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-Usually, one creates a GOF-model-test-class via the function GOF_model(). But this function is actually a wrapper for the class GOF_model_test. That class needs other classes to work properly. In particular it uses the three interfaces GOF_model_info_extractor, GOF_model_simulator, GOF_model_trainer. In general, objects of class lm or glm can be used with GOF_model. However, there might be situations where one has to overwrite the default behavior. For instance, if you want to apply the methodology to new models or simply because a model fit returned by an R-package does not work properly with the GOF-package. Here we show how to implement the three interfaces for some concret cases.
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--Least square estimates
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-\[ -Y = m(\beta^\top X) + \epsilon -\]
-without any knowledge about \(\epsilon\). Then we could try to estimate the \(\beta\) using a least square estimate and using the GOF-package for check our fitted model. First we generate a data set
-library(bootGOF) -set.seed(1) -X = runif(n = 200, min = 6, max = 14) -d = data.frame(x = X, y = sin(0.5 * X) + rnorm(200, sd = 0.2)) -plot(y~x, data = d)
Lets have a short excursion at this point because the plot gives the impression that the following simple model might apply:
- -However, a goodness-of-fit-test rejects this model
-mt <- GOF_model( - data = d, - model = wrong_model, - simulator_type = "parametric", - nmb_boot_samples = 100, - y_name = "y", - Rn1_statistic = Rn1_KS$new()) -mt$get_pvalue() -#> [1] 0
Note that in this simple case the standard diagnostic plots also reveal that this model is not a sufficient. Now we fit the model using a least square estimator:
-library(minpack.lm) -fit <- minpack.lm::nlsLM(y ~ sin(a * x), - data = d, - start = c(a = 0.5), - control = nls.control(maxiter = 500)) -fit -#> Nonlinear regression model -#> model: y ~ sin(a * x) -#> data: d -#> a -#> 0.4993 -#> residual sum-of-squares: 7.873 -#> -#> Number of iterations to convergence: 3 -#> Achieved convergence tolerance: 1.49e-08
In order to create a goodnes-of-fit-test using GOF_model_test we have to implement three interfaces. The first interface requires that we implement three functions yhat, y_minus_yhat and beta_x_covariates, which are the predictions for the dependent variable (also called target-variable), the residuals on the scale of the dependent variable and the inner product of the estimated parameters and the independent variables (also called covariates or features). However, the object returned by minpack.lm::nlsLM does not contain the original data set but that data set is necessary to calculate the inner product. Hence, we make a list that contains the model fit and the data that was used to fit the model.
-fit_and_data <- list(fit = fit, data = d)
Now we can implement the interface
-library(R6) -my_nls_info_extractor <- R6::R6Class( - classname = "my_nls_info_extractor", - inherit = GOF_model_info_extractor, - public = list( - yhat = function(model) { - predict(object = model$fit) - }, - y_minus_yhat = function(model) { - residuals(object = model$fit) - }, - beta_x_covariates = function(model) { - a_hat <- coef(object = model$fit) - x <- model$data$x - ret <- a_hat * x - return(ret) - } - )) -my_info_extractor <- my_nls_info_extractor$new()
Implementing yhat and y_minus_yhat is straight forward using the function already offered by R but beta_x_covariates needs special attention. The reason is that minpack.lm::nlsLM can also fit very general models of the type \(m(\beta, X)\). Hence, there is now built-in-function to extract objects like \(\beta^\top X\). Lets look at the first few data points:
-head(d) -#> x y -#> 1 8.124069 -0.9199426 -#> 2 8.976991 -0.9666172 -#> 3 10.582827 -1.0191812 -#> 4 13.265662 0.3741709 -#> 5 7.613455 -0.7480823 -#> 6 13.187117 0.6588717
Now we are able to predict \(y\):
-head(my_info_extractor$yhat(model = fit_and_data)) -#> [1] -0.7926252 -0.9737135 -0.8407826 0.3343611 -0.6132205 0.2971500
And calculate the difference between \(y\) and our prediction:
-head(my_info_extractor$y_minus_yhat(model = fit_and_data)) -#> [1] -0.127317499 0.007096259 -0.178398577 0.039809752 -0.134861797 -#> [6] 0.361721696
And based on the estimated coefficient we can calculate the inner product with \(x\):
-head(my_info_extractor$beta_x_covariates(model = fit_and_data)) -#> [1] 4.056695 4.482596 5.284458 6.624113 3.801724 6.584892
Since we did not make an assumption about the distribution of \(\epsilon\) we cannot use a parametric resampling scheme. However, we can do a wild bootstrap that uses only the predictions and the residuals. The class GOF_sim_wild_rademacher implements this wild bootstrap but needs an info extractor to obtain the preditions and residuals:
-my_simulator <- GOF_sim_wild_rademacher$new( - gof_model_info_extractor = my_info_extractor -)
This class generates as many observations (according to the fitted model) as are contained in the data set used to fit the model.
-Again looking at first at the original data points:
-head(d) -#> x y -#> 1 8.124069 -0.9199426 -#> 2 8.976991 -0.9666172 -#> 3 10.582827 -1.0191812 -#> 4 13.265662 0.3741709 -#> 5 7.613455 -0.7480823 -#> 6 13.187117 0.6588717
Now lets look at new \(y\)s generated according to the fitted model, i.e. following a negative binomial distribution subject to the independent variables:
-head(my_simulator$resample_y(model = fit_and_data)) -#> [1] -0.6653077 -0.9808097 -1.0191812 0.3741709 -0.4783588 0.6588717
Note that the resampled \(y\)’s sometimes equal the observed \(y\). The reason is that the this wild bootstrap performs ‘predictions +/- residual’ and only the sign is drawn at random.
-Finally, we need to implement the interface GOF_model_trainer which requires a function refit that is able to update the model object by refitting it to a new data set. R already provides the necessary function, i.e. stats::update. However we combined the fitted model with the data set in a list and we need to take into account:
-my_nls_trainer <- R6::R6Class( - classname = "GOF_nls_trainer", - inherit = GOF_model_trainer, - public = list( - refit = function(model, data) { - fit <- update(object = model$fit, data = data) - ret <- list(fit = fit, data = data) - return(ret) - })) -my_trainer <- my_nls_trainer$new()
This implementation basically equals the implementation of GOF_lm/glm/_trainer. The only difference is that we again store the data with the fit because nlsLM() doesn’t do it for us.
-Of course, fitting the model again to the original data set results in the same fitted model. With the defined classes we can now easily generate a new data set and refit the model to that new data set.
-new_data <- d -new_data$y <- my_simulator$resample_y(model = fit_and_data) -my_trainer$refit(model = fit_and_data, data = new_data)$fit -#> Nonlinear regression model -#> model: y ~ sin(a * x) -#> data: data -#> a -#> 0.4997 -#> residual sum-of-squares: 7.871 -#> -#> Number of iterations to convergence: 2 -#> Achieved convergence tolerance: 1.49e-08
Now all ingredients are available for applying the Goodness-of-Fit test:
-set.seed(1) -mt <- GOF_model_test$new( - model = fit_and_data, - data = d, - nmb_boot_samples = 100, - y_name = "y", - Rn1_statistic = Rn1_CvM$new(), - gof_model_info_extractor = my_info_extractor, - gof_model_resample = GOF_model_resample$new( - gof_model_simulator = my_simulator, - gof_model_trainer = my_trainer - ) -) -mt$get_pvalue() -#> [1] 0.8
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- -Negative Binomial using the MASS-package
-A negative binomial model is a generalized linear model. Furthermore, within R MASS::glm.nb returns an object of class glm and this package actually can process glm-classes. However, MASS::glm.nb seems to have a bug that prevents to propoerly update/refit such an object via the stats::update() function. We briefly illustrate this using an artificial data set:
-library(MASS) -set.seed(1) -X1 <- rnorm(100) -X2 <- rnorm(100) -d <- data.frame( - y = MASS::rnegbin(n = 100, mu = exp(0.2 + X1 * 0.2 + X2 * 0.6), theta = 2), - x1 = X1, - x2 = X2) -fit <- MASS::glm.nb(y~x1+x2, data = d) -fit -#> -#> Call: MASS::glm.nb(formula = y ~ x1 + x2, data = d, init.theta = 1.561918046, -#> link = log) -#> -#> Coefficients: -#> (Intercept) x1 x2 -#> 0.1905 0.1532 0.7696 -#> -#> Degrees of Freedom: 99 Total (i.e. Null); 97 Residual -#> Null Deviance: 144.3 -#> Residual Deviance: 102.4 AIC: 317.5
Note that fit-object shows that the call contained the parameter init.theta which obviously was not provided by us. The problem is that this init.theta parameter is used by stats::update during refitting resampled data. So fitting resampled data and fitting the original data is slightly different from the perspective of the fitting algorithm. To circumvent this problem we can reimplement the corresponding interface as follows:
-my_negbin_trainer <- R6::R6Class( - classname = "GOF_glmnb_trainer", - inherit = GOF_model_trainer, - public = list( - refit = function(model, data) { - MASS::glm.nb(formula = formula(model), data = data) - }))
This way we ensure that the original data set and resampled data set are fitted in the same way. Now we can create the GOF-test-class using this new refitting-class
-set.seed(1) -mt <- GOF_model_test$new( - model = fit, - data = d, - nmb_boot_samples = 100, - y_name = "y", - Rn1_statistic = Rn1_CvM$new(), - gof_model_info_extractor = GOF_glm_info_extractor$new(), - gof_model_resample = GOF_model_resample$new( - gof_model_simulator = GOF_glm_sim_param$new(), - gof_model_trainer = my_negbin_trainer$new() - ) -) -mt$get_pvalue() -#> [1] 0.36
Lets compare the result with the default GOF-test for glm’s
-set.seed(1) -mt2 <- GOF_model( - model = fit, - data = d, - nmb_boot_samples = 100, - simulator_type = "parametric", - y_name = "y", - Rn1_statistic = Rn1_CvM$new() -) -#> Warning in GOF_model(model = fit, data = d, nmb_boot_samples = 100, -#> simulator_type = "parametric", : The GOF-test requires to refit the model. -#> Refitting MASS::glm.nb can be problematic, see vignette New-Models -mt2$get_pvalue() -#> [1] 0.36
In this case the p-values do not differ. However, it could be different in other settings.
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- box-shadow: inset 0 -2px 0 0 rgba(105, 105, 105, .5)
-}
-
-.algolia-autocomplete .ds-suggestion.ds-cursor .algolia-docsearch-suggestion--content {
- background-color: rgba(192, 192, 192, .15)
-}
diff --git a/docs/docsearch.js b/docs/docsearch.js
deleted file mode 100644
index b35504c..0000000
--- a/docs/docsearch.js
+++ /dev/null
@@ -1,85 +0,0 @@
-$(function() {
-
- // register a handler to move the focus to the search bar
- // upon pressing shift + "/" (i.e. "?")
- $(document).on('keydown', function(e) {
- if (e.shiftKey && e.keyCode == 191) {
- e.preventDefault();
- $("#search-input").focus();
- }
- });
-
- $(document).ready(function() {
- // do keyword highlighting
- /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */
- var mark = function() {
-
- var referrer = document.URL ;
- var paramKey = "q" ;
-
- if (referrer.indexOf("?") !== -1) {
- var qs = referrer.substr(referrer.indexOf('?') + 1);
- var qs_noanchor = qs.split('#')[0];
- var qsa = qs_noanchor.split('&');
- var keyword = "";
-
- for (var i = 0; i < qsa.length; i++) {
- var currentParam = qsa[i].split('=');
-
- if (currentParam.length !== 2) {
- continue;
- }
-
- if (currentParam[0] == paramKey) {
- keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20"));
- }
- }
-
- if (keyword !== "") {
- $(".contents").unmark({
- done: function() {
- $(".contents").mark(keyword);
- }
- });
- }
- }
- };
-
- mark();
- });
-});
-
-/* Search term highlighting ------------------------------*/
-
-function matchedWords(hit) {
- var words = [];
-
- var hierarchy = hit._highlightResult.hierarchy;
- // loop to fetch from lvl0, lvl1, etc.
