Power Analysis via Monte Carlo Simulation for Correlated Data
Fits a BKMR model with significance criteria: PIP and group-wise PIP
Fits a linear model with Bayesian model selection with significance cr...
Fits a Bayesian weighted sums
Convert a correlation matrix into a partial correlation matrix
Citation: Daniel Lewandowski, Dorota Kurowicka, Harry Joe, Generating ...
Monte Carlo approximation of the SNR
Fits a Bayesian factor model with interactions
Fits the model to given data and gets a list of significance criteria
Generates a matrix of n observations of p predictors
Generates a vector of outcomes
Fits a generalized linear model
Statistical model that returns significance criterion
Correlated predictors generator
Visualize marginals and Gaussian copula correlations of simulated data
mpower: Power analysis using Monte Carlo for correlated predictors.
Outcome generator
Partial correlations between elements in x and elements in y
Plot summaries of power simulation
Fits a linear Quantile G-Computation model with no interactions
Quantile function for the multinomial distribution, size = 1
Convert R-squared value to the SNR
Rescale the mean function of an OutcomeModel to meet a given SNR
Rescale the noise variance of a Gaussian OutcomeModel to meet a given ...
This function updates values in an OutcomeModel object
Power curve using Monte Carlo simulation
Power analysis using Monte Carlo simulation
Tabular summaries of power simulation
A flexible framework for power analysis using Monte Carlo simulation for settings in which considerations of the correlations between predictors are important. Users can set up a data generative model that preserves dependence structures among predictors given existing data (continuous, binary, or ordinal). Users can also generate power curves to assess the trade-offs between sample size, effect size, and power of a design. This package includes several statistical models common in environmental mixtures studies. For more details and tutorials, see Nguyen et al. (2022) <arXiv:2209.08036>.