Input simulation conditions and which term to compute power for, export reported power.
sim_pow_single(
fixed,
fixed_param,
cov_param,
n,
error_var,
with_err_gen,
arima = FALSE,
data_str,
cor_vars = NULL,
fact_vars = list(NULL),
lvl1_err_params = NULL,
arima_mod = list(NULL),
contrasts = NULL,
homogeneity = TRUE,
heterogeneity_var = NULL,
knot_args = list(NULL),
missing = FALSE,
missing_args = list(NULL),
pow_param = NULL,
alpha,
pow_dist = c("z", "t"),
pow_tail = c(1, 2),
lm_fit_mod = NULL,
general_mod = NULL,
general_extract = NULL,
...
)One sided formula for fixed effects in the simulation. To suppress intercept add -1 to formula.
Fixed effect parameter values (i.e. beta weights). Must be same length as fixed.
List of arguments to pass to the continuous generating function, must be the same order as the variables specified in fixed. This list does not include intercept, time, factors, or interactions. Required arguments include:
dist_fun: This is a quoted R distribution function.
var_type: This is the level of variable to generate. Must be 'single'. Must be same order as fixed formula above.
Optional arguments to the distribution functions are in a nested list, see the examples or vignettes for example code.
Cluster sample size.
Scalar of error variance.
Simulated within cluster error distribution. Must be a quoted 'r' distribution function.
TRUE/FALSE flag indicating whether residuals should
be correlated. If TRUE, must specify a valid model to pass to
arima.sim via the arima_mod argument.
See arima.sim for examples.
Type of data. Must be "cross", "long", or "single".
A vector of correlations between variables.
A nested list of factor, categorical, or ordinal variable specification, each list must include:
numlevels: Number of levels for ordinal or factor variables.
var_type: Must be 'single'.
Optional arguments include:
replace
prob
value.labels
See also sample for use of these optional arguments.
Additional parameters passed as a list on to the level one error generating function
A list indicating the ARIMA model to pass to arima.sim.
See arima.sim for examples.
An optional list that specifies the contrasts to be used
for factor variables (i.e. those variables with .f or .c).
See contrasts for more detail.
Either TRUE (default) indicating homogeneity of variance assumption is assumed or FALSE to indicate desire to generate heterogeneity of variance.
Variable name as a character string to use for heterogeneity of variance simulation.
A nested list of named knot arguments. See sim_knot
for more details. Arguments must include:
var
knot_locations
TRUE/FALSE flag indicating whether missing data should be simulated.
Additional missing arguments to pass to the missing_data
function. See missing_data for examples.
Name of variable to calculate power for, must be a name from fixed.
What should the per test alpha rate be used for the hypothesis testing.
Which distribution should be used when testing hypothesis test, z or t?
One-tailed or two-tailed test?
Valid lm syntax to be used for model fitting.
Valid model syntax. This syntax can be from any R package. By default, broom is used to extract model result information. Note, package must be defined or loaded prior to running the sim_pow function.
A valid function to extract model results if general_mod argument is used. This argument is primarily used if extracting model results is not possibly using the broom package. If this is left NULL (default), broom is used to collect model results.
Additional specification needed to pass to the random generating function defined by with_err_gen.
Power function to compute power for a regression term for simple regression
models. This function would need to be replicated to make any statement about
power. Use sim_pow as a convenient wrapper for this.
sim_pow for a wrapper to replicate.