Merge pull request #92 from gmcmacran/betterPrint

Better print

.github/workflows/check-full.yaml → .github/workflows/R-CMD-check.yaml

@@ -1,9 +1,5 @@
 # Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
 # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
-#
-# NOTE: This workflow is overkill for most R packages and
-# check-standard.yaml is likely a better choice.
-# usethis::use_github_action("check-standard") will install it.
 on:
   push:
     branches: [main, master]
@@ -23,19 +19,10 @@ jobs:
       matrix:
         config:
           - {os: macos-latest,   r: 'release'}
-
           - {os: windows-latest, r: 'release'}
-          # Use 3.6 to trigger usage of RTools35
-          - {os: windows-latest, r: '3.6'}
-          # use 4.1 to check with rtools40's older compiler
-          - {os: windows-latest, r: '4.1'}
-
           - {os: ubuntu-latest,   r: 'devel', http-user-agent: 'release'}
           - {os: ubuntu-latest,   r: 'release'}
           - {os: ubuntu-latest,   r: 'oldrel-1'}
-          - {os: ubuntu-latest,   r: 'oldrel-2'}
-          - {os: ubuntu-latest,   r: 'oldrel-3'}
-          - {os: ubuntu-latest,   r: 'oldrel-4'}
 
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/test-coverage.yaml

@@ -0,0 +1,50 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+    branches: [main, master]
+
+name: test-coverage
+
+jobs:
+  test-coverage:
+    runs-on: ubuntu-latest
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+
+    steps:
+      - uses: actions/checkout@v3
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::covr
+          needs: coverage
+
+      - name: Test coverage
+        run: |
+          covr::codecov(
+            quiet = FALSE,
+            clean = FALSE,
+            install_path = file.path(Sys.getenv("RUNNER_TEMP"), "package")
+          )
+        shell: Rscript {0}
+
+      - name: Show testthat output
+        if: always()
+        run: |
+          ## --------------------------------------------------------------------
+          find ${{ runner.temp }}/package -name 'testthat.Rout*' -exec cat '{}' \; || true
+        shell: bash
+
+      - name: Upload test results
+        if: failure()
+        uses: actions/upload-artifact@v3
+        with:
+          name: coverage-test-failures
+          path: ${{ runner.temp }}/package

.lintr

@@ -5,6 +5,7 @@ linters: linters_with_defaults(
  object_length_linter = NULL,
  object_name_linter = NULL,
  object_usage_linter = NULL,
+ indentation_linter = NULL,
  duplicate_argument_linter = duplicate_argument_linter(),
  equals_na_linter = equals_na_linter(),
  namespace_linter = namespace_linter(),

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: LRTesteR
 Title: Likelihood Ratio Tests and Confidence Intervals
-Version: 1.1.0
+Version: 1.1.1
 Author: Greg McMahan
 Maintainer: Greg McMahan <gmcmacran@gmail.com>
 Description: A collection of hypothesis tests and confidence intervals based on the likelihood ratio

NEWS.md

@@ -1,4 +1,8 @@
-# LRTesteR 1.2.0
+# LRTesteR 1.1.1
+* Remove "[1] " from print method.
+* Update github actions.
+
+# LRTesteR 1.1.0
 * Add one sample and one way empirical quantile likelihood tests.
 * Added a second objective optimization function to empirical mu test.
 

