## ----echo = FALSE, message = FALSE--------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE
)

## ----example-0.1--------------------------------------------------------------
set.seed(333)
# Generate data with a medium effect
data <- sprtt::draw_sample_normal(
  k = 3,           # number of groups
  f = 0.25,        # effect size (Cohen's f)
  max_n = 22       # maximum sample size per group
)

## ----example-0.1b-------------------------------------------------------------
# View the first few rows
head(data)

# Check the sample sizes per group for the first 6 (2*k) data points
table(data$x[1:6])

## ----example-0.2--------------------------------------------------------------
# Calculate the sequential ANOVA
anova_results <- sprtt::seq_anova(
  y ~ x,
  f = 0.25,
  data = data[1:6, ],
  verbose = FALSE
)

# View results
anova_results

# Access the decision
anova_results@decision

## ----example-0.3--------------------------------------------------------------
# Calculate sequential ANOVA with larger sample
anova_results <- sprtt::seq_anova(
  y ~ x,
  f = 0.25,
  data = data[1:20, ],
  verbose = FALSE
)

# View results
anova_results

# Check decision
anova_results@decision

## ----example-0.4--------------------------------------------------------------
# Calculate sequential ANOVA with complete dataset
anova_results <- sprtt::seq_anova(
  y ~ x,
  f = 0.25,
  data = data,
  verbose = TRUE
)

# View full results
anova_results

## ----example-0.5--------------------------------------------------------------
# Access the decision
anova_results@decision

# Access the likelihood ratio
anova_results@likelihood_ratio

# Access the total sample size
anova_results@total_sample_size

## ----example-1----------------------------------------------------------------
set.seed(333)
data <- sprtt::draw_sample_normal(3, f = 0.25, max_n = 22)

# calculate the SPRT -----------------------------------------------------------
# Default: plot = TRUE with seq_steps = "single"
anova_results <- sprtt::seq_anova(y~x, f = 0.25,
                                  data = data, plot = TRUE)
# Explicitly specify seq_steps = "single"
anova_results <- sprtt::seq_anova(y~x, f = 0.25,
                                  data = data, plot = TRUE,
                                  seq_steps = "single")
# Use balanced sequential steps
anova_results <- sprtt::seq_anova(y~x, f = 0.25,
                                  data = data, plot = TRUE,
                                  seq_steps = "balanced")

# plot the results -------------------------------------------------------------
sprtt::plot_anova(anova_results)

## ----example-2----------------------------------------------------------------
set.seed(333)
# Generate unbalanced data with a 1:1:2 sampling ratio -------------------------
data <- sprtt::draw_sample_normal(3, f = 0.25, max_n = 37, sample_ratio = c(1,1,2))
# Randomize the order to get a more realistic data collection
data <- data[sample(nrow(data)),] 

# Calculate the SPRT with custom sequential steps ------------------------------
anova_results <- sprtt::seq_anova(
                          y~x,
                          f = 0.25,
                          data = data,
                          plot = TRUE,
                          # Start at n=12, then test after each observation
                          seq_steps = 12:nrow(data)) 

# Plot the results with custom styling -----------------------------------------
sprtt::plot_anova(anova_results,
                 labels = TRUE,
                 position_labels_x = 0.2,
                 position_labels_y = 0.2,
                 position_lr_x = 100,
                 position_lr_y = 1.8,
                 font_size = 20,
                 line_size = 1,
                 highlight_color = "steelblue"
                 )