Input simulation conditions, returns power for term.
sim_pow_glm(
fixed,
random = NULL,
random3 = NULL,
fixed_param,
random_param = list(NULL),
random_param3 = list(NULL),
cov_param,
k = NULL,
n,
p = NULL,
data_str,
cor_vars = NULL,
fact_vars = list(NULL),
unbal = list(level2 = FALSE, level3 = FALSE),
unbal_design = list(level2 = NULL, level3 = NULL),
contrasts = NULL,
outcome_type,
cross_class_params = NULL,
knot_args = list(NULL),
missing = FALSE,
missing_args = list(NULL),
pow_param,
alpha,
pow_dist = c("z", "t"),
pow_tail = c(1, 2),
replicates,
terms_vary = NULL,
raw_power = TRUE,
glm_fit_mod = NULL,
lme4_fit_mod = NULL,
glm_fit_family = NULL,
lme4_fit_family = NULL,
general_mod = NULL,
general_extract = NULL,
...
)One sided formula for fixed effects in the simulation. To suppress intercept add -1 to formula.
One sided formula for random effects in the simulation. Must be a subset of fixed.
One sided formula for random effects at third level in the simulation. Must be a subset of fixed(and likely of random).
Fixed effect parameter values (i.e. beta weights). Must be same length as fixed.
A list of named elements that must contain:
random_var: variance of random parameters,
rand_gen: Name of simulation function for random effects.
Optional elements are:
ther: Theorectial mean and variance from rand_gen,
ther_sim: Simulate mean/variance for standardization purposes,
cor_vars: Correlation between random effects,
...: Additional parameters needed for rand_gen function.
A list of named elements that must contain:
random_var: variance of random parameters,
rand_gen: Name of simulation function for random effects.
Optional elements are:
ther: Theorectial mean and variance from rand_gen,
ther_sim: Simulate mean/variance for standardization purposes,
cor_vars: Correlation between random effects,
...: Additional parameters needed for rand_gen function.
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 either 'single', 'level1', 'level2', or 'level3'. 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.
Number of third level clusters.
Cluster sample size.
Within cluster sample size.
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', 'level1', 'level2', or 'level3'.
Optional arguments include:
replace
prob
value.labels
See also sample for use of these optional arguments.
A named TRUE/FALSE list specifying whether unbalanced simulation design is desired. The named elements must be: "level2" or "level3" representing unbalanced simulation for level two and three respectively. Default is FALSE, indicating balanced sample sizes at both levels.
When unbal = TRUE, this specifies the design for unbalanced simulation in one of two ways. It can represent the minimum and maximum sample size within a cluster via a named list. This will be drawn from a random uniform distribution with min and max specified. Secondly, the actual sample sizes within each cluster can be specified. This takes the form of a vector that must have the same length as the level two or three sample size. These are specified as a named list in which level two sample size is controlled via "level2" and level three sample size is controlled via "level3".
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.
A vector specifying the type of outcome, must be either logistic or poisson. Logitstic outcome will be 0/1 and poisson outcome will be counts.
A list of named parameters when cross classified data structures are desired. Must include the following arguments:
num_ids: The number of cross classified clusters. These are in addition to the typical cluster ids
random_param: This argument is a list of arguments passed to
sim_rand_eff. These must include:
random_var: The variance of the cross classified random effect
rand_gen: The random generating function used to generate the cross classified random effect.
Optional elements are:
ther: Theorectial mean and variance from rand_gen,
ther_sim: Simulate mean/variance for standardization purposes,
cor_vars: Correlation between random effects,
...: Additional parameters needed for rand_gen function.
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.
Number of parameter to calculate power includes intercept where applicable.
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?
How many replications should be done (i.e. the denominator in power calculation).
A named list of terms that should vary as a function for
the power simulation. The names must match arguments to the simulation
function, see sim_glm for examples. Values specified here
should not be included as arguments in the function call.
TRUE/FALSE indicating whether raw power output should be returned. Default is TRUE, which will create a new nested column with raw data by variable(s) manipulated in power analysis.
Valid glm syntax to be used for model fitting.
Valid lme4 syntax to be used for model fitting.
Valid family syntax to pass to the glm function.
Valid lme4 family specification passed to glmer.
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.
Current not used.
This function is a wrapper that replicates the simulation functions for simple generalized regression and the generalized linear mixed model power functions. This function replicates the power call a specified number of times and prints outs a matrix with the results.
# NOT RUN {
# single level dichotomous (glm) example
fixed <- ~ 1 + act + diff
fixed_param <- c(0.1, 0.5, 0.3)
cov_param <- list(dist_fun = c('rnorm', 'rnorm'),
var_type = c("single", "single"),
opts = list(list(mean = 0, sd = 2),
list(mean = 0, sd = 4)))
n <- 50
pow_param <- c('(Intercept)', 'act', 'diff')
alpha <- .01
pow_dist <- "z"
pow_tail <- 2
replicates <- 2
power_out <- sim_pow_glm(fixed = fixed, fixed_param = fixed_param,
cov_param = cov_param,
n = n, data_str = "single",
outcome_type = 'logistic',
pow_param = pow_param, alpha = alpha,
pow_dist = pow_dist, pow_tail = pow_tail,
replicates = replicates, raw_power = FALSE)
# }
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