Summarize 'x_from_power' Results

The summary method of the output of x_from_power().

Usage

# S3 method for class 'x_from_power'
summary(object, ...)

# S3 method for class 'n_region_from_power'
summary(object, ...)

# S3 method for class 'summary.x_from_power'
print(x, digits = 3, ...)

# S3 method for class 'summary.n_region_from_power'
print(x, digits = 3, ...)

Arguments

object: An x_from_power-class object, such as the output of x_from_power(), or an object of the class n_region_from_power, such as the output of n_region_from_power().
...: Additional arguments. Not used for now.
x: The output of summary.x_from_power(), the summary method of an x_from_power object, which is the output of x_from_power(), or the output of summary.n_region_from_power(), the summary method of an n_region_from_power object (the output of n_region_from_power()).
digits: The number of digits after the decimal when printing the results.

Value

The summary method for x_from_power objects returns an object of the class summary.x_from_power, which is simply the output of x_from_power(), with a print method dedicated for detailed summary. Please refer to x_from_power() for the contents.

The print-method of summary.x_from_power objects returns the object x invisibly. It is called for its side effect.

The print-method of summary.n_region_from_power objects returns the object x invisibly. It is called for its side effect.

Details

The summary method simply prepares the results of x_from_power() to be printed in details.

Examples


# Specify the population model

mod <-
"
m ~ x
y ~ m + x
"

# Specify the population values

mod_es <-
"
m ~ x: m
y ~ m: l
y ~ x: n
"

# Generate the datasets

sim_only <- power4test(nrep = 5,
                       model = mod,
                       pop_es = mod_es,
                       n = 100,
                       do_the_test = FALSE,
                       iseed = 2345)
#> Simulate the data:
#> Fit the model(s):

# Do a test

test_out <- power4test(object = sim_only,
                       test_fun = test_parameters,
                       test_args = list(pars = "m~x"))
#> Do the test: test_parameters: CIs (pars: m~x) 

