pval_correct {gateR} | R Documentation |
Calculate p-value corrections for multiple testing
Description
Internal function to calculate various p-value corrections for use within the rrs
and lotrrs
functions.
Usage
pval_correct(
input,
type = c("FDR", "correlated Sidak", "correlated Bonferroni", "uncorrelated Sidak",
"uncorrelated Bonferroni", "Adler and Hasofer", "Friston"),
alpha = 0.05,
nbc = NULL
)
Arguments
input |
|
type |
Character string specifying which correction for multiple comparisons. Options include a False Discovery Rate |
alpha |
Numeric. The two-tailed alpha level for significance threshold (default in |
nbc |
Integer. The number of bins. Similar to |
Details
This function provides functionality for multiple testing correction in five ways:
Computes a False Discovery Rate by Benjamini and Hochberg doi:10.1111/j.2517-6161.1995.tb02031.x (
p_correct = "FDR"
) by: 1) sorting the p-values (p_i) of each knot in ascending order (p_1 <= p_2 <= ... <= p_m), 2) starting from p_m find the first p_i for which p_i <= (i/m) * alpha.Computes an independent Sidak correction doi:10.2307/2283989 (
p_correct = "uncorrelated Sidak"
) by 1 - (1 -alpha
) ^ (1 / total number of gridded knots across the estimated surface). The default in therisk
function is a resolution of 128 x 128 or n = 16,384 knots and a custom resolution can be specified using theresolution
argument within therisk
function.Computes an independent Bonferroni correction (
p_correct = "uncorrelated Bonferroni"
) byalpha
/ total number of gridded knots across the estimated surface. The default in therisk
function is a resolution of 128 x 128 or n = 16,384 knots and a custom resolution can be specified using theresolution
argument within therisk
function.Computes a spatially dependent Sidak correction (
p_correct = "correlated Sidak"
) by taking into account the spatial correlation of the relative risk surface values (if using therrs
function for a single condition gate) or the ratio of relative risk surfaces values (if using thelotrrs
function for a two condition gate). The correction uses the minimum number of knots that are not spatially correlated instead of the total number of knots. The minimum number of knots that are not spatially correlated is computed by counting the knots that are a distance apart that exceeds the minimum distance of non-significant spatial correlation based on a correlogram using themodified.ttest
function.Computes a spatially dependent Bonferroni correction (
p_correct = "correlated Bonferroni"
) by taking into account the spatial correlation of the relative risk surface values (if using therrs
function for a single condition gate) or the ratio of relative risk surfaces values (if using thelotrrs
function for a two condition gate). The correction uses the minimum number of knots that are not spatially correlated instead of the total number of knots. The minimum number of knots that are not spatially correlated is computed by counting the knots that are a distance apart that exceeds the minimum distance of non-significant spatial correlation based on a correlogram using themodified.ttest
function.Computes a critical p-value based on Random Field Theory and the Adler and Hasofer equation (
p_correct = "Euler A&H"
) doi:10.1214/aop/1176996176 and p.111 of doi:10.1137/1.9780898718980. The correction uses the number of knots that are independent based on the bandwidth used in the kernel density estimation of the spatial relative risk function.Computes a critical p-value based on Random Field Theory and the Friston et al. equation (
p_correct = "Euler Friston"
) doi:10.1038/jcbfm.1991.122 which differs from Adler and Hasofer's equation by a factor of 0.79. The correction uses the number of knots that are independent based on the bandwidth used in the kernel density estimation of the spatial relative risk function.
Value
An object of class 'numeric' with the corrected alpha level.