---
title: "cmstatr Validation"
author: "Stefan Kloppenborg"
date: "`r Sys.Date()`"
output:
rmarkdown::html_vignette:
toc: true
toc_depth: 2
vignette: >
%\VignetteIndexEntry{cmstatr Validation}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
csl: ieee.csl
references:
- id: "ASAP2008"
type: "report"
number: "ASAP-2008"
author:
- given: K.S.
family: Raju
- given: J.S.
family: Tomblin
title: "AGATE Statistical Analysis Program"
issued:
year: 2008
publisher: Wichita State University
- id: Stephens1987
type: article
author:
- given: F.W.
family: Scholz
- given: M.A,
family: Stephens
title: K-Sample Anderson-Darling Tests
container-title: Journal of the American Statistical Association
volume: "82"
issue: "399"
page: 918-924
issued:
year: "1987"
month: "09"
DOI: 10.1080/01621459.1987.10478517
URL: https://doi.org/10.1080/01621459.1987.10478517
- id: "CMH-17-1G"
type: report
number: "CMH-17-1G"
title: Composite Materials Handbook, Volume 1. Polymer Matrix Composites
Guideline for Characterization of Structural Materials
publisher: SAE International
issued:
year: "2012"
month: "03"
- id: "STAT-17"
type: report
number: "STAT-17 Rev 5"
author:
- literal: Materials Sciences Corporation
title: CMH-17 Statistical Analysis for B-Basis and A-Basis Values
publisher: Materials Sciences Corporation
publisher-place: Horsham, PA
issued:
year: "2008"
month: "01"
day: "08"
- id: Vangel1994
type: article
author:
- given: Mark
family: Vangel
title: One-Sided Nonparametric Tolerance Limits
container-title: Communications in Statistics - Simulation and Computation
volume: "23"
issue: "4"
page: 1137-1154
issued:
year: "1994"
DOI: 10.1080/03610919408813222
- id: vangel_lot_2002
type: article
author:
- given: Mark
family: Vangel
title: Lot Acceptance and Compliance Testing Using the Sample Mean and an Extremum
container-title: Technometrics
volume: "44"
issue: "3"
page: 242--249
issued:
year: 2002
month: 8
DOI: 10.1198/004017002188618428
---
```{r setup, include = FALSE}
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.
```{r message=FALSE, warning=FALSE}
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.
```{r}
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](#cf-ad), [Section 4.1.2](#c11-adk), [Section 6.1](#cl-adk)
`anderson_darling_normal()` | [Section 4.1.3](#c11-ad), [Section 5.1](#ods-adn)
`anderson_darling_lognormal()` | [Section 4.1.3](#c11-ad), [Section 5.2](#ods-adl)
`anderson_darling_weibull()` | [Section 4.1.3](#c11-ad), [Section 5.3](#ods-adw)
`basis_normal()` | [Section 5.4](#ods-nb)
`basis_lognormal()` | [Section 5.5](#ods-lb)
`basis_weibull()` | [Section 5.6](#ods-wb)
`basis_pooled_cv()` | [Section 4.2.3](#c12-pcv), [Section 4.2.4](#c12-pcvmcv),
`basis_pooled_sd()` | [Section 4.2.1](#c12-psd), [Section 4.2.2](#c12-psdmcv)
`basis_hk_ext()` | [Section 4.1.6](#c11-hk-wf), [Section 5.7](#ods-hkb), [Section 5.8](#ods-hkb2)
`basis_nonpara_large_sample()` | [Section 5.9](#ods-lsnb)
`basis_anova()` | [Section 4.1.7](#c11-anova)
`calc_cv_star()` |
`cv()` |
`equiv_change_mean()` | [Section 5.11](#ods-ecm)
`equiv_mean_extremum()` | [Section 5.10](#ods-eme)
`hk_ext_z()` | [Section 7.3](#pf-hk), [Section 7.4](#pf-hk2)
`hk_ext_z_j_opt()` | [Section 7.5](#pf-hk-opt)
`k_equiv()` | [Section 7.8](#pf-equiv)
`k_factor_normal()` | [Section 7.1](#pf-kb), [Section 7.2](#pf-ka)
`levene_test()` | [Section 4.1.4](#c11-lbc), [Section 4.1.5](#c11-lbb)
`maximum_normed_residual()` | [Section 4.1.1](#c11-mnr)
`nonpara_binomial_rank()` | [Section 7.6](#pf-npbinom), [Section 7.7](#pf-npbinom2)
`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.