R/print.R

@@ -18,17 +18,29 @@
 #' print(test)
 #' @export
 print.lrtest <- function(x, ...) {
-  print(paste("Log Likelihood Statistic:", round(x$statistic, 2), sep = " "))
-  print(paste("p value:", round(x$p.value, 3), sep = " "))
+  cat(paste("Log Likelihood Statistic:", round(x$statistic, 2), sep = " "))
+  cat("\n")
+
+  cat(paste("p value:", round(x$p.value, 3), sep = " "))
+  cat("\n")
   if (class(x)[1] %in% c("one_sample_case_one", "one_sample_case_two", "one_sample_case_three", "one_sample_case_four")) {
-    print(paste("Confidence Level: ", round(x[["conf.level"]][1], 3) * 100, "%", sep = ""))
-    print(paste("Confidence Interval: (", round(x[["conf.int"]][1], 3), ", ", round(x[["conf.int"]][2], 3), ")", sep = ""))
+    cat(paste("Confidence Level: ", round(x[["conf.level"]][1], 3) * 100, "%", sep = ""))
+    cat("\n")
+
+    cat(paste("Confidence Interval: (", round(x[["conf.int"]][1], 3), ", ", round(x[["conf.int"]][2], 3), ")", sep = ""))
+    cat("\n")
   } else {
     CIs <- x$conf.ints
-    print(paste("Confidence Level Of Set: ", round(x[["overall.conf"]][1], 3) * 100, "%", sep = ""))
-    print(paste("Individual Confidence Level: ", round(x[["individ.conf"]][1], 3) * 100, "%", sep = ""))
+    cat(paste("Confidence Level Of Set: ", round(x[["overall.conf"]][1], 3) * 100, "%", sep = ""))
+    cat("\n")
+
+    cat(paste("Individual Confidence Level: ", round(x[["individ.conf"]][1], 3) * 100, "%", sep = ""))
+    cat("\n")
     for (i in seq(1, length(CIs), 1)) {
-      print(paste("Confidence Interval For Group ", names(CIs)[i], ": (", round(CIs[[i]][1], 3), ", ", round(CIs[[i]][2], 3), ")", sep = ""))
+      cat(paste("Confidence Interval For Group ", names(CIs)[i], ": (", round(CIs[[i]][1], 3), ", ", round(CIs[[i]][2], 3), ")", sep = ""))
+      cat("\n")
     }
   }
+
+  invisible(x)
 }

README.Rmd

@@ -14,7 +14,7 @@ knitr::opts_chunk$set(
 ```
 # LRTesteR Overview
 <!-- badges: start -->
-[![R build status](https://github.com/gmcmacran/LRTesteR/workflows/R-CMD-check/badge.svg)](https://github.com/gmcmacran/LRTesteR/actions)
+[![R-CMD-check](https://github.com/gmcmacran/LRTesteR/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/gmcmacran/LRTesteR/actions/workflows/R-CMD-check.yaml)
 [![CRAN status](https://www.r-pkg.org/badges/version/LRTesteR)](https://cran.r-project.org/package=LRTesteR)
 <!-- badges: end -->
 
@@ -26,7 +26,7 @@ Estimated asymptotic type I and type II error rates can be found [here](https://
 
 * Empirical Likelihood
   + mean
-  + quantile
+  + quantile 
 
 # Parametric Tests and Confidence Intervals
 Parametric tests require a sample size of at least 50. 

README.md

@@ -5,8 +5,7 @@
 
 <!-- badges: start -->
 
-[![R build
-status](https://github.com/gmcmacran/LRTesteR/workflows/R-CMD-check/badge.svg)](https://github.com/gmcmacran/LRTesteR/actions)
+[![R-CMD-check](https://github.com/gmcmacran/LRTesteR/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/gmcmacran/LRTesteR/actions/workflows/R-CMD-check.yaml)
 [![CRAN
 status](https://www.r-pkg.org/badges/version/LRTesteR)](https://cran.r-project.org/package=LRTesteR)
 <!-- badges: end -->
@@ -67,10 +66,10 @@ library(LRTesteR)
 set.seed(1)
 x <- rpois(n = 100, lambda = 1)
 poisson_lambda_one_sample(x = x, lambda = 1, alternative = "two.sided")
-#> [1] "Log Likelihood Statistic: 0.01"
-#> [1] "p value: 0.92"
-#> [1] "Confidence Level: 95%"
-#> [1] "Confidence Interval: (0.826, 1.22)"
+#> Log Likelihood Statistic: 0.01
+#> p value: 0.92
+#> Confidence Level: 95%
+#> Confidence Interval: (0.826, 1.22)
 ```
 
 # Example 2: Confidence Interval
@@ -83,30 +82,30 @@ from a Cauchy random variable.
 set.seed(1)
 x <- rcauchy(n = 100, location = 3, scale = 5)
 cauchy_scale_one_sample(x = x, scale = 5, alternative = "two.sided", conf.level = .90)
-#> [1] "Log Likelihood Statistic: 1.21"
-#> [1] "p value: 0.271"
-#> [1] "Confidence Level: 90%"
-#> [1] "Confidence Interval: (4.64, 7.284)"
+#> Log Likelihood Statistic: 1.21
+#> p value: 0.271
+#> Confidence Level: 90%
+#> Confidence Interval: (4.64, 7.284)
 ```
 
 Setting alternative to “less” gets a lower one sided interval.
 
