JumpWithin: Simultaneous move of experiment, alphas and slopes within...
In gpapadog/LERCA: Local Exposure-Response Confounding Adjustment

Description Usage Arguments

Proposing a simultaneous move of the experiment configuration, inclusion indicators and slopes of the exposure in the outcome model while maintaining the order of the experiment configuration. New values for the slopes are proposed ensuring continuous ER. We use the likelihood integrating out the coefficients of the covariates and variance terms.

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JumpWithin(dta, current_cutoffs, current_alphas, current_coefs, cov_cols,
  approx_likelihood = TRUE, omega = 5000, mu_priorY, Sigma_priorY,
  alpha_probs = c(0.01, 0.5, 0.99), min_exper_sample = 20)

`dta`	Data frame including the covariates as C1, C2, ..., the exposure as X and the outcome as Y.
`current_cutoffs`	The current values of the experiment configuration. Vector of length K.
`current_alphas`	The current values of the inclusion indicators. Array of dimensions 2 (exposure & outcome model), experiments, covariates.
`current_coefs`	The current values of the coefficients. Array of dimensions 2 (exposure & outcome model), experiments, and coefficients (intercept, slope, covariates).
`cov_cols`	The indices of the columns including the covariates.
`approx_likelihood`	Logical. If set to true the BIC will be used to calculate the marginal likelihood. FALSE not supported yet.
`omega`	The omega parameter of the BAC prior.
`mu_priorY`	Vector of length equal to the number of covariates + 2 with entries corresponding to the prior mean of the intercept, slope, coefficient in the outcome model.
`Sigma_priorY`	The normal prior covariance matrix of the parameters in mu_priorY.
`alpha_probs`	The probability that a proposed alpha is equal to 1, when 0, 1, and 2 alphas of the surrounding experiments are equal to 1. Vector of length 3. Defaults to (0.01, 0.5, 0.99).
`min_exper_sample`	The minimum number of observations within an experiment. Defaults to 20.