and the reported B-Basis
value using the Optimum Order nonparametric method was 122.36798. This
result matches the results of cmstatr within a small margin.
res <- basis_hk_ext(x = dat, p = 0.9, conf = 0.95,
method = "optimum-order", override = "all")
expect_equal(res$basis, 122.36798, tolerance = 0.001)
res
The last two observations from the above data set were discarded, leaving
26 observations. This smaller data set was input into STAT17 and that
software calculated a B-Basis value of 121.57073 using the Optimum
Order nonparametric method. cmstatr reports a very similar number.
res <- basis_hk_ext(x = head(dat, 26), p = 0.9, conf = 0.95,
method = "optimum-order", override = "all")
expect_equal(res$basis, 121.57073, tolerance = 0.001)
res
The same data set was further reduced such that only the first 22
observations were included. This smaller data set was input into STAT17
and that
software calculated a B-Basis value of 128.82397 using the Optimum
Order nonparametric method. cmstatr reports a very similar number.
res <- basis_hk_ext(x = head(dat, 22), p = 0.9, conf = 0.95,
method = "optimum-order", override = "all")
expect_equal(res$basis, 128.82397, tolerance = 0.001)
res
5.9 Large Sample Nonparametric B-Basis {#ods-lsnb}
The following data was input into STAT17 and the B-Basis value
was computed using the nonparametric (large sample) method.
The results was 122.738297. cmstatr reports very similar values.
dat <- c(
137.3603, 135.6665, 136.6914, 154.7919, 159.2037, 137.3277, 128.821,
138.6304, 138.9004, 147.4598, 148.6622, 144.4948, 131.0851, 149.0203,
131.8232, 146.4471, 123.8124, 126.3105, 140.7609, 134.4875, 128.7508,
117.1854, 129.3088, 141.6789, 138.4073, 136.0295, 128.4164, 141.7733,
134.455, 122.7383, 136.9171, 136.9232, 138.8402, 152.8294, 135.0633,
121.052, 131.035, 138.3248, 131.1379, 147.3771, 130.0681, 132.7467,
137.1444, 141.662, 146.9363, 160.7448, 138.5511, 129.1628, 140.2939,
144.8167, 156.5918, 132.0099, 129.3551, 136.6066, 134.5095, 128.2081,
144.0896, 141.8029, 130.0149, 140.8813, 137.7864
)
res <- basis_nonpara_large_sample(x = dat, p = 0.9, conf = 0.95,
override = "all")
expect_equal(res$basis, 122.738297, tolerance = 0.005)
res
5.10 Acceptance Limits Based on Mean and Extremum {#ods-eme}
Results from cmstatr's equiv_mean_extremum function were compared with
results from HYTEQ. The summary statistics for the qualification data
were set as mean = 141.310 and sd=6.415. For a value of alpha=0.05 and
n = 9,
HYTEQ reported thresholds of 123.725 and 137.197 for minimum individual
and mean, respectively. cmstatr produces very similar results.
res <- equiv_mean_extremum(alpha = 0.05, mean_qual = 141.310, sd_qual = 6.415,
n_sample = 9)
expect_equal(res$threshold_min_indiv, 123.725, tolerance = 0.001)
expect_equal(res$threshold_mean, 137.197, tolerance = 0.001)
res
Using the same parameters, but using the modified CV method,
HYTEQ produces thresholds of 117.024 and 135.630 for minimum individual
and mean, respectively. cmstatr produces very similar results.
res <- equiv_mean_extremum(alpha = 0.05, mean_qual = 141.310, sd_qual = 6.415,
n_sample = 9, modcv = TRUE)
expect_equal(res$threshold_min_indiv, 117.024, tolerance = 0.001)
expect_equal(res$threshold_mean, 135.630, tolerance = 0.001)
res
5.11 Acceptance Based on Change in Mean {#ods-ecm}
Results from cmstatr's equiv_change_mean function were compared with
results from HYTEQ. The following parameters were used. A value of
alpha = 0.05 was selected.
Parameter | Qualification | Sample
----------|---------------|---------
Mean | 9.24 | 9.02
SD | 0.162 | 0.15785
n | 28 | 9
HYTEQ gives an acceptance range of 9.115 to 9.365. cmstatr produces
similar results.
res <- equiv_change_mean(alpha = 0.05, n_sample = 9, mean_sample = 9.02,
sd_sample = 0.15785, n_qual = 28, mean_qual = 9.24,
sd_qual = 0.162)
expect_equal(res$threshold, c(9.115, 9.365), tolerance = 0.001)
res
After selecting the modified CV method, HYTEQ gives an acceptance
range of 8.857 to 9.623. cmstatr produces similar results.
res <- equiv_change_mean(alpha = 0.05, n_sample = 9, mean_sample = 9.02,
sd_sample = 0.15785, n_qual = 28, mean_qual = 9.24,
sd_qual = 0.162, modcv = TRUE)
expect_equal(res$threshold, c(8.857, 9.623), tolerance = 0.001)
res
6. Comparison with Literature
In this section, results from cmstatr are compared with values published
in literature.
6.1 Anderson--Darling K--Sample Test {#cl-adk}
[@Stephens1987] provides example data that compares measurements obtained
in four labs. Their paper gives values of the ADK test statistic as well as
p-values.
The data in [@Stephens1987] is as follows:
dat_ss1987 <- data.frame(
smoothness = c(
38.7, 41.5, 43.8, 44.5, 45.5, 46.0, 47.7, 58.0,
39.2, 39.3, 39.7, 41.4, 41.8, 42.9, 43.3, 45.8,
34.0, 35.0, 39.0, 40.0, 43.0, 43.0, 44.0, 45.0,
34.0, 34.8, 34.8, 35.4, 37.2, 37.8, 41.2, 42.8
),
lab = c(rep("A", 8), rep("B", 8), rep("C", 8), rep("D", 8))
)
dat_ss1987
[@Stephens1987] lists the corresponding test statistics
$A_{akN}^2 = 8.3926$ and $\sigma_N = 1.2038$ with the p-value
$p = 0.0022$. These match the result of cmstatr within a small margin.
res <- ad_ksample(dat_ss1987, smoothness, lab)
expect_equal(res$ad, 8.3926, tolerance = 0.001)
expect_equal(res$sigma, 1.2038, tolerance = 0.001)
expect_equal(res$p, 0.00226, tolerance = 0.01)
res
7. Comparison with Published Factors
Various factors, such as tolerance limit factors, are published in various
publications. This section compares those published factors with those
computed by cmstatr.
7.1 Normal kB Factors {#pf-kb}
B-Basis tolerance limit factors assuming a normal distribution are published in
CMH-17-1G. Those factors are reproduced below and are compared with the
results of cmstatr. The published factors and those computed by cmstatr
are quite similar.
tribble(
~n, ~kB_published,
2, 20.581, 36, 1.725, 70, 1.582, 104, 1.522,
3, 6.157, 37, 1.718, 71, 1.579, 105, 1.521,
4, 4.163, 38, 1.711, 72, 1.577, 106, 1.519,
5, 3.408, 39, 1.704, 73, 1.575, 107, 1.518,
6, 3.007, 40, 1.698, 74, 1.572, 108, 1.517,
7, 2.756, 41, 1.692, 75, 1.570, 109, 1.516,
8, 2.583, 42, 1.686, 76, 1.568, 110, 1.515,
9, 2.454, 43, 1.680, 77, 1.566, 111, 1.513,
10, 2.355, 44, 1.675, 78, 1.564, 112, 1.512,
11, 2.276, 45, 1.669, 79, 1.562, 113, 1.511,
12, 2.211, 46, 1.664, 80, 1.560, 114, 1.510,
13, 2.156, 47, 1.660, 81, 1.558, 115, 1.509,
14, 2.109, 48, 1.655, 82, 1.556, 116, 1.508,
15, 2.069, 49, 1.650, 83, 1.554, 117, 1.507,
16, 2.034, 50, 1.646, 84, 1.552, 118, 1.506,
17, 2.002, 51, 1.642, 85, 1.551, 119, 1.505,
18, 1.974, 52, 1.638, 86, 1.549, 120, 1.504,
19, 1.949, 53, 1.634, 87, 1.547, 121, 1.503,
20, 1.927, 54, 1.630, 88, 1.545, 122, 1.502,
21, 1.906, 55, 1.626, 89, 1.544, 123, 1.501,
22, 1.887, 56, 1.623, 90, 1.542, 124, 1.500,
23, 1.870, 57, 1.619, 91, 1.540, 125, 1.499,
24, 1.854, 58, 1.616, 92, 1.539, 126, 1.498,
25, 1.839, 59, 1.613, 93, 1.537, 127, 1.497,
26, 1.825, 60, 1.609, 94, 1.536, 128, 1.496,
27, 1.812, 61, 1.606, 95, 1.534, 129, 1.495,
28, 1.800, 62, 1.603, 96, 1.533, 130, 1.494,
29, 1.789, 63, 1.600, 97, 1.531, 131, 1.493,
30, 1.778, 64, 1.597, 98, 1.530, 132, 1.492,
31, 1.768, 65, 1.595, 99, 1.529, 133, 1.492,
32, 1.758, 66, 1.592, 100, 1.527, 134, 1.491,
33, 1.749, 67, 1.589, 101, 1.526, 135, 1.490,
34, 1.741, 68, 1.587, 102, 1.525, 136, 1.489,
35, 1.733, 69, 1.584, 103, 1.523, 137, 1.488
) %>%
arrange(n) %>%
mutate(kB_cmstatr = k_factor_normal(n, p = 0.9, conf = 0.95)) %>%
rowwise() %>%
mutate(diff = expect_equal(kB_published, kB_cmstatr, tolerance = 0.001)) %>%
select(-c(diff))
7.2 Normal kA Factors {#pf-ka}
A-Basis tolerance limit factors assuming a normal distribution are published in
CMH-17-1G. Those factors are reproduced below and are compared with the
results of cmstatr. The published factors and those computed by cmstatr
are quite similar.