 ``` r
 cauchy_scale_one_sample(x = x, scale = 5, alternative = "less", conf.level = .90)
-#> [1] "Log Likelihood Statistic: 1.1"
-#> [1] "p value: 0.865"
-#> [1] "Confidence Level: 90%"
-#> [1] "Confidence Interval: (0, 6.93)"
+#> Log Likelihood Statistic: 1.1
+#> p value: 0.865
+#> Confidence Level: 90%
+#> Confidence Interval: (0, 6.93)
 ```
 
 Setting it to “greater” gets an upper one sided interval.
 
 ``` r
 cauchy_scale_one_sample(x = x, scale = 5, alternative = "greater", conf.level = .90)
-#> [1] "Log Likelihood Statistic: 1.1"
-#> [1] "p value: 0.135"
-#> [1] "Confidence Level: 90%"
-#> [1] "Confidence Interval: (4.878, Inf)"
+#> Log Likelihood Statistic: 1.1
+#> p value: 0.135
+#> Confidence Level: 90%
+#> Confidence Interval: (4.878, Inf)
 ```
 
 # Example 3: One-way Analysis
@@ -122,13 +121,13 @@ x <- c(rgamma(n = 50, shape = 1, rate = 2), rgamma(n = 50, shape = 2, rate = 2),
 fctr <- c(rep(1, 50), rep(2, 50), rep(3, 50))
 fctr <- factor(fctr, levels = c("1", "2", "3"))
 gamma_shape_one_way(x = x, fctr = fctr, conf.level = .95)
-#> [1] "Log Likelihood Statistic: 68.59"
-#> [1] "p value: 0"
-#> [1] "Confidence Level Of Set: 95%"
-#> [1] "Individual Confidence Level: 98.3%"
-#> [1] "Confidence Interval For Group 1: (0.65, 1.515)"
-#> [1] "Confidence Interval For Group 2: (1.376, 3.376)"
-#> [1] "Confidence Interval For Group 3: (1.691, 4.192)"
+#> Log Likelihood Statistic: 68.59
+#> p value: 0
+#> Confidence Level Of Set: 95%
+#> Individual Confidence Level: 98.3%
+#> Confidence Interval For Group 1: (0.65, 1.515)
+#> Confidence Interval For Group 2: (1.376, 3.376)
+#> Confidence Interval For Group 3: (1.691, 4.192)
 ```
 
 # Example 4: Empirical Likelihood
@@ -140,10 +139,10 @@ assumptions and work with less data.
 set.seed(1)
 x <- rnorm(n = 25, mean = 1, sd = 1)
 empirical_mu_one_sample(x = x, mu = 1, alternative = "two.sided")
-#> [1] "Log Likelihood Statistic: 0.73"
-#> [1] "p value: 0.392"
-#> [1] "Confidence Level: 95%"
-#> [1] "Confidence Interval: (0.752, 1.501)"
+#> Log Likelihood Statistic: 0.73
+#> p value: 0.392
+#> Confidence Level: 95%
+#> Confidence Interval: (0.752, 1.501)
 ```
 
 # The $\chi^2$ approximation

vignettes/Analysis_of_Deviance.Rmd

@@ -16,7 +16,7 @@ knitr::opts_chunk$set(
 
 # Classic One-Way ANOVA
 
-As an introduction, lets start with one way ANOVA. Here three random variables following a normal distribution with a common standard deviation are created. For this test, the null hypothesis is 
+As an introduction, lets start with one way ANOVA. Here three random variables following a normal distribution with a common standard deviation are created. For this test, the null hypothesis is
 
 $$ H_{0}: \mu_0 = \mu_1 = \mu_2  $$
 

vignettes/Nonparametric_Tests.Rmd

@@ -20,7 +20,7 @@ Empirical likelihood is a nonparametric method of inference based on a data driv
 
 ## Tests For Mu
 
-One sample and one-way tests are implemented. Note the distribution does not need to be symmetrical and less data is needed.
+One sample and one-way tests are implemented. Note the distribution does not need to be symmetrical and less data is needed. 
 
 ```{r part1}
 library(LRTesteR)