LR rest documentation

Author: PrzeChoj

Diff for: DESCRIPTION

@@ -50,5 +50,5 @@ Config/testthat/edition: 3
 Encoding: UTF-8
 Language: en-US
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 LazyData: true

Diff for: NEWS.md

@@ -13,7 +13,7 @@ There was a significant improvement in the speed of calculation. Details in the
 
 -   `plot.gips()` can get `type = "n0"`, which will plot the change of `n0` along the "MH" optimization. Handy for deciding of burn-in time;
 -   `find_MAP(optimizer = "MH")` tracks the `n0` along the optimization;
--   `summary.gips()` calculates Likelihood-Ratio test (in development, TODO: documentation, mention in vignette, LR for estimated mean);
+-   `summary.gips()` calculates Likelihood-Ratio test;
 
 
 # gips 1.2.2

Diff for: R/gips_class.R

@@ -1556,26 +1556,33 @@ get_diagonalized_matrix_for_heatmap <- function(g) {
 #'       It can be less than 1, meaning the identity permutation
 #'       is more likely. Remember that this number can big and
 #'       overflow to `Inf` or small and underflow to 0.
-#'   5. `n0` - the minimum number of observations needed for
+#'   5. `log_times_more_likely_than_id` - log of `times_more_likely_than_id`.
+#'   6. `likelihood_ratio_test_statistics`, `likelihood_ratio_test_p_value` - 
+#'       statistics and p-value of Likelihood Ratio test, where
+#'       the H_0 is that the data was drawn from the normal distribution
+#'       with Covariance matrix invariant under the given permutation.
+#'       The p-value is calculated from the asymptotic distribution.
+#'       Note that this is sensibly defined only for \eqn{n \ge p}.
+#'   7. `n0` - the minimum number of observations needed for
 #'       the covariance matrix's maximum likelihood estimator
 #'       (corresponding to a MAP) to exist. See **\eqn{C\sigma} and `n0`**
 #'       section in `vignette("Theory", package = "gips")` or in its
 #'       [pkgdown page](https://przechoj.github.io/gips/articles/Theory.html).
-#'   6. `S_matrix` - the underlying matrix.
+#'   8. `S_matrix` - the underlying matrix.
 #'       This matrix will be used in calculations of
 #'       the posteriori value in [log_posteriori_of_gips()].
-#'   7. `number_of_observations` - the number of observations that
+#'   9. `number_of_observations` - the number of observations that
 #'       were observed for the `S_matrix` to be calculated.
 #'       This value will be used in calculations of
 #'       the posteriori value in [log_posteriori_of_gips()].
-#'   8. `was_mean_estimated` - given by the user while creating the `gips` object:
+#'   10. `was_mean_estimated` - given by the user while creating the `gips` object:
 #'       * `TRUE` means the `S` parameter was the output of [stats::cov()] function;
 #'       * `FALSE` means the `S` parameter was calculated with
 #'           `S = t(X) %*% X / number_of_observations`.
-#'   9. `delta`, `D_matrix` - the hyperparameters of the Bayesian method.
+#'   11. `delta`, `D_matrix` - the hyperparameters of the Bayesian method.
 #'       See the **Hyperparameters** section of [gips()] documentation.
-#'   10. `AIC`, `BIC` - output of [AIC.gips()] and [BIC.gips()] functions.
-#'   11. `n_parameters` - number of free parameters in the covariance matrix.
+#'   12. `n_parameters` - number of free parameters in the covariance matrix.
+#'   13. `AIC`, `BIC` - output of [AIC.gips()] and [BIC.gips()] functions.
 #' * For optimized `gips` object:
 #'   1. `optimized` - `TRUE`.
 #'   2. `found_permutation` - the permutation this `gips` represents.
@@ -1590,43 +1597,51 @@ get_diagonalized_matrix_for_heatmap <- function(g) {
 #'       the `found_permutation` is over the `start_permutation`.
 #'       It cannot be a number less than 1.
 #'       Remember that this number can big and overflow to `Inf`.
-#'   7. `n0` - the minimal number of observations needed for the existence of
+#'   7. `log_times_more_likely_than_start` - log of
+#'       `times_more_likely_than_start`.