tribble(
~n, ~kA_published,
2, 37.094, 36, 2.983, 70, 2.765, 104, 2.676,
3, 10.553, 37, 2.972, 71, 2.762, 105, 2.674,
4, 7.042, 38, 2.961, 72, 2.758, 106, 2.672,
5, 5.741, 39, 2.951, 73, 2.755, 107, 2.671,
6, 5.062, 40, 2.941, 74, 2.751, 108, 2.669,
7, 4.642, 41, 2.932, 75, 2.748, 109, 2.667,
8, 4.354, 42, 2.923, 76, 2.745, 110, 2.665,
9, 4.143, 43, 2.914, 77, 2.742, 111, 2.663,
10, 3.981, 44, 2.906, 78, 2.739, 112, 2.662,
11, 3.852, 45, 2.898, 79, 2.736, 113, 2.660,
12, 3.747, 46, 2.890, 80, 2.733, 114, 2.658,
13, 3.659, 47, 2.883, 81, 2.730, 115, 2.657,
14, 3.585, 48, 2.876, 82, 2.727, 116, 2.655,
15, 3.520, 49, 2.869, 83, 2.724, 117, 2.654,
16, 3.464, 50, 2.862, 84, 2.721, 118, 2.652,
17, 3.414, 51, 2.856, 85, 2.719, 119, 2.651,
18, 3.370, 52, 2.850, 86, 2.716, 120, 2.649,
19, 3.331, 53, 2.844, 87, 2.714, 121, 2.648,
20, 3.295, 54, 2.838, 88, 2.711, 122, 2.646,
21, 3.263, 55, 2.833, 89, 2.709, 123, 2.645,
22, 3.233, 56, 2.827, 90, 2.706, 124, 2.643,
23, 3.206, 57, 2.822, 91, 2.704, 125, 2.642,
24, 3.181, 58, 2.817, 92, 2.701, 126, 2.640,
25, 3.158, 59, 2.812, 93, 2.699, 127, 2.639,
26, 3.136, 60, 2.807, 94, 2.697, 128, 2.638,
27, 3.116, 61, 2.802, 95, 2.695, 129, 2.636,
28, 3.098, 62, 2.798, 96, 2.692, 130, 2.635,
29, 3.080, 63, 2.793, 97, 2.690, 131, 2.634,
30, 3.064, 64, 2.789, 98, 2.688, 132, 2.632,
31, 3.048, 65, 2.785, 99, 2.686, 133, 2.631,
32, 3.034, 66, 2.781, 100, 2.684, 134, 2.630,
33, 3.020, 67, 2.777, 101, 2.682, 135, 2.628,
34, 3.007, 68, 2.773, 102, 2.680, 136, 2.627,
35, 2.995, 69, 2.769, 103, 2.678, 137, 2.626
) %>%
arrange(n) %>%
mutate(kA_cmstatr = k_factor_normal(n, p = 0.99, conf = 0.95)) %>%
rowwise() %>%
mutate(diff = expect_equal(kA_published, kA_cmstatr, tolerance = 0.001)) %>%
select(-c(diff))
7.3 Nonparametric B-Basis Extended Hanson--Koopmans {#pf-hk}
Vangel [@Vangel1994] provides extensive tables of $z$ for the case where
$i=1$ and $j$ is the median observation. This section checks the results of
cmstatr's function against those tables. Only the odd values of $n$
are checked so that the median is a single observation. The unit tests for
the cmstatr package include checks of a variety of values of $p$ and
confidence, but only the factors for B-Basis are checked here.
tribble(
~n, ~z,
3, 28.820048,
5, 6.1981307,
7, 3.4780112,
9, 2.5168762,
11, 2.0312134,
13, 1.7377374,
15, 1.5403989,
17, 1.3979806,
19, 1.2899172,
21, 1.2048089,
23, 1.1358259,
25, 1.0786237,
27, 1.0303046,
) %>%
rowwise() %>%
mutate(
z_calc = hk_ext_z(n, 1, ceiling(n / 2), p = 0.90, conf = 0.95)
) %>%
mutate(diff = expect_equal(z, z_calc, tolerance = 0.0001)) %>%
select(-c(diff))
7.4 Nonparametric A-Basis Extended Hanson--Koopmans {#pf-hk2}
CMH-17-1G provides Table 8.5.15, which contains factors for calculating
A-Basis values using the Extended Hanson--Koopmans nonparametric method.
That table is reproduced in part here and the factors are compared with
those computed by cmstatr. More extensive checks are performed in the
unit test of the cmstatr package. The factors computed by cmstatr are
very similar to those published in CMH-17-1G.
tribble(
~n, ~k,
2, 80.0038,
4, 9.49579,
6, 5.57681,
8, 4.25011,
10, 3.57267,
12, 3.1554,
14, 2.86924,
16, 2.65889,
18, 2.4966,
20, 2.36683,
25, 2.131,
30, 1.96975,
35, 1.85088,
40, 1.75868,
45, 1.68449,
50, 1.62313,
60, 1.5267,
70, 1.45352,
80, 1.39549,
90, 1.34796,
100, 1.30806,
120, 1.24425,
140, 1.19491,
160, 1.15519,
180, 1.12226,
200, 1.09434,
225, 1.06471,
250, 1.03952,
275, 1.01773
) %>%
rowwise() %>%
mutate(z_calc = hk_ext_z(n, 1, n, 0.99, 0.95)) %>%
mutate(diff = expect_lt(abs(k - z_calc), 0.0001)) %>%
select(-c(diff))
7.5 Factors for Small Sample Nonparametric B-Basis {#pf-hk-opt}
CMH-17-1G Table 8.5.14 provides ranks orders and factors for computing
nonparametric B-Basis values. This table is reproduced below and
compared with the results of cmstatr. The results are similar. In some
cases, the rank order ($r$ in CMH-17-1G or $j$ in cmstatr) and the
the factor ($k$) are different. These differences are discussed in detail in
the vignette
Extended Hanson-Koopmans.
tribble(
~n, ~r, ~k,
2, 2, 35.177,
3, 3, 7.859,
4, 4, 4.505,
5, 4, 4.101,
6, 5, 3.064,
7, 5, 2.858,
8, 6, 2.382,
9, 6, 2.253,
10, 6, 2.137,
11, 7, 1.897,
12, 7, 1.814,
13, 7, 1.738,
14, 8, 1.599,
15, 8, 1.540,
16, 8, 1.485,
17, 8, 1.434,
18, 9, 1.354,
19, 9, 1.311,
20, 10, 1.253,
21, 10, 1.218,
22, 10, 1.184,
23, 11, 1.143,
24, 11, 1.114,
25, 11, 1.087,
26, 11, 1.060,
27, 11, 1.035,
28, 12, 1.010
) %>%
rowwise() %>%
mutate(r_calc = hk_ext_z_j_opt(n, 0.90, 0.95)$j) %>%
mutate(k_calc = hk_ext_z_j_opt(n, 0.90, 0.95)$z)
7.6 Nonparametric B-Basis Binomial Rank {#pf-npbinom}
CMH-17-1G Table 8.5.12 provides factors for computing B-Basis values
using the nonparametric binomial rank method. Part of that table is
reproduced below and compared with the results of cmstatr.
The results of cmstatr are similar to the published values.
