View source: R/score_log_nonparam.R
score_log_nonparam | R Documentation |
Description of the nonparametric score function for distance-based kernel function and a binary outcome.
score_log_nonparam(outcome, dist_mat, grid_gran = 5000)
outcome |
a numeric vector containing the binary outcome variable, 0/1 (in the same ID order as dist_mat) |
dist_mat |
a square distance matrix |
grid_gran |
a numeric value specifying the grid search length, preset to 5000 |
This is the main function that calculates the p-value associated with a nonparametric kernel test of association between the kernel and binary outcome variable. A null model (where the kernel is not associated with the outcome) is initially fit. Then, the variance of Y_i | X_i is used as the basis for the score test,
S(rho) = ( Q_tau (beta_0, rho) - mu_Q) / ( sigma_Q).
However, because rho disappears under the null hypothesis, we run a grid search over a range of values of rho (the bounds of which were derived by Liu et al. in 2008). This grid search gets the upper bound for the score test's p-value. This function is tailored for the underlying model
y_i = h(z_i) + e_i,
where
h(.)
is the kernel function, z_i is a multidimensional array of variables, and y_i is a binary outcome taking values in 0, 1.
The function returns an numeric p-value for the kernel score test of association.
the score function p-value
Liu D, Ghosh D, and Lin X (2008) "Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models." BMC Bioinformatics, 9(1), 292. ISSN 1471-2105. doi: 10.1186/1471-2105-9-292.
hms
, ext_distance
, ham_distance
score_log_semiparam
for semiparametric score function of distance-based kernel functions and binary outcome.
score_cont_nonparam
for nonparametric score function of distance-based kernel function and continuous outcome.
score_cont_semiparam
for semiparametric score function of distance-based kernel function and continuous outcome.
data(simasd_hamil_df) data(simasd_covars) hamil_matrix <- ham_distance(simasd_hamil_df) score_log_nonparam( outcome = simasd_covars$dx_group, dist_mat = hamil_matrix, grid_gran = 5000 )
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