The default is "Bernoulli".Ĭorresponding parameter for the predictor's distribution. "lognormal", "normal", "Poisson", "uniform"). The default is "two.sided".ĭistribution of the predictor ( "Bernoulli", "exponential", It equals 0.05 by default.ĭirection of the alternative hypothesis ( "two.sided" or "less" or "greater"). Prob( Y=1|X=1): the probobility of observieng 1 for the outcome variable Y when the predictor X equals 1. Prob( Y=1|X=0): the probobility of observieng 1 for the outcome variable Y when the predictor X equals 0. Power = NULL, alternative = c("two.sided", "less", "greater"),įamily = c("Bernoulli", "exponential", "lognormal", "normal", "Poisson", Usage wp.logistic(n = NULL, p0 = NULL, p1 = NULL, alpha = 0.05,
The procedure introduced by Demidenko (2007) is adopted here for computing the statistical power. The estimated regression coefficent is assumed to follow a normal distribution.Ī Wald test is use to test the mean difference between the estimated parameter and the null parameter (tipically the null hypothesis assumes it equals 0). Here, Maximum likelihood methods is used to estimate the model parameters. Logistic regression is a type of generalized linear models where the outcome variable follows Bernoulli distribution. This function is for Logistic regression models. Statistical Power Analysis for Logistic Regression Description
wp.mmrm: Power analysis for longitudinal data analysis.wp.mediation: Statistical Power Analysis for Simple Mediation.wp.mc.t: Power analysis for t-test based on Monte Carlo simulation.
wp.mc.: Statistical Power Curve for Structural Equation Modeling /.wp.mc.sem.boot: Statistical Power Analysis for Structural Equation Modeling /.wp.mc.sem.basic: Statistical Power Analysis for Structural Equation Modeling /.wp.mc.chisq.diff: Statistical Power Analysis for SEM Based on Chi-square.wp.logistic: Statistical Power Analysis for Logistic Regression.wp.lcsm: Statistical Power Curve for Univariate Latent Change Score.wp.kanova: Power analysis for two-way, three-way and k-way ANOVA.wp.effect.MRT3arm: Effect size calculatator based on raw data for Multisite.wp.effect.MRT2arm: Effect size calculatator based on raw data for Multisite.wp.effect.CRT3arm: Effect size calculatator based on raw data for Cluster.wp.effect.CRT2arm: Effect size calculatator based on raw data for Cluster.wp.crt3arm: Statistical Power Analysis for Cluster Randomized Trials with.wp.crt2arm: Statistical Power Analysis for Cluster Randomized Trials with.wp.correlation: Statistical Power Analysis for Correlation.wp.blcsm: Statistical Power Curve for Bivariate Latent Change Score.wp.unt: Statistical Power Analysis for One-way ANOVA with Count Data.wp.anova.binary: Statistical Power Analysis for One-way ANOVA with Binary Data.wp.anova: Statistical Power Analysis for One-way ANOVA.WebPower-package: Basic and Advanced Statistical Power Analysis.summary.power: Summary Statistical Power Analysis Results.print.webpower: To Print Statistical Power Analysis Results.plot.webpower: To plot Statistical Power Curve.: Plot the power curve for Latent Change Score Models.estCRT2arm: Estimate multilevel effect size from data.
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