evanverse (version 0.4.0)

stat_samplesize: Calculate Required Sample Size

Description

Compute the minimum sample size required to achieve a specified statistical power for detecting a given effect size. Supports multiple test types including t-tests, ANOVA, proportion tests, correlation tests, and chi-square tests.

Usage

stat_samplesize(
  power = 0.8,
  effect_size,
  test = c("t.test", "anova", "proportion", "correlation", "chisq"),
  type = c("two.sample", "one.sample", "paired"),
  alternative = c("two.sided", "less", "greater"),
  alpha = 0.05,
  k = NULL,
  df = NULL,
  plot = TRUE,
  plot_range = NULL,
  palette = "qual_vivid",
  verbose = TRUE
)

Value

An object of class stat_samplesize_result containing:

n

Required sample size (per group for t-tests and ANOVA)

n_total

Total sample size across all groups

power

Target statistical power

effect_size

Effect size used in the calculation

alpha

Significance level

test_type

Type of statistical test

plot

ggplot2 object showing the sample size curve (if plot = TRUE)

details

List with interpretation and recommendations

Arguments

power

Numeric. Target statistical power (probability of correctly rejecting the null hypothesis). Default: 0.8 (80%).

effect_size

Numeric. Effect size appropriate for the test:

• Cohen's d for t-tests (small: 0.2, medium: 0.5, large: 0.8)

• Cohen's f for ANOVA (small: 0.1, medium: 0.25, large: 0.4)

• Cohen's h for proportion tests (small: 0.2, medium: 0.5, large: 0.8)

• Correlation coefficient r for correlation tests (small: 0.1, medium: 0.3, large: 0.5)

• Cohen's w for chi-square tests (small: 0.1, medium: 0.3, large: 0.5)

test

Character. Type of statistical test: "t.test" (default), "anova", "proportion", "correlation", or "chisq".

type

Character. For t-tests only: "two.sample" (default), "one.sample", or "paired".

alternative

Character. Direction of alternative hypothesis: "two.sided" (default), "less", or "greater".

alpha

Numeric. Significance level (Type I error rate). Default: 0.05.

k

Integer. Number of groups (required for ANOVA).

df

Integer. Degrees of freedom (required for chi-square tests).

plot

Logical. Generate a sample size curve plot? Default: TRUE.

plot_range

Numeric vector of length 2. Range of effect sizes for the curve. If NULL (default), automatically determined.

palette

Character. evanverse palette name for the plot. Default: "qual_vivid".

verbose

Logical. Print detailed diagnostic information? Default: TRUE.

Sample Size Curve

When plot = TRUE, a sample size curve is generated showing how required sample size changes with effect size. The curve helps visualize:

• The current required sample size (marked with a red point)

• Reference lines for small, medium, and large effects

• How detecting smaller effects requires larger samples

Important Notes

• Sample sizes are calculated per group for t-tests and ANOVA

• Consider adding 10-15% to account for potential dropout

• Very small effect sizes may require impractically large samples

Details

Sample size estimation is a critical step in research planning. This function calculates the minimum number of participants needed to achieve a specified statistical power (typically 0.8 or 80%) for detecting an effect of a given size.

The function uses the pwr package for all calculations, ensuring accurate results based on well-established statistical theory. Sample sizes are always rounded up to the nearest integer.

See Also

stat_power for calculating statistical power.

Examples

if (FALSE) {
# Example 1: Sample size for a two-sample t-test
result <- stat_samplesize(
  power = 0.8,
  effect_size = 0.5,
  test = "t.test",
  type = "two.sample"
)
print(result)
plot(result)

# Example 2: Sample size for ANOVA with 3 groups
stat_samplesize(
  power = 0.8,
  effect_size = 0.25,
  test = "anova",
  k = 3
)

# Example 3: Sample size for correlation test
stat_samplesize(
  power = 0.9,
  effect_size = 0.3,
  test = "correlation"
)
}