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Predict Method for a 'power_curve' Object

Source: R/power_curve_predict.R
predict.power_curve.Rd

Compute the predicted values in a model fitted by power_curve().

Usage

# S3 method for class 'power_curve'
predict(object, newdata, ...)

Arguments

object

A power_curve object.

newdata

A data frame with a column named x. It can also be a named list, with one element named x and is a vector of the values. If not supplied, values of x stored in object will be used.

...

Additional arguments. Passed to the corresponding predict method.

Value

It returns a numeric vector of the predicted rejection rates.

Details

The predict method of power_curve objects works in two modes.

If new data is not supplied (through newdata), it retrieves the stored results and calls the corresponding methods to compute the predicted values, which are the predicted rejection rates (power levels if the null hypothesis is false, e.g., the population effect size is equal to zero).

If new data is supplied, such as a named list with a vector of sample sizes, they will be used to compute the predicted rejection rates.

See also

power_curve().

Examples


# Specify the population model

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

# Specify the effect sizes (population parameter values)

model_simple_med_es <-
"
y ~ m: l
m ~ x: m
y ~ x: s
"

# Simulate datasets to check the model
# Set `parallel` to TRUE for faster, usually much faster, analysis
# Set `progress` to TRUE to display the progress of the analysis

sim_only <- power4test(nrep = 10,
                       model = model_simple_med,
                       pop_es = model_simple_med_es,
                       n = 50,
                       fit_model_args = list(fit_function = "lm"),
                       do_the_test = FALSE,
                       iseed = 1234,
                       parallel = FALSE,
                       progress = FALSE)

# By n: Do a test for different sample sizes

out1 <- power4test_by_n(sim_only,
                        nrep = 10,
                        test_fun = test_parameters,
                        test_args = list(par = "y~x"),
                        n = c(25, 100, 200, 1000),
                        by_seed = 1234,
                        parallel = FALSE,
                        progress = FALSE)

pout1 <- power_curve(out1)
pout1
#> Call:
#> power_curve(object = out1)
#> 
#> Predictor: n (Sample Size)
#> 
#> Model:
#> 
#> Call:  stats::glm(formula = reject ~ x, family = "binomial", data = reject1)
#> 
#> Coefficients:
#> (Intercept)            x  
#>   -1.550280     0.004213  
#> 
#> Degrees of Freedom: 39 Total (i.e. Null);  38 Residual
#> Null Deviance:	    54.55 
#> Residual Deviance: 38.37 	AIC: 42.37
predict(pout1,
        newdata = list(x = c(150, 250, 500)))
#>         1         2         3 
#> 0.2852909 0.3782246 0.6355494 

# By pop_es: Do a test for different population values of a model parameter

out2 <- power4test_by_es(sim_only,
                             nrep = 10,
                             test_fun = test_parameters,
                             test_args = list(par = "y~x"),
                             pop_es_name = "y ~ x",
                             pop_es_values = seq(0, .7, .15),
                             by_seed = 1234,
                             parallel = FALSE,
                             progress = FALSE)

pout2 <- power_curve(out2)
pout2
#> Call:
#> power_curve(object = out2)
#> 
#> Predictor: es (Effect Size)
#> 
#> Model:
#> 
#> Call:  stats::glm(formula = reject ~ x, family = "binomial", data = reject1)
#> 
#> Coefficients:
#> (Intercept)            x  
#>      -3.347       17.231  
#> 
#> Degrees of Freedom: 49 Total (i.e. Null);  48 Residual
#> Null Deviance:	    65.34 
#> Residual Deviance: 25 	AIC: 29
predict(pout2,
        newdata = list(x = c(.25, .55)))
#>         1         2 
#> 0.7232684 0.9978282