R/outliers.R
In qzhang503/proteoQ: Processing and Informatic Analysis of Mass Spectrometrirc Data
Defines functions
Rosner_outliers
Grubbs_outliers
Dixon_outliers
Documented in
Dixon_outliers
Grubbs_outliers
Rosner_outliers
#' Locates the positions of outliers
#'
#' @param df A data frame containing the PSM table from database searches.
#' @param range_colRatios The range of columns.
#' @return A data frame.
#' @examples \donttest{locate_outliers(df, 2:3)}
locate_outliers <- function (df, range_colRatios)
{
for(col_index in range_colRatios) {
n <- colSums(!is.na(df[col_index]))
if (n > 25) {
df[, col_index] <- Rosner_outliers(df[, col_index])
}
else if (n > 2) {
df[, col_index] <- Dixon_outliers(df[, col_index])
}
}
invisible(df)
}
#' Outlier removals with Dixon's method
#'
#' @param x A data frame.
#' @param type The type of Dixon method. See also outliers::dixon.test.
#' @param opposite The high or low. See also outliers::dixon.test.
#' @param two.sided Logical; is two-sided or not. See also
#' outliers::dixon.test.
dixon_test <- function (x, type = 0, opposite = FALSE, two.sided = TRUE)
{
x <- sort(x[complete.cases(x)])
n <- length(x)
if ((type == 10 || type == 0) && (n < 3 || n > 30))
stop("Sample size must be in range 3-30")
if (type == 11 && (n < 4 || n > 30))
stop("Sample size must be in range 4-30")
if (type == 12 && (n < 5 || n > 30))
stop("Sample size must be in range 5-30")
if (type == 20 && (n < 4 || n > 30))
stop("Sample size must be in range 4-30")
if (type == 21 && (n < 5 || n > 30))
stop("Sample size must be in range 5-30")
if (type == 22 && (n < 6 || n > 30))
stop("Sample size must be in range 6-30")
if (sum(c(0, 10, 11, 12, 20, 21, 22) == type) == 0)
stop("Incorrect type")
if (type == 0) {
if (n <= 7)
type <- 10
else if (n > 7 & n <= 10)
type <- 11
else if (n > 10 & n <= 13)
type <- 21
else
type <- 22
}
if (xor(((x[n] - mean(x)) < (mean(x) - x[1])), opposite)) {
alt <- "low"
if (type == 10) {
Q = (x[2] - x[1])/(x[n] - x[1])
}
else if (type == 11) {
Q = (x[2] - x[1])/(x[n - 1] - x[1])
}
else if (type == 12) {
Q = (x[2] - x[1])/(x[n - 2] - x[1])
}
else if (type == 20) {
Q = (x[3] - x[1])/(x[n] - x[1])
}
else if (type == 21) {
Q = (x[3] - x[1])/(x[n - 1] - x[1])
}
else {
Q = (x[3] - x[1])/(x[n - 2] - x[1])
}
}
else {
alt <- "high"
if (type == 10) {
Q = (x[n] - x[n - 1])/(x[n] - x[1])
}
else if (type == 11) {
Q = (x[n] - x[n - 1])/(x[n] - x[2])
}
else if (type == 12) {
Q = (x[n] - x[n - 1])/(x[n] - x[3])
}
else if (type == 20) {
Q = (x[n] - x[n - 2])/(x[n] - x[1])
}
else if (type == 21) {
Q = (x[n] - x[n - 2])/(x[n] - x[2])
}
else {
Q = (x[n] - x[n - 2])/(x[n] - x[3])
}
}
pval <- p_dixon(Q, n, type)
if (two.sided) {
pval <- 2 * pval
if (pval > 1)
pval <- 2 - pval
}
out <- list(statistic = c(Q = Q), alternative = alt, p.value = pval)
invisible(out)
}
#' Distribution function
#'
#' @param p Vector of probabilities. See also outliers::dixon.test.
#' @param n Length of sample Length of sample.
#' @param rev Logical; if TRUE, acts as p_dixon.
#' @inheritParams dixon_test
q_dixon <- function (p, n, type = 10, rev = FALSE)
{
if ((type == 10 || type == 0) & (n < 3 || n > 30))
stop("Sample size must be in range 3-30")
if (type == 11 & (n < 4 || n > 30))
stop("Sample size must be in range 4-30")
if (type == 12 & (n < 5 || n > 30))
stop("Sample size must be in range 5-30")
if (type == 20 & (n < 4 || n > 30))
stop("Sample size must be in range 4-30")
if (type == 21 & (n < 5 || n > 30))
stop("Sample size must be in range 5-30")
if (type == 22 & (n < 6 || n > 30))
stop("Sample size must be in range 6-30")
if (sum(c(0, 10, 11, 12, 20, 21, 22) == type) == 0)
stop("Incorrect type")
if (type == 0) {
if (n <= 7)
type <- 10
else if (n > 7 & n <= 10)
type <- 11
else if (n > 10 & n <= 13)
type <- 21
else
type <- 22
}
pp <- c(0.005, 0.01, 0.02, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4,
0.5, 0.6, 0.7, 0.8, 0.9, 0.95)
q10 <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 0.994, 0.988, 0.976, 0.97, 0.941, 0.886,
0.781, 0.684, 0.591, 0.5, 0.409, 0.316, 0.219, 0.114,
0.059, 0.926, 0.889, 0.846, 0.829, 0.765, 0.679, 0.56,
0.471, 0.394, 0.324, 0.257, 0.193, 0.13, 0.065, 0.033,
0.821, 0.78, 0.729, 0.71, 0.642, 0.557, 0.451, 0.373,
0.308, 0.25, 0.196, 0.146, 0.097, 0.048, 0.023, 0.74,
0.698, 0.644, 0.625, 0.56, 0.482, 0.386, 0.318, 0.261,
0.21, 0.164, 0.121, 0.079, 0.038, 0.018, 0.68, 0.637,
0.586, 0.568, 0.507, 0.434, 0.344, 0.281, 0.23, 0.184,
0.143, 0.105, 0.068, 0.032, 0.016, 0.634, 0.59, 0.543,
0.526, 0.468, 0.399, 0.314, 0.255, 0.208, 0.166, 0.128,
0.094, 0.06, 0.029, 0.014, 0.598, 0.555, 0.51, 0.493,
0.437, 0.37, 0.29, 0.234, 0.191, 0.152, 0.118, 0.086,
0.055, 0.026, 0.013, 0.568, 0.527, 0.483, 0.466, 0.412,
0.349, 0.273, 0.219, 0.178, 0.142, 0.11, 0.08, 0.051,
0.025, 0.012, 0.542, 0.502, 0.46, 0.444, 0.392, 0.332,
0.259, 0.208, 0.168, 0.133, 0.103, 0.074, 0.048, 0.023,
0.011, 0.522, 0.482, 0.441, 0.426, 0.376, 0.318, 0.247,
0.197, 0.16, 0.126, 0.097, 0.07, 0.047, 0.022, 0.011,
0.503, 0.465, 0.425, 0.41, 0.361, 0.305, 0.237, 0.188,
0.153, 0.12, 0.092, 0.067, 0.043, 0.021, 0.01, 0.488,
0.45, 0.411, 0.396, 0.349, 0.294, 0.228, 0.181, 0.147,
0.115, 0.088, 0.064, 0.041, 0.02, 0.01, 0.475, 0.438,
0.399, 0.384, 0.338, 0.285, 0.22, 0.175, 0.141, 0.111,
0.085, 0.062, 0.04, 0.019, 0.01, 0.463, 0.426, 0.388,
