tests/testthat/_snaps/show.md
In MeikeSteinhilber/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
MeikeSteinhilber/sprtt documentation built on Jan. 19, 2024, 1:56 a.m.
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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
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Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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