A more complete comparison is performed in the units tests of the
cmstatr package.
tribble(
~n, ~rb,
29, 1,
46, 2,
61, 3,
76, 4,
89, 5,
103, 6,
116, 7,
129, 8,
142, 9,
154, 10,
167, 11,
179, 12,
191, 13,
203, 14
) %>%
rowwise() %>%
mutate(r_calc = nonpara_binomial_rank(n, 0.9, 0.95)) %>%
mutate(test = expect_equal(rb, r_calc)) %>%
select(-c(test))
7.7 Nonparametric A-Basis Binomial Rank {#pf-npbinom2}
CMH-17-1G Table 8.5.13 provides factors for computing B-Basis values
using the nonparametric binomial rank method. Part of that table is
reproduced below and compared with the results of cmstatr.
The results of cmstatr are similar to the published values.
A more complete comparison is performed in the units tests of the
cmstatr package.
tribble(
~n, ~ra,
299, 1,
473, 2,
628, 3,
773, 4,
913, 5
) %>%
rowwise() %>%
mutate(r_calc = nonpara_binomial_rank(n, 0.99, 0.95)) %>%
mutate(test = expect_equal(ra, r_calc)) %>%
select(-c(test))
7.8 Factors for Equivalency {#pf-equiv}
Vangel's 2002 paper provides factors for calculating limits for sample
mean and sample extremum for various values of $\alpha$ and sample size ($n$).
A subset of those factors are reproduced below and compared with results
from cmstatr. The results are very similar for values of $\alpha$ and $n$
that are common for composite materials.
read.csv(system.file("extdata", "k1.vangel.csv",
package = "cmstatr")) %>%
gather(n, k1, X2:X10) %>%
mutate(n = as.numeric(substring(n, 2))) %>%
inner_join(
read.csv(system.file("extdata", "k2.vangel.csv",
package = "cmstatr")) %>%
gather(n, k2, X2:X10) %>%
mutate(n = as.numeric(substring(n, 2))),
by = c("n" = "n", "alpha" = "alpha")
) %>%
filter(n >= 5 & (alpha == 0.01 | alpha == 0.05)) %>%
group_by(n, alpha) %>%
nest() %>%
mutate(equiv = map2(alpha, n, ~k_equiv(.x, .y))) %>%
mutate(k1_calc = map(equiv, function(e) e[1]),
k2_calc = map(equiv, function(e) e[2])) %>%
select(-c(equiv)) %>%
unnest(cols = c(data, k1_calc, k2_calc)) %>%
mutate(check = expect_equal(k1, k1_calc, tolerance = 0.0001)) %>%
select(-c(check)) %>%
mutate(check = expect_equal(k2, k2_calc, tolerance = 0.0001)) %>%
select(-c(check))
8. Session Info
This copy of this vignette was build on the following system.
sessionInfo()
9. References
ComtekAdvancedStructures/cmstatr documentation built on Nov. 20, 2024, 6:39 p.m.
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.
knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
1. Introduction
This vignette is intended to contain the same validation that is included
in the test suite within the cmstatr package, but in a format that is
easier for a human to read. The intent is that this vignette will include
only those validations that are included in the test suite, but that the
test suite may include more tests than are shown in this vignette.
The following packages will be used in this validation. The version of each package used is listed at the end of this vignette.
library(cmstatr) library(dplyr) library(purrr) library(tidyr) library(testthat)
Throughout this vignette, the testthat package will be used. Expressions
such as expect_equal are used to ensure that the two values are equal
(within some tolerance). If this expectation is not true, the vignette will
fail to build. The tolerance is a relative tolerance: a tolerance of 0.01
means that the two values must be within $1\%$ of each other.
As an example, the following expression checks that the value
10 is equal to 10.1 within a tolerance of 0.01. Such an expectation
should be satisfied.
expect_equal(10, 10.1, tolerance = 0.01)
The basis_... functions automatically perform certain diagnostic tests.
When those diagnostic tests are not relevant to the validation, the
diagnostic tests are overridden by passing the argument override = "all".
2. Validation Table
The following table provides a cross-reference between the various
functions of the cmstatr package and the tests shown within this
vignette. The sections in this vignette are organized by data set.
Not all checks are performed on all data sets.
Function | Tests
--------------------------------|---------------------------
ad_ksample() | Section 3.1, Section 4.1.2, Section 6.1
anderson_darling_normal() | Section 4.1.3, Section 5.1
anderson_darling_lognormal() | Section 4.1.3, Section 5.2
anderson_darling_weibull() | Section 4.1.3, Section 5.3
basis_normal() | Section 5.4
basis_lognormal() | Section 5.5
basis_weibull() | Section 5.6
basis_pooled_cv() | Section 4.2.3, Section 4.2.4,
basis_pooled_sd() | Section 4.2.1, Section 4.2.2
basis_hk_ext() | Section 4.1.6, Section 5.7, Section 5.8
basis_nonpara_large_sample() | Section 5.9
basis_anova() | Section 4.1.7
calc_cv_star() |
cv() |
equiv_change_mean() | Section 5.11
equiv_mean_extremum() | Section 5.10
hk_ext_z() | Section 7.3, Section 7.4
hk_ext_z_j_opt() | Section 7.5
k_equiv() | Section 7.8
k_factor_normal() | Section 7.1, Section 7.2
levene_test() | Section 4.1.4, Section 4.1.5
maximum_normed_residual() | Section 4.1.1
nonpara_binomial_rank() | Section 7.6, Section 7.7
normalize_group_mean() |
normalize_ply_thickness() |
transform_mod_cv_ad() |
transform_mod_cv() |
3. carbon.fabric Data Set
This data set is example data that is provided with cmstatr.
The first few rows of this data are shown below.
head(carbon.fabric)
3.1. Anderson--Darling k-Sample Test {#cf-ad}
This data was entered into ASAP 2008 [@ASAP2008] and the reported Anderson--Darling k--Sample test statistics were recorded, as were the conclusions.
The value of the test statistic reported by cmstatr and that reported
by ASAP 2008 differ by a factor of $k - 1$, as do the critical values
used. As such, the conclusion of the tests are identical. This is
described in more detail in the
Anderson--Darling k--Sample Vignette.
When the RTD warp-tension data from this data set is entered into ASAP 2008,
it reports a test statistic of 0.456 and fails to reject the null hypothesis
that the batches are drawn from the same distribution. Adjusting for the
different definition of the test statistic, the results given by cmstatr
are very similar.
res <- carbon.fabric %>% filter(test == "WT") %>% filter(condition == "RTD") %>% ad_ksample(strength, batch) expect_equal(res$ad / (res$k - 1), 0.456, tolerance = 0.002) expect_false(res$reject_same_dist) res
When the ETW warp-tension data from this data set are entered into ASAP 2008,
the reported test statistic is 1.604 and it fails to reject the null
hypothesis that the batches are drawn from the same distribution. Adjusting
for the different definition of the test statistic, cmstatr gives nearly
identical results.