0.374, 0.329, 0.277, 0.213, 0.169, 0.136, 0.107, 0.082,
0.06, 0.039, 0.019, 0.009, 0.452, 0.416, 0.379, 0.365,
0.32, 0.269, 0.207, 0.165, 0.132, 0.104, 0.08, 0.058,
0.038, 0.018, 0.009, 0.442, 0.407, 0.37, 0.356, 0.313,
0.263, 0.202, 0.16, 0.128, 0.101, 0.078, 0.056, 0.036,
0.018, 0.009, 0.433, 0.398, 0.363, 0.349, 0.306, 0.258,
0.197, 0.157, 0.125, 0.098, 0.076, 0.055, 0.036, 0.017,
0.008, 0.425, 0.391, 0.356, 0.342, 0.3, 0.252, 0.193,
0.153, 0.122, 0.096, 0.074, 0.053, 0.035, 0.017, 0.008,
0.418, 0.384, 0.35, 0.337, 0.295, 0.247, 0.189, 0.15,
0.119, 0.094, 0.072, 0.052, 0.034, 0.016, 0.008, 0.411,
0.378, 0.344, 0.331, 0.29, 0.242, 0.185, 0.147, 0.117,
0.092, 0.071, 0.051, 0.033, 0.016, 0.008, 0.404, 0.372,
0.338, 0.326, 0.285, 0.238, 0.182, 0.144, 0.115, 0.09,
0.069, 0.05, 0.033, 0.016, 0.008, 0.399, 0.367, 0.333,
0.321, 0.281, 0.234, 0.179, 0.142, 0.113, 0.089, 0.068,
0.049, 0.032, 0.016, 0.008, 0.393, 0.362, 0.329, 0.317,
0.277, 0.23, 0.176, 0.139, 0.111, 0.088, 0.067, 0.048,
0.032, 0.015, 0.008, 0.388, 0.357, 0.324, 0.312, 0.273,
0.227, 0.173, 0.137, 0.109, 0.086, 0.066, 0.047, 0.031,
0.015, 0.007, 0.384, 0.353, 0.32, 0.308, 0.269, 0.224,
0.171, 0.135, 0.108, 0.085, 0.065, 0.047, 0.031, 0.015,
0.007, 0.38, 0.349, 0.316, 0.305, 0.266, 0.22, 0.168,
0.133, 0.106, 0.084, 0.064, 0.046, 0.03, 0.015, 0.007,
0.376, 0.345, 0.312, 0.301, 0.263, 0.218, 0.166, 0.131,
0.105, 0.083, 0.063, 0.046, 0.03, 0.014, 0.007, 0.372,
0.341, 0.309, 0.298, 0.26, 0.215, 0.164, 0.13, 0.103,
0.082, 0.062, 0.045, 0.029, 0.014, 0.007)
q11 <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 0.995, 0.991, 0.981, 0.977, 0.955,
0.91, 0.822, 0.737, 0.648, 0.554, 0.459, 0.362, 0.25,
0.131, 0.069, 0.937, 0.916, 0.876, 0.863, 0.807, 0.728,
0.615, 0.524, 0.444, 0.369, 0.296, 0.224, 0.151, 0.078,
0.039, 0.839, 0.805, 0.763, 0.748, 0.689, 0.609, 0.502,
0.42, 0.35, 0.288, 0.227, 0.169, 0.113, 0.056, 0.028,
0.782, 0.74, 0.689, 0.673, 0.61, 0.53, 0.432, 0.359,
0.298, 0.241, 0.189, 0.14, 0.093, 0.045, 0.022, 0.725,
0.683, 0.631, 0.615, 0.554, 0.479, 0.385, 0.318, 0.26,
0.21, 0.164, 0.121, 0.079, 0.037, 0.019, 0.677, 0.635,
0.587, 0.57, 0.512, 0.441, 0.352, 0.288, 0.236, 0.189,
0.148, 0.107, 0.07, 0.033, 0.016, 0.639, 0.597, 0.551,
0.534, 0.477, 0.409, 0.325, 0.265, 0.216, 0.173, 0.134,
0.098, 0.063, 0.03, 0.014, 0.606, 0.566, 0.521, 0.505,
0.45, 0.385, 0.305, 0.248, 0.202, 0.161, 0.124, 0.09,
0.058, 0.028, 0.013, 0.58, 0.541, 0.498, 0.481, 0.428,
0.367, 0.289, 0.234, 0.19, 0.15, 0.116, 0.084, 0.055,
0.026, 0.012, 0.558, 0.52, 0.477, 0.461, 0.41, 0.35,
0.275, 0.222, 0.18, 0.142, 0.109, 0.079, 0.052, 0.025,
0.012, 0.539, 0.502, 0.46, 0.445, 0.395, 0.336, 0.264,
0.212, 0.171, 0.135, 0.104, 0.075, 0.049, 0.024, 0.011,
0.522, 0.486, 0.445, 0.43, 0.381, 0.323, 0.253, 0.203,
0.164, 0.129, 0.099, 0.072, 0.047, 0.023, 0.011, 0.508,
0.472, 0.432, 0.417, 0.369, 0.313, 0.244, 0.196, 0.158,
0.124, 0.095, 0.096, 0.045, 0.022, 0.011, 0.495, 0.46,
0.42, 0.406, 0.359, 0.303, 0.236, 0.19, 0.152, 0.119,
0.092, 0.067, 0.044, 0.021, 0.01, 0.484, 0.449, 0.41,
0.396, 0.349, 0.295, 0.229, 0.184, 0.148, 0.116, 0.089,
0.065, 0.042, 0.02, 0.01, 0.473, 0.439, 0.4, 0.386, 0.341,
0.288, 0.223, 0.179, 0.143, 0.112, 0.087, 0.063, 0.041,
0.02, 0.01, 0.464, 0.43, 0.392, 0.379, 0.334, 0.282,
0.218, 0.174, 0.139, 0.11, 0.084, 0.061, 0.04, 0.019,
0.01, 0.455, 0.421, 0.384, 0.371, 0.327, 0.276, 0.213,
0.17, 0.136, 0.107, 0.082, 0.059, 0.039, 0.019, 0.009,
0.446, 0.414, 0.377, 0.364, 0.32, 0.27, 0.208, 0.166,
0.132, 0.104, 0.081, 0.058, 0.038, 0.018, 0.009, 0.439,
0.407, 0.371, 0.357, 0.314, 0.265, 0.204, 0.163, 0.13,
0.102, 0.079, 0.056, 0.037, 0.018, 0.009, 0.432, 0.4,
0.365, 0.352, 0.309, 0.26, 0.2, 0.16, 0.127, 0.1, 0.077,
0.055, 0.036, 0.018, 0.009, 0.426, 0.394, 0.359, 0.346,
0.304, 0.255, 0.197, 0.156, 0.124, 0.098, 0.076, 0.054,
0.036, 0.017, 0.009, 0.42, 0.389, 0.354, 0.341, 0.299,
0.25, 0.193, 0.154, 0.122, 0.096, 0.074, 0.053, 0.035,
0.017, 0.008, 0.414, 0.383, 0.349, 0.337, 0.295, 0.246,
0.19, 0.151, 0.12, 0.095, 0.073, 0.052, 0.034, 0.017,
0.008, 0.409, 0.378, 0.344, 0.332, 0.291, 0.243, 0.188,
0.149, 0.118, 0.093, 0.072, 0.051, 0.034, 0.016, 0.008,
0.404, 0.374, 0.34, 0.328, 0.287, 0.239, 0.185, 0.146,
0.116, 0.092, 0.07, 0.051, 0.033, 0.016, 0.008, 0.399,
0.369, 0.336, 0.324, 0.283, 0.236, 0.182, 0.144, 0.115,
0.09, 0.069, 0.05, 0.032, 0.016, 0.008)
q12 <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 0.996, 0.992, 0.984, 0.98, 0.96,
0.919, 0.838, 0.755, 0.669, 0.579, 0.483, 0.381, 0.268,
0.143, 0.074, 0.951, 0.925, 0.891, 0.878, 0.824, 0.745,
0.635, 0.545, 0.465, 0.39, 0.316, 0.24, 0.165, 0.088,
0.049, 0.875, 0.836, 0.791, 0.773, 0.712, 0.636, 0.528,
0.445, 0.374, 0.307, 0.245, 0.183, 0.123, 0.064, 0.031,
0.797, 0.76, 0.708, 0.692, 0.632, 0.557, 0.456, 0.382,
0.317, 0.258, 0.203, 0.152, 0.101, 0.056, 0.025, 0.739,
0.702, 0.656, 0.639, 0.58, 0.504, 0.409, 0.339, 0.27,
0.227, 0.177, 0.13, 0.086, 0.044, 0.021, 0.694, 0.655,
0.61, 0.594, 0.537, 0.464, 0.373, 0.308, 0.258, 0.204,
0.158, 0.116, 0.075, 0.038, 0.019, 0.658, 0.619, 0.575,
0.559, 0.502, 0.431, 0.345, 0.283, 0.232, 0.187, 0.145,
0.106, 0.069, 0.035, 0.017, 0.629, 0.59, 0.546, 0.529,
0.473, 0.406, 0.324, 0.265, 0.217, 0.174, 0.135, 0.098,