# Determine the sample size with a power of .80 (default)

power_vs_n <- x_from_power(test_out,
                           x = "n",
                           progress = TRUE,
                           target_power = .80,
                           final_nrep = 5,
                           max_trials = 1,
                           seed = 1234)
#> 
#> --- Setting ---
#> 
#> Algorithm:  bisection 
#> Goal:  ci_hit 
#> What:  point   (Estimated Power) 
#> 
#> --- Progress  ---
#> 
#> - Set 'progress = FALSE' to suppress displaying the progress.
#> - Set 'simulation progress = FALSE' to suppress displaying the progress
#>   in the simulation.
#> 
#> Initial interval: [100, 125] 
#> 
#> 
#> Do the simulation for the upper bound:
#> 
#> Try x = 125 
#> 
#> Updating the simulation for sample size: 125 
#> Re-simulate the data:
#> Fit the model(s):
#> Update the test(s):
#> Update test_parameters: CIs (pars: m~x) :
#> 
#> Estimated power at n: 1.000, 95.0% confidence interval: [1.000,1.000]
#> 
#> Initial interval: [100, 125] 
#> 
#> - Rejection Rates:
#> [test]: test_parameters: CIs (pars: m~x) 
#> [test_label]: m~x 
#>     n   est   p.v reject r.cilo r.cihi
#> 1 100 0.298 1.000  0.800  0.449  1.151
#> 2 125 0.310 1.000  1.000  1.000  1.000
#> 
#> One of the bounds in the interval is already a solution.
#> 
#> - 'nls()' estimation skipped when less than 4 values of predictor examined.
#> Solution found.
#> 
#> 
#> --- Final Stage ---
#> 
#> - Start at 2025-09-05 00:16:33 
#> - Rejection Rates:
#> 
#> [test]: test_parameters: CIs (pars: m~x) 
#> [test_label]: m~x 
#>     n   est   p.v reject r.cilo r.cihi
#> 1 100 0.298 1.000  0.800  0.449  1.151
#> 2 125 0.310 1.000  1.000  1.000  1.000
#> Notes:
#> - n: The sample size in a trial.
#> - p.v: The proportion of valid replications.
#> - est: The mean of the estimates in a test across replications.
#> - reject: The proportion of 'significant' replications, that is, the
#>   rejection rate. If the null hypothesis is true, this is the Type I
#>   error rate. If the null hypothesis is false, this is the power.
#> - r.cilo,r.cihi: The confidence interval of the rejection rate, based
#>   on normal approximation.
#> - Refer to the tests for the meanings of other columns.
#> 
#> - Estimated Power Curve:
#> 
#> Call:
#> power_curve(object = by_x_1, formula = power_model, start = power_curve_start, 
#>     lower_bound = lower_bound, upper_bound = upper_bound, nls_args = nls_args, 
#>     nls_control = nls_control, verbose = progress)
#> 
#> Predictor: n (Sample Size)
#> 
#> Model:
#> 
#> Call:  stats::glm(formula = reject ~ x, family = "binomial", data = reject1)
#> 
#> Coefficients:
#> (Intercept)            x  
#>    -75.3328       0.7672  
#> 
#> Degrees of Freedom: 9 Total (i.e. Null);  8 Residual
#> Null Deviance:	    6.502 
#> Residual Deviance: 5.004 	AIC: 9.004
#> 
#> 
#> - Final Value: 100 
#> 
#> - Final Estimated Power: 0.8000 
#> - Confidence Interval: [0.4494; 1.1506]
#> - CI Level: 95.00%
summary(power_vs_n)
#> 
#> ====== x_from_power Results ======
#> 
#> Call:
#> x_from_power(object = test_out, x = "n", target_power = 0.8, 
#>     progress = TRUE, max_trials = 1, final_nrep = 5, seed = 1234)
#> 
#> Predictor (x): Sample Size 
#> 
#> - Target Power: 0.800 
#> - Goal: Find 'x' with the confidence interval of the estimated power
#>   enclosing the target power.
#> 
#> === Major Results ===
#> 
#> - Final Value (Sample Size): 100
#> 
#> - Final Estimated Power: 0.800 
#> - Confidence Interval: [0.449; 1.151]
#> - Level of confidence: 95.0%
#> - Based on 5 replications.
#> 
#> === Technical Information ===
#> 
#> - Algorithm: bisection 
#> - The range of values explored: 100 to 125 
#> - Time spent in the search: 0.5368 secs 
#> - The final crude model for the power-predictor relation:
#> 
#> Model Type: Logistic Regression 
#> 
#> Call:
#> power_curve(object = by_x_1, formula = power_model, start = power_curve_start, 
#>     lower_bound = lower_bound, upper_bound = upper_bound, nls_args = nls_args, 
#>     nls_control = nls_control, verbose = progress)
#> 
#> Predictor: n (Sample Size)
#> 
#> Model:
#> 
#> Call:  stats::glm(formula = reject ~ x, family = "binomial", data = reject1)
#> 
#> Coefficients:
#> (Intercept)            x  
#>    -75.3328       0.7672  
#> 
#> Degrees of Freedom: 9 Total (i.e. Null);  8 Residual
#> Null Deviance:	    6.502 
#> Residual Deviance: 5.004 	AIC: 9.004
#> 
#> - Detailed Results:
#> 
#> [test]: test_parameters: CIs (pars: m~x) 
#> [test_label]: m~x 
#>     n   est   p.v reject r.cilo r.cihi
#> 1 100 0.298 1.000  0.800  0.449  1.151
#> 2 125 0.310 1.000  1.000  1.000  1.000
#> Notes:
#> - n: The sample size in a trial.
#> - p.v: The proportion of valid replications.
#> - est: The mean of the estimates in a test across replications.
#> - reject: The proportion of 'significant' replications, that is, the
#>   rejection rate. If the null hypothesis is true, this is the Type I
#>   error rate. If the null hypothesis is false, this is the power.
#> - r.cilo,r.cihi: The confidence interval of the rejection rate, based
#>   on normal approximation.
#> - Refer to the tests for the meanings of other columns.
#>

Usage

Arguments

Value

Details

See also

Examples