ORTH.Ord {ORTH.Ord} | R Documentation |
function: ORTH.Ord
Description
This function is designed for analyzing correlated ordinal data with ability to correct small-sample bias.
Usage
ORTH.Ord(
formula_mean,
data_mean,
cluster,
formula_por = NULL,
data_por = NULL,
MMORTH = FALSE,
BC = NULL,
init_beta = NULL,
init_alpha = NULL,
miter = 30,
crit_level = 1e-04
)
Arguments
formula_mean |
the symbolic description of the marginal mean model that contains the ordinal outcome and marginal mean covariates. |
data_mean |
the data set containing the ordinal outcome and marginal mean covariates. |
cluster |
cluster ID (consecutive integers) in data_mean. |
formula_por |
the symbolic description of marginal association model in the form of a one-sided formula, default is NULL. When leaving formula_por as default, independence working correlation will be used. |
data_por |
a data set for marginal association model, default is NULL. When leaving data_por as default, independence working correlation will be used. |
MMORTH |
a logical value to indicate if matrix-adjusted estimating equations will be applied for the association estimation, default is FALSE. |
BC |
an option to apply bias-correction on covariance estimation, default is NULL. Possible values are "BC1", "BC2", or "BC3". |
init_beta |
pre-specified starting values for parameters in the mean model, default is NULL. |
init_alpha |
pre-specified starting values for parameters in the association model, default is NULL. |
miter |
maximum number of iterations for Fisher scoring, default is 30. |
crit_level |
tolerance for convergence, default is 0.0001. |
Details
The method is a modified version of alternating logistic regressions with estimation based on orthogonalized residuals (ORTH). The within-cluster association between ordinal responses is modeled by global pairwise odds ratios (POR). A small-sample bias correction to POR parameter estimates based on matrix multiplicative adjusted orthogonalized residuals (MMORTH) for correcting estimating equations, and bias-corrected sandwich estimators with different options for covariance estimation, i.e. BC1 (Kauermann & Zeger (1986)), BC2 (Mancl & DeRouen (2001)), and BC3 (Fay & Graubard (2001)).
Value
A list is returned. The first element is a matrix for model parameter estimates; the second element is a variance-covariance matrix for model parameters without bias correction (BC0). Additional variance-covarianc matrices will be added if argument BC is specified.
References
Can Meng, Mary Ryan, Paul Rathouz, Elizabeth Turner, John S Preisser, and Fan Li. 2023. ORTH.Ord: An R package for analyzing correlated ordinal outcomes using alternating logistic regressions with orthogonalized residuals. Computer Methods and Programs in Biomedicine, 237, doi:10.1016/j.cmpb.2023.107567.