0.063, 0.032, 0.016, 0.602, 0.564, 0.521, 0.505, 0.451,
0.387, 0.307, 0.25, 0.204, 0.163, 0.126, 0.092, 0.059,
0.03, 0.015, 0.58, 0.542, 0.501, 0.485, 0.432, 0.369,
0.292, 0.237, 0.193, 0.153, 0.118, 0.086, 0.055, 0.028,
0.014, 0.56, 0.523, 0.482, 0.467, 0.416, 0.354, 0.28,
0.226, 0.184, 0.146, 0.112, 0.082, 0.053, 0.026, 0.013,
0.544, 0.508, 0.467, 0.452, 0.401, 0.341, 0.269, 0.217,
0.177, 0.139, 0.107, 0.078, 0.05, 0.025, 0.013, 0.529,
0.493, 0.453, 0.438, 0.388, 0.33, 0.259, 0.209, 0.17,
0.134, 0.103, 0.075, 0.048, 0.024, 0.012, 0.516, 0.48,
0.44, 0.426, 0.377, 0.32, 0.251, 0.202, 0.163, 0.129,
0.099, 0.072, 0.047, 0.023, 0.012, 0.504, 0.469, 0.429,
0.415, 0.367, 0.311, 0.243, 0.196, 0.157, 0.125, 0.096,
0.069, 0.045, 0.022, 0.011, 0.493, 0.458, 0.419, 0.405,
0.358, 0.303, 0.237, 0.191, 0.153, 0.121, 0.093, 0.067,
0.044, 0.022, 0.011, 0.483, 0.449, 0.41, 0.396, 0.349,
0.296, 0.231, 0.186, 0.148, 0.118, 0.09, 0.065, 0.042,
0.021, 0.01, 0.474, 0.44, 0.402, 0.388, 0.342, 0.29,
0.225, 0.181, 0.145, 0.114, 0.088, 0.063, 0.041, 0.02,
0.01, 0.465, 0.432, 0.394, 0.381, 0.336, 0.284, 0.22,
0.176, 0.141, 0.112, 0.086, 0.062, 0.04, 0.02, 0.01,
0.457, 0.423, 0.387, 0.374, 0.33, 0.278, 0.216, 0.173,
0.138, 0.109, 0.084, 0.06, 0.039, 0.019, 0.01, 0.45,
0.417, 0.381, 0.368, 0.324, 0.273, 0.212, 0.169, 0.135,
0.107, 0.082, 0.059, 0.038, 0.019, 0.009, 0.443, 0.411,
0.375, 0.362, 0.319, 0.268, 0.208, 0.166, 0.132, 0.105,
0.08, 0.058, 0.037, 0.019, 0.009, 0.437, 0.405, 0.37,
0.357, 0.314, 0.263, 0.204, 0.163, 0.13, 0.103, 0.079,
0.057, 0.037, 0.018, 0.009, 0.431, 0.399, 0.365, 0.352,
0.309, 0.259, 0.201, 0.16, 0.128, 0.101, 0.077, 0.056,
0.036, 0.018, 0.009, 0.426, 0.394, 0.36, 0.347, 0.305,
0.255, 0.197, 0.157, 0.126, 0.009, 0.076, 0.055, 0.035,
0.017, 0.009, 0.42, 0.389, 0.355, 0.343, 0.301, 0.251,
0.194, 0.154, 0.124, 0.098, 0.075, 0.054, 0.035, 0.017,
0.009)
q20 <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 0.996, 0.992, 0.987, 0.983, 0.967,
0.935, 0.871, 0.807, 0.743, 0.676, 0.606, 0.529, 0.44,
0.321, 0.235, 0.95, 0.929, 0.901, 0.89, 0.845, 0.782,
0.694, 0.623, 0.56, 0.5, 0.44, 0.377, 0.306, 0.218, 0.155,
0.865, 0.836, 0.8, 0.786, 0.736, 0.67, 0.585, 0.52, 0.463,
0.411, 0.358, 0.305, 0.245, 0.172, 0.126, 0.814, 0.778,
0.732, 0.716, 0.661, 0.596, 0.516, 0.454, 0.402, 0.355,
0.306, 0.261, 0.208, 0.144, 0.099, 0.746, 0.71, 0.67,
0.657, 0.607, 0.545, 0.468, 0.41, 0.361, 0.317, 0.274,
0.23, 0.184, 0.125, 0.085, 0.7, 0.667, 0.627, 0.614,
0.565, 0.505, 0.432, 0.378, 0.331, 0.288, 0.25, 0.208,
0.166, 0.114, 0.077, 0.664, 0.632, 0.592, 0.579, 0.531,
0.474, 0.404, 0.354, 0.307, 0.268, 0.231, 0.192, 0.153,
0.104, 0.07, 0.627, 0.603, 0.564, 0.551, 0.504, 0.449,
0.381, 0.334, 0.29, 0.253, 0.217, 0.181, 0.143, 0.097,
0.065, 0.612, 0.579, 0.54, 0.527, 0.481, 0.429, 0.362,
0.216, 0.274, 0.239, 0.205, 0.172, 0.136, 0.091, 0.06,
0.59, 0.557, 0.52, 0.506, 0.461, 0.411, 0.345, 0.301,
0.261, 0.227, 0.195, 0.164, 0.129, 0.086, 0.057, 0.571,
0.538, 0.502, 0.489, 0.445, 0.395, 0.332, 0.288, 0.25,
0.217, 0.187, 0.157, 0.123, 0.082, 0.054, 0.554, 0.522,
0.486, 0.473, 0.43, 0.382, 0.32, 0.277, 0.241, 0.209,
0.179, 0.15, 0.118, 0.079, 0.052, 0.539, 0.508, 0.472,
0.46, 0.418, 0.37, 0.31, 0.268, 0.233, 0.202, 0.173,
0.144, 0.113, 0.076, 0.05, 0.526, 0.495, 0.46, 0.447,
0.406, 0.359, 0.301, 0.26, 0.226, 0.195, 0.167, 0.139,
0.109, 0.074, 0.049, 0.514, 0.484, 0.449, 0.437, 0.397,
0.35, 0.293, 0.252, 0.219, 0.189, 0.162, 0.134, 0.105,
0.071, 0.048, 0.503, 0.473, 0.439, 0.427, 0.387, 0.341,
0.286, 0.246, 0.213, 0.184, 0.157, 0.13, 0.101, 0.069,
0.047, 0.494, 0.464, 0.43, 0.418, 0.378, 0.333, 0.279,
0.24, 0.208, 0.179, 0.152, 0.126, 0.098, 0.067, 0.046,
0.485, 0.455, 0.422, 0.41, 0.371, 0.326, 0.273, 0.235,
0.203, 0.175, 0.148, 0.123, 0.096, 0.065, 0.045, 0.477,
0.447, 0.414, 0.402, 0.364, 0.32, 0.267, 0.23, 0.199,
0.171, 0.145, 0.12, 0.094, 0.064, 0.044, 0.469, 0.44,
0.407, 0.395, 0.358, 0.314, 0.262, 0.225, 0.195, 0.167,
0.142, 0.117, 0.092, 0.062, 0.043, 0.462, 0.434, 0.401,
0.39, 0.352, 0.309, 0.258, 0.221, 0.192, 0.164, 0.139,
0.114, 0.09, 0.061, 0.042, 0.456, 0.428, 0.395, 0.383,
0.346, 0.304, 0.254, 0.217, 0.189, 0.161, 0.136, 0.112,
0.089, 0.06, 0.041, 0.45, 0.422, 0.39, 0.379, 0.342,
0.3, 0.25, 0.214, 0.186, 0.158, 0.134, 0.11, 0.087, 0.059,
0.041, 0.444, 0.417, 0.385, 0.374, 0.338, 0.296, 0.246,
0.211, 0.183, 0.156, 0.132, 0.109, 0.086, 0.058, 0.04,
0.439, 0.412, 0.381, 0.37, 0.333, 0.292, 0.243, 0.208,
0.18, 0.154, 0.13, 0.107, 0.085, 0.058, 0.04, 0.434,
0.407, 0.376, 0.365, 0.329, 0.288, 0.239, 0.205, 0.177,
0.151, 0.128, 0.106, 0.083, 0.057, 0.039, 0.428, 0.402,
0.372, 0.361, 0.326, 0.285, 0.236, 0.202, 0.175, 0.149,
0.126, 0.104, 0.082, 0.056, 0.039)
q21 <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 0.998, 0.995, 0.99, 0.987, 0.976,
0.952, 0.902, 0.82, 0.795, 0.735, 0.669, 0.594, 0.504,
0.374, 0.273, 0.97, 0.951, 0.924, 0.913, 0.872, 0.821,
0.745, 0.68, 0.621, 0.563, 0.504, 0.439, 0.364, 0.268,
0.195, 0.919, 0.885, 0.842, 0.828, 0.78, 0.725, 0.637,
0.575, 0.517, 0.462, 0.408, 350, 0.285, 0.198, 0.138,
0.868, 0.829, 0.78, 0.763, 0.71, 0.65, 0.57, 0.509, 0.454,
0.402, 0.352, 0.298, 0.24, 0.166, 0.117, 0.816, 0.776,
0.725, 0.71, 0.657, 0.594, 0.516, 0.458, 0.407, 0.36,
0.313, 0.265, 0.212, 0.146, 0.103, 0.76, 0.726, 0.678,
