sandwich_se {CIEE} | R Documentation |
Sandwich standard error estimates
Description
Function to obtain consistent and robust sandwich standard error estimates
based on estimating equations, for the parameter estimates of the
get_estimates
function, under the GLM or AFT setting
for the analysis of a normally-distributed or censored time-to-event primary
outcome.
Usage
sandwich_se(setting = "GLM", scores = NULL, hessian = NULL)
Arguments
setting |
String with value |
scores |
Score matrix of the parameters, which can be obtained using the
|
hessian |
Hessian matrix of the parameters, which can be obtained using the
|
Details
Under the GLM setting for the analysis of a normally-distributed primary
outcome Y, robust sandwich standard error estimates are obtained for the
estimates of the parameters
\alpha_0, \alpha_1, \alpha_2, \alpha_3, \sigma_1^2, \alpha_4, \alpha_{XY}, \sigma_2^2
in the model
Y = \alpha_0 + \alpha_1 \cdot K + \alpha_2 \cdot X + \alpha_3 \cdot L + \epsilon_1, \epsilon_1 \sim N(0,\sigma_1^2)
Y^* = Y - \overline{Y} - \alpha_1 \cdot (K-\overline{K})
Y^* = \alpha_0 + \alpha_{XY} \cdot X + \epsilon_2, \epsilon_2 \sim N(0,\sigma_2^2)
by using the score and hessian matrices of the parameters.
Under the AFT setting for the analysis of a censored time-to-event primary
outcome, robust sandwich standard error estimates are similarly obtained of
the parameter estimates of
\alpha_0, \alpha_1, \alpha_2, \alpha_3, \sigma_1, \alpha_4, \alpha_{XY}, \sigma_2^2
.
For more details and the underlying model, see the vignette.
Value
Returns a vector with the CIEE sandwich standard error estimates of the parameter estimates.
Examples
# Generate data including Y, K, L, X under the GLM setting
dat <- generate_data(setting = "GLM")
# Obtain estimating functions expressions
estfunct <- est_funct_expr(setting = "GLM")
# Obtain point estimates of the parameters
estimates <- get_estimates(setting = "GLM", Y = dat$Y, X = dat$X,
K = dat$K, L = dat$L)
# Obtain matrices with all first and second derivatives
derivobj <- deriv_obj(setting = "GLM", logL1 = estfunct$logL1,
logL2 = estfunct$logL2, Y = dat$Y, X = dat$X,
K = dat$K, L = dat$L, estimates = estimates)
# Obtain score and hessian matrices
results_scores <- scores(derivobj)
results_hessian <- hessian(derivobj)
# Obtain sandwich standard error estimates of the parameters
sandwich_se(scores = results_scores, hessian = results_hessian)