Plot Case-Control Minimum Sample Size Necessary

Sweeps one x-axis parameter and repeatedly calls cc_mssn() to plot the minimum sample size necessary (MSSN) for case-control tests.

plot_cc_mssn(
  x_var,
  x_values,
  test = c("genotypes", "trend"),
  input_mode = c("model_based", "model_free"),
  sample_size = c("total", "case", "control"),
  compare_tests = FALSE,
  title = NULL,
  x_label = NULL,
  y_label = NULL,
  return_data = FALSE,
  ...
)

Arguments

x_var

Character. Parameter to vary on the x-axis.

x_values

Numeric vector of x-axis values.

test

One of "genotypes" or "trend".

input_mode

One of "model_based" or "model_free".

sample_size

One of "total", "case", or "control".

compare_tests

Logical. If TRUE, plot genotype and trend tests together and ignore test.

title

Optional character title override.

x_label

Optional x-axis label override.

y_label

Optional y-axis label override.

return_data

Logical. If TRUE, return the data frame instead of a ggplot.

...

Arguments passed to cc_mssn().

Value

A ggplot object, or a data frame if return_data = TRUE.

Details

The supported test values are "genotypes" and "trend". sample_size selects whether to plot required cases, controls, or total sample size. compare_tests = TRUE plots both tests together.

Supported x_var values are the same heterogeneity and misclassification variables documented for plot_cc_power(), plus "power" for target power. Fixed sample-size variables such as "N_case" are not valid because sample size is the MSSN output.

Multiplier behavior matches plot_cc_power(): pheno_error_multiplier multiplies baseline theta_base and phi_base; geno_error_multiplier multiplies the corresponding baseline genotype-error parameters for the selected genotype misclassification model. All arguments in ... remain fixed while x_var is swept. The y-axis is the required number of selected cases, controls, or total individuals returned by cc_mssn(), according to sample_size. For locus-heterogeneity sweeps, an exact pi = 0 design has no finite MSSN when target power exceeds alpha. That scientifically structural boundary is retained with MSSN NA, finite_mssn = FALSE, and status = "no finite MSSN"; other errors are propagated unchanged.

See also

cc_mssn, plot_cc_power.

Examples

plot_cc_mssn(
  x_var = "geno_error_multiplier",
  x_values = c(0, 1, 2),
  test = "trend",
  input_mode = "model_based",
  power = 0.80,
  alpha = 0.05,
  prev = 0.10,
  pd = 0.25,
  R2 = 2,
  MOI = "M",
  geno_misclass = "3p",
  e01_base = 0.02,
  e02_base = 0.01,
  e03_base = 0.005,
  k = 1
)