Performs one and two sample sequential t-tests on vectors of data. For more information on the sequential t-test, see Schnuerch & Erdfelder (2019) doi:10.1037/met0000234.
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:
numericandformula. 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.
- data
an optional
data.frame, which you can use only in combination with a"formula input"in argumentx.- 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 effect size (Cohen's d)
- alpha
the type I error. A number between 0 and 1.
- power
1 - beta (beta is the type II error probability). A number between 0 and 1.
- alternative
a character string specifying the alternative hypothesis, must be one of
two.sided(default),greaterorless. You can specify just the initial letter.- paired
a logical indicating whether you want a paired t-test.
- na.rm
a logical value indicating whether
NAvalues should be stripped 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.
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
#> [1] 965.0728
# [] Operator
results["likelihood_ratio"]
#> [1] 965.0728
# 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)
#>
#> ***** Sequential Two Sample t-test *****
#>
#> formula: treatment_group and control_group
#> test statistic:
#> log-likelihood ratio = 5.347, decision = accept H1
#> SPRT thresholds:
#> lower log(B) = -2.944, upper log(A) = 2.944
#> Log-Likelihood of the:
#> alternative hypothesis = -4.211
#> null hypothesis = -9.558
#> alternative hypothesis: true difference in means is not equal to 0.
#> specified effect size: Cohen's d = 0.8
#> degrees of freedom: df = 38
#> sample estimates:
#> mean of x mean of y
#> -0.05204 1.18768
#> *Note: to get access to the object of the results use the @ or [] instead of the $ operator.
# 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)
#>
#> ***** Sequential Two Sample t-test *****
#>
#> formula: stress_level ~ sex
#> test statistic:
#> log-likelihood ratio = -1.455, decision = continue sampling
#> SPRT thresholds:
#> lower log(B) = -2.944, upper log(A) = 2.944
#> Log-Likelihood of the:
#> alternative hypothesis = -1.233
#> null hypothesis = 0.222
#> alternative hypothesis: true difference in means is not equal to 0.
#> specified effect size: Cohen's d = 0.8
#> degrees of freedom: df = 18
#> sample estimates:
#> mean of x mean of y
#> -0.23286 -0.08217
#> *Note: to get access to the object of the results use the @ or [] instead of the $ operator.
# 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)
#>
#> ***** Sequential Two Sample t-test *****
#>
#> formula: stress_level ~ sex
#> test statistic:
#> log-likelihood ratio = -0.359, decision = continue sampling
#> SPRT thresholds:
#> lower log(B) = -2.944, upper log(A) = 2.944
#> Log-Likelihood of the:
#> alternative hypothesis = -1.923
#> null hypothesis = -1.564
#> alternative hypothesis: true difference in means is not equal to 0.
#> specified effect size: Cohen's d = 0.8
#> degrees of freedom: df = 18
#> sample estimates:
#> mean of x mean of y
#> -0.40818 0.42068
#> *Note: to get access to the object of the results use the @ or [] instead of the $ operator.
# work with dataset (data are in the package included) ------------------------
seq_ttest(monthly_income ~ sex, data = df_income, d = 0.8)
#>
#> ***** Sequential Two Sample t-test *****
#>
#> formula: monthly_income ~ sex
#> test statistic:
#> log-likelihood ratio = -9.514, decision = accept H0
#> SPRT thresholds:
#> lower log(B) = -2.944, upper log(A) = 2.944
#> Log-Likelihood of the:
#> alternative hypothesis = -8.093
#> null hypothesis = 1.421
#> alternative hypothesis: true difference in means is not equal to 0.
#> specified effect size: Cohen's d = 0.8
#> degrees of freedom: df = 118
#> sample estimates:
#> mean of x mean of y
#> 3072.086 3080.715
#> *Note: to get access to the object of the results use the @ or [] instead of the $ operator.