0.664, 0.612, 0.551, 0.474, 0.42, 0.374, 0.329, 0.286,
0.24, 0.189, 0.13, 0.089, 0.713, 0.679, 0.638, 0.625,
0.576, 0.517, 0.442, 0.391, 0.348, 0.305, 0.265, 0.221,
0.173, 0.118, 0.08, 0.675, 0.642, 0.605, 0.592, 0.546,
0.49, 0.419, 0.37, 0.326, 0.285, 0.247, 0.206, 0.161,
0.11, 0.074, 0.649, 0.615, 0.578, 0.565, 0.521, 0.467,
0.399, 0.351, 0.308, 0.269, 0.232, 0.194, 0.152, 0.104,
0.07, 0.627, 0.593, 0.556, 0.544, 0.501, 0.448, 0.381,
0.334, 0.293, 0.256, 0.219, 0.184, 0.144, 0.099, 0.066,
0.607, 0.574, 0.537, 0.525, 0.483, 0.431, 0.366, 0.319,
0.28, 0.245, 0.208, 0.175, 0.138, 0.094, 0.062, 0.58,
0.557, 0.521, 0.509, 0.467, 0.416, 0.353, 0.307, 0.269,
0.235, 0.199, 0.167, 0.132, 0.09, 0.059, 0.573, 0.542,
0.507, 0.495, 0.453, 0.403, 0.341, 0.296, 0.259, 0.225,
0.192, 0.161, 0.127, 0.086, 0.057, 0.559, 0.529, 0.494,
0.482, 0.44, 0.391, 0.331, 0.287, 0.25, 0.218, 0.186,
0.155, 0.122, 0.082, 0.054, 0.547, 0.517, 0.482, 0.469,
0.428, 0.38, 0.322, 0.279, 0.243, 0.211, 0.18, 0.15,
0.117, 0.078, 0.052, 0.536, 0.506, 0.472, 0.46, 0.419,
0.371, 0.314, 0.271, 0.236, 0.205, 0.174, 0.145, 0.113,
0.075, 0.05, 0.526, 0.496, 0.462, 0.45, 0.41, 0.363,
0.306, 0.264, 0.229, 0.199, 0.17, 0.141, 0.11, 0.073,
0.049, 0.517, 0.487, 0.453, 0.441, 0.402, 0.356, 0.299,
0.258, 0.223, 0.194, 0.165, 0.137, 0.107, 0.071, 0.048,
0.509, 0.479, 0.445, 0.434, 0.395, 0.349, 0.293, 0.252,
0.218, 0.189, 0.161, 0.133, 0.105, 0.069, 0.046, 0.501,
0.471, 0.438, 0.427, 0.388, 0.343, 0.287, 0.247, 0.214,
0.185, 0.158, 0.13, 0.103, 0.068, 0.045, 0.493, 0.464,
0.431, 0.42, 0.382, 0.337, 0.282, 0.242, 0.21, 0.181,
0.154, 0.127, 0.1, 0.067, 0.043, 0.486, 0.457, 0.424,
0.414, 0.376, 0.331, 0.277, 0.238, 0.206, 0.178, 0.151,
0.125, 0.098, 0.066, 0.042, 0.479, 0.45, 0.418, 0.407,
0.37, 0.325, 0.273, 0.234, 0.203, 0.175, 0.149, 0.123,
0.096, 0.064, 0.041, 0.472, 0.444, 0.412, 0.402, 0.365,
0.32, 0.269, 0.23, 0.2, 0.172, 0.146, 0.121, 0.094, 0.063,
0.041, 0.466, 0.438, 0.406, 0.396, 0.36, 0.316, 0.265,
0.227, 0.197, 0.17, 0.144, 0.119, 0.092, 0.062, 0.04,
0.46, 0.433, 0.401, 0.391, 0.355, 0.312, 0.261, 0.224,
0.194, 0.167, 0.142, 0.117, 0.091, 0.061, 0.04)
q22 <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 0.998, 0.995, 0.992, 0.99,
0.983, 0.965, 0.93, 0.88, 830, 0.78, 0.72, 0.64, 0.54,
0.41, 0.3, 0.97, 0.945, 0.919, 0.909, 0.881, 0.85, 0.78,
0.73, 0.67, 0.61, 0.54, 0.47, 0.39, 0.27, 0.2, 0.922,
0.89, 0.857, 0.846, 0.803, 0.745, 0.664, 0.602, 0.546,
0.49, 0.434, 0.375, 0.309, 0.218, 0.156, 0.873, 0.84,
0.8, 0.787, 0.737, 0.676, 0.592, 0.53, 0.478, 0.425,
0.373, 0.32, 0.261, 0.186, 0.128, 0.826, 0.791, 0.749,
0.734, 0.682, 0.62, 0.543, 0.483, 0.433, 0.384, 0.335,
0.285, 0.231, 0.15, 0.111, 0.781, 0.745, 0.703, 0.688,
0.637, 0.578, 0.503, 0.446, 0.397, 0.351, 0.305, 0.258,
0.208, 0.142, 0.099, 0.74, 0.704, 0.661, 0.648, 0.6,
0.543, 0.47, 0.416, 0.37, 0.325, 0.282, 0.238, 0.19,
0.13, 0.09, 0.705, 0.67, 0.628, 0.616, 0.57, 0.515, 0.443,
0.391, 0.347, 0.304, 0.263, 0.222, 0.177, 0.122, 0.084,
0.674, 0.641, 0.602, 0.59, 0.546, 0.492, 0.421, 0.37,
0.328, 0.287, 0.247, 0.208, 0.166, 0.115, 0.079, 0.647,
0.616, 0.579, 0.568, 0.525, 0.472, 0.402, 0.353, 0.312,
0.273, 0.234, 0.196, 0.156, 0.109, 0.075, 0.624, 0.595,
0.559, 0.548, 0.507, 0.454, 0.386, 0.338, 0.298, 0.261,
0.223, 0.186, 0.148, 0.104, 0.071, 0.605, 0.577, 0.542,
0.531, 0.49, 0.438, 0.373, 0.325, 0.286, 0.25, 0.214,
0.178, 0.142, 0.099, 0.067, 0.589, 0.561, 0.527, 0.516,
0.475, 0.424, 0.361, 0.314, 0.276, 0.241, 0.206, 0.171,
0.135, 0.094, 0.063, 0.575, 0.547, 0.514, 0.503, 0.462,
0.412, 0.35, 0.304, 0.268, 0.233, 0.199, 0.165, 0.103,
0.09, 0.06, 0.562, 0.535, 0.502, 0.491, 0.45, 0.401,
0.34, 0.295, 0.26, 0.226, 0.193, 0.16, 0.125, 0.086,
0.057, 0.551, 0.524, 0.491, 0.48, 0.44, 0.391, 0.331,
0.287, 0.252, 0.22, 0.187, 0.155, 0.12, 0.082, 0.054,
0.541, 0.514, 0.481, 0.47, 0.43, 0.382, 0.323, 0.28,
0.245, 0.213, 0.182, 0.15, 0.116, 0.078, 0.051, 0.532,
0.505, 0.472, 0.461, 0.421, 0.374, 0.316, 0.274, 0.239,
0.207, 0.177, 0.146, 0.113, 0.075, 0.049, 0.524, 0.497,
0.464, 0.452, 0.413, 0.367, 0.31, 0.268, 0.232, 0.201,
0.172, 0.142, 0.111, 0.074, 0.047, 0.516, 0.489, 0.457,
0.445, 0.406, 0.36, 0.304, 0.262, 0.227, 0.196, 0.168,
0.138, 0.108, 0.073, 0.045, 0.508, 0.482, 0.45, 0.438,
0.399, 0.354, 0.298, 0.257, 0.222, 0.192, 0.164, 0.135,
0.106, 0.072, 0.044, 0.501, 0.475, 0.443, 0.432, 0.393,
0.348, 0.292, 0.252, 0.218, 0.189, 0.161, 0.132, 0.104,
0.071, 0.043, 0.495, 0.469, 0.437, 0.426, 0.387, 0.342,
0.287, 0.247, 0.215, 0.186, 0.158, 0.13, 0.102, 0.069,
0.042, 0.489, 0.463, 0.431, 0.419, 0.381, 0.337, 0.282,
0.243, 0.211, 0.183, 0.155, 0.128, 0.1, 0.068, 0.041,
0.483, 0.457, 0.425, 0.414, 0.376, 0.332, 0.278, 0.239,
0.208, 0.18, 0.153, 0.126, 0.098, 0.067, 0.041)
dim(q10) <- c(15, 30)
dim(q11) <- c(15, 30)
dim(q12) <- c(15, 30)
dim(q20) <- c(15, 30)
dim(q21) <- c(15, 30)
dim(q22) <- c(15, 30)
if (type == 10)
q0 <- q10[, n]
else if (type == 11)
q0 <- q11[, n]
else if (type == 12)
q0 <- q12[, n]
else if (type == 20)
q0 <- q20[, n]
else if (type == 21)
q0 <- q21[, n]
else
q0 <- q22[, n]
if (rev) {
res <- q_table(p, q0, pp)
}
else {
res <- q_table(p, pp, q0)
}
res[res < 0] <- 0
res[res > 1] <- 1
invisible(res)
}
#' Outlier removals with Dixon's method
#'
#' @param q Vector of quantiles.