- for (var idx in hierarchy) {
- words = words.concat(hierarchy[idx].matchedWords);
- }
-
- var content = hit._highlightResult.content;
- if (content) {
- words = words.concat(content.matchedWords);
- }
-
- // return unique words
- var words_uniq = [...new Set(words)];
- return words_uniq;
-}
-
-function updateHitURL(hit) {
-
- var words = matchedWords(hit);
- var url = "";
-
- if (hit.anchor) {
- url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor;
- } else {
- url = hit.url + '?q=' + escape(words.join(" "));
- }
-
- return url;
-}
diff --git a/docs/index.html b/docs/index.html
deleted file mode 100644
index 36681a8..0000000
--- a/docs/index.html
+++ /dev/null
@@ -1,243 +0,0 @@
-
-
-
-
-
-
-
-Bootstrap Based Goodness-of-Fit Tests • bootGOF
-
-
-
-
-
-
-
-
-
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-
-Bootstrap based goodness-of-fit tests for (linear) models. Assume you have fitted a statistical model, e.g. classical linear model or generalized linear model or a model that follows Y = m(β⊤X) + ϵ. This package allows to perform a rigorous statistical test to check if the chosen model family is correct.
-
-
-Example
-First we generate a data-set in order to apply the package.
-set.seed(1)
-N <- 100
-X1 <- rnorm(N)
-X2 <- rnorm(N)
-d <- data.frame(
- y = rpois(n = N, lambda = exp(4 + X1 * 2 + X2 * 6)),
- x1 = X1,
- x2 = X2)
-Note that both covariates influence the dependent variable Y. Taking only one of the covariates into account obviously leads to a model family that is not correct and the GOF-test should reveal that:
-fit <- glm(y ~ x1, data = d, family = poisson())
-
-library(bootGOF)
-mt <- GOF_model(
- model = fit,
- data = d,
- nmb_boot_samples = 100,
- simulator_type = "parametric",
- y_name = "y",
- Rn1_statistic = Rn1_KS$new())
-mt$get_pvalue()
-#> [1] 0
-On the other hand assuming the correct model family should in general not be rejected by the GOF-test:
-fit <- glm(y ~ x1 + x2, data = d, family = poisson())
-mt <- GOF_model(
- model = fit,
- data = d,
- nmb_boot_samples = 100,
- simulator_type = "parametric",
- y_name = "y",
- Rn1_statistic = Rn1_KS$new())
-mt$get_pvalue()
-#> [1] 0.61
-…
-
-
-
-Installation
-You can install it from CRAN
-install.packages("bootGOF")
-or github
-devtools::install_github("MarselScheer/bootGOF")
-
-
-
-
-sessionInfo
-sessionInfo()
-#> R version 4.0.0 (2020-04-24)
-#> Platform: x86_64-pc-linux-gnu (64-bit)
-#> Running under: Ubuntu 20.04 LTS
-#>
-#> Matrix products: default
-#> BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
-#>
-#> locale:
-#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
-#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
-#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C
-#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
-#> [9] LC_ADDRESS=C LC_TELEPHONE=C
-#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
-#>
-#> attached base packages:
-#> [1] stats graphics grDevices datasets utils methods base
-#>
-#> other attached packages:
-#> [1] bootGOF_0.1.0 badgecreatr_0.2.0
-#>
-#> loaded via a namespace (and not attached):
-#> [1] digest_0.6.25 R6_2.4.1 backports_1.1.8 git2r_0.27.1
-#> [5] magrittr_1.5 evaluate_0.14 rlang_0.4.10 stringi_1.4.6
-#> [9] renv_0.10.0 checkmate_2.0.0 rmarkdown_2.3 tools_4.0.0
-#> [13] stringr_1.4.0 xfun_0.15 yaml_2.2.1 compiler_4.0.0
-#> [17] htmltools_0.5.0 knitr_1.29
-
-
-
-
-
-
-
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-
-
diff --git a/docs/link.svg b/docs/link.svg
deleted file mode 100644
index 88ad827..0000000
--- a/docs/link.svg
+++ /dev/null
@@ -1,12 +0,0 @@
-
-
-
diff --git a/docs/news/index.html b/docs/news/index.html
deleted file mode 100644
index 751b359..0000000
--- a/docs/news/index.html
+++ /dev/null
@@ -1,171 +0,0 @@
-
-
-
-
-
-
-
-
-Changelog • bootGOF
-
-
-
-
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-
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-
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-
-
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- Changelog
- Source: NEWS.md
-
-
-
-
-
-
-
-
-
-
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-
-
-
-
-
-
-
-
-
diff --git a/docs/pkgdown.css b/docs/pkgdown.css
deleted file mode 100644
index c01e592..0000000
--- a/docs/pkgdown.css
+++ /dev/null
@@ -1,367 +0,0 @@
-/* Sticky footer */
-
-/**
- * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/
- * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css
- *
- * .Site -> body > .container
- * .Site-content -> body > .container .row
- * .footer -> footer
- *
- * Key idea seems to be to ensure that .container and __all its parents__
- * have height set to 100%
- *
- */
-
-html, body {
- height: 100%;
-}
-
-body {
- position: relative;
-}
-
-body > .container {
- display: flex;
- height: 100%;
- flex-direction: column;
-}
-
-body > .container .row {
- flex: 1 0 auto;
-}
-
-footer {
- margin-top: 45px;
- padding: 35px 0 36px;
- border-top: 1px solid #e5e5e5;
- color: #666;
- display: flex;
- flex-shrink: 0;
-}
-footer p {
- margin-bottom: 0;
-}
-footer div {
- flex: 1;
-}
-footer .pkgdown {
- text-align: right;
-}
-footer p {
- margin-bottom: 0;
-}
-
-img.icon {
- float: right;
-}
-
-img {
- max-width: 100%;
-}
-
-/* Fix bug in bootstrap (only seen in firefox) */
-summary {
- display: list-item;
-}
-
-/* Typographic tweaking ---------------------------------*/
-
-.contents .page-header {
- margin-top: calc(-60px + 1em);
-}
-
-dd {
- margin-left: 3em;
-}
-
-/* Section anchors ---------------------------------*/
-
-a.anchor {
- margin-left: -30px;
- display:inline-block;
- width: 30px;
- height: 30px;
- visibility: hidden;
-
- background-image: url(./link.svg);
- background-repeat: no-repeat;
- background-size: 20px 20px;
- background-position: center center;
-}
-
-.hasAnchor:hover a.anchor {
- visibility: visible;
-}
-
-@media (max-width: 767px) {
- .hasAnchor:hover a.anchor {
- visibility: hidden;
- }
-}
-
-
-/* Fixes for fixed navbar --------------------------*/
-
-.contents h1, .contents h2, .contents h3, .contents h4 {
- padding-top: 60px;
- margin-top: -40px;
-}
-
-/* Navbar submenu --------------------------*/
-
-.dropdown-submenu {
- position: relative;
-}
-
-.dropdown-submenu>.dropdown-menu {
- top: 0;
- left: 100%;
- margin-top: -6px;
- margin-left: -1px;
- border-radius: 0 6px 6px 6px;
-}
-
-.dropdown-submenu:hover>.dropdown-menu {
- display: block;
-}
-
-.dropdown-submenu>a:after {
- display: block;
- content: " ";
- float: right;
- width: 0;
- height: 0;
- border-color: transparent;
- border-style: solid;
- border-width: 5px 0 5px 5px;
- border-left-color: #cccccc;
- margin-top: 5px;
- margin-right: -10px;
-}
-
-.dropdown-submenu:hover>a:after {
- border-left-color: #ffffff;
-}
-
-.dropdown-submenu.pull-left {
- float: none;
-}
-
-.dropdown-submenu.pull-left>.dropdown-menu {
- left: -100%;
- margin-left: 10px;
- border-radius: 6px 0 6px 6px;
-}
-
-/* Sidebar --------------------------*/
-
-#pkgdown-sidebar {
- margin-top: 30px;
- position: -webkit-sticky;
- position: sticky;
- top: 70px;
-}
-
-#pkgdown-sidebar h2 {
- font-size: 1.5em;
- margin-top: 1em;
-}
-
-#pkgdown-sidebar h2:first-child {
- margin-top: 0;
-}
-
-#pkgdown-sidebar .list-unstyled li {
- margin-bottom: 0.5em;
-}
-
-/* bootstrap-toc tweaks ------------------------------------------------------*/
-
-/* All levels of nav */
-
-nav[data-toggle='toc'] .nav > li > a {
- padding: 4px 20px 4px 6px;
- font-size: 1.5rem;
- font-weight: 400;
- color: inherit;
-}
-
-nav[data-toggle='toc'] .nav > li > a:hover,
-nav[data-toggle='toc'] .nav > li > a:focus {
- padding-left: 5px;
- color: inherit;
- border-left: 1px solid #878787;
-}
-
-nav[data-toggle='toc'] .nav > .active > a,
-nav[data-toggle='toc'] .nav > .active:hover > a,
-nav[data-toggle='toc'] .nav > .active:focus > a {
- padding-left: 5px;
- font-size: 1.5rem;
- font-weight: 400;
- color: inherit;
- border-left: 2px solid #878787;
-}
-
-/* Nav: second level (shown on .active) */
-
-nav[data-toggle='toc'] .nav .nav {
- display: none; /* Hide by default, but at >768px, show it */
- padding-bottom: 10px;
-}
-
-nav[data-toggle='toc'] .nav .nav > li > a {
- padding-left: 16px;
- font-size: 1.35rem;
-}
-
-nav[data-toggle='toc'] .nav .nav > li > a:hover,
-nav[data-toggle='toc'] .nav .nav > li > a:focus {
- padding-left: 15px;
-}
-
-nav[data-toggle='toc'] .nav .nav > .active > a,
-nav[data-toggle='toc'] .nav .nav > .active:hover > a,
-nav[data-toggle='toc'] .nav .nav > .active:focus > a {
- padding-left: 15px;
- font-weight: 500;
- font-size: 1.35rem;
-}
-
-/* orcid ------------------------------------------------------------------- */
-
-.orcid {
- font-size: 16px;
- color: #A6CE39;
- /* margins are required by official ORCID trademark and display guidelines */
- margin-left:4px;
- margin-right:4px;
- vertical-align: middle;
-}
-
-/* Reference index & topics ----------------------------------------------- */
-
-.ref-index th {font-weight: normal;}
-
-.ref-index td {vertical-align: top;}
-.ref-index .icon {width: 40px;}
-.ref-index .alias {width: 40%;}
-.ref-index-icons .alias {width: calc(40% - 40px);}
-.ref-index .title {width: 60%;}
-
-.ref-arguments th {text-align: right; padding-right: 10px;}
-.ref-arguments th, .ref-arguments td {vertical-align: top;}
-.ref-arguments .name {width: 20%;}
-.ref-arguments .desc {width: 80%;}
-
-/* Nice scrolling for wide elements --------------------------------------- */
-
-table {
- display: block;
- overflow: auto;
-}
-
-/* Syntax highlighting ---------------------------------------------------- */
-
-pre {
- word-wrap: normal;
- word-break: normal;
- border: 1px solid #eee;
-}
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-pre, code {
- background-color: #f8f8f8;
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-pre .img {
- margin: 5px 0;
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- background-color: #fff;
- display: block;
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-code a, pre a {
- color: #375f84;
-}
-
-a.sourceLine:hover {
- text-decoration: none;
-}
-
-.fl {color: #1514b5;}
-.fu {color: #000000;} /* function */
-.ch,.st {color: #036a07;} /* string */
-.kw {color: #264D66;} /* keyword */
-.co {color: #888888;} /* comment */
-
-.message { color: black; font-weight: bolder;}
-.error { color: orange; font-weight: bolder;}
-.warning { color: #6A0366; font-weight: bolder;}
-
-/* Clipboard --------------------------*/
-
-.hasCopyButton {
- position: relative;
-}
-
-.btn-copy-ex {
- position: absolute;
- right: 0;
- top: 0;
- visibility: hidden;
-}
-
-.hasCopyButton:hover button.btn-copy-ex {
- visibility: visible;
-}
-
-/* headroom.js ------------------------ */
-
-.headroom {
- will-change: transform;
- transition: transform 200ms linear;
-}
-.headroom--pinned {
- transform: translateY(0%);
-}
-.headroom--unpinned {
- transform: translateY(-100%);
-}
-
-/* mark.js ----------------------------*/
-
-mark {
- background-color: rgba(255, 255, 51, 0.5);
- border-bottom: 2px solid rgba(255, 153, 51, 0.3);
- padding: 1px;
-}
-
-/* vertical spacing after htmlwidgets */
-.html-widget {
- margin-bottom: 10px;
-}
-
-/* fontawesome ------------------------ */
-
-.fab {
- font-family: "Font Awesome 5 Brands" !important;
-}
-
-/* don't display links in code chunks when printing */
-/* source: https://stackoverflow.com/a/10781533 */
-@media print {
- code a:link:after, code a:visited:after {
- content: "";
- }
-}
diff --git a/docs/pkgdown.js b/docs/pkgdown.js
deleted file mode 100644
index 7e7048f..0000000
--- a/docs/pkgdown.js
+++ /dev/null
@@ -1,108 +0,0 @@
-/* http://gregfranko.com/blog/jquery-best-practices/ */
-(function($) {
- $(function() {
-
- $('.navbar-fixed-top').headroom();
-
- $('body').css('padding-top', $('.navbar').height() + 10);
- $(window).resize(function(){
- $('body').css('padding-top', $('.navbar').height() + 10);
- });
-
- $('[data-toggle="tooltip"]').tooltip();
-
- var cur_path = paths(location.pathname);
- var links = $("#navbar ul li a");
- var max_length = -1;
- var pos = -1;
- for (var i = 0; i < links.length; i++) {
- if (links[i].getAttribute("href") === "#")
- continue;
- // Ignore external links
- if (links[i].host !== location.host)
- continue;
-
- var nav_path = paths(links[i].pathname);
-
- var length = prefix_length(nav_path, cur_path);
- if (length > max_length) {
- max_length = length;
- pos = i;
- }
- }
-
- // Add class to parent
`).
