added CI of p-value in print()

R/CDF.R

@@ -78,7 +78,7 @@ CDF <- R6Class(
             )
         },
 
-        .print = function(...) {
+        .print = function() {
             cat(format(self), sep = "\n")
         },
 

R/MultipleComparison.R

@@ -84,7 +84,7 @@ MultipleComparison <- R6Class(
             private$.calculate_extra()
         },
 
-        .print = function(digits) {
+        .print = function() {
             cat("\n\t", private$.name, "\n\n")
 
             cat(
@@ -93,7 +93,7 @@ MultipleComparison <- R6Class(
                     "type:",
                     if ((type <- private$.type) == "permu") {
                         n_used <- as.numeric(attr(private$.n_permu, "n_used"))
-                        paste0(type, "(", format(n_used, digits = digits), ")")
+                        paste0(type, "(", format(n_used), ")")
                     } else type
                 ),
                 paste("method:", private$.method),
@@ -103,7 +103,7 @@ MultipleComparison <- R6Class(
 
             cat(
                 "family-wise confidence level:",
-                paste0(format(private$.conf_level * 100, digits = digits), "%")
+                paste0(format(private$.conf_level * 100), "%")
             )
             cat("\n\n")
 
@@ -117,8 +117,9 @@ MultipleComparison <- R6Class(
                         data_names[private$.group_ij$i],
                         data_names[private$.group_ij$j],
                         sep = " ~ "
-                    ), check.names = FALSE, fix.empty.names = FALSE
-                ), digits = digits
+                    ),
+                    check.names = FALSE, fix.empty.names = FALSE
+                )
             )
         },
 

R/PermuTest.R

@@ -27,14 +27,12 @@ PermuTest <- R6Class(
 
         #' @description Print the results of the test.
         #' 
-        #' @param digits an integer specifying the minimum number of significant digits to be printed in values.
-        #' 
         #' @return The object itself (invisibly).
-        print = function(digits = getOption("digits")) {
+        print = function() {
             if (is.null(private$.raw_data)) {
                 cat(format(self), sep = "\n")
             } else {
-                private$.print(digits = digits)
+                private$.print()
             }
 
             invisible(self)
@@ -201,7 +199,7 @@ PermuTest <- R6Class(
             private$.calculate_p_permu()
         },
 
-        .print = function(digits) {
+        .print = function() {
             cat("\n", "\t", private$.name, "\n\n")
 
             cat(
@@ -210,7 +208,7 @@ PermuTest <- R6Class(
                     "type:",
                     if ((type <- private$.type) == "permu") {
                         n_used <- as.numeric(attr(private$.n_permu, "n_used"))
-                        paste0(type, "(", format(n_used, digits = digits), ")")
+                        paste0(type, "(", format(n_used), ")")
                     } else type
                 ),
                 paste("method:", private$.method),
@@ -219,15 +217,18 @@ PermuTest <- R6Class(
             cat("\n")
 
             cat(
-                paste(
-                    "statistic", "=",
-                    format(private$.statistic, digits = digits)
-                ),
+                paste("statistic", "=", format(private$.statistic)),
                 {
-                    p <- format.pval(private$.p_value, digits = digits)
+                    p <- private$.p_value
+                    eps <- .Machine$double.eps
                     paste(
                         "p-value",
-                        if (!startsWith(p, "<")) paste("=", p) else p
+                        if (p < eps) "<" else "=",
+                        if (p < eps) format(eps) else format(p),
+                        if (private$.type == "permu" && private$.n_permu != 0) {
+                            q <- qnorm(0.975) * sqrt(p * (1 - p) / n_used)
+                            paste("(\u00B1", format(q), "at 95% confidence)")
+                        }
                     )
                 },
                 sep = ", "
@@ -254,17 +255,17 @@ PermuTest <- R6Class(
             }
 
             if (!is.null(private$.estimate)) {
-                cat("estimate:", format(private$.estimate, digits = digits))
+                cat("estimate:", format(private$.estimate))
                 cat("\n")
             }
 
             if (!is.null(private$.conf_int)) {
                 cat(
                     paste0(
-                        format(private$.conf_level * 100, digits = digits), "%",
+                        format(private$.conf_level * 100), "%",
                         " confidence interval: ",
-                        "(", format(private$.conf_int[1], digits = digits), ",",
-                        " ", format(private$.conf_int[2], digits = digits), ")"
+                        "(", format(private$.conf_int[1]), ",",
+                        " ", format(private$.conf_int[2]), ")"
                     )
                 )
                 cat("\n")

README.Rmd

@@ -37,7 +37,7 @@ options(
 
 ## Overview
 
-This R package implements some of the non-parametric tests in chapters 1-5 of [@Higgins2003](#references).
+This R package implements several non-parametric tests in chapters 1-5 of [@Higgins2003](#references).
 