#' @inheritParams q_dixon
p_dixon <- function (q, n, type = 10)
{
q_dixon(q, n, type, rev = TRUE)
}
#' Distribution table
#'
#' @param probs Vector of given probabilities.
#' @param quants Vector of given corresponding quantiles.
#' @inheritParams q_dixon
q_table <- function (p, probs, quants)
{
quants <- quants[order(probs)]
probs <- sort(probs)
res <- vector()
for (n in 1:length(p)) {
pp <- p[n]
if (pp <= probs[1]) {
q0 <- quants[c(1, 2)]
p0 <- probs[c(1, 2)]
fit <- lm(q0 ~ p0)
}
else if (pp >= probs[length(probs)]) {
q0 <- quants[c(length(quants) - 1, length(quants))]
p0 <- probs[c(length(probs) - 1, length(probs))]
fit <- lm(q0 ~ p0)
}
else {
x0 <- which(abs(pp - probs) == min(abs(pp - probs)))
x1 <- which(abs(pp - probs) == sort(abs(pp - probs))[2])
x <- min(c(x0, x1))
if (x == 1)
x <- 2
if (x > length(probs) - 2)
x <- length(probs) - 2
i <- c(x - 1, x, x + 1, x + 2)
q0 <- quants[i]
p0 <- probs[i]
fit <- lm(q0 ~ poly(p0, 3))
}
res <- c(res, predict(fit, newdata = list(p0 = pp)))
}
invisible(res)
}
#' Outlier removals with Dixon's method
#'
#' @param x A numeric vector.
Dixon_outliers <- function(x)
{
# x = c(0.0000000, 0.0000000, 1.0271542, 0.0000000, 0.2080097)
# x = c(0.0000000, 0.0000000, NA, 0.0000000, 0.2080097)
# x = c(0.0000000, 0.0000000, 0.0000000, 0.2080097)
# x = c(NA, NA, NA, 0.2080097)
x2 <- x[!is.na(x)]
if (length(x2) > 2L && length(unique(x2)) > 1L) {
out <- dixon_test(as.numeric(x), type = 0)
while(out$p.value < 0.05) {
if (out$alternative == "high") {
x[which.max(x)] <- NA
} else {
x[which.min(x)] <- NA
}
x2 <- x[!is.na(x)]
if (length(x2) > 2 && length(unique(x2)) > 1) {
out <- dixon_test(as.numeric(x), type = 0)
} else {
out$p.value <- 1
}
}
}
invisible(x)
}
#' Outlier removals with Grubbs's method
#'
#' @param x A data frame.
#' @param type Type for grubbs.test.
Grubbs_outliers <- function(x, type = 10)
{
x2 <- x[!is.na(x)]
if (length(x2) > 2 && length(unique(x2)) > 1) {
out <- grubbs.test(as.numeric(x, type))
while(out$p.value < 0.05) {
if (grepl("^highest", out$alternative)) {
x[which.max(x)] <- NA
}
else {
x[which.min(x)] <- NA
}
x2 <- x[!is.na(x)]
if (length(x2) > 2 && length(unique(x2)) > 1) {
out <- grubbs.test(as.numeric(x), type = type)
}
else {
out$p.value <- 1
}
}
}
invisible(x)
}
#' Outlier removals with Grubbs' method
#'
#' See also outliers::grubbs.test.
#'
#' @inheritParams dixon_test
grubbs_test <- function (x, type = 10, opposite = FALSE, two.sided = FALSE)
{
if (sum(c(10, 11, 20) == type) == 0) {
stop("Incorrect type", call. = FALSE)
}
DNAME <- deparse(substitute(x))
x <- sort(x[complete.cases(x)])
n <- length(x)
if (type == 11) {
g <- (x[n] - x[1])/sd(x)
u <- var(x[2:(n - 1)])/var(x) * (n - 3)/(n - 1)
pval = 1 - p_grubbs(g, n, type = 11)
method <- "Grubbs test for two opposite outliers"
alt = paste(x[1], "and", x[n], "are outliers")
}
else if (type == 10) {
if (xor(((x[n] - mean(x)) < (mean(x) - x[1])), opposite)) {
alt = paste("lowest value", x[1], "is an outlier")
o <- x[1]
d <- x[2:n]
}
else {
alt = paste("highest value", x[n], "is an outlier")
o <- x[n]
d <- x[1:(n - 1)]
}
g <- abs(o - mean(x))/sd(x)
u <- var(d)/var(x) * (n - 2)/(n - 1)
pval <- 1 - p_grubbs(g, n, type = 10)
method <- "Grubbs test for one outlier"
}
else {
if (xor(((x[n] - mean(x)) < (mean(x) - x[1])), opposite)) {
alt = paste("lowest values", x[1], ",",
x[2], "are outliers")
u <- var(x[3:n])/var(x) * (n - 3)/(n - 1)
}
else {
alt = paste("highest values", x[n - 1], ",",
x[n], "are outliers")
u <- var(x[1:(n - 2)])/var(x) * (n - 3)/(n - 1)
}
g <- NULL
pval <- p_grubbs(u, n, type = 20)
method <- "Grubbs test for two outliers"
}
if (two.sided) {
pval <- 2 * pval
if (pval > 1)
pval <- 2 - pval
}
RVAL <- list(statistic = c(G = g, U = u), alternative = alt,
p.value = pval, method = method, data.name = DNAME)
class(RVAL) <- "htest"
invisible(RVAL)
}
#' Distribution function
#'
#' @param p Vector of probabilities. See also outliers::dixon.test.
#' @param n Length of sample Length of sample.
#' @param rev Logical; if TRUE, acts as p_dixon.