res <- carbon.fabric %>% filter(test == "WT") %>% filter(condition == "ETW") %>% ad_ksample(strength, batch) expect_equal(res$ad / (res$k - 1), 1.604, tolerance = 0.002) expect_false(res$reject_same_dist) res
4. Comparison with Examples from CMH-17-1G
4.1 Dataset From Section 8.3.11.1.1
CMH-17-1G [@CMH-17-1G] provides an example data set and results from ASAP [@ASAP2008] and STAT17 [@STAT-17]. This example data set is duplicated below:
dat_8_3_11_1_1 <- tribble( ~batch, ~strength, ~condition, 1, 118.3774604, "CTD", 1, 84.9581364, "RTD", 1, 83.7436035, "ETD", 1, 123.6035612, "CTD", 1, 92.4891822, "RTD", 1, 84.3831677, "ETD", 1, 115.2238092, "CTD", 1, 96.8212659, "RTD", 1, 94.8030433, "ETD", 1, 112.6379744, "CTD", 1, 109.030325, "RTD", 1, 94.3931537, "ETD", 1, 116.5564277, "CTD", 1, 97.8212659, "RTD", 1, 101.702222, "ETD", 1, 123.1649896, "CTD", 1, 100.921519, "RTD", 1, 86.5372121, "ETD", 2, 128.5589027, "CTD", 1, 103.699444, "RTD", 1, 92.3772684, "ETD", 2, 113.1462103, "CTD", 2, 93.7908212, "RTD", 2, 89.2084024, "ETD", 2, 121.4248107, "CTD", 2, 107.526709, "RTD", 2, 100.686001, "ETD", 2, 134.3241906, "CTD", 2, 94.5769704, "RTD", 2, 81.0444192, "ETD", 2, 129.6405117, "CTD", 2, 93.8831373, "RTD", 2, 91.3398070, "ETD", 2, 117.9818658, "CTD", 2, 98.2296605, "RTD", 2, 93.1441939, "ETD", 3, 115.4505226, "CTD", 2, 111.346590, "RTD", 2, 85.8204168, "ETD", 3, 120.0369467, "CTD", 2, 100.817538, "RTD", 3, 94.8966273, "ETD", 3, 117.1631088, "CTD", 3, 100.382203, "RTD", 3, 95.8068520, "ETD", 3, 112.9302797, "CTD", 3, 91.5037811, "RTD", 3, 86.7842252, "ETD", 3, 117.9114501, "CTD", 3, 100.083233, "RTD", 3, 94.4011973, "ETD", 3, 120.1900159, "CTD", 3, 95.6393615, "RTD", 3, 96.7231171, "ETD", 3, 110.7295966, "CTD", 3, 109.304779, "RTD", 3, 89.9010384, "ETD", 3, 100.078562, "RTD", 3, 99.1205847, "RTD", 3, 89.3672306, "ETD", 1, 106.357525, "ETW", 1, 99.0239966, "ETW2", 1, 105.898733, "ETW", 1, 103.341238, "ETW2", 1, 88.4640082, "ETW", 1, 100.302130, "ETW2", 1, 103.901744, "ETW", 1, 98.4634133, "ETW2", 1, 80.2058219, "ETW", 1, 92.2647280, "ETW2", 1, 109.199597, "ETW", 1, 103.487693, "ETW2", 1, 61.0139431, "ETW", 1, 113.734763, "ETW2", 2, 99.3207107, "ETW", 2, 108.172659, "ETW2", 2, 115.861770, "ETW", 2, 108.426732, "ETW2", 2, 82.6133082, "ETW", 2, 116.260375, "ETW2", 2, 85.3690411, "ETW", 2, 121.049610, "ETW2", 2, 115.801622, "ETW", 2, 111.223082, "ETW2", 2, 44.3217741, "ETW", 2, 104.574843, "ETW2", 2, 117.328077, "ETW", 2, 103.222552, "ETW2", 2, 88.6782903, "ETW", 3, 99.3918538, "ETW2", 3, 107.676986, "ETW", 3, 87.3421658, "ETW2", 3, 108.960241, "ETW", 3, 102.730741, "ETW2", 3, 116.122640, "ETW", 3, 96.3694916, "ETW2", 3, 80.2334815, "ETW", 3, 99.5946088, "ETW2", 3, 106.145570, "ETW", 3, 97.0712407, "ETW2", 3, 104.667866, "ETW", 3, 104.234953, "ETW" ) dat_8_3_11_1_1
4.1.1 Maximum Normed Residual Test {#c11-mnr}
CMH-17-1G Table 8.3.11.1.1(a) provides results of the MNR test from
ASAP for this data set. Batches 2 and 3 of the ETW data is considered
here and the results of cmstatr are compared with those published in
CMH-17-1G.
For Batch 2 of the ETW data, the results match those published in the handbook within a small tolerance. The published test statistic is 2.008.
res <- dat_8_3_11_1_1 %>% filter(condition == "ETW" & batch == 2) %>% maximum_normed_residual(strength, alpha = 0.05) expect_equal(res$mnr, 2.008, tolerance = 0.001) expect_equal(res$crit, 2.127, tolerance = 0.001) expect_equal(res$n_outliers, 0) res
Similarly, for Batch 3 of the ETW data, the results of cmstatr match
the results published in the handbook within a small tolerance. The published
test statistic is 2.119
res <- dat_8_3_11_1_1 %>% filter(condition == "ETW" & batch == 3) %>% maximum_normed_residual(strength, alpha = 0.05) expect_equal(res$mnr, 2.119, tolerance = 0.001) expect_equal(res$crit, 2.020, tolerance = 0.001) expect_equal(res$n_outliers, 1) res
4.1.2 Anderson--Darling k--Sample Test {#c11-adk}
For the ETW condition, the ADK test statistic given in [@CMH-17-1G] is
$ADK = 0.793$ and the test concludes that the samples come from the
same distribution. Noting that cmstatr uses the definition of the
test statistic given in [@Stephens1987], so the test statistic given
by cmstatr differs from that given by ASAP by a factor of $k - 1$,
as described in the Anderson--Darling k--Sample Vignette.
res <- dat_8_3_11_1_1 %>% filter(condition == "ETW") %>% ad_ksample(strength, batch) expect_equal(res$ad / (res$k - 1), 0.793, tolerance = 0.003) expect_false(res$reject_same_dist) res
Similarly, for the ETW2 condition, the test statistic given in [@CMH-17-1G]
is $ADK = 3.024$ and the test concludes that the samples come from different
distributions. This matches cmstatr
res <- dat_8_3_11_1_1 %>% filter(condition == "ETW2") %>% ad_ksample(strength, batch) expect_equal(res$ad / (res$k - 1), 3.024, tolerance = 0.001) expect_true(res$reject_same_dist) res
4.1.3 Anderson--Darling Tests for Distribution {#c11-ad}
CMH-17-1G Section 8.3.11.2.1 contains results from STAT17 for
the "observed significance level" from the Anderson--Darling test
for various distributions. In this section, the ETW condition from the
present data set is used. The published results are given in the
following table. The results from cmstatr are below and are very
similar to those from STAT17.
Distribution | OSL -------------|------------ Normal | 0.006051 Lognormal | 0.000307 Weibull | 0.219
res <- dat_8_3_11_1_1 %>% filter(condition == "ETW") %>% anderson_darling_normal(strength) expect_equal(res$osl, 0.006051, tolerance = 0.001) res
res <- dat_8_3_11_1_1 %>% filter(condition == "ETW") %>% anderson_darling_lognormal(strength) expect_equal(res$osl, 0.000307, tolerance = 0.001) res
res <- dat_8_3_11_1_1 %>% filter(condition == "ETW") %>% anderson_darling_weibull(strength) expect_equal(res$osl, 0.0219, tolerance = 0.002) res
4.1.4 Levene's Test (Between Conditions) {#c11-lbc}
CMH-17-1G Section 8.3.11.1.1 provides results from ASAP for Levene's test
for equality of variance between conditions after the ETW and ETW2 conditions
are removed. The handbook shows an F statistic of 0.58, however if this
data is entered into ASAP directly, ASAP gives an F statistic of 0.058,
which matches the result of cmstatr.
res <- dat_8_3_11_1_1 %>% filter(condition != "ETW" & condition != "ETW2") %>% levene_test(strength, condition) expect_equal(res$f, 0.058, tolerance = 0.01) res
4.1.5 Levene's Test (Between Batches) {#c11-lbb}
CMH-17-1G Section 8.3.11.2.2 provides output from STAT17. The
ETW2 condition from the present data set was analyzed by STAT17
and that software reported an F statistic of 0.123 from Levene's
test when comparing the variance of the batches within this condition.
The result from cmstatr is similar.
res <- dat_8_3_11_1_1 %>% filter(condition == "ETW2") %>% levene_test(strength, batch) expect_equal(res$f, 0.123, tolerance = 0.005) res
Similarly, the published value of the F statistic for the CTD condition is
$3.850$. cmstatr produces very similar results.
res <- dat_8_3_11_1_1 %>% filter(condition == "CTD") %>% levene_test(strength, batch) expect_equal(res$f, 3.850, tolerance = 0.005) res
4.1.6 Nonparametric Basis Values {#c11-hk-wf}
CMH-17-1G Section 8.3.11.2.1 provides STAT17 outputs for the ETW condition of the present data set. The nonparametric Basis values are listed. In this case, the Hanson--Koopmans method is used. The published A-Basis value is 13.0 and the B-Basis is 37.9.
res <- dat_8_3_11_1_1 %>% filter(condition == "ETW") %>% basis_hk_ext(strength, method = "woodward-frawley", p = 0.99, conf = 0.95, override = "all") expect_equal(res$basis, 13.0, tolerance = 0.001) res
res <- dat_8_3_11_1_1 %>% filter(condition == "ETW") %>% basis_hk_ext(strength, method = "optimum-order", p = 0.90, conf = 0.95, override = "all") expect_equal(res$basis, 37.9, tolerance = 0.001) res
4.1.7 Single-Point ANOVA Basis Value {#c11-anova}
CMH-17-1G Section 8.3.11.2.2 provides output from STAT17 for
the ETW2 condition from the present data set. STAT17 reports
A- and B-Basis values based on the ANOVA method of 34.6 and
63.2, respectively. The results from cmstatr are similar.
