Merge pull request #4 from JavierMtzRdz/develop

Update notes

.Rbuildignore

@@ -0,0 +1 @@
+^README\.Rmd$

.gitignore

@@ -16,6 +16,7 @@
 .Rproj.user
 .Rproj
 inst/
+misc/
 ^tidydelta\.Rproj$
 ^\.Rproj\.user$
 ^LICENSE\.md$

DESCRIPTION

@@ -1,9 +1,9 @@
 Package: tidydelta
-Title: Estimation of standard errors using Delta Method. 
+Title: Estimation of standard errors using Delta Method
 Version: 0.0.0.9000
 Authors@R: 
-    person("First", "Last", , "[email protected]", role = c("aut", "cre"),
-           comment = c(ORCID = "YOUR-ORCID-ID"))
+    person("Javier", "Martinez-Rodriguez", , "[email protected]", 
+    role = c("aut", "cre"))
 Description: What the package does (one paragraph).
 License: MIT + file LICENSE
 Imports:
@@ -12,6 +12,8 @@ Imports:
     purrr,
     rlang, 
     tibble
+Suggests:
+    tidyverse
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.2.3

LICENSE.md

@@ -1,6 +1,6 @@
 # MIT License
 
-Copyright (c) 2023 tidydelta authors
+Copyright (c) 2023 Javier Martínez-Rodríguez
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

R/tidydelta.R

@@ -50,22 +50,41 @@ ext_bd_var <- function(formula) {
 }
 
 
-#' Estimate means and standard errors
+#' Delta Method implementation
 #'
-#' Estimates means and standard errors for a given formula using Monte Carlo simulation.
+#' Estimates standard errors for transformations of random variables using Delta method.
 #'
 #' @param formula A formula object specifying the variables of interest.
 #' @param normality_eval Logical value to run normality test in case of being
 #' possible.
 #' @param formula_vars The function(s) to apply to the variables in the formula.
-#' @param mean_dta Data frame containing the means of the variables.
+#' @param mean_dta Vector containing the means of the variables.
 #' @param cov_dta Covariance matrix of the variables.
 #' @param conf_lev Confidence level for confidence intervals.
-#' @param n Sample size evaluate (in case that we can evaluate the confidence
+#' @param n Sample size evaluation (in case that we can evaluate the confidence
 #' intervals with different hypnotic sample sizes).
 #'
 #' @return A tibble with columns for means, standard errors, and optionally, confidence intervals.
 #'
+#' @examples
+#' # Equivalent ways to use tidydelta_m()
+#' library(tidyverse)
+#'
+#' x = rnorm(1000, mean = 5, sd = 2)
+#' y = rnorm(1000, mean = 15, sd = 3)
+#'
+#' bd <- tibble(x, y)
+#'
+#' tidydelta_m(~ y/x,
+#'             conf_lev = .95)
+#'
+#' tidydelta_m(~ bd$y/bd$x,
+#'             conf_lev = .95)
+#' bd %>%
+#'   summarise(tidydelta_m(~ y/x,
+#'                         conf_lev = .95))
+#'
+#'
 #' @export
 tidydelta_m <- function(
     formula,

README.Rmd

@@ -0,0 +1,80 @@
+---
+output: github_document
+---
+
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  fig.path = "man/figures/README-",
+  out.width = "100%"
+)
+```
+
+# tidydelta
+
+<!-- badges: start -->
+<!-- badges: end -->
+
+Delta Method implementation to estimate standard errors in a {tidyverse} workflow. 
+
+## Installation
+
+You can install the development version of tidydelta from [GitHub](https://github.com/JavierMtzRdz/tidydelta) with:
+
+``` r
+remotes::install_github("JavierMtzRdz/tidydelta")
+# Or
+devtools::install_github("JavierMtzRdz/tidydelta")	
+```
+
+## Example
+
+Using tidydelta_m(), the following commands are equivalent:
+
+```{r example}
+# Load packages
+library(tidydelta)
+library(tidyverse)
+
+# Simulate samples
+set.seed(547)
+x = rnorm(10000, mean = 5, sd = 2)
+y = rnorm(10000, mean = 15, sd = 3)
+
+bd <- tibble(x, y)
+
+# Equivalent uses of tidydelta_m()
+tidydelta_m(~ y/x,
+            conf_lev = .95)
+
+tidydelta_m(~ bd$y/bd$x,
+            conf_lev = .95)
+bd %>%
+  summarise(tidydelta_m(~ y/x,
+                        conf_lev = .95))
+```
+
+Now, the data frame is divided into samples to compare the transformation of the sample with the estimation of `tidydelta()`. In the real world, you would not need to compute the Delta Method if you have many samples, but it shows how it can be incorporated in a workflow with tidyverse. 
+
+```{r message=FALSE, warning=FALSE, fig.height=3.5, dpi=300}
+(result <- bd %>%
+  summarise(tidydelta_m(~ x/y,
+                        conf_lev = .95)))
+
+ggplot() +
+  geom_histogram(data = bd %>%
+                   mutate(t = x/y),
+                 aes(x = t)) +
+  geom_vline(aes(xintercept = result$T_n,
+                 color = "T_n")) +
+  geom_vline(aes(xintercept = c(result$lower_ci,
+                                result$upper_ci),
+                 color = "CI"),
+             linetype = "dashed") +
+  labs(color = element_blank()) +
+  theme_minimal() +
+  theme(text = element_text(family = "Times New Roman"))
+```

