EM-Algorithm Estimation of the Binary Outcome Misclassification Model while Assuming Perfect Sensitivity
perfect_sensitivity_EM.RdCode is adapted by the SAMBA R package from Lauren Beesley and Bhramar Mukherjee.
Usage
perfect_sensitivity_EM(
Ystar,
Z,
X,
start,
beta0_fixed = NULL,
weights = NULL,
expected = TRUE,
tolerance = 1e-07,
max_em_iterations = 1500
)Arguments
- Ystar
A numeric vector of indicator variables (1, 0) for the observed outcome
Y*. The reference category is 0.- Z
A numeric matrix of covariates in the true outcome mechanism.
Zshould not contain an intercept.- X
A numeric matrix of covariates in the observation mechanism.
Xshould not contain an intercept.- start
Numeric vector of starting values for parameters in the true outcome mechanism (\(\theta\)) and the observation mechanism (\(\beta\)), respectively.
- beta0_fixed
Optional numeric vector of values of the observation mechanism intercept to profile over. If a single value is entered, this corresponds to fixing the intercept at the specified value. The default is
NULL.- weights
Optional vector of row-specific weights used for selection bias adjustment. The default is
NULL.- expected
A logical value indicating whether or not to calculate the covariance matrix via the expected Fisher information matrix. The default is
TRUE.- tolerance
A numeric value specifying when to stop estimation, based on the difference of subsequent log-likelihood estimates. The default is
1e-7.- max_em_iterations
An integer specifying the maximum number of iterations of the EM algorithm. The default is
1500.
Value
perfect_sensitivity_EM returns a list containing nine elements.
The elements are detailed in ?SAMBA::obsloglikEM documentation. Code
is adapted from the SAMBA::obsloglikEM function.