- getTopLevel: function($scope) {
- for (var i = 1; i <= 6; i++) {
- var $headings = this.findOrFilter($scope, 'h' + i);
- if ($headings.length > 1) {
- return i;
- }
- }
-
- return 1;
- },
-
- // returns the elements for the top level, and the next below it
- getHeadings: function($scope, topLevel) {
- var topSelector = 'h' + topLevel;
-
- var secondaryLevel = topLevel + 1;
- var secondarySelector = 'h' + secondaryLevel;
-
- return this.findOrFilter($scope, topSelector + ',' + secondarySelector);
- },
-
- getNavLevel: function(el) {
- return parseInt(el.tagName.charAt(1), 10);
- },
-
- populateNav: function($topContext, topLevel, $headings) {
- var $context = $topContext;
- var $prevNav;
-
- var helpers = this;
- $headings.each(function(i, el) {
- var $newNav = helpers.generateNavItem(el);
- var navLevel = helpers.getNavLevel(el);
-
- // determine the proper $context
- if (navLevel === topLevel) {
- // use top level
- $context = $topContext;
- } else if ($prevNav && $context === $topContext) {
- // create a new level of the tree and switch to it
- $context = helpers.createChildNavList($prevNav);
- } // else use the current $context
-
- $context.append($newNav);
-
- $prevNav = $newNav;
- });
- },
-
- parseOps: function(arg) {
- var opts;
- if (arg.jquery) {
- opts = {
- $nav: arg
- };
- } else {
- opts = arg;
- }
- opts.$scope = opts.$scope || $(document.body);
- return opts;
- }
- },
-
- // accepts a jQuery object, or an options object
- init: function(opts) {
- opts = this.helpers.parseOps(opts);
-
- // ensure that the data attribute is in place for styling
- opts.$nav.attr('data-toggle', 'toc');
-
- var $topContext = this.helpers.createChildNavList(opts.$nav);
- var topLevel = this.helpers.getTopLevel(opts.$scope);
- var $headings = this.helpers.getHeadings(opts.$scope, topLevel);
- this.helpers.populateNav($topContext, topLevel, $headings);
- }
- };
-
- $(function() {
- $('nav[data-toggle="toc"]').each(function(i, el) {
- var $nav = $(el);
- Toc.init($nav);
- });
- });
-})();
diff --git a/docs/docsearch.css b/docs/docsearch.css
deleted file mode 100644
index e5f1fe1..0000000
--- a/docs/docsearch.css
+++ /dev/null
@@ -1,148 +0,0 @@
-/* Docsearch -------------------------------------------------------------- */
-/*
- Source: https://github.com/algolia/docsearch/
- License: MIT
-*/
-
-.algolia-autocomplete {
- display: block;
- -webkit-box-flex: 1;
- -ms-flex: 1;
- flex: 1
-}
-
-.algolia-autocomplete .ds-dropdown-menu {
- width: 100%;
- min-width: none;
- max-width: none;
- padding: .75rem 0;
- background-color: #fff;
- background-clip: padding-box;
- border: 1px solid rgba(0, 0, 0, .1);
- box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175);
-}
-
-@media (min-width:768px) {
- .algolia-autocomplete .ds-dropdown-menu {
- width: 175%
- }
-}
-
-.algolia-autocomplete .ds-dropdown-menu::before {
- display: none
-}
-
-.algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] {
- padding: 0;
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- border: 0;
- max-height: 80vh;
-}
-
-.algolia-autocomplete .ds-dropdown-menu .ds-suggestions {
- margin-top: 0
-}
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-.algolia-autocomplete .algolia-docsearch-suggestion {
- padding: 0;
- overflow: visible
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--category-header {
- padding: .125rem 1rem;
- margin-top: 0;
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- color: #00008B;
- border-bottom: 0
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--wrapper {
- float: none;
- padding-top: 0
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--subcategory-column {
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- width: auto;
- padding: 0;
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-}
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- display: none
-}
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-.algolia-autocomplete .ds-suggestion:not(:first-child) .algolia-docsearch-suggestion--category-header {
- padding-top: .75rem;
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-}
-
-.algolia-autocomplete .ds-suggestion .algolia-docsearch-suggestion--subcategory-column {
- display: block;
- padding: .1rem 1rem;
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- font-size: 1.0em;
- font-weight: 400
- /* display: none */
-}
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-.algolia-autocomplete .algolia-docsearch-suggestion--title {
- display: block;
- padding: .25rem 1rem;
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- font-weight: 400
-}
-
-.algolia-autocomplete .algolia-docsearch-suggestion--text {
- padding: 0 1rem .5rem;
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diff --git a/docs/index.html b/docs/index.html
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-
-Bootstrap Based Goodness-of-Fit Tests • bootGOF
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-Bootstrap based goodness-of-fit tests for (linear) models. Assume you have fitted a statistical model, e.g. classical linear model or generalized linear model or a model that follows Y = m(β⊤X) + ϵ. This package allows to perform a rigorous statistical test to check if the chosen model family is correct.
-
-
-Example
-First we generate a data-set in order to apply the package.
-set.seed(1)
-N <- 100
-X1 <- rnorm(N)
-X2 <- rnorm(N)
-d <- data.frame(
- y = rpois(n = N, lambda = exp(4 + X1 * 2 + X2 * 6)),
- x1 = X1,
- x2 = X2)
-Note that both covariates influence the dependent variable Y. Taking only one of the covariates into account obviously leads to a model family that is not correct and the GOF-test should reveal that:
-fit <- glm(y ~ x1, data = d, family = poisson())
-
-library(bootGOF)
-mt <- GOF_model(
- model = fit,
- data = d,
- nmb_boot_samples = 100,
- simulator_type = "parametric",
- y_name = "y",
- Rn1_statistic = Rn1_KS$new())
-mt$get_pvalue()
-#> [1] 0
-On the other hand assuming the correct model family should in general not be rejected by the GOF-test:
-fit <- glm(y ~ x1 + x2, data = d, family = poisson())
-mt <- GOF_model(
- model = fit,
- data = d,
- nmb_boot_samples = 100,
- simulator_type = "parametric",
- y_name = "y",
- Rn1_statistic = Rn1_KS$new())
-mt$get_pvalue()
-#> [1] 0.61
-…
-
-
-
-Installation
-You can install it from CRAN
-install.packages("bootGOF")
-or github
-devtools::install_github("MarselScheer/bootGOF")
-
-
-
-
-sessionInfo
-sessionInfo()
-#> R version 4.0.0 (2020-04-24)
-#> Platform: x86_64-pc-linux-gnu (64-bit)
-#> Running under: Ubuntu 20.04 LTS
-#>
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-#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
-#> [9] LC_ADDRESS=C LC_TELEPHONE=C
-#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
-#>
-#> attached base packages:
-#> [1] stats graphics grDevices datasets utils methods base
-#>
-#> other attached packages:
-#> [1] bootGOF_0.1.0 badgecreatr_0.2.0
-#>
-#> loaded via a namespace (and not attached):
-#> [1] digest_0.6.25 R6_2.4.1 backports_1.1.8 git2r_0.27.1
-#> [5] magrittr_1.5 evaluate_0.14 rlang_0.4.10 stringi_1.4.6
-#> [9] renv_0.10.0 checkmate_2.0.0 rmarkdown_2.3 tools_4.0.0
-#> [13] stringr_1.4.0 xfun_0.15 yaml_2.2.1 compiler_4.0.0
-#> [17] htmltools_0.5.0 knitr_1.29
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diff --git a/docs/news/index.html b/docs/news/index.html
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-Changelog • bootGOF
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- Source: NEWS.md
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diff --git a/docs/pkgdown.css b/docs/pkgdown.css
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-/* Sticky footer */
-
-/**
- * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/
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- *
- * .Site -> body > .container
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- *
- * Key idea seems to be to ensure that .container and __all its parents__
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-/* Sidebar --------------------------*/
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-#pkgdown-sidebar {
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-}
-
-/* bootstrap-toc tweaks ------------------------------------------------------*/
-
-/* All levels of nav */
-
-nav[data-toggle='toc'] .nav > li > a {
- padding: 4px 20px 4px 6px;
- font-size: 1.5rem;
- font-weight: 400;
- color: inherit;
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diff --git a/docs/pkgdown.js b/docs/pkgdown.js
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-/* http://gregfranko.com/blog/jquery-best-practices/ */
-(function($) {
- $(function() {
-
- $('.navbar-fixed-top').headroom();
-
- $('body').css('padding-top', $('.navbar').height() + 10);
- $(window).resize(function(){
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- });
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-
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-
- var length = prefix_length(nav_path, cur_path);
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-Bootstrap based goodness-of-fit tests for (linear) models. Assume you have fitted a statistical model, e.g. classical linear model or generalized linear model or a model that follows Y = m(β⊤X) + ϵ. This package allows to perform a rigorous statistical test to check if the chosen model family is correct.
-
-
-
-
-
--Example
-First we generate a data-set in order to apply the package.