```{r}
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](adktest.html).
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.
```{r}
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$reject_same_dist)
res
```
When the ETW warp-tension data from this data set are entered into ASAP 2008,
the reported test statistic is 1.604 and it fails to reject the null
hypothesis that the batches are drawn from the same distribution. Adjusting
for the different definition of the test statistic, `cmstatr` gives nearly
identical results.
```{r}
res <- carbon.fabric %>%
filter(test == "WT") %>%
filter(condition == "ETW") %>%
ad_ksample(strength, batch)
expect_equal(res$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:
```{r}
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.
```{r}
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$n_outliers, 0)
res
```
Similarly, for Batch 3 of the ETW data, the results of `cmstatr` match
the results published in the handbook within a small tolerance. The published
test statistic is 2.119
```{r}
res <- dat_8_3_11_1_1 %>%
filter(condition == "ETW" & batch == 3) %>%
maximum_normed_residual(strength, alpha = 0.05)
expect_equal(res$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](adktest.html).
```{r}
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`
```{r}
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$reject_same_dist)
res
```
### 4.1.3 Anderson--Darling Tests for Distribution {#c11-ad}
CMH-17-1G Section 8.3.11.2.1 contains results from STAT17 for
the "observed significance level" from the Anderson--Darling test
for various distributions. In this section, the ETW condition from the
present data set is used. The published results are given in the
following table. The results from `cmstatr` are below and are very
similar to those from STAT17.
Distribution | OSL
-------------|------------
Normal | 0.006051
Lognormal | 0.000307
Weibull | 0.219
```{r}
res <- dat_8_3_11_1_1 %>%
filter(condition == "ETW") %>%
anderson_darling_normal(strength)
expect_equal(res$osl, 0.006051, tolerance = 0.001)
res
```
```{r}
res <- dat_8_3_11_1_1 %>%
filter(condition == "ETW") %>%
anderson_darling_lognormal(strength)
expect_equal(res$osl, 0.000307, tolerance = 0.001)
res
```
```{r}
res <- dat_8_3_11_1_1 %>%
filter(condition == "ETW") %>%
anderson_darling_weibull(strength)
expect_equal(res$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`.
```{r}
res <- dat_8_3_11_1_1 %>%
filter(condition != "ETW" & condition != "ETW2") %>%
levene_test(strength, condition)
expect_equal(res$f, 0.058, tolerance = 0.01)
res
```
### 4.1.5 Levene's Test (Between Batches) {#c11-lbb}
CMH-17-1G Section 8.3.11.2.2 provides output from STAT17. The
ETW2 condition from the present data set was analyzed by STAT17
and that software reported an F statistic of 0.123 from Levene's
test when comparing the variance of the batches within this condition.
The result from `cmstatr` is similar.
```{r}
res <- dat_8_3_11_1_1 %>%
filter(condition == "ETW2") %>%
levene_test(strength, batch)
expect_equal(res$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.
```{r}
res <- dat_8_3_11_1_1 %>%
filter(condition == "CTD") %>%
levene_test(strength, batch)
expect_equal(res$f, 3.850, tolerance = 0.005)
res
```
### 4.1.6 Nonparametric Basis Values {#c11-hk-wf}
CMH-17-1G Section 8.3.11.2.1 provides STAT17 outputs for the ETW
condition of the present data set. The nonparametric Basis values are listed.
In this case, the Hanson--Koopmans method is used. The published A-Basis
value is 13.0 and the B-Basis is 37.9.
