View source: R/factorial_power_plan.R
FactorialPowerPlan | R Documentation |
There are three ways to use this function:
Estimate power available from a given sample size and a given effect size.
Estimate sample size needed for a given power and a given effect size.
Estimate effect size detectable from a given power at a given sample size.
That is, there are three main pieces of information: power, sample size, and effect size. The user provides two of them, and this function calculates the third.
FactorialPowerPlan( alpha = 0.05, assignment = "unclustered", change_score_icc = NULL, cluster_size = NULL, cluster_size_sd = NULL, d_main = NULL, effect_size_ratio = NULL, icc = NULL, model_order = 1, nclusters = NULL, nfactors = 1, ntotal = NULL, power = NULL, pre_post_corr = NULL, pretest = "none", raw_coef = NULL, raw_main = NULL, sigma_y = NULL, std_coef = NULL )
alpha |
Two sided Type I error level for the test to be performed(default=0.05). |
assignment |
One of three options: (default=unclustered)
Clusters in this context are preexisting units within which responses may be dependent (e.g., clinics or schools). A within-cluster experiment involves randomizing individual members, while a between-cluster experiment involves randomizing clusters as whole units (see Dziak, Nahum-Shani, and Collins, 2012) <DOI:10.1037/a0026972> |
change_score_icc |
The intraclass correlation of the change scores (posttest minus pretest). Relevant only if assignment is between clusters and there is a pretest. |
cluster_size |
The mean number of members in each cluster. Relevant only if assignment is between clusters or within clusters. |
cluster_size_sd |
Relevant only if assignment is between clusters. The standard deviation of the number of members in each cluster (the default is 0 which means that the clusters are expected to be of equal size). |
d_main |
Effect size measure: standardized mean difference raw_main/sigma_y. |
effect_size_ratio |
Effect size measure: signal to noise ratio raw_coef^2/sigma_y^2. |
icc |
Relevant only if assignment is between clusters or within clusters. The intraclass correlation of the variable of interest in the absence of treatment. |
model_order |
The highest order term to be included in the regression model in the planned analysis (1=main effects, 2=two-way interactions, 3=three-way interactions, etc.); must be >= 1 and <= nfactors (default=1). |
nclusters |
The total number of clusters available (for between clusters or within clusters assignment). |
nfactors |
The number of factors (independent variables) in the planned experiment(default=1). |
ntotal |
The total sample size available (for unclustered assignment. For clustered assignment, use “cluster_size” and “nclusters.” |
power |
If specified: The desired power of the test. If returned in the output list: The expected power of the test. |
pre_post_corr |
Relevant only if there is a pretest. The correlation between the pretest and the posttest. |
pretest |
One of three options:
The option “yes” is also allowed and is interpreted as “repeated.” The option “covariate” is not allowed if assignment is between clusters. This is because predicting power for covariate-adjusted cluster-level randomization is somewhat complicated, although it can be approximated in practice by using the formula for the repeated-measures cluster-level randomization (see simulations in Dziak, Nahum-Shani, and Collins, 2012). |
raw_coef |
Effect size measure: unstandardized effect-coded regression coefficient. |
raw_main |
Effect size measure: unstandardized mean difference. |
sigma_y |
The assumed standard deviation of the response variable after treatment, within each treatment condition (i.e., adjusting for treatment but not adjusting for post-test). This statement must be used if the effect size argument used is either “raw_main” or “raw_coef”. |
std_coef |
Effect size measure: standardized effect-coded regression coefficient raw_coef/sigma_y. |
A list with power, sample size and effect size.
FactorialPowerPlan(assignment="independent", model_order=2, nfactors=5, ntotal=300, raw_main=3, sigma_y=10) FactorialPowerPlan(assignment="independent", model_order=2, nfactors=5, ntotal=300, pre_post_corr=.6, pretest="covariate", raw_main=3, sigma_y=10)
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