res <- dat_8_3_11_1_1 %>% filter(condition == "ETW2") %>% basis_anova(strength, batch, override = "number_of_groups", p = 0.99, conf = 0.95) expect_equal(res$basis, 34.6, tolerance = 0.001) res
res <- dat_8_3_11_1_1 %>% filter(condition == "ETW2") %>% basis_anova(strength, batch, override = "number_of_groups") expect_equal(res$basis, 63.2, tolerance = 0.001) res
4.2 Dataset From Section 8.3.11.1.2
[@CMH-17-1G] provides an example data set and results from ASAP [@ASAP2008]. This example data set is duplicated below:
dat_8_3_11_1_2 <- tribble( ~batch, ~strength, ~condition, 1, 79.04517, "CTD", 1, 103.2006, "RTD", 1, 63.22764, "ETW", 1, 54.09806, "ETW2", 1, 102.6014, "CTD", 1, 105.1034, "RTD", 1, 70.84454, "ETW", 1, 58.87615, "ETW2", 1, 97.79372, "CTD", 1, 105.1893, "RTD", 1, 66.43223, "ETW", 1, 61.60167, "ETW2", 1, 92.86423, "CTD", 1, 100.4189, "RTD", 1, 75.37771, "ETW", 1, 60.23973, "ETW2", 1, 117.218, "CTD", 2, 85.32319, "RTD", 1, 72.43773, "ETW", 1, 61.4808, "ETW2", 1, 108.7168, "CTD", 2, 92.69923, "RTD", 1, 68.43073, "ETW", 1, 64.55832, "ETW2", 1, 112.2773, "CTD", 2, 98.45242, "RTD", 1, 69.72524, "ETW", 2, 57.76131, "ETW2", 1, 114.0129, "CTD", 2, 104.1014, "RTD", 2, 66.20343, "ETW", 2, 49.91463, "ETW2", 2, 106.8452, "CTD", 2, 91.51841, "RTD", 2, 60.51251, "ETW", 2, 61.49271, "ETW2", 2, 112.3911, "CTD", 2, 101.3746, "RTD", 2, 65.69334, "ETW", 2, 57.7281, "ETW2", 2, 115.5658, "CTD", 2, 101.5828, "RTD", 2, 62.73595, "ETW", 2, 62.11653, "ETW2", 2, 87.40657, "CTD", 2, 99.57384, "RTD", 2, 59.00798, "ETW", 2, 62.69353, "ETW2", 2, 102.2785, "CTD", 2, 88.84826, "RTD", 2, 62.37761, "ETW", 3, 61.38523, "ETW2", 2, 110.6073, "CTD", 3, 92.18703, "RTD", 3, 64.3947, "ETW", 3, 60.39053, "ETW2", 3, 105.2762, "CTD", 3, 101.8234, "RTD", 3, 72.8491, "ETW", 3, 59.17616, "ETW2", 3, 110.8924, "CTD", 3, 97.68909, "RTD", 3, 66.56226, "ETW", 3, 60.17616, "ETW2", 3, 108.7638, "CTD", 3, 101.5172, "RTD", 3, 66.56779, "ETW", 3, 46.47396, "ETW2", 3, 110.9833, "CTD", 3, 100.0481, "RTD", 3, 66.00123, "ETW", 3, 51.16616, "ETW2", 3, 101.3417, "CTD", 3, 102.0544, "RTD", 3, 59.62108, "ETW", 3, 100.0251, "CTD", 3, 60.61167, "ETW", 3, 57.65487, "ETW", 3, 66.51241, "ETW", 3, 64.89347, "ETW", 3, 57.73054, "ETW", 3, 68.94086, "ETW", 3, 61.63177, "ETW" )
4.2.1 Pooled SD A- and B-Basis {#c12-psd}
CMH-17-1G Table 8.3.11.2(k) provides outputs from ASAP for the data set
above. ASAP uses the pooled SD method. ASAP produces the following
results, which are quite similar to those produced by cmstatr.
Condition | CTD | RTD | ETW | ETW2 ----------|-------|-------|-------|------ B-Basis | 93.64 | 87.30 | 54.33 | 47.12 A-Basis | 89.19 | 79.86 | 46.84 | 39.69
res <- basis_pooled_sd(dat_8_3_11_1_2, strength, condition, override = "all") expect_equal(res$basis$value[res$basis$group == "CTD"], 93.64, tolerance = 0.001) expect_equal(res$basis$value[res$basis$group == "RTD"], 87.30, tolerance = 0.001) expect_equal(res$basis$value[res$basis$group == "ETW"], 54.33, tolerance = 0.001) expect_equal(res$basis$value[res$basis$group == "ETW2"], 47.12, tolerance = 0.001) res
res <- basis_pooled_sd(dat_8_3_11_1_2, strength, condition, p = 0.99, conf = 0.95, override = "all") expect_equal(res$basis$value[res$basis$group == "CTD"], 86.19, tolerance = 0.001) expect_equal(res$basis$value[res$basis$group == "RTD"], 79.86, tolerance = 0.001) expect_equal(res$basis$value[res$basis$group == "ETW"], 46.84, tolerance = 0.001) expect_equal(res$basis$value[res$basis$group == "ETW2"], 39.69, tolerance = 0.001) res
4.2.2 Pooled SD A- and B-Basis (Mod CV) {#c12-psdmcv}
After removal of the ETW2 condition, CMH17-STATS reports the pooled A- and
B-Basis (mod CV) shown in the following table.
cmstatr computes very similar values.
Condition | CTD | RTD | ETW ----------|-------|-------|------ B-Basis | 92.25 | 85.91 | 52.97 A-Basis | 83.81 | 77.48 | 44.47
res <- dat_8_3_11_1_2 %>% filter(condition != "ETW2") %>% basis_pooled_sd(strength, condition, modcv = TRUE, override = "all") expect_equal(res$basis$value[res$basis$group == "CTD"], 92.25, tolerance = 0.001) expect_equal(res$basis$value[res$basis$group == "RTD"], 85.91, tolerance = 0.001) expect_equal(res$basis$value[res$basis$group == "ETW"], 52.97, tolerance = 0.001) res
res <- dat_8_3_11_1_2 %>% filter(condition != "ETW2") %>% basis_pooled_sd(strength, condition, p = 0.99, conf = 0.95, modcv = TRUE, override = "all") expect_equal(res$basis$value[res$basis$group == "CTD"], 83.81, tolerance = 0.001) expect_equal(res$basis$value[res$basis$group == "RTD"], 77.48, tolerance = 0.001) expect_equal(res$basis$value[res$basis$group == "ETW"], 44.47, tolerance = 0.001) res
4.2.3 Pooled CV A- and B-Basis {#c12-pcv}
This data set was input into CMH17-STATS and the Pooled CV method was selected.
The results from CMH17-STATS were as follows. cmstatr produces very similar
results.
Condition | CTD | RTD | ETW | ETW2 ----------|-------|-------|-------|------ B-Basis | 90.89 | 85.37 | 56.79 | 50.55 A-Basis | 81.62 | 76.67 | 50.98 | 45.40
res <- basis_pooled_cv(dat_8_3_11_1_2, strength, condition, override = "all") expect_equal(res$basis$value[res$basis$group == "CTD"], 90.89, tolerance = 0.001) expect_equal(res$basis$value[res$basis$group == "RTD"], 85.37, tolerance = 0.001) expect_equal(res$basis$value[res$basis$group == "ETW"], 56.79, tolerance = 0.001) expect_equal(res$basis$value[res$basis$group == "ETW2"], 50.55, tolerance = 0.001) res
res <- basis_pooled_cv(dat_8_3_11_1_2, strength, condition, p = 0.99, conf = 0.95, override = "all") expect_equal(res$basis$value[res$basis$group == "CTD"], 81.62, tolerance = 0.001) expect_equal(res$basis$value[res$basis$group == "RTD"], 76.67, tolerance = 0.001) expect_equal(res$basis$value[res$basis$group == "ETW"], 50.98, tolerance = 0.001) expect_equal(res$basis$value[res$basis$group == "ETW2"], 45.40, tolerance = 0.001) res
4.2.4 Pooled CV A- and B-Basis (Mod CV) {#c12-pcvmcv}
This data set was input into CMH17-STATS and the Pooled CV method was selected
with the modified CV transform. Additionally, the ETW2 condition was removed.
The results from CMH17-STATS were as follows. cmstatr produces very similar
results.
Condition | CTD | RTD | ETW
----------|-------|-------|-------
B-Basis | 90.31 | 84.83 | 56.43
A-Basis | 80.57 | 75.69 | 50.33
res <- dat_8_3_11_1_2 %>% filter(condition != "ETW2") %>% basis_pooled_cv(strength, condition, modcv = TRUE, override = "all") expect_equal(res$basis$value[res$basis$group == "CTD"], 90.31, tolerance = 0.001) expect_equal(res$basis$value[res$basis$group == "RTD"], 84.83, tolerance = 0.001) expect_equal(res$basis$value[res$basis$group == "ETW"], 56.43, tolerance = 0.001) res
res <- dat_8_3_11_1_2 %>% filter(condition != "ETW2") %>% basis_pooled_cv(strength, condition, modcv = TRUE, p = 0.99, conf = 0.95, override = "all") expect_equal(res$basis$value[res$basis$group == "CTD"], 80.57, tolerance = 0.001) expect_equal(res$basis$value[res$basis$group == "RTD"], 75.69, tolerance = 0.001) expect_equal(res$basis$value[res$basis$group == "ETW"], 50.33, tolerance = 0.001) res
5. Other Data Sets
This section contains various small data sets. In most cases, these data sets
were generated randomly for the purpose of comparing cmstatr to other
software.