 It depends on [R6](https://CRAN.R-project.org/package=R6) for object oriented design and [Rcpp](https://CRAN.R-project.org/package=Rcpp) for integration of R and C++.
 
@@ -67,8 +67,9 @@ options(LearnNonparam.pmt_progress = TRUE)
     t <- Wilcoxon$new(n_permu = 1e6)
     ```
 
-    - using the `pmt` (**p**er**m**utation **t**est) function (*recommended*)
+    - using the `pmt` (**p**er**m**u**t**ation test) wrapper
     ```{r, create_pmt, eval = FALSE}
+    # recommended for a unified API
     t <- pmt("twosample.wilcoxon", n_permu = 1e6)
     ```
 
@@ -95,11 +96,14 @@ options(LearnNonparam.pmt_progress = TRUE)
     ```
 
     ```{asciicast, print}
+    options(digits = 2)
+
     t$print()
     ```
 
     ```{asciicast, plot}
     ggplot2::theme_set(ggplot2::theme_minimal())
+
     t$plot(style = "ggplot2", binwidth = 1)
     ```
 
@@ -114,7 +118,7 @@ options(LearnNonparam.pmt_progress = TRUE)
     knitr::include_graphics("./man/figures/README/histogram.svg")
     ```
 
-- Modify some active bindings and see how the results change
+- Modify some settings and observe the change
     ```{asciicast, modify}
     t$type <- "asymp"
     t$p_value
@@ -127,14 +131,14 @@ LearnNonparam::pmts()
 ```
 </details>
 
-The `define_pmt` function allows users to define new permutation tests. Take Cramér-Von Mises test as an example:
+`define_pmt` allows users to define new permutation tests. Take the two-sample Cramér-Von Mises test as an example:
 
 ```{asciicast, define}
 t <- define_pmt(
     # this is a two-sample permutation test
     inherit = "twosample",
     statistic = function(x, y) {
-        # pre-calculate certain constants that remain invariant during permutation
+        # (optional) pre-calculate certain constants that remain invariant during permutation
         n_x <- length(x)
         n_y <- length(y)
         F_x <- seq_len(n_x) / n_x

README.md

@@ -14,8 +14,8 @@ check](https://github.com/qddyy/LearnNonparam/actions/workflows/R-CMD-check.yaml
 
 ## Overview
 
-This R package implements some of the non-parametric tests in chapters
-1-5 of [Higgins (2003)](#references).
+This R package implements several non-parametric tests in chapters 1-5
+of [Higgins (2003)](#references).
 
 It depends on [R6](https://CRAN.R-project.org/package=R6) for object
 oriented design and [Rcpp](https://CRAN.R-project.org/package=Rcpp) for
@@ -52,10 +52,10 @@ options(LearnNonparam.pmt_progress = TRUE)
   t <- Wilcoxon$new(n_permu = 1e6)
   ```
 
-  - using the `pmt` (**p**er**m**utation **t**est) function
-    (*recommended*)
+  - using the `pmt` (**p**er**m**u**t**ation test) wrapper
 
   ``` r
+  # recommended for a unified API
   t <- pmt("twosample.wilcoxon", n_permu = 1e6)
   ```
 
@@ -91,6 +91,8 @@ options(LearnNonparam.pmt_progress = TRUE)
   </picture>
 
   ``` r
+  options(digits = 2)
+
   t$print()
   ```
 
@@ -101,6 +103,7 @@ options(LearnNonparam.pmt_progress = TRUE)
 
   ``` r
   ggplot2::theme_set(ggplot2::theme_minimal())
+
   t$plot(style = "ggplot2", binwidth = 1)
   ```
 
@@ -111,7 +114,7 @@ options(LearnNonparam.pmt_progress = TRUE)
 
   <img src="./man/figures/README/histogram.svg" width="100%" style="display: block; margin: auto;" />
 
-- Modify some active bindings and see how the results change
+- Modify some settings and observe the change
 
   ``` r
   t$type <- "asymp"
@@ -157,15 +160,15 @@ See <code>pmts()</code> for tests implemented in this package.
 
 </details>
 
-The `define_pmt` function allows users to define new permutation tests.
-Take Cramér-Von Mises test as an example:
+`define_pmt` allows users to define new permutation tests. Take the
+two-sample Cramér-Von Mises test as an example:
 
 ``` r
 t <- define_pmt(
     # this is a two-sample permutation test
     inherit = "twosample",
     statistic = function(x, y) {
-        # pre-calculate certain constants that remain invariant during permutation
+        # (optional) pre-calculate certain constants that remain invariant during permutation
         n_x <- length(x)
         n_y <- length(y)
         F_x <- seq_len(n_x) / n_x