```{r}
res <- dat_8_3_11_1_1 %>%
filter(condition == "ETW") %>%
basis_hk_ext(strength, method = "woodward-frawley", p = 0.99, conf = 0.95,
override = "all")
expect_equal(res$basis, 13.0, tolerance = 0.001)
res
```
```{r}
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$basis, 37.9, tolerance = 0.001)
res
```
### 4.1.7 Single-Point ANOVA Basis Value {#c11-anova}
CMH-17-1G Section 8.3.11.2.2 provides output from STAT17 for
the ETW2 condition from the present data set. STAT17 reports
A- and B-Basis values based on the ANOVA method of 34.6 and
63.2, respectively. The results from `cmstatr` are similar.
```{r}
res <- dat_8_3_11_1_1 %>%
filter(condition == "ETW2") %>%
basis_anova(strength, batch, override = "number_of_groups",
p = 0.99, conf = 0.95)
expect_equal(res$basis, 34.6, tolerance = 0.001)
res
```
```{r}
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:
```{r}
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
```{r}
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$group == "ETW2"],
47.12, tolerance = 0.001)
res
```
```{r}
res <- basis_pooled_sd(dat_8_3_11_1_2, strength, condition,
p = 0.99, conf = 0.95,
override = "all")
expect_equal(res$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$group == "ETW2"],
39.69, tolerance = 0.001)
res
```
### 4.2.2 Pooled SD A- and B-Basis (Mod CV) {#c12-psdmcv}
After removal of the ETW2 condition, CMH17-STATS reports the pooled A- and
B-Basis (mod CV) shown in the following table.
`cmstatr` computes very similar values.
Condition | CTD | RTD | ETW
----------|-------|-------|------
B-Basis | 92.25 | 85.91 | 52.97
A-Basis | 83.81 | 77.48 | 44.47
```{r}
res <- dat_8_3_11_1_2 %>%
filter(condition != "ETW2") %>%
basis_pooled_sd(strength, condition, modcv = TRUE, override = "all")
expect_equal(res$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$group == "ETW"],
52.97, tolerance = 0.001)
res
```
```{r}
res <- dat_8_3_11_1_2 %>%
filter(condition != "ETW2") %>%
basis_pooled_sd(strength, condition,
p = 0.99, conf = 0.95, modcv = TRUE, override = "all")
expect_equal(res$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$group == "ETW"],
44.47, tolerance = 0.001)
res
```
### 4.2.3 Pooled CV A- and B-Basis {#c12-pcv}
This data set was input into CMH17-STATS and the Pooled CV method was selected.
The results from CMH17-STATS were as follows. `cmstatr` produces very similar
results.
Condition | CTD | RTD | ETW | ETW2
----------|-------|-------|-------|------
B-Basis | 90.89 | 85.37 | 56.79 | 50.55
A-Basis | 81.62 | 76.67 | 50.98 | 45.40
```{r}
res <- basis_pooled_cv(dat_8_3_11_1_2, strength, condition, override = "all")
expect_equal(res$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$group == "ETW2"],
50.55, tolerance = 0.001)
res
```
```{r}
res <- basis_pooled_cv(dat_8_3_11_1_2, strength, condition,
p = 0.99, conf = 0.95, override = "all")
expect_equal(res$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$group == "ETW2"],
45.40, tolerance = 0.001)
res
```
### 4.2.4 Pooled CV A- and B-Basis (Mod CV) {#c12-pcvmcv}
This data set was input into CMH17-STATS and the Pooled CV method was selected
with the modified CV transform. Additionally, the ETW2 condition was removed.
The results from CMH17-STATS were as follows. `cmstatr` produces very similar
results.
Condition | CTD | RTD | ETW
----------|-------|-------|-------
B-Basis | 90.31 | 84.83 | 56.43
A-Basis | 80.57 | 75.69 | 50.33
```{r}
res <- dat_8_3_11_1_2 %>%
filter(condition != "ETW2") %>%
basis_pooled_cv(strength, condition, modcv = TRUE, override = "all")
expect_equal(res$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$group == "ETW"],
56.43, tolerance = 0.001)
res
```
```{r}
res <- dat_8_3_11_1_2 %>%
filter(condition != "ETW2") %>%
basis_pooled_cv(strength, condition, modcv = TRUE,
p = 0.99, conf = 0.95, override = "all")
expect_equal(res$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.