-set.seed(1) -N <- 100 -X1 <- rnorm(N) -X2 <- rnorm(N) -d <- data.frame( - y = rpois(n = N, lambda = exp(4 + X1 * 2 + X2 * 6)), - x1 = X1, - x2 = X2)
Note that both covariates influence the dependent variable Y. Taking only one of the covariates into account obviously leads to a model family that is not correct and the GOF-test should reveal that:
-fit <- glm(y ~ x1, data = d, family = poisson()) - -library(bootGOF) -mt <- GOF_model( - model = fit, - data = d, - nmb_boot_samples = 100, - simulator_type = "parametric", - y_name = "y", - Rn1_statistic = Rn1_KS$new()) -mt$get_pvalue() -#> [1] 0
On the other hand assuming the correct model family should in general not be rejected by the GOF-test:
-fit <- glm(y ~ x1 + x2, data = d, family = poisson()) -mt <- GOF_model( - model = fit, - data = d, - nmb_boot_samples = 100, - simulator_type = "parametric", - y_name = "y", - Rn1_statistic = Rn1_KS$new()) -mt$get_pvalue() -#> [1] 0.61
…
-
-
-
-
--Installation
-You can install it from CRAN
-install.packages("bootGOF")
or github
-devtools::install_github("MarselScheer/bootGOF")
-
-
-
- -sessionInfo
-sessionInfo() -#> R version 4.0.0 (2020-04-24) -#> Platform: x86_64-pc-linux-gnu (64-bit) -#> Running under: Ubuntu 20.04 LTS -#> -#> Matrix products: default -#> BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so -#> -#> locale: -#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C -#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 -#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C -#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C -#> [9] LC_ADDRESS=C LC_TELEPHONE=C -#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C -#> -#> attached base packages: -#> [1] stats graphics grDevices datasets utils methods base -#> -#> other attached packages: -#> [1] bootGOF_0.1.0 badgecreatr_0.2.0 -#> -#> loaded via a namespace (and not attached): -#> [1] digest_0.6.25 R6_2.4.1 backports_1.1.8 git2r_0.27.1 -#> [5] magrittr_1.5 evaluate_0.14 rlang_0.4.10 stringi_1.4.6 -#> [9] renv_0.10.0 checkmate_2.0.0 rmarkdown_2.3 tools_4.0.0 -#> [13] stringr_1.4.0 xfun_0.15 yaml_2.2.1 compiler_4.0.0 -#> [17] htmltools_0.5.0 knitr_1.29
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diff --git a/docs/reference/GOF_glm_sim_param.html b/docs/reference/GOF_glm_sim_param.html
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- Implements the "interface" GOF_model_info_extractor for - for generalized linear models
- Source:R/GOF_model_info_extractor.R
- GOF_glm_info_extractor.Rd
-
-
-
-
- This class is specialized in extracting various information - from an object of class "glm"
-Super class
- -bootGOF::GOF_model_info_extractor -> GOF_glm_info_extractor
Methods
- - -Public methods
- --
-
- -
- -
- -
- -
-
-Inherited methods
-
-
-
-
Method yhat()
-Usage
-GOF_glm_info_extractor$yhat(model)
Arguments
-modelReturns
- --
Method y_minus_yhat()
-Usage
-GOF_glm_info_extractor$y_minus_yhat(model)
Arguments
-modelReturns
- --
Method beta_x_covariates()
-Usage
-GOF_glm_info_extractor$beta_x_covariates(model)
Arguments
-modelReturns
- --
Method clone()
-The objects of this class are cloneable with this method.
Usage
-GOF_glm_info_extractor$clone(deep = FALSE)
Arguments
-deepWhether to make a deep clone.
-
-
-
-
-
-
-
-
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diff --git a/docs/reference/GOF_glm_trainer.html b/docs/reference/GOF_glm_trainer.html
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- Implements the "interface" GOF_model_simulator for - for generalized linear models
- Source:R/GOF_model_simulator.R
- GOF_glm_sim_param.Rd
-
-
-
-
- after the GLM was fitted the distribution of the
- of the dependent variable is fully specified and used here
- to generate new depenedent variables that follow model
Methods
- - -Public methods
- - --
Method resample_y()
-Usage
-GOF_glm_sim_param$resample_y(model)
Arguments
-modelReturns
- --
Method clone()
-The objects of this class are cloneable with this method.
Usage
-GOF_glm_sim_param$clone(deep = FALSE)
Arguments
-deepWhether to make a deep clone.
-
-
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-
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diff --git a/docs/reference/GOF_lm_info_extractor.html b/docs/reference/GOF_lm_info_extractor.html
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- Implements the "interface" GOF_model_trainer for - for generalized linear models
- Source:R/GOF_model_trainer.R
- GOF_glm_trainer.Rd
-
-
-
-
- refits an object of class "glm" to a new data set
-Methods
- - -Public methods
- - --
Method refit()
-Usage
-GOF_glm_trainer$refit(model, data)
Arguments
-modeldataReturns
- --
Method clone()
-The objects of this class are cloneable with this method.
Usage
-GOF_glm_trainer$clone(deep = FALSE)
Arguments
-deepWhether to make a deep clone.
-
-
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diff --git a/docs/reference/GOF_lm_sim_param.html b/docs/reference/GOF_lm_sim_param.html
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- Implements the "interface" GOF_model_info_extractor - for linear models
- Source:R/GOF_model_info_extractor.R
- GOF_lm_info_extractor.Rd
-
-
-
-
- This class is specialized in extracting various information - from an object of class "lm"
-Super class
- -bootGOF::GOF_model_info_extractor -> GOF_lm_info_extractor
Methods
- - -Public methods
- --
-
- -
- -
- -
- -
-
-Inherited methods
-
-
-
-
Method yhat()
-Usage
-GOF_lm_info_extractor$yhat(model)
Arguments
-modelReturns
- --
Method y_minus_yhat()
-Usage
-GOF_lm_info_extractor$y_minus_yhat(model)
Arguments
-modelReturns
- --
Method beta_x_covariates()
-Usage
-GOF_lm_info_extractor$beta_x_covariates(model)
Arguments
-modelReturns
- --
Method clone()
-The objects of this class are cloneable with this method.
Usage
-GOF_lm_info_extractor$clone(deep = FALSE)
Arguments
-deepWhether to make a deep clone.
-
-
-
-
-
-
-
-
-
-
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diff --git a/docs/reference/GOF_lm_sim_wild_rademacher.html b/docs/reference/GOF_lm_sim_wild_rademacher.html
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- Implements the "interface" GOF_model_simulator for - for linear models
- Source:R/GOF_model_simulator.R
- GOF_lm_sim_param.Rd
-
-
-
-
- after the classical linear model was fitted the
- normal distribution of the
- of the dependent variable is fully specified and used here
- to generate new depenedent variables that follow model
Methods
- - -Public methods
- - --
Method resample_y()
-generates/resamples the dependent variables based
- on the parameteric nature defined by model
Usage
-GOF_lm_sim_param$resample_y(model)
Arguments
-modelReturns
- --
Method clone()
-The objects of this class are cloneable with this method.
Usage
-GOF_lm_sim_param$clone(deep = FALSE)
Arguments
-deepWhether to make a deep clone.
-
-
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diff --git a/docs/reference/GOF_lm_trainer.html b/docs/reference/GOF_lm_trainer.html
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-
- Implements the "interface" GOF_model_simulator for - for linear models
- Source:R/GOF_model_simulator.R
- GOF_lm_sim_wild_rademacher.Rd
-
-
-
-
- Rademacher random variables are used to change -add or substract the residuals from the fitted values
-Methods
- - -Public methods
- - --
Method resample_y()
-a wild bootstrap using Rademacher random - variables to resample the dependent variable
Usage
-GOF_lm_sim_wild_rademacher$resample_y(model)
Arguments
-modelReturns
- --
Method clone()
-The objects of this class are cloneable with this method.
Usage
-GOF_lm_sim_wild_rademacher$clone(deep = FALSE)
Arguments
-deepWhether to make a deep clone.
-
-
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diff --git a/docs/reference/GOF_model.html b/docs/reference/GOF_model.html
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- Implements the "interface" GOF_model_trainer for - for linear models
- Source:R/GOF_model_trainer.R
- GOF_lm_trainer.Rd
-
-
-
-
- refits an object of class "lm" to a new data set
-Methods
- - -Public methods
- - --
Method refit()
-Usage
-GOF_lm_trainer$refit(model, data)
Arguments
-modeldataReturns
- --
Method clone()
-The objects of this class are cloneable with this method.
Usage
-GOF_lm_trainer$clone(deep = FALSE)
Arguments
-deepWhether to make a deep clone.
-
-
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diff --git a/docs/reference/GOF_model_info_extractor.html b/docs/reference/GOF_model_info_extractor.html
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- Convenience function for creating a GOF-test for statistical models
- Source:R/GOF_model.R
- GOF_model.Rd
-
-
- Simplifies the creation of an instance of - GOF_model_test, the actual work horse for performing a - goodness-of-fit-test.
-GOF_model( - model, - data, - nmb_boot_samples, - simulator_type, - y_name, - Rn1_statistic, - gof_model_resample_class = GOF_model_resample, - gof_model_test_class = GOF_model_test -)- -
Arguments
-| model | -of class 'lm' or 'glm'. Caution with MASS::glm.nb, see -vignette 'New-Models' for more details. |
-
|---|---|
| data | -see GOF_model_test |
-
| nmb_boot_samples | -see GOF_model_test |
-
| simulator_type | -either "parameteric" or "semi_parameteric_rademacher" |
-
| y_name | -see GOF_model_test |
-
| Rn1_statistic | -see GOF_model_test |
-
| gof_model_resample_class | -no need to change this parameter. Here the -class used for resampling the model (GOF_model_resample) -is injected. This parameter simply makes it easier to test the -convenience function properly. |
-
| gof_model_test_class | -no need to change this parameter. Here the -class used for performing the GOF test (GOF_model_test) -is injected. This parameter simply makes it easier to test the -convenience function properly. |
-
Value
- -instance of GOF_model_test
- -Examples
--set.seed(1) -N <- 100 -X1 <- rnorm(N) -X2 <- rnorm(N) -d <- data.frame( - y = rpois(n = N, lambda = exp(4 + X1 * 2 + X2 * 6)), - x1 = X1, - x2 = X2) -fit <- glm(y ~ x1, data = d, family = poisson()) -mt <- GOF_model( - model = fit, - data = d, - nmb_boot_samples = 100, - simulator_type = "parametric", - y_name = "y", - Rn1_statistic = Rn1_KS$new()) -mt$get_pvalue()#> [1] 0fit <- glm(y ~ x1 + x2, data = d, family = poisson()) -mt <- GOF_model( - model = fit, - data = d, - nmb_boot_samples = 100, - simulator_type = "parametric", - y_name = "y", - Rn1_statistic = Rn1_KS$new()) -mt$get_pvalue()#> [1] 0.61
-
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diff --git a/docs/reference/GOF_model_resample.html b/docs/reference/GOF_model_resample.html
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- R6 Class representing model information
- Source:R/GOF_model_info_extractor.R
- GOF_model_info_extractor.Rd
-
-
-
-
- R6 does not offer interfaces. Hence all methods -are considered as abstract.
-Methods
- - -Public methods
- --
-
- -
- -
- -
- -
-
Method yhat()
-Abstract function that estimates/predicts the
- the dependent variable in model
Usage
-GOF_model_info_extractor$yhat(model)
Arguments
-modelfitted model
Returns
-estimate/prediction of the dependent variable
- fitted by model
-
Method y_minus_yhat()
-abstract function that calculates the residuals - on the scale of the dependent variable.
Usage
-GOF_model_info_extractor$y_minus_yhat(model)
Arguments
-modelfitted model
Returns
-residuals on the scale of the dependent variable
--
Method beta_x_covariates()
-abstract function that calculates the inner product - of estimated parameters and the independent variables.
Usage
-GOF_model_info_extractor$beta_x_covariates(model)
Arguments
-modelfitted model
Returns
-inner product of the estimated parameters and the - independent variables.
--
Method clone()
-The objects of this class are cloneable with this method.
Usage
-GOF_model_info_extractor$clone(deep = FALSE)
Arguments
-deepWhether to make a deep clone.
-
-
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diff --git a/docs/reference/GOF_model_simulator.html b/docs/reference/GOF_model_simulator.html
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- R6 Class representing the resampling scheme for -Goodness-of-fit-tests for (linear) models
- Source:R/GOF_model_resample.R
- GOF_model_resample.Rd
-
-
-
-
- Class is able to resample model fit, i.e. generate a - new data set and refit the model to the new data.
-Methods
- - -Public methods
- - --
Method new()
-
-Usage
-GOF_model_resample$new(gof_model_simulator, gof_model_trainer)
Arguments
-gof_model_simulatoran instance that implements GOF_model_simulator
gof_model_traineran instance that implements GOF_model_trainer
Returns
-No explicit return
--
Method resample()
-resamples the dependent variable in data and refits
- model to that new data set
Usage
-GOF_model_resample$resample(model, data, y_name)
Arguments
-modelfitted model based on data
dataused to fit model
y_namestring specifying the name of the dependent variable
Returns
-a resampled version of model
-
Method clone()
-The objects of this class are cloneable with this method.
Usage
-GOF_model_resample$clone(deep = FALSE)
Arguments
-deepWhether to make a deep clone.
-
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diff --git a/docs/reference/GOF_model_test.html b/docs/reference/GOF_model_test.html
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- R6 Class representing a generator/resample of the dependent variable
- Source:R/GOF_model_simulator.R
- GOF_model_simulator.Rd
-
-
-
-
- R6 does not offer interfaces. Hence all methods - are considered as abstract.