5.1 Anderson--Darling Test (Normal) {#ods-adn}
The following data set was randomly generated. When this is
entered into STAT17 [@STAT-17], that software gives the value
$OSL = 0.465$, which matches the result of cmstatr
within a small margin.
dat <- data.frame( strength = c( 137.4438, 139.5395, 150.89, 141.4474, 141.8203, 151.8821, 143.9245, 132.9732, 136.6419, 138.1723, 148.7668, 143.283, 143.5429, 141.7023, 137.4732, 152.338, 144.1589, 128.5218 ) ) res <- anderson_darling_normal(dat, strength) expect_equal(res$osl, 0.465, tolerance = 0.001) res
5.2 Anderson--Darling Test (Lognormal) {#ods-adl}
The following data set was randomly generated. When this is
entered into STAT17 [@STAT-17], that software gives the value
$OSL = 0.480$, which matches the result of cmstatr
within a small margin.
dat <- data.frame( strength = c( 137.4438, 139.5395, 150.89, 141.4474, 141.8203, 151.8821, 143.9245, 132.9732, 136.6419, 138.1723, 148.7668, 143.283, 143.5429, 141.7023, 137.4732, 152.338, 144.1589, 128.5218 ) ) res <- anderson_darling_lognormal(dat, strength) expect_equal(res$osl, 0.480, tolerance = 0.001) res
5.3 Anderson--Darling Test (Weibull) {#ods-adw}
The following data set was randomly generated. When this is
entered into STAT17 [@STAT-17], that software gives the value
$OSL = 0.179$, which matches the result of cmstatr
within a small margin.
dat <- data.frame( strength = c( 137.4438, 139.5395, 150.89, 141.4474, 141.8203, 151.8821, 143.9245, 132.9732, 136.6419, 138.1723, 148.7668, 143.283, 143.5429, 141.7023, 137.4732, 152.338, 144.1589, 128.5218 ) ) res <- anderson_darling_weibull(dat, strength) expect_equal(res$osl, 0.179, tolerance = 0.002) res
5.4 Normal A- and B-Basis {#ods-nb}
The following data was input into STAT17 and the A- and B-Basis values
were computed assuming normally distributed data. The results were 120.336
and 129.287, respectively. cmstatr reports very similar values.
dat <- c( 137.4438, 139.5395, 150.8900, 141.4474, 141.8203, 151.8821, 143.9245, 132.9732, 136.6419, 138.1723, 148.7668, 143.2830, 143.5429, 141.7023, 137.4732, 152.3380, 144.1589, 128.5218 ) res <- basis_normal(x = dat, p = 0.99, conf = 0.95, override = "all") expect_equal(res$basis, 120.336, tolerance = 0.0005) res
res <- basis_normal(x = dat, p = 0.9, conf = 0.95, override = "all") expect_equal(res$basis, 129.287, tolerance = 0.0005) res
5.5 Lognormal A- and B-Basis {#ods-lb}
The following data was input into STAT17 and the A- and B-Basis values
were computed assuming distributed according to a lognormal distribution.
The results were 121.710
and 129.664, respectively. cmstatr reports very similar values.
dat <- c( 137.4438, 139.5395, 150.8900, 141.4474, 141.8203, 151.8821, 143.9245, 132.9732, 136.6419, 138.1723, 148.7668, 143.2830, 143.5429, 141.7023, 137.4732, 152.3380, 144.1589, 128.5218 ) res <- basis_lognormal(x = dat, p = 0.99, conf = 0.95, override = "all") expect_equal(res$basis, 121.710, tolerance = 0.0005) res
res <- basis_lognormal(x = dat, p = 0.9, conf = 0.95, override = "all") expect_equal(res$basis, 129.664, tolerance = 0.0005) res
5.6 Weibull A- and B-Basis {#ods-wb}
The following data was input into STAT17 and the A- and B-Basis values
were computed assuming data following the Weibull distribution.
The results were 109.150
and 125.441, respectively. cmstatr reports very similar values.
dat <- c( 137.4438, 139.5395, 150.8900, 141.4474, 141.8203, 151.8821, 143.9245, 132.9732, 136.6419, 138.1723, 148.7668, 143.2830, 143.5429, 141.7023, 137.4732, 152.3380, 144.1589, 128.5218 ) res <- basis_weibull(x = dat, p = 0.99, conf = 0.95, override = "all") expect_equal(res$basis, 109.150, tolerance = 0.005) res
res <- basis_weibull(x = dat, p = 0.9, conf = 0.95, override = "all") expect_equal(res$basis, 125.441, tolerance = 0.005) res
5.7 Extended Hanson--Koopmans A- and B-Basis {#ods-hkb}
The following data was input into STAT17 and the A- and B-Basis values
were computed using the nonparametric (small sample) method.
The results were 99.651
and 124.156, respectively. cmstatr reports very similar values.
dat <- c( 137.4438, 139.5395, 150.8900, 141.4474, 141.8203, 151.8821, 143.9245, 132.9732, 136.6419, 138.1723, 148.7668, 143.2830, 143.5429, 141.7023, 137.4732, 152.3380, 144.1589, 128.5218 ) res <- basis_hk_ext(x = dat, p = 0.99, conf = 0.95, method = "woodward-frawley", override = "all") expect_equal(res$basis, 99.651, tolerance = 0.005) res
res <- basis_hk_ext(x = dat, p = 0.9, conf = 0.95, method = "optimum-order", override = "all") expect_equal(res$basis, 124.156, tolerance = 0.005) res
5.8 Extended Hanson--Koopmans B-Basis {#ods-hkb2}
The following random numbers were generated.
dat <- c( 139.6734, 143.0032, 130.4757, 144.8327, 138.7818, 136.7693, 148.636, 131.0095, 131.4933, 142.8856, 158.0198, 145.2271, 137.5991, 139.8298, 140.8557, 137.6148, 131.3614, 152.7795, 145.8792, 152.9207, 160.0989, 145.1920, 128.6383, 141.5992, 122.5297, 159.8209, 151.6720, 159.0156 )
All of the numbers above were input into STAT17 and the reported B-Basis
value using the Optimum Order nonparametric method was 122.36798. This
result matches the results of cmstatr within a small margin.
res <- basis_hk_ext(x = dat, p = 0.9, conf = 0.95, method = "optimum-order", override = "all") expect_equal(res$basis, 122.36798, tolerance = 0.001) res
The last two observations from the above data set were discarded, leaving
26 observations. This smaller data set was input into STAT17 and that
software calculated a B-Basis value of 121.57073 using the Optimum
Order nonparametric method. cmstatr reports a very similar number.
res <- basis_hk_ext(x = head(dat, 26), p = 0.9, conf = 0.95, method = "optimum-order", override = "all") expect_equal(res$basis, 121.57073, tolerance = 0.001) res
The same data set was further reduced such that only the first 22
observations were included. This smaller data set was input into STAT17
and that
software calculated a B-Basis value of 128.82397 using the Optimum
Order nonparametric method. cmstatr reports a very similar number.
res <- basis_hk_ext(x = head(dat, 22), p = 0.9, conf = 0.95, method = "optimum-order", override = "all") expect_equal(res$basis, 128.82397, tolerance = 0.001) res
5.9 Large Sample Nonparametric B-Basis {#ods-lsnb}
The following data was input into STAT17 and the B-Basis value
was computed using the nonparametric (large sample) method.
