score_log_nonparam {CommKern} | R Documentation |
Nonparametric score function for distance-based kernel and binary outcome
Description
Description of the nonparametric score function for distance-based kernel function and a binary outcome.
Usage
score_log_nonparam(outcome, dist_mat, grid_gran = 5000)
Arguments
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 |
Details
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
is used as the basis for the score test,
However, because disappears under the null hypothesis, we run a
grid search over a range of values of
(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
where
is
the kernel function, is a multidimensional array of variables, and
is a binary outcome taking values in
0, 1.
The function returns an numeric p-value for the kernel score test of association.
Value
the score function p-value
References
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.
See Also
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.
Examples
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
)