seq_ttest: Sequential Probability Ratio Test using t-statistic

In sprtt: Sequential Probability Ratio Tests Toolbox

Sequential Probability Ratio Test using t-statistic

Description

Performs one-sample, two-sample, and paired sequential t-tests, which are variants of Sequential Probability Ratio Tests (SPRT). The test allows for continuous monitoring of data collection and provides stopping boundaries based on likelihood ratios, offering efficiency gains over traditional fixed-N designs.

The sequential t-test continuously evaluates the likelihood ratio after each observation (or pair of observations), stopping when sufficient evidence accumulates for either H0 or H1.

For methodological details, see Schnuerch & Erdfelder (2019) doi:10.1037/met0000234. For practical guidance, see vignette("t_test", package = "sprtt").

Usage

seq_ttest(
  x,
  y = NULL,
  data = NULL,
  mu = 0,
  d,
  alpha = 0.05,
  power = 0.95,
  alternative = "two.sided",
  paired = FALSE,
  na.rm = TRUE,
  verbose = TRUE
)

Arguments

x	Works with two classes: numeric and formula. Therefore you can write "x" or "x~y". "numeric input": a (non-empty) numeric vector of data values. "formula input": a formula of the form lhs ~ rhs where lhs is a numeric variable giving the data values and rhs either 1 for a one-sample test or a factor with two levels giving the corresponding groups.
y	An optional (non-empty) numeric vector of data values. Only used for two-sample tests when x is numeric.
data	An optional data frame containing the variables specified in the formula. Only used when x is a formula.
mu	a number indicating the true value of the mean (or difference in means if you are performing a two sample test).
d	a number indicating the specified (expected) effect size (Cohen's d)
alpha	Type I error rate (alpha level). The probability of rejecting H0 when it is true. Default is 0.05. Must be between 0 and 1.
power	Statistical power (1 - beta), where beta is the Type II error rate. The probability of correctly rejecting H0 when H1 is true with effect size d. Default is 0.95. Must be between 0 and 1. Higher values lead to wider stopping boundaries and potentially larger sample sizes.
alternative	a character string specifying the alternative hypothesis, must be one of two.sided (default), greater or less. You can specify just the initial letter.
paired	Logical indicating whether to perform a paired t-test. Default is FALSE.
na.rm	a logical value indicating whether NA values should be removed before the computation proceeds.
verbose	a logical value whether you want a verbose output or not.

Value

An object of the S4 class seq_ttest_results. Click on the class link to see the full description of the slots. To get access to the object use the @-operator or ⁠[]⁠-brackets instead of $. See the examples below.

See Also

• seq_anova() for sequential one-way ANOVA

• draw_sample_normal() for simulating test data

• vignette("t_test", package = "sprtt") for detailed tutorial

• vignette("usage_sprtt", package = "sprtt") for package overview

• Schnuerch & Erdfelder (2019) doi:10.1037/met0000234 for theoretical background

Examples

# set seed --------------------------------------------------------------------
set.seed(333)

# load library ----------------------------------------------------------------
library(sprtt)

# one sample: numeric input ---------------------------------------------------
treatment_group <- rnorm(20, mean = 0, sd = 1)
results <- seq_ttest(treatment_group, mu = 1, d = 0.8)

# get access to the slots -----------------------------------------------------
# @ Operator
results@likelihood_ratio

# [] Operator
results["likelihood_ratio"]

# two sample: numeric input----------------------------------------------------
treatment_group <- stats::rnorm(20, mean = 0, sd = 1)
control_group <- stats::rnorm(20, mean = 1, sd = 1)
seq_ttest(treatment_group, control_group, d = 0.8)

# two sample: formula input ---------------------------------------------------
stress_level <- stats::rnorm(20, mean = 0, sd = 1)
sex <- as.factor(c(rep(1, 10), rep(2, 10)))
seq_ttest(stress_level ~ sex, d = 0.8)

# NA in the data --------------------------------------------------------------
stress_level <- c(NA, stats::rnorm(20, mean = 0, sd = 2), NA)
sex <- as.factor(c(rep(1, 11), rep(2, 11)))
seq_ttest(stress_level ~ sex, d = 0.8, na.rm = TRUE)

# work with dataset (data are in the package included) ------------------------
seq_ttest(monthly_income ~ sex, data = df_income, d = 0.8)

sprtt documentation built on May 6, 2026, 5:06 p.m.