-Methods
- - -Public methods
- - --
Method resample_y()
-Abstract function that resamples/generates - the dependent variable
Usage
-GOF_model_simulator$resample_y(model)
Arguments
-modelfitted model
Returns
-generates the dependent variable according to
- the model
-
Method clone()
-The objects of this class are cloneable with this method.
Usage
-GOF_model_simulator$clone(deep = FALSE)
Arguments
-deepWhether to make a deep clone.
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diff --git a/docs/reference/GOF_model_test_injector.html b/docs/reference/GOF_model_test_injector.html
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- R6 Class representing the Goodness-of-Fit test - for (linear) models.
- Source:R/GOF_model_test.R
- GOF_model_test.Rd
-
-
-
-
- This class can test the null hypothesis that data follows - a particular linear model, i.e. classical linear models, generalized - linear models or models of the type \(m(\beta^\top X) + \epsilon\).
-Methods
- - -Public methods
- --
-
- -
- -
- -
- -
- -
-
Method new()
-
-Usage
-GOF_model_test$new( - model, - data, - nmb_boot_samples, - y_name, - Rn1_statistic, - gof_model_info_extractor, - gof_model_resample -)
Arguments
-modela fitted model
dataused to fit model
nmb_boot_samplesinteger specifying the number of bootstrap -samples to perform
y_namestring specifying the name of the dependent variable in
-in data
Rn1_statisticstatistic used to map the marked empirical -process to the real line. Needs to be an instance of the class -that implements Rn1_statistic
gof_model_info_extractoran instance that implements
-GOF_model_info_extractor in order to apply it to
-model
gof_model_resamplean instance that implements
-GOF_model_resample in order to apply it to
-model
Returns
-An instance of the Class
--
Method get_Rn1_org()
-calculates the marked empricial process for model
Usage
-GOF_model_test$get_Rn1_org()
Returns
-vector ordered by the inner product of the estimated - parameter and the independent variables
--
Method get_Rn1_boot()
-calculates the marked empricial process for the
- resampled versions of model
Usage
-GOF_model_test$get_Rn1_boot()
Returns
-list of length nmb_boot_samples where every element
- is a vector ordered by the inner product of the estimated
- parameter and the dependent variables
-
Method get_pvalue()
-p-value for Goodness-of-Fit-test for model
Usage
-GOF_model_test$get_pvalue()
Returns
-p-value for the null hypothesis that the dependent variable
- was generated according to model
-
Method clone()
-The objects of this class are cloneable with this method.
Usage
-GOF_model_test$clone(deep = FALSE)
Arguments
-deepWhether to make a deep clone.
-
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diff --git a/docs/reference/GOF_model_trainer.html b/docs/reference/GOF_model_trainer.html
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- Creates a GOF-model-test-class
- Source:R/GOF_model_test_injector.R
- GOF_model_test_injector.Rd
-
-
- Simplifies the creation of a GOF-model-test-class
-GOF_model_test_injector( - model, - data, - nmb_boot_samples, - simulator_type, - y_name, - Rn1_statistic, - gof_model_resample_class, - gof_model_test_class -)- -
Arguments
-| model | -see GOF_model_test |
-
|---|---|
| data | -see GOF_model_test |
-
| nmb_boot_samples | -see GOF_model_test |
-
| simulator_type | -see GOF_model |
-
| y_name | -see GOF_model_test |
-
| Rn1_statistic | -see GOF_model_test |
-
| gof_model_resample_class | -GOF_model_resample class |
-
| gof_model_test_class | -GOF_model_test class |
-
Value
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diff --git a/docs/reference/GOF_sim_wild_rademacher.html b/docs/reference/GOF_sim_wild_rademacher.html
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- R6 Class representing a trainer for fitting models
- Source:R/GOF_model_trainer.R
- GOF_model_trainer.Rd
-
-
-
-
- R6 does not offer interfaces. Hence all methods -are considered as abstract.
-Methods
- - -Public methods
- - --
Method refit()
-Abstract function refits the model to - a new data set
Usage
-GOF_model_trainer$refit(model, data)
Arguments
-modelfitted model
dataused for refitting the model
Returns
-model refitted on data
-
Method clone()
-The objects of this class are cloneable with this method.
Usage
-GOF_model_trainer$clone(deep = FALSE)
Arguments
-deepWhether to make a deep clone.
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diff --git a/docs/reference/Rn1_CvM.html b/docs/reference/Rn1_CvM.html
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-
-
-
-Method
-
-
-
-
-
-
-
-
-
-
-
-
-
- Implements the "interface" GOF_model_simulator - in a semi-parametric fashion
- Source:R/GOF_model_simulator.R
- GOF_sim_wild_rademacher.Rd
-
-
-
-
- This is a model agnostic resampling class, where -Rademacher random variables are used to add or substract -the residuals from the fitted values.
-Methods
- - -Public methods
- --
-
- -
- -
- -
-
Method new()
-
-Usage
-GOF_sim_wild_rademacher$new(gof_model_info_extractor)
Arguments
-gof_model_info_extractorthe info extractor that is used -to derive the residuals and fitted values for resampling.
-
Method resample_y()
-a wild bootstrap using Rademacher random - variables to resample the dependent variable
Usage
-GOF_sim_wild_rademacher$resample_y(model)
Arguments
-modelReturns
- --
Method clone()
-The objects of this class are cloneable with this method.
Usage
-GOF_sim_wild_rademacher$clone(deep = FALSE)
Arguments
-deepWhether to make a deep clone.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/docs/reference/Rn1_KS.html b/docs/reference/Rn1_KS.html
deleted file mode 100644
index 4feff36..0000000
--- a/docs/reference/Rn1_KS.html
+++ /dev/null
@@ -1,212 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-Method
-
-
-
-
-
-Method
-
-
-
-
-
-
-
-
-
-
-
-
-
- Cramer-von-Mises-statistic for marked empirical - process
- Source:R/Rn1_statistic.R
- Rn1_CvM.Rd
-
-
-
-
- Implements the "interface" Rn1_statistic
-Super class
- -bootGOF::Rn1_statistic -> Rn1_CvM
Methods
- - -Public methods
- - -
-
-Inherited methods
-
-
-
-
Method calc_statistic()
-calculates the calculates the Cramer-von-Mises - statistic
Usage
-Rn1_CvM$calc_statistic(Rn1)
Arguments
-Rn1see Rn1_statistic
Returns
-see Rn1_statistic
--
Method clone()
-The objects of this class are cloneable with this method.
Usage
-Rn1_CvM$clone(deep = FALSE)
Arguments
-deepWhether to make a deep clone.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/docs/reference/Rn1_statistic.html b/docs/reference/Rn1_statistic.html
deleted file mode 100644
index 2f5ee37..0000000
--- a/docs/reference/Rn1_statistic.html
+++ /dev/null
@@ -1,207 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-Method
-
-
-
-
-
-Method
-
-
-
-
-
-
-
-
-
-
-
-
-
- Kolmogorov-Smirnov-statistic for marked empirical - process
- Source:R/Rn1_statistic.R
- Rn1_KS.Rd
-
-
-
-
- Implements the "interface" Rn1_statistic
-Super class
- -bootGOF::Rn1_statistic -> Rn1_KS
Methods
- - -Public methods
- - -
-
-Inherited methods
-
-
-
-
Method calc_statistic()
-calculates the Kolmogorov-Smirnov-statistic
Usage
-Rn1_KS$calc_statistic(Rn1)
Arguments
-Rn1see Rn1_statistic
Returns
-see Rn1_statistic
--
Method clone()
-The objects of this class are cloneable with this method.
Usage
-Rn1_KS$clone(deep = FALSE)
Arguments
-deepWhether to make a deep clone.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/docs/reference/index.html b/docs/reference/index.html
deleted file mode 100644
index 70c5d68..0000000
--- a/docs/reference/index.html
+++ /dev/null
@@ -1,355 +0,0 @@
-
-
-
-
-
-
-
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-Method
-
-
-
-
-
-Method
-
-
-
-
-
-
-
-
-
-
-
-
-
- R6 Class representing statistics for marked - empirical processes
- Source:R/Rn1_statistic.R
- Rn1_statistic.Rd
-
-
-
-
- R6 does not offer interfaces. Hence all methods - are considered as abstract.
-Methods
- - -Public methods
- - --
Method calc_statistic()
-Abstract function that calculates the statistic - for a given marked empirical process
Usage
-Rn1_statistic$calc_statistic(Rn1)
Arguments
-Rn1marked empirical process as a double vector
Returns
-statistic based on Rn1
-
Method clone()
-The objects of this class are cloneable with this method.
Usage
-Rn1_statistic$clone(deep = FALSE)
Arguments
-deepWhether to make a deep clone.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/docs/reference/not_covered.html b/docs/reference/not_covered.html
deleted file mode 100644
index c39a481..0000000
--- a/docs/reference/not_covered.html
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@@ -1,156 +0,0 @@
-
-
-
-
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-
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-
-
-
-
-
- Reference
-
-
-
-
-
-
-
-
-
-
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-
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-
-
-
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diff --git a/docs/reference/rrademacher.html b/docs/reference/rrademacher.html
deleted file mode 100644
index b015d17..0000000
--- a/docs/reference/rrademacher.html
+++ /dev/null
@@ -1,180 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- example of a function that is not covered by unit-tests
- Source:R/coverage-example.R
- not_covered.Rd
-
-
- example of a function that is not covered by unit-tests
-not_covered()
-
-
- Value
- -no explicit return value
- -Examples
--not_covered()#> NULL
-
-
-
-
-
-
-
-
-
-
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-
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diff --git a/docs/sitemap.xml b/docs/sitemap.xml
deleted file mode 100644
index 23b5876..0000000
--- a/docs/sitemap.xml
+++ /dev/null
@@ -1,63 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Generates Rademacher distributed random variables
- Source:R/GOF_model_simulator.R
- rrademacher.Rd
-
-
- Generates Rademacher distributed random variables
-rrademacher(n)- -
Arguments
-| n | -number of random variables to be generated |
-
|---|
Value
- -vector of values following the Rademacher distribution
- -Inherited methods
} -\itemize{ -} -\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-yhat}{}}} -\subsection{Method \code{yhat()}}{ -see \link{GOF_model_info_extractor} -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_glm_info_extractor$yhat(model)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{model}}{see \link{GOF_model_info_extractor}}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-see \link{GOF_model_info_extractor}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-y_minus_yhat}{}}} -\subsection{Method \code{y_minus_yhat()}}{ -see \link{GOF_model_info_extractor} -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_glm_info_extractor$y_minus_yhat(model)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{model}}{see \link{GOF_model_info_extractor}}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-see \link{GOF_model_info_extractor}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-beta_x_covariates}{}}} -\subsection{Method \code{beta_x_covariates()}}{ -see \link{GOF_model_info_extractor} -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_glm_info_extractor$beta_x_covariates(model)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{model}}{see \link{GOF_model_info_extractor}}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-see \link{GOF_model_info_extractor}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-clone}{}}} -\subsection{Method \code{clone()}}{ -The objects of this class are cloneable with this method. -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_glm_info_extractor$clone(deep = FALSE)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{deep}}{Whether to make a deep clone.}
-}
-\if{html}{\out{
}}
-}
-}
-}
diff --git a/man/GOF_glm_sim_param.Rd b/man/GOF_glm_sim_param.Rd
deleted file mode 100644
index 23e4425..0000000
--- a/man/GOF_glm_sim_param.Rd
+++ /dev/null
@@ -1,56 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/GOF_model_simulator.R
-\name{GOF_glm_sim_param}
-\alias{GOF_glm_sim_param}
-\title{Implements the "interface" GOF_model_simulator for
- for generalized linear models}
-\description{
-after the GLM was fitted the distribution of the
- of the dependent variable is fully specified and used here
- to generate new depenedent variables that follow \code{model}
-}
-\section{Methods}{
-\subsection{Public methods}{
-\itemize{
-\item \href{#method-resample_y}{\code{GOF_glm_sim_param$resample_y()}}
-\item \href{#method-clone}{\code{GOF_glm_sim_param$clone()}}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-resample_y}{}}} -\subsection{Method \code{resample_y()}}{ -see \link{GOF_model_simulator} -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_glm_sim_param$resample_y(model)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{model}}{see \link{GOF_model_simulator}}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-see \link{GOF_model_simulator}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-clone}{}}} -\subsection{Method \code{clone()}}{ -The objects of this class are cloneable with this method. -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_glm_sim_param$clone(deep = FALSE)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{deep}}{Whether to make a deep clone.}
