show.md
In sprtt: Sequential Probability Ratio Tests Toolbox

show: print output ttest?

Code
  show(seq_ttest(rnorm(20), d = 0.8))
Output

  *****  Sequential One Sample t-test *****

  formula: rnorm(20)
  test statistic:
   log-likelihood ratio = -5.783, decision = accept H0
  SPRT thresholds:
   lower log(B) = -2.944, upper log(A) = 2.944
  Log-Likelihood of the:
   alternative hypothesis = -5.676
   null hypothesis = 0.107
  alternative hypothesis: true mean is not equal to 0.
  specified effect size: Cohen's d = 0.8
  degrees of freedom: df = 19
  sample estimates:
  mean of x 
    0.05238 
  *Note: to get access to the object of the results use the @ or [] instead of the $ operator.

Code
  show(seq_ttest(x_special_name, d = 0.8, alternative = "less", mu = 2))
Output

  *****  Sequential One Sample t-test *****

  formula: x_special_name
  test statistic:
   log-likelihood ratio = 12.358, decision = accept H1
  SPRT thresholds:
   lower log(B) = -2.944, upper log(A) = 2.944
  Log-Likelihood of the:
   alternative hypothesis = -17.916
   null hypothesis = -30.275
  alternative hypothesis: true mean is less than 2.
  specified effect size: Cohen's d = 0.8
  degrees of freedom: df = 19
  sample estimates:
  mean of x 
   -0.50342 
  *Note: to get access to the object of the results use the @ or [] instead of the $ operator.

Code
  show(seq_ttest(x, d = 0.8, alternative = "greater"))
Output

  *****  Sequential One Sample t-test *****

  formula: x
  test statistic:
   log-likelihood ratio = -9.385, decision = accept H0
  SPRT thresholds:
   lower log(B) = -2.944, upper log(A) = 2.944
  Log-Likelihood of the:
   alternative hypothesis = -10.711
   null hypothesis = -1.326
  alternative hypothesis: true mean is greater than 0.
  specified effect size: Cohen's d = 0.8
  degrees of freedom: df = 19
  sample estimates:
  mean of x 
   -0.20286 
  *Note: to get access to the object of the results use the @ or [] instead of the $ operator.

Code
  show(seq_ttest(x_special_name, y_secial_name, d = 0.8))
Output

  *****  Sequential  Two Sample t-test *****

  formula: x_special_name and  y_secial_name
  test statistic:
   log-likelihood ratio = -3.2, decision = accept H0
  SPRT thresholds:
   lower log(B) = -2.944, upper log(A) = 2.944
  Log-Likelihood of the:
   alternative hypothesis = 3.455
   null hypothesis = 6.655
  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 
    0.14016   0.13999 
  *Note: to get access to the object of the results use the @ or [] instead of the $ operator.

Code
  show(seq_ttest(x, y, d = 0.8, alternative = "less"))
Output

  *****  Sequential  Two Sample t-test *****

  formula: x and  y
  test statistic:
   log-likelihood ratio = -5.225, decision = accept H0
  SPRT thresholds:
   lower log(B) = -2.944, upper log(A) = 2.944
  Log-Likelihood of the:
   alternative hypothesis = -6.486
   null hypothesis = -1.261
  alternative hypothesis: true difference in means is less than 0.
  specified effect size: Cohen's d = 0.8
  degrees of freedom: df = 38
  sample estimates:
  mean of x mean of y 
    0.11723  -0.14143 
  *Note: to get access to the object of the results use the @ or [] instead of the $ operator.

Code
  show(seq_ttest(x ~ y, d = 0.8))
Output

  *****  Sequential  Two Sample t-test *****

  formula: x ~ y
  test statistic:
   log-likelihood ratio = -1.596, decision = continue sampling
  SPRT thresholds:
   lower log(B) = -2.944, upper log(A) = 2.944
  Log-Likelihood of the:
   alternative hypothesis = 0.499
   null hypothesis = 2.095
  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 
    0.18073   0.16120 
  *Note: to get access to the object of the results use the @ or [] instead of the $ operator.

show: verbose

Code
  show(calc_seq_ttest(build_prototype_seq_ttest_arguments()))
Output

  *****  Sequential  Two Sample t-test *****

  formula: x and y
  test statistic:
   log-likelihood ratio = 2.193, decision = continue sampling
  SPRT thresholds:
   lower log(B) = -1.558, upper log(A) = 2.773
  Log-Likelihood of the:
   alternative hypothesis = -3.201
   null hypothesis = -5.393
  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 
   -0.13828   1.05060 
  *Note: to get access to the object of the results use the @ or [] instead of the $ operator.