```{r}
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.
```{r}
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.
```{r}
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$osl, 0.179, tolerance = 0.002)
res
```
## 5.4 Normal A- and B-Basis {#ods-nb}
The following data was input into STAT17 and the A- and B-Basis values
were computed assuming normally distributed data. The results were 120.336
and 129.287, respectively. `cmstatr` reports very similar values.
```{r}
dat <- c(
137.4438, 139.5395, 150.8900, 141.4474, 141.8203, 151.8821, 143.9245,
132.9732, 136.6419, 138.1723, 148.7668, 143.2830, 143.5429, 141.7023,
137.4732, 152.3380, 144.1589, 128.5218
)
res <- basis_normal(x = dat, p = 0.99, conf = 0.95, override = "all")
expect_equal(res$basis, 120.336, tolerance = 0.0005)
res
```
```{r}
res <- basis_normal(x = dat, p = 0.9, conf = 0.95, override = "all")
expect_equal(res$basis, 129.287, tolerance = 0.0005)
res
```
## 5.5 Lognormal A- and B-Basis {#ods-lb}
The following data was input into STAT17 and the A- and B-Basis values
were computed assuming distributed according to a lognormal distribution.
The results were 121.710
and 129.664, respectively. `cmstatr` reports very similar values.
```{r}
dat <- c(
137.4438, 139.5395, 150.8900, 141.4474, 141.8203, 151.8821, 143.9245,
132.9732, 136.6419, 138.1723, 148.7668, 143.2830, 143.5429, 141.7023,
137.4732, 152.3380, 144.1589, 128.5218
)
res <- basis_lognormal(x = dat, p = 0.99, conf = 0.95, override = "all")
expect_equal(res$basis, 121.710, tolerance = 0.0005)
res
```
```{r}
res <- basis_lognormal(x = dat, p = 0.9, conf = 0.95, override = "all")
expect_equal(res$basis, 129.664, tolerance = 0.0005)
res
```
## 5.6 Weibull A- and B-Basis {#ods-wb}
The following data was input into STAT17 and the A- and B-Basis values
were computed assuming data following the Weibull distribution.
The results were 109.150
and 125.441, respectively. `cmstatr` reports very similar values.
```{r}
dat <- c(
137.4438, 139.5395, 150.8900, 141.4474, 141.8203, 151.8821, 143.9245,
132.9732, 136.6419, 138.1723, 148.7668, 143.2830, 143.5429, 141.7023,
137.4732, 152.3380, 144.1589, 128.5218
)
res <- basis_weibull(x = dat, p = 0.99, conf = 0.95, override = "all")
expect_equal(res$basis, 109.150, tolerance = 0.005)
res
```
```{r}
res <- basis_weibull(x = dat, p = 0.9, conf = 0.95, override = "all")
expect_equal(res$basis, 125.441, tolerance = 0.005)
res
```
## 5.7 Extended Hanson--Koopmans A- and B-Basis {#ods-hkb}
The following data was input into STAT17 and the A- and B-Basis values
were computed using the nonparametric (small sample) method.
The results were 99.651
and 124.156, respectively. `cmstatr` reports very similar values.
```{r}
dat <- c(
137.4438, 139.5395, 150.8900, 141.4474, 141.8203, 151.8821, 143.9245,
132.9732, 136.6419, 138.1723, 148.7668, 143.2830, 143.5429, 141.7023,
137.4732, 152.3380, 144.1589, 128.5218
)
res <- basis_hk_ext(x = dat, p = 0.99, conf = 0.95,
method = "woodward-frawley", override = "all")
expect_equal(res$basis, 99.651, tolerance = 0.005)
res
```
```{r}
res <- basis_hk_ext(x = dat, p = 0.9, conf = 0.95,
method = "optimum-order", override = "all")
expect_equal(res$basis, 124.156, tolerance = 0.005)
res
```
## 5.8 Extended Hanson--Koopmans B-Basis {#ods-hkb2}
The following random numbers were generated.