+#'   8. `likelihood_ratio_test_statistics`, `likelihood_ratio_test_p_value` - 
+#'       statistics and p-value of Likelihood Ratio test, where
+#'       the H_0 is that the data was drawn from the normal distribution
+#'       with Covariance matrix invariant under `found_permutation`.
+#'       The p-value is calculated from the asymptotic distribution.
+#'       Note that this is sensibly defined only for \eqn{n \ge p}.
+#'   9. `n0` - the minimal number of observations needed for the existence of
 #'       the maximum likelihood estimator (corresponding to a MAP) of
 #'       the covariance matrix (see **\eqn{C\sigma} and `n0`**
 #'       section in `vignette("Theory", package = "gips")` or in its
 #'       [pkgdown page](https://przechoj.github.io/gips/articles/Theory.html)).
-#'   8. `S_matrix` - the underlying matrix.
+#'   10. `S_matrix` - the underlying matrix.
 #'       This matrix will be used in calculations of
 #'       the posteriori value in [log_posteriori_of_gips()].
-#'   9. `number_of_observations` - the number of observations that
+#'   11. `number_of_observations` - the number of observations that
 #'       were observed for the `S_matrix` to be calculated.
 #'       This value will be used in calculations of
 #'       the posteriori value in [log_posteriori_of_gips()].
-#'   10. `was_mean_estimated` - given by the user while creating the `gips` object:
+#'   12. `was_mean_estimated` - given by the user while creating the `gips` object:
 #'       * `TRUE` means the `S` parameter was output of the [stats::cov()] function;
 #'       * `FALSE` means the `S` parameter was calculated with
 #'           `S = t(X) %*% X / number_of_observations`.
-#'   11. `delta`, `D_matrix` - the hyperparameters of the Bayesian method.
+#'   13. `delta`, `D_matrix` - the hyperparameters of the Bayesian method.
 #'       See the **Hyperparameters** section of [gips()] documentation.
-#'   12. `AIC`, `BIC` - output of [AIC.gips()] and [BIC.gips()] functions.
-#'   13. `n_parameters` - number of free parameters in the covariance matrix.
-#'   14. `optimization_algorithm_used` - all used optimization algorithms
+#'   14. `n_parameters` - number of free parameters in the covariance matrix.
+#'   15. `AIC`, `BIC` - output of [AIC.gips()] and [BIC.gips()] functions.
+#'   16. `optimization_algorithm_used` - all used optimization algorithms
 #'       in order (one could start optimization with "MH", and then
 #'       do an "HC").
-#'   15. `did_converge` - a boolean, did the last used algorithm converge.
-#'   16. `number_of_log_posteriori_calls` - how many times was
+#'   17. `did_converge` - a boolean, did the last used algorithm converge.
+#'   18. `number_of_log_posteriori_calls` - how many times was
 #'       the [log_posteriori_of_gips()] function called during
 #'       the optimization.
-#'   17. `whole_optimization_time` - how long was the optimization process;
+#'   19. `whole_optimization_time` - how long was the optimization process;
 #'       the sum of all optimization times (when there were multiple).
-#'   18. `log_posteriori_calls_after_best` - how many times was
+#'   20. `log_posteriori_calls_after_best` - how many times was
 #'       the [log_posteriori_of_gips()] function called after
 #'       the `found_permutation`; in other words, how long ago
 #'       could the optimization be stopped and have the same result.
 #'       If this value is small, consider running [find_MAP()]
 #'       again with `optimizer = "continue"`.
 #'       For `optimizer = "BF"`, it is `NULL`.
-#'   19. `acceptance_rate` - only interesting for `optimizer = "MH"`.
+#'   21. `acceptance_rate` - only interesting for `optimizer = "MH"`.
 #'       How often was the algorithm accepting the change of permutation
 #'       in an iteration.
 #' @export

Diff for: inst/WORDLIST

@@ -8,7 +8,6 @@ CRAN's
 DOI
 Diaconis
 EDA
-EPYC
 Eq
 Graczyk
 HC
@@ -26,6 +25,7 @@ Optimizers
 Piotr
 Posteriori
 Pseudocode
+RStudio
 Schwarz's
 Sym
 Validator