-}
-\if{html}{\out{
}}
-}
-}
-}
diff --git a/man/GOF_glm_trainer.Rd b/man/GOF_glm_trainer.Rd
deleted file mode 100644
index 57e1a19..0000000
--- a/man/GOF_glm_trainer.Rd
+++ /dev/null
@@ -1,56 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/GOF_model_trainer.R
-\name{GOF_glm_trainer}
-\alias{GOF_glm_trainer}
-\title{Implements the "interface" GOF_model_trainer for
- for generalized linear models}
-\description{
-refits an object of class "glm" to a new data set
-}
-\section{Methods}{
-\subsection{Public methods}{
-\itemize{
-\item \href{#method-refit}{\code{GOF_glm_trainer$refit()}}
-\item \href{#method-clone}{\code{GOF_glm_trainer$clone()}}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-refit}{}}} -\subsection{Method \code{refit()}}{ -see \link{GOF_model_trainer} -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_glm_trainer$refit(model, data)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{model}}{see \link{GOF_model_trainer}}
-
-\item{\code{data}}{see \link{GOF_model_trainer}}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-see \link{GOF_model_trainer}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-clone}{}}} -\subsection{Method \code{clone()}}{ -The objects of this class are cloneable with this method. -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_glm_trainer$clone(deep = FALSE)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{deep}}{Whether to make a deep clone.}
-}
-\if{html}{\out{
}}
-}
-}
-}
diff --git a/man/GOF_lm_info_extractor.Rd b/man/GOF_lm_info_extractor.Rd
deleted file mode 100644
index de0008e..0000000
--- a/man/GOF_lm_info_extractor.Rd
+++ /dev/null
@@ -1,106 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/GOF_model_info_extractor.R
-\name{GOF_lm_info_extractor}
-\alias{GOF_lm_info_extractor}
-\title{Implements the "interface" GOF_model_info_extractor
- for linear models}
-\description{
-This class is specialized in extracting various information
- from an object of class "lm"
-}
-\section{Super class}{
-\code{\link[bootGOF:GOF_model_info_extractor]{bootGOF::GOF_model_info_extractor}} -> \code{GOF_lm_info_extractor}
-}
-\section{Methods}{
-\subsection{Public methods}{
-\itemize{
-\item \href{#method-yhat}{\code{GOF_lm_info_extractor$yhat()}}
-\item \href{#method-y_minus_yhat}{\code{GOF_lm_info_extractor$y_minus_yhat()}}
-\item \href{#method-beta_x_covariates}{\code{GOF_lm_info_extractor$beta_x_covariates()}}
-\item \href{#method-clone}{\code{GOF_lm_info_extractor$clone()}}
-}
-}
-\if{html}{
-\out{Inherited methods
} -\itemize{ -} -\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-yhat}{}}} -\subsection{Method \code{yhat()}}{ -see \link{GOF_model_info_extractor} -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_lm_info_extractor$yhat(model)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{model}}{see \link{GOF_model_info_extractor}}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-see \link{GOF_model_info_extractor}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-y_minus_yhat}{}}} -\subsection{Method \code{y_minus_yhat()}}{ -see \link{GOF_model_info_extractor} -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_lm_info_extractor$y_minus_yhat(model)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{model}}{see \link{GOF_model_info_extractor}}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-see \link{GOF_model_info_extractor}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-beta_x_covariates}{}}} -\subsection{Method \code{beta_x_covariates()}}{ -see \link{GOF_model_info_extractor} -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_lm_info_extractor$beta_x_covariates(model)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{model}}{see \link{GOF_model_info_extractor}}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-see \link{GOF_model_info_extractor}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-clone}{}}} -\subsection{Method \code{clone()}}{ -The objects of this class are cloneable with this method. -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_lm_info_extractor$clone(deep = FALSE)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{deep}}{Whether to make a deep clone.}
-}
-\if{html}{\out{
}}
-}
-}
-}
diff --git a/man/GOF_lm_sim_param.Rd b/man/GOF_lm_sim_param.Rd
deleted file mode 100644
index 61a824b..0000000
--- a/man/GOF_lm_sim_param.Rd
+++ /dev/null
@@ -1,58 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/GOF_model_simulator.R
-\name{GOF_lm_sim_param}
-\alias{GOF_lm_sim_param}
-\title{Implements the "interface" GOF_model_simulator for
- for linear models}
-\description{
-after the classical linear model was fitted the
- normal distribution of the
- of the dependent variable is fully specified and used here
- to generate new depenedent variables that follow \code{model}
-}
-\section{Methods}{
-\subsection{Public methods}{
-\itemize{
-\item \href{#method-resample_y}{\code{GOF_lm_sim_param$resample_y()}}
-\item \href{#method-clone}{\code{GOF_lm_sim_param$clone()}}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-resample_y}{}}} -\subsection{Method \code{resample_y()}}{ -generates/resamples the dependent variables based - on the parameteric nature defined by \code{model} -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_lm_sim_param$resample_y(model)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{model}}{see \link{GOF_model_simulator}}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-see \link{GOF_model_simulator}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-clone}{}}} -\subsection{Method \code{clone()}}{ -The objects of this class are cloneable with this method. -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_lm_sim_param$clone(deep = FALSE)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{deep}}{Whether to make a deep clone.}
-}
-\if{html}{\out{
}}
-}
-}
-}
diff --git a/man/GOF_lm_trainer.Rd b/man/GOF_lm_trainer.Rd
deleted file mode 100644
index c8b6c50..0000000
--- a/man/GOF_lm_trainer.Rd
+++ /dev/null
@@ -1,56 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/GOF_model_trainer.R
-\name{GOF_lm_trainer}
-\alias{GOF_lm_trainer}
-\title{Implements the "interface" GOF_model_trainer for
- for linear models}
-\description{
-refits an object of class "lm" to a new data set
-}
-\section{Methods}{
-\subsection{Public methods}{
-\itemize{
-\item \href{#method-refit}{\code{GOF_lm_trainer$refit()}}
-\item \href{#method-clone}{\code{GOF_lm_trainer$clone()}}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-refit}{}}} -\subsection{Method \code{refit()}}{ -see \link{GOF_model_trainer} -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_lm_trainer$refit(model, data)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{model}}{see \link{GOF_model_trainer}}
-
-\item{\code{data}}{see \link{GOF_model_trainer}}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-see \link{GOF_model_trainer}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-clone}{}}} -\subsection{Method \code{clone()}}{ -The objects of this class are cloneable with this method. -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_lm_trainer$clone(deep = FALSE)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{deep}}{Whether to make a deep clone.}
-}
-\if{html}{\out{
}}
-}
-}
-}
diff --git a/man/GOF_model.Rd b/man/GOF_model.Rd
deleted file mode 100644
index 0bbbaf3..0000000
--- a/man/GOF_model.Rd
+++ /dev/null
@@ -1,77 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/GOF_model.R
-\name{GOF_model}
-\alias{GOF_model}
-\title{Convenience function for creating a GOF-test for statistical models}
-\usage{
-GOF_model(
- model,
- data,
- nmb_boot_samples,
- simulator_type,
- y_name,
- Rn1_statistic,
- gof_model_resample_class = GOF_model_resample,
- gof_model_test_class = GOF_model_test
-)
-}
-\arguments{
-\item{model}{of class 'lm' or 'glm'. Caution with MASS::glm.nb, see
-vignette 'New-Models' for more details.}
-
-\item{data}{see \link{GOF_model_test}}
-
-\item{nmb_boot_samples}{see \link{GOF_model_test}}
-
-\item{simulator_type}{either "parameteric" or "semi_parameteric_rademacher"}
-
-\item{y_name}{see \link{GOF_model_test}}
-
-\item{Rn1_statistic}{see \link{GOF_model_test}}
-
-\item{gof_model_resample_class}{no need to change this parameter. Here the
-class used for resampling the model (\link{GOF_model_resample})
-is injected. This parameter simply makes it easier to test the
-convenience function properly.}
-
-\item{gof_model_test_class}{no need to change this parameter. Here the
-class used for performing the GOF test (\link{GOF_model_test})
-is injected. This parameter simply makes it easier to test the
-convenience function properly.}
-}
-\value{
-instance of \link{GOF_model_test}
-}
-\description{
-Simplifies the creation of an instance of
- \link{GOF_model_test}, the actual work horse for performing a
- goodness-of-fit-test.
-}
-\examples{
-set.seed(1)
-N <- 100
-X1 <- rnorm(N)
-X2 <- rnorm(N)
-d <- data.frame(
- y = rpois(n = N, lambda = exp(4 + X1 * 2 + X2 * 6)),
- x1 = X1,
- x2 = X2)
-fit <- glm(y ~ x1, data = d, family = poisson())
-mt <- GOF_model(
- model = fit,
- data = d,
- nmb_boot_samples = 100,
- simulator_type = "parametric",
- y_name = "y",
- Rn1_statistic = Rn1_KS$new())
-mt$get_pvalue()
-fit <- glm(y ~ x1 + x2, data = d, family = poisson())
-mt <- GOF_model(
- model = fit,
- data = d,
- nmb_boot_samples = 100,
- simulator_type = "parametric",
- y_name = "y",
- Rn1_statistic = Rn1_KS$new())
-mt$get_pvalue()
-}
diff --git a/man/GOF_model_info_extractor.Rd b/man/GOF_model_info_extractor.Rd
deleted file mode 100644
index d6cb296..0000000
--- a/man/GOF_model_info_extractor.Rd
+++ /dev/null
@@ -1,101 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/GOF_model_info_extractor.R
-\name{GOF_model_info_extractor}
-\alias{GOF_model_info_extractor}
-\title{R6 Class representing model information}
-\description{
-R6 does not offer interfaces. Hence all methods
-are considered as abstract.
-}
-\section{Methods}{
-\subsection{Public methods}{
-\itemize{
-\item \href{#method-yhat}{\code{GOF_model_info_extractor$yhat()}}
-\item \href{#method-y_minus_yhat}{\code{GOF_model_info_extractor$y_minus_yhat()}}
-\item \href{#method-beta_x_covariates}{\code{GOF_model_info_extractor$beta_x_covariates()}}
-\item \href{#method-clone}{\code{GOF_model_info_extractor$clone()}}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-yhat}{}}} -\subsection{Method \code{yhat()}}{ -Abstract function that estimates/predicts the - the dependent variable in \code{model} -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_model_info_extractor$yhat(model)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{model}}{fitted model}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-estimate/prediction of the dependent variable
- fitted by \code{model}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-y_minus_yhat}{}}} -\subsection{Method \code{y_minus_yhat()}}{ -abstract function that calculates the residuals - on the scale of the dependent variable. -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_model_info_extractor$y_minus_yhat(model)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{model}}{fitted model}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-residuals on the scale of the dependent variable
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-beta_x_covariates}{}}} -\subsection{Method \code{beta_x_covariates()}}{ -abstract function that calculates the inner product - of estimated parameters and the independent variables. -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_model_info_extractor$beta_x_covariates(model)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{model}}{fitted model}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-inner product of the estimated parameters and the
- independent variables.
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-clone}{}}} -\subsection{Method \code{clone()}}{ -The objects of this class are cloneable with this method. -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_model_info_extractor$clone(deep = FALSE)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{deep}}{Whether to make a deep clone.}
-}
-\if{html}{\out{
}}
-}
-}
-}
diff --git a/man/GOF_model_resample.Rd b/man/GOF_model_resample.Rd
deleted file mode 100644
index 09141c0..0000000
--- a/man/GOF_model_resample.Rd
+++ /dev/null
@@ -1,82 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/GOF_model_resample.R
-\name{GOF_model_resample}
-\alias{GOF_model_resample}
-\title{R6 Class representing the resampling scheme for
-Goodness-of-fit-tests for (linear) models}
-\description{
-Class is able to resample model fit, i.e. generate a
- new data set and refit the model to the new data.