```{r}
dat <- c(
139.6734, 143.0032, 130.4757, 144.8327, 138.7818, 136.7693, 148.636,
131.0095, 131.4933, 142.8856, 158.0198, 145.2271, 137.5991, 139.8298,
140.8557, 137.6148, 131.3614, 152.7795, 145.8792, 152.9207, 160.0989,
145.1920, 128.6383, 141.5992, 122.5297, 159.8209, 151.6720, 159.0156
)
```
All of the numbers above were input into STAT17 and the reported B-Basis
value using the Optimum Order nonparametric method was 122.36798. This
result matches the results of `cmstatr` within a small margin.
```{r}
res <- basis_hk_ext(x = dat, p = 0.9, conf = 0.95,
method = "optimum-order", override = "all")
expect_equal(res$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.
```{r}
res <- basis_hk_ext(x = head(dat, 26), p = 0.9, conf = 0.95,
method = "optimum-order", override = "all")
expect_equal(res$basis, 121.57073, tolerance = 0.001)
res
```
The same data set was further reduced such that only the first 22
observations were included. This smaller data set was input into STAT17
and that
software calculated a B-Basis value of 128.82397 using the Optimum
Order nonparametric method. `cmstatr` reports a very similar number.
```{r}
res <- basis_hk_ext(x = head(dat, 22), p = 0.9, conf = 0.95,
method = "optimum-order", override = "all")
expect_equal(res$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.
```{r}
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$basis, 122.738297, tolerance = 0.005)
res
```
## 5.10 Acceptance Limits Based on Mean and Extremum {#ods-eme}
Results from `cmstatr`'s `equiv_mean_extremum` function were compared with
results from HYTEQ. The summary statistics for the qualification data
were set as `mean = 141.310` and `sd=6.415`. For a value of `alpha=0.05` and
`n = 9`,
HYTEQ reported thresholds of 123.725 and 137.197 for minimum individual
and mean, respectively. `cmstatr` produces very similar results.
```{r}
res <- equiv_mean_extremum(alpha = 0.05, mean_qual = 141.310, sd_qual = 6.415,
n_sample = 9)
expect_equal(res$threshold_min_indiv, 123.725, tolerance = 0.001)
expect_equal(res$threshold_mean, 137.197, tolerance = 0.001)
res
```
Using the same parameters, but using the modified CV method,
HYTEQ produces thresholds of 117.024 and 135.630 for minimum individual
and mean, respectively. `cmstatr` produces very similar results.
```{r}
res <- equiv_mean_extremum(alpha = 0.05, mean_qual = 141.310, sd_qual = 6.415,
n_sample = 9, modcv = TRUE)
expect_equal(res$threshold_min_indiv, 117.024, tolerance = 0.001)
expect_equal(res$threshold_mean, 135.630, tolerance = 0.001)
res
```
## 5.11 Acceptance Based on Change in Mean {#ods-ecm}
Results from `cmstatr`'s `equiv_change_mean` function were compared with
results from HYTEQ. The following parameters were used. A value of
`alpha = 0.05` was selected.
Parameter | Qualification | Sample
----------|---------------|---------
Mean | 9.24 | 9.02
SD | 0.162 | 0.15785
n | 28 | 9
HYTEQ gives an acceptance range of 9.115 to 9.365. `cmstatr` produces
similar results.
```{r}
res <- equiv_change_mean(alpha = 0.05, n_sample = 9, mean_sample = 9.02,
sd_sample = 0.15785, n_qual = 28, mean_qual = 9.24,
sd_qual = 0.162)
expect_equal(res$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.
```{r}
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:
```{r}
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.
```{r}
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.
```{r}
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.
```{r}
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.
```{r}
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.
```{r}
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](hk_ext.html).
```{r}
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.
```{r}
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.
```{r}
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.
```{r}
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.
```{r}
sessionInfo()
```
# 9. References