nRegression {nRegression}

nRegression

Description

The nRegression package was designed to estimate the minimal sample size required to attain a specific statistical power in the context of linear regression and logistic regression models through simulations.

Usage

nRegression(
  the.steps,
  num.experiments,
  model.type = "lm",
  the.formula,
  the.variable,
  seed,
  n.start = 50,
  n.min = 1,
  n.max = 10000,
  increment = 50,
  stop.threshold = 5,
  power = 0.8,
  conf.level = 0.95,
  verbose = TRUE,
  vstr = 3.6
)

Arguments

the.steps

The steps are ordered to describe which variables will be calculated. Note that the variables with a dependence on other variables must be specified after all of its inputs.

num.experiments

The number of experiments to perform at each sample size to estimate the power.

model.type

The type of regression model to fit.By default it is linear regression

the.formula

The formula for the regression model to fit.

the.variable

the variable to test

seed

This parameter is used to set a specific random seed, ensuring that the results are reproducible if the same seed is used.

n.start

The initial sample size to start the simulations. By default it is 50.

n.min

The minimum possible sample size. By default it is 1

n.max

The maximum possible sample size. By default it is 10000

increment

The increment to increase the sample size at each iteration. By default it is 50.

stop.threshold

The number of iterations to stop after if the desired power is not achieved.

power

describes the statistical power

conf.level

The confidence level for the statistical power.

verbose

Logical. If TRUE, the function will print information about each iteration.

vstr

Variance inflation factor. Used in the simulation of the response variable.

Value

A list containing the following elements:

• 'min.sample.size': The estimated minimum sample size to achieve the desired power.

• 'power': The statistical power achieved with the estimated sample size.

• 'iterations': A data frame with details about each iteration of the simulation. Each row represents an iteration and contains the sample size, the estimated power, and the upper and lower bounds for the sample size for that iteration.

Examples

require(data.table)
require(simitation)
power = 0.9
step.age <- "Age ~ N(45, 10)"
step.female <- "Female ~ binary(0.53)"
step.health.percentile <- "Health.Percentile ~ U(0,100)"
step.exercise.sessions <- "Exercise.Sessions ~ Poisson(2)"
step.diet <- "Diet ~ sample(('Light', 'Moderate', 'Heavy'), (0.2, 0.45, 0.35))"
step.healthy.lifestyle <- "Healthy.Lifestyle ~ logistic(log(0.45) - 0.1 *
(Age -45) + 0.05 * Female + 0.01 * Health.Percentile + 0.5 *
Exercise.Sessions - 0.1 * (Diet == 'Moderate') - 0.4 * (Diet == 'Heavy'))"
step.weight <- "Weight ~ lm(150 - 15 * Female + 0.5 * Age - 0.1 *
Health.Percentile - 0.2 * Exercise.Sessions  + 5 * (Diet == 'Moderate') + 15 *
(Diet == 'Heavy') - 2 * Healthy.Lifestyle + N(0, 10))"
the.steps <- c(step.age, step.female, step.health.percentile, step.exercise.sessions,
 step.diet, step.healthy.lifestyle, step.weight)
the.formula.logistic <- Healthy.Lifestyle ~ Age + Female + Health.Percentile +
 Exercise.Sessions + Weight
the.variable = "Exercise.Sessions"
conf.level = 0.95
model.type = "logistic"
seed = 41
vstr = 3.6
num.experiments = 10
n.start = 200
n.min = 1
n.max = 300
increment = 100
stop.threshold = 1
n.logistic = nRegression(the.steps = the.steps, num.experiments = num.experiments,
 the.formula = the.formula.logistic, the.variable = the.variable,
 seed = seed, n.start = n.start, n.min = n.min, n.max = n.max,
 increment = increment, stop.threshold = stop.threshold, power = power,
 model.type = model.type, verbose = TRUE)