Code
  show(calc_seq_ttest(build_prototype_seq_ttest_arguments(), verbose = FALSE))
Output

  *****  Sequential  Two Sample t-test *****

  formula: x and y
  test statistic:
   log-likelihood ratio = 2.193, decision = continue sampling
  SPRT thresholds:
   lower log(B) = -1.558, upper log(A) = 2.773

show: print output anova?

Code
  show(seq_anova(y ~ x, f = 0.25, data = data))
Output

  *****  Sequential ANOVA *****

  formula: y ~ x
  test statistic:
   log-likelihood ratio = 5.579, decision = accept H1
  SPRT thresholds:
   lower log(B) = -2.944, upper log(A) = 2.944
  Log-Likelihood of the:
   alternative hypothesis = -1.768
   null hypothesis = -7.348
  alternative hypothesis: true difference in means is not equal to 0.
  specified effect size: Cohen's f = 0.25
  empirical Cohen's f = 0.255582, 95% CI[0.1175887, 0.3478401]
  Cohen's f adjusted = 0.228
  degrees of freedom: df1 = 4, df2 = 325
  SS effect = 20.49968, SS residual = 313.8243, SS total = 334.324
  *Note: to get access to the object of the results use the @ or [] instead of the $ operator.

Code
  show(seq_anova(happiness ~ job_satisfaction, f = 0.25, data = df_job))
Output

  *****  Sequential ANOVA *****

  formula: happiness ~ job_satisfaction
  test statistic:
   log-likelihood ratio = 4.66, decision = accept H1
  SPRT thresholds:
   lower log(B) = -2.944, upper log(A) = 2.944
  Log-Likelihood of the:
   alternative hypothesis = -6.628
   null hypothesis = -11.288
  alternative hypothesis: true difference in means is not equal to 0.
  specified effect size: Cohen's f = 0.25
  empirical Cohen's f = 0.6758475, 95% CI[0.3452897, 0.9720489]
  Cohen's f adjusted = 0.631
  degrees of freedom: df1 = 1, df2 = 46
  SS effect = 19.50919, SS residual = 42.71121, SS total = 62.2204
  *Note: to get access to the object of the results use the @ or [] instead of the $ operator.

Code
  show(seq_anova(y ~ x, f = 0.1, data = data))
Output

  *****  Sequential ANOVA *****

  formula: y ~ x
  test statistic:
   log-likelihood ratio = -0.036, decision = continue sampling
  SPRT thresholds:
   lower log(B) = -2.944, upper log(A) = 2.944
  Log-Likelihood of the:
   alternative hypothesis = -0.799
   null hypothesis = -0.763
  alternative hypothesis: true difference in means is not equal to 0.
  specified effect size: Cohen's f = 0.1
  empirical Cohen's f = 0.1958652, 95% CI[NA, 0.3634934]
  Cohen's f adjusted = 0
  degrees of freedom: df1 = 3, df2 = 76
  SS effect = 3.948363, SS residual = 102.9206, SS total = 106.869
  *Note: to get access to the object of the results use the @ or [] instead of the $ operator.

Code
  show(seq_anova(y ~ x, f = 0.25, alpha = 0.3, power = 0.95, data = data))
Output

  *****  Sequential ANOVA *****

  formula: y ~ x
  test statistic:
   log-likelihood ratio = 7.792, decision = accept H1
  SPRT thresholds:
   lower log(B) = -2.639, upper log(A) = 1.153
  Log-Likelihood of the:
   alternative hypothesis = -5.07
   null hypothesis = -12.862
  alternative hypothesis: true difference in means is not equal to 0.
  specified effect size: Cohen's f = 0.25
  empirical Cohen's f = 0.4366689, 95% CI[0.2449371, 0.6194111]
  Cohen's f adjusted = 0.42
  degrees of freedom: df1 = 1, df2 = 118
  SS effect = 25.09296, SS residual = 131.5974, SS total = 156.6904
  *Note: to get access to the object of the results use the @ or [] instead of the $ operator.

Code
  show(seq_anova(y ~ x, f = 0.25, data = data, verbose = FALSE))
Output

  *****  Sequential ANOVA *****

  formula: y ~ x
  test statistic:
   log-likelihood ratio = 10.2, decision = accept H1
  SPRT thresholds:
   lower log(B) = -2.944, upper log(A) = 2.944