An S4 class to represent the results of a sequential t-test.

`likelihood_ratio_log`

the logarithmic test statistic.

`decision`

the test decision: "accept H1", "accept H0", or "continue sampling".

`A_boundary_log`

the lower logarithmic boundary of the test.

`B_boundary_log`

the upper logarithmic boundary of the test.

`d`

a number indicating the specified effect size (Cohen's d).

`mu`

a number indicating the true value of the mean (or difference in means if you are performing a two sample test).

`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.

`likelihood_ratio`

the likelihood ratio of the test without logarithm.

`likelihood_1`

the likelihood of the alternative Hypothesis (H1).

`likelihood_0`

the likelihood of the null Hypothesis (H0).

`likelihood_1_log`

the logarithmic likelihood of the alternative Hypothesis (H1).

`likelihood_0_log`

the logarithmic likelihood of the null Hypothesis (H0).

`non_centrality_parameter`

parameter to calculate the likelihoods

`t_value`

the t-value of the t-statistic.

`p_value`

the p-value of the t-test.

`df`

degrees of freedom.

`mean_estimate`

the estimated mean or difference in means depending on whether it was a one-sample test or a two-sample test.

`alternative`

a character string specifying the alternative hypothesis: "two.sided" (default), "greater" or "less".

`one_sample`

"true" if it is a one-sample test, "false" if it is a two-sample test.

`ttest_method`

a character string indicating what type of t-test was performed.

`data_name`

a character string giving the name(s) of the data.

`verbose`

a logical value whether you want a verbose output or not.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.