The results was 122.738297. cmstatr reports very similar values.
dat <- c( 137.3603, 135.6665, 136.6914, 154.7919, 159.2037, 137.3277, 128.821, 138.6304, 138.9004, 147.4598, 148.6622, 144.4948, 131.0851, 149.0203, 131.8232, 146.4471, 123.8124, 126.3105, 140.7609, 134.4875, 128.7508, 117.1854, 129.3088, 141.6789, 138.4073, 136.0295, 128.4164, 141.7733, 134.455, 122.7383, 136.9171, 136.9232, 138.8402, 152.8294, 135.0633, 121.052, 131.035, 138.3248, 131.1379, 147.3771, 130.0681, 132.7467, 137.1444, 141.662, 146.9363, 160.7448, 138.5511, 129.1628, 140.2939, 144.8167, 156.5918, 132.0099, 129.3551, 136.6066, 134.5095, 128.2081, 144.0896, 141.8029, 130.0149, 140.8813, 137.7864 ) res <- basis_nonpara_large_sample(x = dat, p = 0.9, conf = 0.95, override = "all") expect_equal(res$basis, 122.738297, tolerance = 0.005) res
5.10 Acceptance Limits Based on Mean and Extremum {#ods-eme}
Results from cmstatr's equiv_mean_extremum function were compared with
results from HYTEQ. The summary statistics for the qualification data
were set as mean = 141.310 and sd=6.415. For a value of alpha=0.05 and
n = 9,
HYTEQ reported thresholds of 123.725 and 137.197 for minimum individual
and mean, respectively. cmstatr produces very similar results.
res <- equiv_mean_extremum(alpha = 0.05, mean_qual = 141.310, sd_qual = 6.415, n_sample = 9) expect_equal(res$threshold_min_indiv, 123.725, tolerance = 0.001) expect_equal(res$threshold_mean, 137.197, tolerance = 0.001) res
Using the same parameters, but using the modified CV method,
HYTEQ produces thresholds of 117.024 and 135.630 for minimum individual
and mean, respectively. cmstatr produces very similar results.
res <- equiv_mean_extremum(alpha = 0.05, mean_qual = 141.310, sd_qual = 6.415, n_sample = 9, modcv = TRUE) expect_equal(res$threshold_min_indiv, 117.024, tolerance = 0.001) expect_equal(res$threshold_mean, 135.630, tolerance = 0.001) res
5.11 Acceptance Based on Change in Mean {#ods-ecm}
Results from cmstatr's equiv_change_mean function were compared with
results from HYTEQ. The following parameters were used. A value of
alpha = 0.05 was selected.
Parameter | Qualification | Sample ----------|---------------|--------- Mean | 9.24 | 9.02 SD | 0.162 | 0.15785 n | 28 | 9
HYTEQ gives an acceptance range of 9.115 to 9.365. cmstatr produces
similar results.
res <- equiv_change_mean(alpha = 0.05, n_sample = 9, mean_sample = 9.02, sd_sample = 0.15785, n_qual = 28, mean_qual = 9.24, sd_qual = 0.162) expect_equal(res$threshold, c(9.115, 9.365), tolerance = 0.001) res
After selecting the modified CV method, HYTEQ gives an acceptance
range of 8.857 to 9.623. cmstatr produces similar results.
res <- equiv_change_mean(alpha = 0.05, n_sample = 9, mean_sample = 9.02, sd_sample = 0.15785, n_qual = 28, mean_qual = 9.24, sd_qual = 0.162, modcv = TRUE) expect_equal(res$threshold, c(8.857, 9.623), tolerance = 0.001) res
6. Comparison with Literature
In this section, results from cmstatr are compared with values published
in literature.
6.1 Anderson--Darling K--Sample Test {#cl-adk}
[@Stephens1987] provides example data that compares measurements obtained in four labs. Their paper gives values of the ADK test statistic as well as p-values.
The data in [@Stephens1987] is as follows:
dat_ss1987 <- data.frame( smoothness = c( 38.7, 41.5, 43.8, 44.5, 45.5, 46.0, 47.7, 58.0, 39.2, 39.3, 39.7, 41.4, 41.8, 42.9, 43.3, 45.8, 34.0, 35.0, 39.0, 40.0, 43.0, 43.0, 44.0, 45.0, 34.0, 34.8, 34.8, 35.4, 37.2, 37.8, 41.2, 42.8 ), lab = c(rep("A", 8), rep("B", 8), rep("C", 8), rep("D", 8)) ) dat_ss1987
[@Stephens1987] lists the corresponding test statistics
$A_{akN}^2 = 8.3926$ and $\sigma_N = 1.2038$ with the p-value
$p = 0.0022$. These match the result of cmstatr within a small margin.
res <- ad_ksample(dat_ss1987, smoothness, lab) expect_equal(res$ad, 8.3926, tolerance = 0.001) expect_equal(res$sigma, 1.2038, tolerance = 0.001) expect_equal(res$p, 0.00226, tolerance = 0.01) res
7. Comparison with Published Factors
Various factors, such as tolerance limit factors, are published in various
publications. This section compares those published factors with those
computed by cmstatr.
7.1 Normal kB Factors {#pf-kb}
B-Basis tolerance limit factors assuming a normal distribution are published in
CMH-17-1G. Those factors are reproduced below and are compared with the
results of cmstatr. The published factors and those computed by cmstatr
are quite similar.
tribble( ~n, ~kB_published, 2, 20.581, 36, 1.725, 70, 1.582, 104, 1.522, 3, 6.157, 37, 1.718, 71, 1.579, 105, 1.521, 4, 4.163, 38, 1.711, 72, 1.577, 106, 1.519, 5, 3.408, 39, 1.704, 73, 1.575, 107, 1.518, 6, 3.007, 40, 1.698, 74, 1.572, 108, 1.517, 7, 2.756, 41, 1.692, 75, 1.570, 109, 1.516, 8, 2.583, 42, 1.686, 76, 1.568, 110, 1.515, 9, 2.454, 43, 1.680, 77, 1.566, 111, 1.513, 10, 2.355, 44, 1.675, 78, 1.564, 112, 1.512, 11, 2.276, 45, 1.669, 79, 1.562, 113, 1.511, 12, 2.211, 46, 1.664, 80, 1.560, 114, 1.510, 13, 2.156, 47, 1.660, 81, 1.558, 115, 1.509, 14, 2.109, 48, 1.655, 82, 1.556, 116, 1.508, 15, 2.069, 49, 1.650, 83, 1.554, 117, 1.507, 16, 2.034, 50, 1.646, 84, 1.552, 118, 1.506, 17, 2.002, 51, 1.642, 85, 1.551, 119, 1.505, 18, 1.974, 52, 1.638, 86, 1.549, 120, 1.504, 19, 1.949, 53, 1.634, 87, 1.547, 121, 1.503, 20, 1.927, 54, 1.630, 88, 1.545, 122, 1.502, 21, 1.906, 55, 1.626, 89, 1.544, 123, 1.501, 22, 1.887, 56, 1.623, 90, 1.542, 124, 1.500, 23, 1.870, 57, 1.619, 91, 1.540, 125, 1.499, 24, 1.854, 58, 1.616, 92, 1.539, 126, 1.498, 25, 1.839, 59, 1.613, 93, 1.537, 127, 1.497, 26, 1.825, 60, 1.609, 94, 1.536, 128, 1.496, 27, 1.812, 61, 1.606, 95, 1.534, 129, 1.495, 28, 1.800, 62, 1.603, 96, 1.533, 130, 1.494, 29, 1.789, 63, 1.600, 97, 1.531, 131, 1.493, 30, 1.778, 64, 1.597, 98, 1.530, 132, 1.492, 31, 1.768, 65, 1.595, 99, 1.529, 133, 1.492, 32, 1.758, 66, 1.592, 100, 1.527, 134, 1.491, 33, 1.749, 67, 1.589, 101, 1.526, 135, 1.490, 34, 1.741, 68, 1.587, 102, 1.525, 136, 1.489, 35, 1.733, 69, 1.584, 103, 1.523, 137, 1.488 ) %>% arrange(n) %>% mutate(kB_cmstatr = k_factor_normal(n, p = 0.9, conf = 0.95)) %>% rowwise() %>% mutate(diff = expect_equal(kB_published, kB_cmstatr, tolerance = 0.001)) %>% select(-c(diff))
7.2 Normal kA Factors {#pf-ka}
A-Basis tolerance limit factors assuming a normal distribution are published in
CMH-17-1G. Those factors are reproduced below and are compared with the
results of cmstatr. The published factors and those computed by cmstatr
are quite similar.