-}
-\section{Methods}{
-\subsection{Public methods}{
-\itemize{
-\item \href{#method-new}{\code{GOF_model_resample$new()}}
-\item \href{#method-resample}{\code{GOF_model_resample$resample()}}
-\item \href{#method-clone}{\code{GOF_model_resample$clone()}}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-new}{}}} -\subsection{Method \code{new()}}{ -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_model_resample$new(gof_model_simulator, gof_model_trainer)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{gof_model_simulator}}{an instance that implements \link{GOF_model_simulator}}
-
-\item{\code{gof_model_trainer}}{an instance that implements \link{GOF_model_trainer}}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-No explicit return
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-resample}{}}} -\subsection{Method \code{resample()}}{ -resamples the dependent variable in \code{data} and refits - \code{model} to that new data set -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_model_resample$resample(model, data, y_name)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{model}}{fitted model based on \code{data}}
-
-\item{\code{data}}{used to fit \code{model}}
-
-\item{\code{y_name}}{string specifying the name of the dependent variable}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-a resampled version of \code{model}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-clone}{}}} -\subsection{Method \code{clone()}}{ -The objects of this class are cloneable with this method. -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_model_resample$clone(deep = FALSE)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{deep}}{Whether to make a deep clone.}
-}
-\if{html}{\out{
}}
-}
-}
-}
diff --git a/man/GOF_model_simulator.Rd b/man/GOF_model_simulator.Rd
deleted file mode 100644
index 28eff07..0000000
--- a/man/GOF_model_simulator.Rd
+++ /dev/null
@@ -1,56 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/GOF_model_simulator.R
-\name{GOF_model_simulator}
-\alias{GOF_model_simulator}
-\title{R6 Class representing a generator/resample of the dependent variable}
-\description{
-R6 does not offer interfaces. Hence all methods
- are considered as abstract.
-}
-\section{Methods}{
-\subsection{Public methods}{
-\itemize{
-\item \href{#method-resample_y}{\code{GOF_model_simulator$resample_y()}}
-\item \href{#method-clone}{\code{GOF_model_simulator$clone()}}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-resample_y}{}}} -\subsection{Method \code{resample_y()}}{ -Abstract function that resamples/generates - the dependent variable -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_model_simulator$resample_y(model)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{model}}{fitted model}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-generates the dependent variable according to
- the \code{model}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-clone}{}}} -\subsection{Method \code{clone()}}{ -The objects of this class are cloneable with this method. -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_model_simulator$clone(deep = FALSE)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{deep}}{Whether to make a deep clone.}
-}
-\if{html}{\out{
}}
-}
-}
-}
diff --git a/man/GOF_model_test.Rd b/man/GOF_model_test.Rd
deleted file mode 100644
index 20c7baf..0000000
--- a/man/GOF_model_test.Rd
+++ /dev/null
@@ -1,130 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/GOF_model_test.R
-\name{GOF_model_test}
-\alias{GOF_model_test}
-\title{R6 Class representing the Goodness-of-Fit test
- for (linear) models.}
-\description{
-This class can test the null hypothesis that data follows
- a particular linear model, i.e. classical linear models, generalized
- linear models or models of the type \eqn{m(\beta^\top X) + \epsilon}.
-}
-\section{Methods}{
-\subsection{Public methods}{
-\itemize{
-\item \href{#method-new}{\code{GOF_model_test$new()}}
-\item \href{#method-get_Rn1_org}{\code{GOF_model_test$get_Rn1_org()}}
-\item \href{#method-get_Rn1_boot}{\code{GOF_model_test$get_Rn1_boot()}}
-\item \href{#method-get_pvalue}{\code{GOF_model_test$get_pvalue()}}
-\item \href{#method-clone}{\code{GOF_model_test$clone()}}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-new}{}}} -\subsection{Method \code{new()}}{ -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_model_test$new(
- model,
- data,
- nmb_boot_samples,
- y_name,
- Rn1_statistic,
- gof_model_info_extractor,
- gof_model_resample
-)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{model}}{a fitted model}
-
-\item{\code{data}}{used to fit \code{model}}
-
-\item{\code{nmb_boot_samples}}{integer specifying the number of bootstrap
-samples to perform}
-
-\item{\code{y_name}}{string specifying the name of the dependent variable in
-in \code{data}}
-
-\item{\code{Rn1_statistic}}{statistic used to map the marked empirical
-process to the real line. Needs to be an instance of the class
-that implements \link{Rn1_statistic}}
-
-\item{\code{gof_model_info_extractor}}{an instance that implements
-\link{GOF_model_info_extractor} in order to apply it to
-\code{model}}
-
-\item{\code{gof_model_resample}}{an instance that implements
-\link{GOF_model_resample} in order to apply it to
-\code{model}}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-An instance of the Class
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-get_Rn1_org}{}}} -\subsection{Method \code{get_Rn1_org()}}{ -calculates the marked empricial process for \code{model} -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_model_test$get_Rn1_org()}\if{html}{\out{
}}
-}
-
-\subsection{Returns}{
-vector ordered by the inner product of the estimated
- parameter and the independent variables
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-get_Rn1_boot}{}}} -\subsection{Method \code{get_Rn1_boot()}}{ -calculates the marked empricial process for the - resampled versions of \code{model} -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_model_test$get_Rn1_boot()}\if{html}{\out{
}}
-}
-
-\subsection{Returns}{
-list of length \code{nmb_boot_samples} where every element
- is a vector ordered by the inner product of the estimated
- parameter and the dependent variables
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-get_pvalue}{}}} -\subsection{Method \code{get_pvalue()}}{ -p-value for Goodness-of-Fit-test for \code{model} -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_model_test$get_pvalue()}\if{html}{\out{
}}
-}
-
-\subsection{Returns}{
-p-value for the null hypothesis that the dependent variable
- was generated according to \code{model}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-clone}{}}} -\subsection{Method \code{clone()}}{ -The objects of this class are cloneable with this method. -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_model_test$clone(deep = FALSE)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{deep}}{Whether to make a deep clone.}
-}
-\if{html}{\out{
}}
-}
-}
-}
diff --git a/man/GOF_model_trainer.Rd b/man/GOF_model_trainer.Rd
deleted file mode 100644
index e05898e..0000000
--- a/man/GOF_model_trainer.Rd
+++ /dev/null
@@ -1,57 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/GOF_model_trainer.R
-\name{GOF_model_trainer}
-\alias{GOF_model_trainer}
-\title{R6 Class representing a trainer for fitting models}
-\description{
-R6 does not offer interfaces. Hence all methods
-are considered as abstract.
-}
-\section{Methods}{
-\subsection{Public methods}{
-\itemize{
-\item \href{#method-refit}{\code{GOF_model_trainer$refit()}}
-\item \href{#method-clone}{\code{GOF_model_trainer$clone()}}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-refit}{}}} -\subsection{Method \code{refit()}}{ -Abstract function refits the model to - a new data set -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_model_trainer$refit(model, data)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{model}}{fitted model}
-
-\item{\code{data}}{used for refitting the model}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-\code{model} refitted on \code{data}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-clone}{}}} -\subsection{Method \code{clone()}}{ -The objects of this class are cloneable with this method. -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_model_trainer$clone(deep = FALSE)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{deep}}{Whether to make a deep clone.}
-}
-\if{html}{\out{
}}
-}
-}
-}
diff --git a/man/GOF_sim_wild_rademacher.Rd b/man/GOF_sim_wild_rademacher.Rd
deleted file mode 100644
index 17cbdcf..0000000
--- a/man/GOF_sim_wild_rademacher.Rd
+++ /dev/null
@@ -1,75 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/GOF_model_simulator.R
-\name{GOF_sim_wild_rademacher}
-\alias{GOF_sim_wild_rademacher}
-\title{Implements the "interface" GOF_model_simulator
- in a semi-parametric fashion}
-\description{
-This is a model agnostic resampling class, where
-Rademacher random variables are used to add or substract
-the residuals from the fitted values.
-}
-\section{Methods}{
-\subsection{Public methods}{
-\itemize{
-\item \href{#method-new}{\code{GOF_sim_wild_rademacher$new()}}
-\item \href{#method-resample_y}{\code{GOF_sim_wild_rademacher$resample_y()}}
-\item \href{#method-clone}{\code{GOF_sim_wild_rademacher$clone()}}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-new}{}}} -\subsection{Method \code{new()}}{ -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_sim_wild_rademacher$new(gof_model_info_extractor)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{gof_model_info_extractor}}{the info extractor that is used
-to derive the residuals and fitted values for resampling.}
-}
-\if{html}{\out{
}}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-resample_y}{}}} -\subsection{Method \code{resample_y()}}{ -a wild bootstrap using Rademacher random - variables to resample the dependent variable -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_sim_wild_rademacher$resample_y(model)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{model}}{see \link{GOF_model_simulator}}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-see \link{GOF_model_simulator}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-clone}{}}} -\subsection{Method \code{clone()}}{ -The objects of this class are cloneable with this method. -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{GOF_sim_wild_rademacher$clone(deep = FALSE)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{deep}}{Whether to make a deep clone.}
-}
-\if{html}{\out{
}}
-}
-}
-}
diff --git a/man/Rn1_CvM.Rd b/man/Rn1_CvM.Rd
deleted file mode 100644
index 872138c..0000000
--- a/man/Rn1_CvM.Rd
+++ /dev/null
@@ -1,64 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/Rn1_statistic.R
-\name{Rn1_CvM}
-\alias{Rn1_CvM}
-\title{Cramer-von-Mises-statistic for marked empirical
- process}
-\description{
-Implements the "interface" Rn1_statistic
-}
-\section{Super class}{
-\code{\link[bootGOF:Rn1_statistic]{bootGOF::Rn1_statistic}} -> \code{Rn1_CvM}
-}
-\section{Methods}{
-\subsection{Public methods}{
-\itemize{
-\item \href{#method-calc_statistic}{\code{Rn1_CvM$calc_statistic()}}
-\item \href{#method-clone}{\code{Rn1_CvM$clone()}}
-}
-}
-\if{html}{
-\out{Inherited methods
} -\itemize{ -} -\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-calc_statistic}{}}} -\subsection{Method \code{calc_statistic()}}{ -calculates the calculates the Cramer-von-Mises - statistic -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{Rn1_CvM$calc_statistic(Rn1)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{Rn1}}{see \link{Rn1_statistic}}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-see \link{Rn1_statistic}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-clone}{}}} -\subsection{Method \code{clone()}}{ -The objects of this class are cloneable with this method. -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{Rn1_CvM$clone(deep = FALSE)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{deep}}{Whether to make a deep clone.}
-}
-\if{html}{\out{
}}
-}
-}
-}
diff --git a/man/Rn1_KS.Rd b/man/Rn1_KS.Rd
deleted file mode 100644
index 994040f..0000000
--- a/man/Rn1_KS.Rd
+++ /dev/null
@@ -1,63 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/Rn1_statistic.R
-\name{Rn1_KS}
-\alias{Rn1_KS}
-\title{Kolmogorov-Smirnov-statistic for marked empirical
- process}
-\description{
-Implements the "interface" Rn1_statistic
-}
-\section{Super class}{
-\code{\link[bootGOF:Rn1_statistic]{bootGOF::Rn1_statistic}} -> \code{Rn1_KS}
-}
-\section{Methods}{
-\subsection{Public methods}{
-\itemize{
-\item \href{#method-calc_statistic}{\code{Rn1_KS$calc_statistic()}}
-\item \href{#method-clone}{\code{Rn1_KS$clone()}}
-}
-}
-\if{html}{
-\out{Inherited methods
} -\itemize{ -} -\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-calc_statistic}{}}} -\subsection{Method \code{calc_statistic()}}{ -calculates the Kolmogorov-Smirnov-statistic -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{Rn1_KS$calc_statistic(Rn1)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{Rn1}}{see \link{Rn1_statistic}}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-see \link{Rn1_statistic}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-clone}{}}} -\subsection{Method \code{clone()}}{ -The objects of this class are cloneable with this method. -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{Rn1_KS$clone(deep = FALSE)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{deep}}{Whether to make a deep clone.}
-}
-\if{html}{\out{
}}
-}
-}
-}
diff --git a/man/Rn1_statistic.Rd b/man/Rn1_statistic.Rd
deleted file mode 100644
index 101f403..0000000
--- a/man/Rn1_statistic.Rd
+++ /dev/null
@@ -1,56 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/Rn1_statistic.R
-\name{Rn1_statistic}
-\alias{Rn1_statistic}
-\title{R6 Class representing statistics for marked
- empirical processes}
-\description{
-R6 does not offer interfaces. Hence all methods
- are considered as abstract.