#' @inheritParams dixon_test
q_grubbs <- function (p, n, type = 10, rev = FALSE)
{
if (type == 10) {
if (!rev) {
return(((n - 1)/sqrt(n)) *
sqrt(qt((1 - p)/n, n - 2)^2/(n - 2 + qt((1 - p)/n, n - 2)^2)))
}
else {
s <- (p^2 * n * (2 - n))/(p^2 * n - (n - 1)^2)
t <- sqrt(s)
if (is.nan(t)) {
res <- 0
}
else {
res <- n * (1 - pt(t, n - 2))
res[res > 1] <- 1
}
return(1 - res)
}
}
else if (type == 11) {
if (!rev) {
return(sqrt((2 * (n - 1) * qt((1 - p)/(n * (n - 1)),
n - 2)^2)/(n - 2 + qt((1 - p)/(n * (n - 1)),
n - 2)^2)))
}
else {
q <- p
p <- vector()
for (i in 1:length(q)) {
if (q[i] > qgrubbs(0.9999, n, type = 11)) {
pp <- 1
}
else if (q[i] < qgrubbs(2e-16, n, type = 11)) {
pp <- 0
}
else {
f <- function(x, q, n) {
qgrubbs(x, n, type = 11) - q
}
pp <- uniroot(f, c(0.001, 0.9999), q = q[i],
n = n)$root
}
p <- c(p, pp)
}
return(p)
}
}
else {
if (n > 30)
stop("n must be in range 3-30")
pp <- c(0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 0.4, 0.6,
0.8, 0.9, 0.95, 0.975, 0.99)
gtwo <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 1e-05, 2e-04, 8e-04, 0.0031, 0.007, 0.013,
0.055, 0.138, 0.283, 0.399, 0.482, 0.54, 0.589, 0.0035,
0.009, 0.0183, 0.0376, 0.058, 0.078, 0.169, 0.276,
0.41, 0.502, 0.571, 0.63, 0.689, 0.0186, 0.0349,
0.0565, 0.0921, 0.124, 0.153, 0.257, 0.361, 0.478,
0.562, 0.626, 0.674, 0.724, 0.044, 0.0708, 0.102,
0.1479, 0.186, 0.217, 0.325, 0.423, 0.53, 0.605,
0.659, 0.701, 0.743, 0.075, 0.1101, 0.1478, 0.1994,
0.238, 0.271, 0.376, 0.468, 0.568, 0.635, 0.684,
0.722, 0.763, 0.1082, 0.1492, 0.1909, 0.2454, 0.285,
0.319, 0.42, 0.507, 0.598, 0.658, 0.703, 0.738, 0.776,
0.1415, 0.1865, 0.2305, 0.2863, 0.326, 0.358, 0.455,
0.537, 0.622, 0.678, 0.721, 0.753, 0.787, 0.1736,
0.2212, 0.2666, 0.3226, 0.363, 0.394, 0.487, 0.564,
0.624, 0.695, 0.734, 0.765, 0.797, 0.2044, 0.2536,
0.2996, 0.3552, 0.393, 0.424, 0.514, 0.585, 0.66,
0.709, 0.745, 0.773, 0.802, 0.2333, 0.2836, 0.3295,
0.3843, 0.421, 0.451, 0.537, 0.605, 0.676, 0.721,
0.755, 0.782, 0.809, 0.2605, 0.3112, 0.3568, 0.4106,
0.447, 0.477, 0.558, 0.622, 0.689, 0.733, 0.765,
0.789, 0.817, 0.2859, 0.3367, 0.3818, 0.4345, 0.469,
0.497, 0.576, 0.639, 0.701, 0.742, 0.773, 0.797,
0.822, 0.3098, 0.3603, 0.4048, 0.4562, 0.491, 0.518,
0.593, 0.653, 0.713, 0.751, 0.78, 0.803, 0.826, 0.3321,
0.3822, 0.4259, 0.4761, 0.511, 0.536, 0.609, 0.666,
0.723, 0.76, 0.787, 0.808, 0.831, 0.353, 0.4025,
0.4455, 0.4944, 0.526, 0.552, 0.622, 0.676, 0.732,
0.767, 0.793, 0.814, 0.835, 0.3725, 0.4214, 0.4636,
0.5113, 0.543, 0.567, 0.635, 0.688, 0.74, 0.774,
0.799, 0.818, 0.838, 0.3909, 0.4391, 0.4804, 0.5269,
0.559, 0.582, 0.647, 0.697, 0.748, 0.781, 0.805,
0.823, 0.843, 0.408, 0.457, 0.496, 0.542, 0.571,
0.594, 0.657, 0.706, 0.755, 0.786, 0.81, 0.828, 0.847,
0.425, 0.474, 0.512, 0.556, 0.584, 0.606, 0.668,
0.715, 0.762, 0.792, 0.815, 0.834, 0.85, 0.442, 0.486,
0.524, 0.568, 0.596, 0.618, 0.677, 0.723, 0.769,
0.797, 0.819, 0.836, 0.853, 0.453, 0.5, 0.538, 0.581,
0.608, 0.628, 0.686, 0.73, 0.774, 0.802, 0.823, 0.84,
0.857, 0.466, 0.511, 0.547, 0.589, 0.616, 0.637,
0.693, 0.736, 0.779, 0.807, 0.827, 0.843, 0.86, 0.482,
0.525, 0.561, 0.601, 0.627, 0.647, 0.701, 0.743,
0.784, 0.811, 0.83, 0.845, 0.861, 0.492, 0.536, 0.572,
0.611, 0.636, 0.655, 0.709, 0.749, 0.789, 0.815,
0.834, 0.849, 0.864, 0.505, 0.548, 0.583, 0.621,
0.646, 0.664, 0.716, 0.755, 0.794, 0.819, 0.837,
0.851, 0.866, 0.516, 0.558, 0.592, 0.629, 0.654,
0.672, 0.722, 0.76, 0.798, 0.822, 0.84, 0.854, 0.869,
0.528, 0.568, 0.602, 0.638, 0.661, 0.679, 0.728,
0.765, 0.802, 0.826, 0.842, 0.856, 0.87)
dim(gtwo) <- c(13, 30)
if (!rev)
res <- qtable(p, pp, gtwo[, n])
else
res <- qtable(p, gtwo[, n], pp)
res[res < 0] <- 0
res[res > 1] <- 1
invisible(res)
}
}
#' Outlier removals with Grubbs' method
#'
#' @param q Vector of quantiles.
#' @inheritParams q_dixon
p_grubbs <- function (q, n, type = 10)
{
q_grubbs(q, n, type, rev = TRUE)
}
#' Outlier removals with Rosner's method
#'
#' @param x A matrix or data.frame.
Rosner_outliers <- function(x)
{
if (length(unique(x)) < 5)
return(x)
# up to 9-number outliers; may get warnings with NA being an outlier
suppressWarnings(gofOutlier_obj <- EnvStats::rosnerTest(as.numeric(x), 9))
if (gofOutlier_obj$n.outliers > 0) {
Index <- with(gofOutlier_obj$all.stat, Obs.Num[Outlier == TRUE])
x[Index] <- NA
}
invisible(x)
}
qzhang503/proteoQ documentation built on March 16, 2024, 5:27 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions Rosner_outliers Grubbs_outliers Dixon_outliers
Documented in Dixon_outliers Grubbs_outliers Rosner_outliers
#' Locates the positions of outliers
#'
#' @param df A data frame containing the PSM table from database searches.
#' @param range_colRatios The range of columns.
#' @return A data frame.
#' @examples \donttest{locate_outliers(df, 2:3)}
locate_outliers <- function (df, range_colRatios)
{
for(col_index in range_colRatios) {
n <- colSums(!is.na(df[col_index]))
if (n > 25) {
df[, col_index] <- Rosner_outliers(df[, col_index])
}
else if (n > 2) {
df[, col_index] <- Dixon_outliers(df[, col_index])
}
}
invisible(df)
}
#' Outlier removals with Dixon's method
#'
#' @param x A data frame.
#' @param type The type of Dixon method. See also outliers::dixon.test.
#' @param opposite The high or low. See also outliers::dixon.test.
#' @param two.sided Logical; is two-sided or not. See also
#' outliers::dixon.test.
dixon_test <- function (x, type = 0, opposite = FALSE, two.sided = TRUE)
{
x <- sort(x[complete.cases(x)])
n <- length(x)
if ((type == 10 || type == 0) && (n < 3 || n > 30))
stop("Sample size must be in range 3-30")
if (type == 11 && (n < 4 || n > 30))
stop("Sample size must be in range 4-30")
if (type == 12 && (n < 5 || n > 30))
stop("Sample size must be in range 5-30")
if (type == 20 && (n < 4 || n > 30))
stop("Sample size must be in range 4-30")
if (type == 21 && (n < 5 || n > 30))
stop("Sample size must be in range 5-30")
if (type == 22 && (n < 6 || n > 30))
stop("Sample size must be in range 6-30")
if (sum(c(0, 10, 11, 12, 20, 21, 22) == type) == 0)
stop("Incorrect type")
if (type == 0) {
if (n <= 7)
type <- 10
else if (n > 7 & n <= 10)
type <- 11
else if (n > 10 & n <= 13)
type <- 21
else
type <- 22
}
if (xor(((x[n] - mean(x)) < (mean(x) - x[1])), opposite)) {
alt <- "low"
if (type == 10) {
Q = (x[2] - x[1])/(x[n] - x[1])
}
else if (type == 11) {
Q = (x[2] - x[1])/(x[n - 1] - x[1])
}
else if (type == 12) {
Q = (x[2] - x[1])/(x[n - 2] - x[1])
}
else if (type == 20) {
Q = (x[3] - x[1])/(x[n] - x[1])
}
else if (type == 21) {
Q = (x[3] - x[1])/(x[n - 1] - x[1])
}
else {
Q = (x[3] - x[1])/(x[n - 2] - x[1])
}
}
else {
alt <- "high"
if (type == 10) {
Q = (x[n] - x[n - 1])/(x[n] - x[1])
}
else if (type == 11) {
Q = (x[n] - x[n - 1])/(x[n] - x[2])
}
else if (type == 12) {
Q = (x[n] - x[n - 1])/(x[n] - x[3])
}
else if (type == 20) {
Q = (x[n] - x[n - 2])/(x[n] - x[1])
}
else if (type == 21) {
Q = (x[n] - x[n - 2])/(x[n] - x[2])
}
else {
Q = (x[n] - x[n - 2])/(x[n] - x[3])
}
}
pval <- p_dixon(Q, n, type)
if (two.sided) {
pval <- 2 * pval
if (pval > 1)
pval <- 2 - pval
}
out <- list(statistic = c(Q = Q), alternative = alt, p.value = pval)
invisible(out)
}
#' Distribution function
#'
#' @param p Vector of probabilities. See also outliers::dixon.test.
