## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----message=FALSE------------------------------------------------------------
library(PRDA)

## ----eval=FALSE, echo = T-----------------------------------------------------
# retrospective(effect_size, power, ratio_n = 1,
#               test_method = c("pearson", "two_sample", "welch",
#                                         "paired", "one_sample"),
#               alternative = c("two_sided","less","greater"),
#               sig_level = .05, ratio_sd = 1, B = 1e4,
#               tl = -Inf, tu = Inf, B_effect = 1e3,
#               sample_range = c(2, 1000), tol = .01,
#               display_message = TRUE)

## ----example1-----------------------------------------------------------------
set.seed(2020) # set seed to make results reproducible

prospective(effect_size = .25, power = .60, test_method = "pearson",
            display_message = TRUE)

## ----example2-----------------------------------------------------------------
prospective(effect_size = .35, power = .8, test_method = "paired",
            ratio_n = 1, display_message = FALSE)

## ----example3-----------------------------------------------------------------
prospective(effect_size = .35, power = .80, ratio_n = .5, 
            test_method = "two_sample", alternative = "great", sig_level = .10, 
            display_message = FALSE)

## ----example4-----------------------------------------------------------------
prospective(effect_size = .35, power = .80, ratio_n = .5, test_method = "welch",
            ratio_sd = 1.5, alternative = "great", sig_level = .10, 
            display_message = FALSE)

## ----example5-----------------------------------------------------------------
prospective(effect_size = function(n) rnorm(n, .3, .1), power = .60, 
            test_method = "pearson", tl = .15, tu = .45, B_effect = 500, 
            B = 500, display_message = FALSE)

## ----data_plot----------------------------------------------------------------
da_fit <- prospective(effect_size = function(n) rnorm(n, .3, .1), power = .60,
                      test_method = "pearson", tl = .15, tu = .45, 
                      B_effect = 500, B = 500, display_message = FALSE)

str(da_fit, max.level = 1)