README.md

@@ -1 +1,104 @@
-# stat547c_topics-in-probability
+
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+# tidydelta
+
+<!-- badges: start -->
+<!-- badges: end -->
+
+Delta Method implementation to estimate standard errors in a {tidyverse}
+workflow.
+
+## Installation
+
+You can install the development version of tidydelta from
+[GitHub](https://github.com/JavierMtzRdz/tidydelta) with:
+
+``` r
+remotes::install_github("JavierMtzRdz/tidydelta")
+# Or
+devtools::install_github("JavierMtzRdz/tidydelta")  
+```
+
+## Example
+
+Using tidydelta_m(), the following commands are equivalent:
+
+``` r
+# Load packages
+library(tidydelta)
+library(tidyverse)
+#> Warning: package 'dplyr' was built under R version 4.3.1
+#> Warning: package 'stringr' was built under R version 4.3.1
+#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
+#> ✔ dplyr     1.1.4     ✔ readr     2.1.4
+#> ✔ forcats   1.0.0     ✔ stringr   1.5.1
+#> ✔ ggplot2   3.4.3     ✔ tibble    3.2.1
+#> ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
+#> ✔ purrr     1.0.2     
+#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
+#> ✖ dplyr::filter() masks stats::filter()
+#> ✖ dplyr::lag()    masks stats::lag()
+#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
+
+# Simulate samples
+set.seed(547)
+x = rnorm(10000, mean = 5, sd = 2)
+y = rnorm(10000, mean = 15, sd = 3)
+
+bd <- tibble(x, y)
+
+# Equivalent uses of tidydelta_m()
+tidydelta_m(~ y/x,
+            conf_lev = .95)
+#> # A tibble: 1 × 6
+#>       y     x   T_n    se lower_ci upper_ci
+#>   <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
+#> 1  15.0  5.02  2.99  1.33    0.378     5.61
+
+tidydelta_m(~ bd$y/bd$x,
+            conf_lev = .95)
+#> # A tibble: 1 × 6
+#>       y     x   T_n    se lower_ci upper_ci
+#>   <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
+#> 1  15.0  5.02  2.99  1.33    0.378     5.61
+bd %>%
+  summarise(tidydelta_m(~ y/x,
+                        conf_lev = .95))
+#> # A tibble: 1 × 6
+#>       y     x   T_n    se lower_ci upper_ci
+#>   <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
+#> 1  15.0  5.02  2.99  1.33    0.378     5.61
+```
+
+Now, the data frame is divided into samples to compare the
+transformation of the sample with the estimation of `tidydelta()`. In
+the real world, you would not need to compute the Delta Method if you
+have many samples, but it shows how it can be incorporated in a workflow
+with tidyverse.
+
+``` r
+(result <- bd %>%
+  summarise(tidydelta_m(~ x/y,
+                        conf_lev = .95)))
+#> # A tibble: 1 × 6
+#>       x     y   T_n    se lower_ci upper_ci
+#>   <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
+#> 1  5.02  15.0 0.334 0.149   0.0422    0.626
+
+ggplot() +
+  geom_histogram(data = bd %>%
+                   mutate(t = x/y),
+                 aes(x = t)) +
+  geom_vline(aes(xintercept = result$T_n,
+                 color = "T_n")) +
+  geom_vline(aes(xintercept = c(result$lower_ci,
+                                result$upper_ci),
+                 color = "CI"),
+             linetype = "dashed") +
+  labs(color = element_blank()) +
+  theme_minimal() +
+  theme(text = element_text(family = "Times New Roman"))
+```
+
+<img src="man/figures/README-unnamed-chunk-2-1.png" width="100%" />