#' @param n Length of sample Length of sample.
#' @param rev Logical; if TRUE, acts as p_dixon.
#' @inheritParams dixon_test
q_dixon <- function (p, n, type = 10, rev = FALSE)
{
if ((type == 10 || type == 0) & (n < 3 || n > 30))
stop("Sample size must be in range 3-30")
if (type == 11 & (n < 4 || n > 30))
stop("Sample size must be in range 4-30")
if (type == 12 & (n < 5 || n > 30))
stop("Sample size must be in range 5-30")
if (type == 20 & (n < 4 || n > 30))
stop("Sample size must be in range 4-30")
if (type == 21 & (n < 5 || n > 30))
stop("Sample size must be in range 5-30")
if (type == 22 & (n < 6 || n > 30))
stop("Sample size must be in range 6-30")
if (sum(c(0, 10, 11, 12, 20, 21, 22) == type) == 0)
stop("Incorrect type")
if (type == 0) {
if (n <= 7)
type <- 10
else if (n > 7 & n <= 10)
type <- 11
else if (n > 10 & n <= 13)
type <- 21
else
type <- 22
}
pp <- c(0.005, 0.01, 0.02, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4,
0.5, 0.6, 0.7, 0.8, 0.9, 0.95)
q10 <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 0.994, 0.988, 0.976, 0.97, 0.941, 0.886,
0.781, 0.684, 0.591, 0.5, 0.409, 0.316, 0.219, 0.114,
0.059, 0.926, 0.889, 0.846, 0.829, 0.765, 0.679, 0.56,
0.471, 0.394, 0.324, 0.257, 0.193, 0.13, 0.065, 0.033,
0.821, 0.78, 0.729, 0.71, 0.642, 0.557, 0.451, 0.373,
0.308, 0.25, 0.196, 0.146, 0.097, 0.048, 0.023, 0.74,
0.698, 0.644, 0.625, 0.56, 0.482, 0.386, 0.318, 0.261,
0.21, 0.164, 0.121, 0.079, 0.038, 0.018, 0.68, 0.637,
0.586, 0.568, 0.507, 0.434, 0.344, 0.281, 0.23, 0.184,
0.143, 0.105, 0.068, 0.032, 0.016, 0.634, 0.59, 0.543,
0.526, 0.468, 0.399, 0.314, 0.255, 0.208, 0.166, 0.128,
0.094, 0.06, 0.029, 0.014, 0.598, 0.555, 0.51, 0.493,
0.437, 0.37, 0.29, 0.234, 0.191, 0.152, 0.118, 0.086,
0.055, 0.026, 0.013, 0.568, 0.527, 0.483, 0.466, 0.412,
0.349, 0.273, 0.219, 0.178, 0.142, 0.11, 0.08, 0.051,
0.025, 0.012, 0.542, 0.502, 0.46, 0.444, 0.392, 0.332,
0.259, 0.208, 0.168, 0.133, 0.103, 0.074, 0.048, 0.023,
0.011, 0.522, 0.482, 0.441, 0.426, 0.376, 0.318, 0.247,
0.197, 0.16, 0.126, 0.097, 0.07, 0.047, 0.022, 0.011,
0.503, 0.465, 0.425, 0.41, 0.361, 0.305, 0.237, 0.188,
0.153, 0.12, 0.092, 0.067, 0.043, 0.021, 0.01, 0.488,
0.45, 0.411, 0.396, 0.349, 0.294, 0.228, 0.181, 0.147,
0.115, 0.088, 0.064, 0.041, 0.02, 0.01, 0.475, 0.438,
0.399, 0.384, 0.338, 0.285, 0.22, 0.175, 0.141, 0.111,
0.085, 0.062, 0.04, 0.019, 0.01, 0.463, 0.426, 0.388,
0.374, 0.329, 0.277, 0.213, 0.169, 0.136, 0.107, 0.082,
0.06, 0.039, 0.019, 0.009, 0.452, 0.416, 0.379, 0.365,
0.32, 0.269, 0.207, 0.165, 0.132, 0.104, 0.08, 0.058,
0.038, 0.018, 0.009, 0.442, 0.407, 0.37, 0.356, 0.313,
0.263, 0.202, 0.16, 0.128, 0.101, 0.078, 0.056, 0.036,
0.018, 0.009, 0.433, 0.398, 0.363, 0.349, 0.306, 0.258,
0.197, 0.157, 0.125, 0.098, 0.076, 0.055, 0.036, 0.017,
0.008, 0.425, 0.391, 0.356, 0.342, 0.3, 0.252, 0.193,
0.153, 0.122, 0.096, 0.074, 0.053, 0.035, 0.017, 0.008,
0.418, 0.384, 0.35, 0.337, 0.295, 0.247, 0.189, 0.15,
0.119, 0.094, 0.072, 0.052, 0.034, 0.016, 0.008, 0.411,
0.378, 0.344, 0.331, 0.29, 0.242, 0.185, 0.147, 0.117,
0.092, 0.071, 0.051, 0.033, 0.016, 0.008, 0.404, 0.372,
0.338, 0.326, 0.285, 0.238, 0.182, 0.144, 0.115, 0.09,
0.069, 0.05, 0.033, 0.016, 0.008, 0.399, 0.367, 0.333,
0.321, 0.281, 0.234, 0.179, 0.142, 0.113, 0.089, 0.068,
0.049, 0.032, 0.016, 0.008, 0.393, 0.362, 0.329, 0.317,
0.277, 0.23, 0.176, 0.139, 0.111, 0.088, 0.067, 0.048,
0.032, 0.015, 0.008, 0.388, 0.357, 0.324, 0.312, 0.273,
0.227, 0.173, 0.137, 0.109, 0.086, 0.066, 0.047, 0.031,
0.015, 0.007, 0.384, 0.353, 0.32, 0.308, 0.269, 0.224,
0.171, 0.135, 0.108, 0.085, 0.065, 0.047, 0.031, 0.015,
0.007, 0.38, 0.349, 0.316, 0.305, 0.266, 0.22, 0.168,
0.133, 0.106, 0.084, 0.064, 0.046, 0.03, 0.015, 0.007,
0.376, 0.345, 0.312, 0.301, 0.263, 0.218, 0.166, 0.131,
0.105, 0.083, 0.063, 0.046, 0.03, 0.014, 0.007, 0.372,
0.341, 0.309, 0.298, 0.26, 0.215, 0.164, 0.13, 0.103,
0.082, 0.062, 0.045, 0.029, 0.014, 0.007)
q11 <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 0.995, 0.991, 0.981, 0.977, 0.955,
0.91, 0.822, 0.737, 0.648, 0.554, 0.459, 0.362, 0.25,
0.131, 0.069, 0.937, 0.916, 0.876, 0.863, 0.807, 0.728,
0.615, 0.524, 0.444, 0.369, 0.296, 0.224, 0.151, 0.078,
0.039, 0.839, 0.805, 0.763, 0.748, 0.689, 0.609, 0.502,
0.42, 0.35, 0.288, 0.227, 0.169, 0.113, 0.056, 0.028,
0.782, 0.74, 0.689, 0.673, 0.61, 0.53, 0.432, 0.359,
0.298, 0.241, 0.189, 0.14, 0.093, 0.045, 0.022, 0.725,
0.683, 0.631, 0.615, 0.554, 0.479, 0.385, 0.318, 0.26,
0.21, 0.164, 0.121, 0.079, 0.037, 0.019, 0.677, 0.635,
0.587, 0.57, 0.512, 0.441, 0.352, 0.288, 0.236, 0.189,
0.148, 0.107, 0.07, 0.033, 0.016, 0.639, 0.597, 0.551,
