Description Usage Arguments Details Value
Calculates maximum likelihood estimates for measurement error/unmeasured confounding scenario where true disease model is logistic regression and measurement error model is linear regression. Requires validation data.
1 2 3 4 5 6 | ml_logistic_linear(all_data = NULL, main = NULL, internal = NULL,
external = NULL, y_var, z_var, d_vars = NULL, c_vars = NULL,
b_vars = NULL, tdm_covariates = NULL, mem_covariates = NULL,
approx_integral = TRUE, integrate_tol = 1e-08,
integrate_tol_hessian = integrate_tol, estimate_var = TRUE,
fix_posdef = FALSE, ...)
|
all_data |
Data frame with data for main study and validation study. |
main |
Data frame with data for the main study. |
internal |
Data frame with data for internal validation study. |
external |
Data frame with data for the external validation study. |
y_var |
Character string specifying name of Y variable. |
z_var |
Character string specifying name of Z variable. |
d_vars |
Character string specifying name of D variables. |
c_vars |
Character vector specifying names of C variables. |
b_vars |
Character vector specifying names of variables in true disease model but not in measurement error model. |
tdm_covariates |
Character vector specifying variables in true disease
model. The Z variable is automatically included whether you include it in
|
mem_covariates |
Character vector specifying variables in measurement error model. |
approx_integral |
Logical value for whether to use the probit
approximation for the logistic-normal integral, to avoid numerically
integrating |
integrate_tol |
Numeric value specifying |
integrate_tol_hessian |
Same as |
estimate_var |
Logical value for whether to return variance-covariance matrix for parameter estimates. |
fix_posdef |
Logical value for whether to repeatedly reduce
|
... |
Additional arguments to pass to |
The true disease model is:
logit[P(Y = 1)] = beta_0 + beta_z Z + beta_c^T C + beta_b^T B
The measurement error model is:
Z = alpha_0 + alpha_d^T D + alpha_c^T C + d, d ~ N(0, sigsq_d)
There should be main study data with (Y, D, C, B) as well as internal validation data with (Y, Z, D, C, B) and/or external validation data with (Z, D, C).
List containing:
Numeric vector of parameter estimates.
Variance-covariance matrix (if estimate_var = TRUE
).
Returned nlminb
object from maximizing the
log-likelihood function.
Akaike information criterion (AIC).
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.