-}
-\section{Methods}{
-\subsection{Public methods}{
-\itemize{
-\item \href{#method-calc_statistic}{\code{Rn1_statistic$calc_statistic()}}
-\item \href{#method-clone}{\code{Rn1_statistic$clone()}}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-calc_statistic}{}}} -\subsection{Method \code{calc_statistic()}}{ -Abstract function that calculates the statistic - for a given marked empirical process -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{Rn1_statistic$calc_statistic(Rn1)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{Rn1}}{marked empirical process as a double vector}
-}
-\if{html}{\out{
}}
-}
-\subsection{Returns}{
-statistic based on \code{Rn1}
-}
-}
-\if{html}{\out{}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-clone}{}}} -\subsection{Method \code{clone()}}{ -The objects of this class are cloneable with this method. -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{Rn1_statistic$clone(deep = FALSE)}\if{html}{\out{
}}
-}
-
-\subsection{Arguments}{
-\if{html}{\out{}}
-\describe{
-\item{\code{deep}}{Whether to make a deep clone.}
-}
-\if{html}{\out{
}}
-}
-}
-}
diff --git a/man/rrademacher.Rd b/man/rrademacher.Rd
deleted file mode 100644
index c7c9bd4..0000000
--- a/man/rrademacher.Rd
+++ /dev/null
@@ -1,17 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/GOF_model_simulator.R
-\name{rrademacher}
-\alias{rrademacher}
-\title{Generates Rademacher distributed random variables}
-\usage{
-rrademacher(n)
-}
-\arguments{
-\item{n}{number of random variables to be generated}
-}
-\value{
-vector of values following the Rademacher distribution
-}
-\description{
-Generates Rademacher distributed random variables
-}
diff --git a/renv.lock b/renv.lock
index 2d5aa26..5dd2b2e 100644
--- a/renv.lock
+++ b/renv.lock
@@ -1,6 +1,6 @@
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{
"Name": "CRAN",
@@ -9,75 +9,33 @@
]
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+ "Hash": "c39f4155b3ceb1a9a2799d700fbd4b6a"
},
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+ "Hash": "e1e1b9d75c37401117b636b7ae50827a"
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"Package": "base64enc",
@@ -88,213 +46,206 @@
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+ "Repository": "CRAN",
+ "Hash": "8f4a384e19dccd8c65356dc096847b76"
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+ "Repository": "CRAN",
+ "Hash": "c1ee497a6d999947c2c224ae46799b1a"
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"Repository": "CRAN",
- "Hash": "92a5f887f9ae3035ac7afde22ba73ee9"
+ "Hash": "70a6489cc254171fb9b4a7f130f44dca"
+ },
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+ "Repository": "CRAN",
+ "Hash": "cd9a672193789068eb5a2aad65a0dedf"
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+ "Repository": "CRAN",
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@@ -305,66 +256,66 @@
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@@ -375,36 +326,17 @@
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@@ -413,236 +345,206 @@
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- "Hash": "1c3ef87cbb81c23ac96797781ec7aecc"
+ "Version": "1.1.5",
+ "Source": "Repository"
},
"rex": {
"Package": "rex",
- "Version": "1.2.0",
+ "Version": "1.2.1",
"Source": "Repository",
"Repository": "CRAN",
- "Hash": "093584b944440c5cd07a696b3c8e0e4c"
+ "Hash": "ae34cd56890607370665bee5bd17812f"
},
"rlang": {
"Package": "rlang",
- "Version": "0.4.7",
+ "Version": "1.1.6",
"Source": "Repository",
"Repository": "CRAN",
- "Hash": "c06d2a6887f4b414f8e927afd9ee976a"
+ "Hash": "892124978869b74935dc3934c42bfe5a"
},
"rmarkdown": {
"Package": "rmarkdown",
- "Version": "2.3",
+ "Version": "2.29",
"Source": "Repository",
"Repository": "CRAN",
- "Hash": "202260e1b2c410edc086d5b8f1ed946e"
+ "Hash": "df99277f63d01c34e95e3d2f06a79736"
},
"roxygen2": {
"Package": "roxygen2",
- "Version": "7.1.0",
+ "Version": "7.3.2",
"Source": "Repository",
- "Repository": "RSPM",
- "Hash": "f173062c04dc8e91d7376d914df6efee"
+ "Repository": "CRAN",
+ "Hash": "6ee25f9054a70f44d615300ed531ba8d"
},
"rprojroot": {
"Package": "rprojroot",
- "Version": "1.3-2",
- "Source": "Repository",
- "Repository": "CRAN",
- "Hash": "f6a407ae5dd21f6f80a6708bbb6eb3ae"
- },
- "rstudioapi": {
- "Package": "rstudioapi",
- "Version": "0.11",
- "Source": "Repository",
- "Repository": "CRAN",
- "Hash": "33a5b27a03da82ac4b1d43268f80088a"
- },
- "rversions": {
- "Package": "rversions",
- "Version": "2.0.2",
+ "Version": "2.1.0",
"Source": "Repository",
"Repository": "CRAN",
- "Hash": "0ec41191f744d0f5afad8c6f35cc36e4"
+ "Hash": "74c506458effb43d21f74750e8cd42f6"
},
- "sessioninfo": {
- "Package": "sessioninfo",
- "Version": "1.1.1",
+ "sass": {
+ "Package": "sass",
+ "Version": "0.4.10",
"Source": "Repository",
"Repository": "CRAN",
- "Hash": "308013098befe37484df72c39cf90d6e"
+ "Hash": "3fb78d066fb92299b1d13f6a7c9a90a8"
},
"stringi": {
"Package": "stringi",
- "Version": "1.4.6",
+ "Version": "1.8.7",
"Source": "Repository",
"Repository": "CRAN",
- "Hash": "e99d8d656980d2dd416a962ae55aec90"
+ "Hash": "2b56088e23bdd58f89aebf43a0913457"
},
"stringr": {
"Package": "stringr",
- "Version": "1.4.0",
+ "Version": "1.5.1",
"Source": "Repository",
"Repository": "CRAN",
- "Hash": "0759e6b6c0957edb1311028a49a35e76"
+ "Hash": "960e2ae9e09656611e0b8214ad543207"
},
"sys": {
"Package": "sys",
- "Version": "3.3",
+ "Version": "3.4.3",
"Source": "Repository",
"Repository": "CRAN",
- "Hash": "507f3116a38d37ad330a038b3be07b66"
+ "Hash": "de342ebfebdbf40477d0758d05426646"
+ },
+ "systemfonts": {
+ "Package": "systemfonts",
+ "Version": "1.2.3",
+ "Source": "Repository",
+ "Repository": "CRAN",
+ "Hash": "fe31683d2c6fd9a5724bcdf8ed44ded9"
},
"testthat": {
"Package": "testthat",
- "Version": "2.3.2",
+ "Version": "3.2.3",
+ "Source": "Repository",
+ "Repository": "CRAN",
+ "Hash": "42f889439ccb14c55fc3d75c9c755056"
+ },
+ "textshaping": {
+ "Package": "textshaping",
+ "Version": "1.0.1",
"Source": "Repository",
"Repository": "CRAN",
- "Hash": "0829b987b8961fb07f3b1b64a2fbc495"
+ "Hash": "75b5813527f4154cb467e4cf60911333"
},
"tibble": {
"Package": "tibble",
- "Version": "3.0.3",
+ "Version": "3.3.0",
"Source": "Repository",
"Repository": "CRAN",
- "Hash": "08bd36bd34b20d4f7971d49e81deaab0"
+ "Hash": "784b27d0801c3829de602105757b2cd7"
},
"tinytest": {
"Package": "tinytest",
- "Version": "1.2.1",
+ "Version": "1.4.1",
"Source": "Repository",
- "Repository": "RSPM",
- "Hash": "af2182641bc7c5b954f1b6bb56583b45"
+ "Repository": "CRAN",
+ "Hash": "1f344373b4f0fe61b6a0d4e3d3c842ca"
},
"tinytex": {
"Package": "tinytex",
- "Version": "0.24",
- "Source": "Repository",
- "Repository": "CRAN",
- "Hash": "8f24b65b86f4d6d7b1e2d8a4ce2c02fb"
- },
- "usethis": {
- "Package": "usethis",
- "Version": "1.6.1",
+ "Version": "0.57",
"Source": "Repository",
"Repository": "CRAN",
- "Hash": "e1985f5a9985fea2e338fa7eb99018ca"
+ "Hash": "02d65e0c0415bf36a7ddc0d2ba50a840"
},
"utf8": {
"Package": "utf8",
- "Version": "1.1.4",
+ "Version": "1.2.6",
"Source": "Repository",
"Repository": "CRAN",
- "Hash": "4a5081acfb7b81a572e4384a7aaf2af1"
+ "Hash": "d526d558be176e9ceb68c3d1e83479b7"
},
"vctrs": {
"Package": "vctrs",
- "Version": "0.3.2",
+ "Version": "0.6.5",
"Source": "Repository",
"Repository": "CRAN",
- "Hash": "5ba3006888ac62fd5e97b208d00d3317"
+ "Hash": "c03fa420630029418f7e6da3667aac4a"
+ },
+ "waldo": {
+ "Package": "waldo",
+ "Version": "0.6.2",
+ "Source": "Repository",
+ "Repository": "CRAN",
+ "Hash": "b7b91294ee9418a54527a9a9d8677597"
},
"whisker": {
"Package": "whisker",
- "Version": "0.4",
+ "Version": "0.4.1",
"Source": "Repository",
"Repository": "CRAN",
- "Hash": "ca970b96d894e90397ed20637a0c1bbe"
+ "Hash": "c6abfa47a46d281a7d5159d0a8891e88"
},
"withr": {
"Package": "withr",
- "Version": "2.2.0",
+ "Version": "3.0.2",
"Source": "Repository",
"Repository": "CRAN",
- "Hash": "ecd17882a0b4419545691e095b74ee89"
+ "Hash": "cc2d62c76458d425210d1eb1478b30b4"
},
"xfun": {
"Package": "xfun",
- "Version": "0.15",
+ "Version": "0.53",
"Source": "Repository",
"Repository": "CRAN",
- "Hash": "ddeca7650052ff9131ac7c41a9a77b3b"
+ "Hash": "1befe97a0d56060b3964e3986d408df9"
},
"xml2": {
"Package": "xml2",
- "Version": "1.3.2",
- "Source": "Repository",
- "Repository": "CRAN",
- "Hash": "d4d71a75dd3ea9eb5fa28cc21f9585e2"
- },
- "xopen": {
- "Package": "xopen",
- "Version": "1.0.0",
+ "Version": "1.3.8",
"Source": "Repository",
"Repository": "CRAN",
- "Hash": "6c85f015dee9cc7710ddd20f86881f58"
+ "Hash": "f5130b2f3d461964bac93cc618013231"
},
"yaml": {
"Package": "yaml",
- "Version": "2.2.1",
+ "Version": "2.3.10",
"Source": "Repository",
"Repository": "CRAN",
- "Hash": "2826c5d9efb0a88f657c7a679c7106db"
+ "Hash": "51dab85c6c98e50a18d7551e9d49f76c"
}
}
}
diff --git a/tests/testthat.R b/tests/testthat.R
deleted file mode 100644
index 4ea3f11..0000000
--- a/tests/testthat.R
+++ /dev/null
@@ -1,4 +0,0 @@
-library(testthat)
-library(bootGOF)
-
-test_check("bootGOF")
diff --git a/tests/testthat/test-dummy.R b/tests/testthat/test-dummy.R
deleted file mode 100644
index 8849056..0000000
--- a/tests/testthat/test-dummy.R
+++ /dev/null
@@ -1,3 +0,0 @@
-test_that("multiplication works", {
- expect_equal(2 * 2, 4)
-})
diff --git a/tests/tinytest.R b/tests/tinytest.R
index e2ca361..b8d6e2b 100644
--- a/tests/tinytest.R
+++ b/tests/tinytest.R
@@ -1,5 +1,3 @@
-
-if ( requireNamespace("tinytest", quietly=TRUE) ){
+if (requireNamespace("tinytest", quietly = TRUE)) {
tinytest::test_package("bootGOF")
}
-