tribble( ~n, ~kA_published, 2, 37.094, 36, 2.983, 70, 2.765, 104, 2.676, 3, 10.553, 37, 2.972, 71, 2.762, 105, 2.674, 4, 7.042, 38, 2.961, 72, 2.758, 106, 2.672, 5, 5.741, 39, 2.951, 73, 2.755, 107, 2.671, 6, 5.062, 40, 2.941, 74, 2.751, 108, 2.669, 7, 4.642, 41, 2.932, 75, 2.748, 109, 2.667, 8, 4.354, 42, 2.923, 76, 2.745, 110, 2.665, 9, 4.143, 43, 2.914, 77, 2.742, 111, 2.663, 10, 3.981, 44, 2.906, 78, 2.739, 112, 2.662, 11, 3.852, 45, 2.898, 79, 2.736, 113, 2.660, 12, 3.747, 46, 2.890, 80, 2.733, 114, 2.658, 13, 3.659, 47, 2.883, 81, 2.730, 115, 2.657, 14, 3.585, 48, 2.876, 82, 2.727, 116, 2.655, 15, 3.520, 49, 2.869, 83, 2.724, 117, 2.654, 16, 3.464, 50, 2.862, 84, 2.721, 118, 2.652, 17, 3.414, 51, 2.856, 85, 2.719, 119, 2.651, 18, 3.370, 52, 2.850, 86, 2.716, 120, 2.649, 19, 3.331, 53, 2.844, 87, 2.714, 121, 2.648, 20, 3.295, 54, 2.838, 88, 2.711, 122, 2.646, 21, 3.263, 55, 2.833, 89, 2.709, 123, 2.645, 22, 3.233, 56, 2.827, 90, 2.706, 124, 2.643, 23, 3.206, 57, 2.822, 91, 2.704, 125, 2.642, 24, 3.181, 58, 2.817, 92, 2.701, 126, 2.640, 25, 3.158, 59, 2.812, 93, 2.699, 127, 2.639, 26, 3.136, 60, 2.807, 94, 2.697, 128, 2.638, 27, 3.116, 61, 2.802, 95, 2.695, 129, 2.636, 28, 3.098, 62, 2.798, 96, 2.692, 130, 2.635, 29, 3.080, 63, 2.793, 97, 2.690, 131, 2.634, 30, 3.064, 64, 2.789, 98, 2.688, 132, 2.632, 31, 3.048, 65, 2.785, 99, 2.686, 133, 2.631, 32, 3.034, 66, 2.781, 100, 2.684, 134, 2.630, 33, 3.020, 67, 2.777, 101, 2.682, 135, 2.628, 34, 3.007, 68, 2.773, 102, 2.680, 136, 2.627, 35, 2.995, 69, 2.769, 103, 2.678, 137, 2.626 ) %>% arrange(n) %>% mutate(kA_cmstatr = k_factor_normal(n, p = 0.99, conf = 0.95)) %>% rowwise() %>% mutate(diff = expect_equal(kA_published, kA_cmstatr, tolerance = 0.001)) %>% select(-c(diff))
7.3 Nonparametric B-Basis Extended Hanson--Koopmans {#pf-hk}
Vangel [@Vangel1994] provides extensive tables of $z$ for the case where
$i=1$ and $j$ is the median observation. This section checks the results of
cmstatr's function against those tables. Only the odd values of $n$
are checked so that the median is a single observation. The unit tests for
the cmstatr package include checks of a variety of values of $p$ and
confidence, but only the factors for B-Basis are checked here.
tribble( ~n, ~z, 3, 28.820048, 5, 6.1981307, 7, 3.4780112, 9, 2.5168762, 11, 2.0312134, 13, 1.7377374, 15, 1.5403989, 17, 1.3979806, 19, 1.2899172, 21, 1.2048089, 23, 1.1358259, 25, 1.0786237, 27, 1.0303046, ) %>% rowwise() %>% mutate( z_calc = hk_ext_z(n, 1, ceiling(n / 2), p = 0.90, conf = 0.95) ) %>% mutate(diff = expect_equal(z, z_calc, tolerance = 0.0001)) %>% select(-c(diff))
7.4 Nonparametric A-Basis Extended Hanson--Koopmans {#pf-hk2}
CMH-17-1G provides Table 8.5.15, which contains factors for calculating
A-Basis values using the Extended Hanson--Koopmans nonparametric method.
That table is reproduced in part here and the factors are compared with
those computed by cmstatr. More extensive checks are performed in the
unit test of the cmstatr package. The factors computed by cmstatr are
very similar to those published in CMH-17-1G.
tribble( ~n, ~k, 2, 80.0038, 4, 9.49579, 6, 5.57681, 8, 4.25011, 10, 3.57267, 12, 3.1554, 14, 2.86924, 16, 2.65889, 18, 2.4966, 20, 2.36683, 25, 2.131, 30, 1.96975, 35, 1.85088, 40, 1.75868, 45, 1.68449, 50, 1.62313, 60, 1.5267, 70, 1.45352, 80, 1.39549, 90, 1.34796, 100, 1.30806, 120, 1.24425, 140, 1.19491, 160, 1.15519, 180, 1.12226, 200, 1.09434, 225, 1.06471, 250, 1.03952, 275, 1.01773 ) %>% rowwise() %>% mutate(z_calc = hk_ext_z(n, 1, n, 0.99, 0.95)) %>% mutate(diff = expect_lt(abs(k - z_calc), 0.0001)) %>% select(-c(diff))
7.5 Factors for Small Sample Nonparametric B-Basis {#pf-hk-opt}
CMH-17-1G Table 8.5.14 provides ranks orders and factors for computing
nonparametric B-Basis values. This table is reproduced below and
compared with the results of cmstatr. The results are similar. In some
cases, the rank order ($r$ in CMH-17-1G or $j$ in cmstatr) and the
the factor ($k$) are different. These differences are discussed in detail in
the vignette
Extended Hanson-Koopmans.
tribble( ~n, ~r, ~k, 2, 2, 35.177, 3, 3, 7.859, 4, 4, 4.505, 5, 4, 4.101, 6, 5, 3.064, 7, 5, 2.858, 8, 6, 2.382, 9, 6, 2.253, 10, 6, 2.137, 11, 7, 1.897, 12, 7, 1.814, 13, 7, 1.738, 14, 8, 1.599, 15, 8, 1.540, 16, 8, 1.485, 17, 8, 1.434, 18, 9, 1.354, 19, 9, 1.311, 20, 10, 1.253, 21, 10, 1.218, 22, 10, 1.184, 23, 11, 1.143, 24, 11, 1.114, 25, 11, 1.087, 26, 11, 1.060, 27, 11, 1.035, 28, 12, 1.010 ) %>% rowwise() %>% mutate(r_calc = hk_ext_z_j_opt(n, 0.90, 0.95)$j) %>% mutate(k_calc = hk_ext_z_j_opt(n, 0.90, 0.95)$z)
7.6 Nonparametric B-Basis Binomial Rank {#pf-npbinom}
CMH-17-1G Table 8.5.12 provides factors for computing B-Basis values
using the nonparametric binomial rank method. Part of that table is
reproduced below and compared with the results of cmstatr.
The results of cmstatr are similar to the published values.
A more complete comparison is performed in the units tests of the
cmstatr package.
tribble( ~n, ~rb, 29, 1, 46, 2, 61, 3, 76, 4, 89, 5, 103, 6, 116, 7, 129, 8, 142, 9, 154, 10, 167, 11, 179, 12, 191, 13, 203, 14 ) %>% rowwise() %>% mutate(r_calc = nonpara_binomial_rank(n, 0.9, 0.95)) %>% mutate(test = expect_equal(rb, r_calc)) %>% select(-c(test))
7.7 Nonparametric A-Basis Binomial Rank {#pf-npbinom2}
CMH-17-1G Table 8.5.13 provides factors for computing B-Basis values
using the nonparametric binomial rank method. Part of that table is
reproduced below and compared with the results of cmstatr.
The results of cmstatr are similar to the published values.
A more complete comparison is performed in the units tests of the
cmstatr package.
tribble( ~n, ~ra, 299, 1, 473, 2, 628, 3, 773, 4, 913, 5 ) %>% rowwise() %>% mutate(r_calc = nonpara_binomial_rank(n, 0.99, 0.95)) %>% mutate(test = expect_equal(ra, r_calc)) %>% select(-c(test))
7.8 Factors for Equivalency {#pf-equiv}
Vangel's 2002 paper provides factors for calculating limits for sample
mean and sample extremum for various values of $\alpha$ and sample size ($n$).
A subset of those factors are reproduced below and compared with results
from cmstatr. The results are very similar for values of $\alpha$ and $n$
that are common for composite materials.
read.csv(system.file("extdata", "k1.vangel.csv", package = "cmstatr")) %>% gather(n, k1, X2:X10) %>% mutate(n = as.numeric(substring(n, 2))) %>% inner_join( read.csv(system.file("extdata", "k2.vangel.csv", package = "cmstatr")) %>% gather(n, k2, X2:X10) %>% mutate(n = as.numeric(substring(n, 2))), by = c("n" = "n", "alpha" = "alpha") ) %>% filter(n >= 5 & (alpha == 0.01 | alpha == 0.05)) %>% group_by(n, alpha) %>% nest() %>% mutate(equiv = map2(alpha, n, ~k_equiv(.x, .y))) %>% mutate(k1_calc = map(equiv, function(e) e[1]), k2_calc = map(equiv, function(e) e[2])) %>% select(-c(equiv)) %>% unnest(cols = c(data, k1_calc, k2_calc)) %>% mutate(check = expect_equal(k1, k1_calc, tolerance = 0.0001)) %>% select(-c(check)) %>% mutate(check = expect_equal(k2, k2_calc, tolerance = 0.0001)) %>% select(-c(check))
8. Session Info
This copy of this vignette was build on the following system.
sessionInfo()
9. References
R Package Documentation
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