0.534, 0.477, 0.409, 0.325, 0.265, 0.216, 0.173, 0.134,
0.098, 0.063, 0.03, 0.014, 0.606, 0.566, 0.521, 0.505,
0.45, 0.385, 0.305, 0.248, 0.202, 0.161, 0.124, 0.09,
0.058, 0.028, 0.013, 0.58, 0.541, 0.498, 0.481, 0.428,
0.367, 0.289, 0.234, 0.19, 0.15, 0.116, 0.084, 0.055,
0.026, 0.012, 0.558, 0.52, 0.477, 0.461, 0.41, 0.35,
0.275, 0.222, 0.18, 0.142, 0.109, 0.079, 0.052, 0.025,
0.012, 0.539, 0.502, 0.46, 0.445, 0.395, 0.336, 0.264,
0.212, 0.171, 0.135, 0.104, 0.075, 0.049, 0.024, 0.011,
0.522, 0.486, 0.445, 0.43, 0.381, 0.323, 0.253, 0.203,
0.164, 0.129, 0.099, 0.072, 0.047, 0.023, 0.011, 0.508,
0.472, 0.432, 0.417, 0.369, 0.313, 0.244, 0.196, 0.158,
0.124, 0.095, 0.096, 0.045, 0.022, 0.011, 0.495, 0.46,
0.42, 0.406, 0.359, 0.303, 0.236, 0.19, 0.152, 0.119,
0.092, 0.067, 0.044, 0.021, 0.01, 0.484, 0.449, 0.41,
0.396, 0.349, 0.295, 0.229, 0.184, 0.148, 0.116, 0.089,
0.065, 0.042, 0.02, 0.01, 0.473, 0.439, 0.4, 0.386, 0.341,
0.288, 0.223, 0.179, 0.143, 0.112, 0.087, 0.063, 0.041,
0.02, 0.01, 0.464, 0.43, 0.392, 0.379, 0.334, 0.282,
0.218, 0.174, 0.139, 0.11, 0.084, 0.061, 0.04, 0.019,
0.01, 0.455, 0.421, 0.384, 0.371, 0.327, 0.276, 0.213,
0.17, 0.136, 0.107, 0.082, 0.059, 0.039, 0.019, 0.009,
0.446, 0.414, 0.377, 0.364, 0.32, 0.27, 0.208, 0.166,
0.132, 0.104, 0.081, 0.058, 0.038, 0.018, 0.009, 0.439,
0.407, 0.371, 0.357, 0.314, 0.265, 0.204, 0.163, 0.13,
0.102, 0.079, 0.056, 0.037, 0.018, 0.009, 0.432, 0.4,
0.365, 0.352, 0.309, 0.26, 0.2, 0.16, 0.127, 0.1, 0.077,
0.055, 0.036, 0.018, 0.009, 0.426, 0.394, 0.359, 0.346,
0.304, 0.255, 0.197, 0.156, 0.124, 0.098, 0.076, 0.054,
0.036, 0.017, 0.009, 0.42, 0.389, 0.354, 0.341, 0.299,
0.25, 0.193, 0.154, 0.122, 0.096, 0.074, 0.053, 0.035,
0.017, 0.008, 0.414, 0.383, 0.349, 0.337, 0.295, 0.246,
0.19, 0.151, 0.12, 0.095, 0.073, 0.052, 0.034, 0.017,
0.008, 0.409, 0.378, 0.344, 0.332, 0.291, 0.243, 0.188,
0.149, 0.118, 0.093, 0.072, 0.051, 0.034, 0.016, 0.008,
0.404, 0.374, 0.34, 0.328, 0.287, 0.239, 0.185, 0.146,
0.116, 0.092, 0.07, 0.051, 0.033, 0.016, 0.008, 0.399,
0.369, 0.336, 0.324, 0.283, 0.236, 0.182, 0.144, 0.115,
0.09, 0.069, 0.05, 0.032, 0.016, 0.008)
q12 <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 0.996, 0.992, 0.984, 0.98, 0.96,
0.919, 0.838, 0.755, 0.669, 0.579, 0.483, 0.381, 0.268,
0.143, 0.074, 0.951, 0.925, 0.891, 0.878, 0.824, 0.745,
0.635, 0.545, 0.465, 0.39, 0.316, 0.24, 0.165, 0.088,
0.049, 0.875, 0.836, 0.791, 0.773, 0.712, 0.636, 0.528,
0.445, 0.374, 0.307, 0.245, 0.183, 0.123, 0.064, 0.031,
0.797, 0.76, 0.708, 0.692, 0.632, 0.557, 0.456, 0.382,
0.317, 0.258, 0.203, 0.152, 0.101, 0.056, 0.025, 0.739,
0.702, 0.656, 0.639, 0.58, 0.504, 0.409, 0.339, 0.27,
0.227, 0.177, 0.13, 0.086, 0.044, 0.021, 0.694, 0.655,
0.61, 0.594, 0.537, 0.464, 0.373, 0.308, 0.258, 0.204,
0.158, 0.116, 0.075, 0.038, 0.019, 0.658, 0.619, 0.575,
0.559, 0.502, 0.431, 0.345, 0.283, 0.232, 0.187, 0.145,
0.106, 0.069, 0.035, 0.017, 0.629, 0.59, 0.546, 0.529,
0.473, 0.406, 0.324, 0.265, 0.217, 0.174, 0.135, 0.098,
0.063, 0.032, 0.016, 0.602, 0.564, 0.521, 0.505, 0.451,
0.387, 0.307, 0.25, 0.204, 0.163, 0.126, 0.092, 0.059,
0.03, 0.015, 0.58, 0.542, 0.501, 0.485, 0.432, 0.369,
0.292, 0.237, 0.193, 0.153, 0.118, 0.086, 0.055, 0.028,
0.014, 0.56, 0.523, 0.482, 0.467, 0.416, 0.354, 0.28,
0.226, 0.184, 0.146, 0.112, 0.082, 0.053, 0.026, 0.013,
0.544, 0.508, 0.467, 0.452, 0.401, 0.341, 0.269, 0.217,
0.177, 0.139, 0.107, 0.078, 0.05, 0.025, 0.013, 0.529,
0.493, 0.453, 0.438, 0.388, 0.33, 0.259, 0.209, 0.17,
0.134, 0.103, 0.075, 0.048, 0.024, 0.012, 0.516, 0.48,
0.44, 0.426, 0.377, 0.32, 0.251, 0.202, 0.163, 0.129,
0.099, 0.072, 0.047, 0.023, 0.012, 0.504, 0.469, 0.429,
0.415, 0.367, 0.311, 0.243, 0.196, 0.157, 0.125, 0.096,
0.069, 0.045, 0.022, 0.011, 0.493, 0.458, 0.419, 0.405,
0.358, 0.303, 0.237, 0.191, 0.153, 0.121, 0.093, 0.067,
0.044, 0.022, 0.011, 0.483, 0.449, 0.41, 0.396, 0.349,
0.296, 0.231, 0.186, 0.148, 0.118, 0.09, 0.065, 0.042,
0.021, 0.01, 0.474, 0.44, 0.402, 0.388, 0.342, 0.29,
0.225, 0.181, 0.145, 0.114, 0.088, 0.063, 0.041, 0.02,
0.01, 0.465, 0.432, 0.394, 0.381, 0.336, 0.284, 0.22,
0.176, 0.141, 0.112, 0.086, 0.062, 0.04, 0.02, 0.01,
0.457, 0.423, 0.387, 0.374, 0.33, 0.278, 0.216, 0.173,
0.138, 0.109, 0.084, 0.06, 0.039, 0.019, 0.01, 0.45,
0.417, 0.381, 0.368, 0.324, 0.273, 0.212, 0.169, 0.135,
0.107, 0.082, 0.059, 0.038, 0.019, 0.009, 0.443, 0.411,
0.375, 0.362, 0.319, 0.268, 0.208, 0.166, 0.132, 0.105,
0.08, 0.058, 0.037, 0.019, 0.009, 0.437, 0.405, 0.37,
0.357, 0.314, 0.263, 0.204, 0.163, 0.13, 0.103, 0.079,
0.057, 0.037, 0.018, 0.009, 0.431, 0.399, 0.365, 0.352,
0.309, 0.259, 0.201, 0.16, 0.128, 0.101, 0.077, 0.056,
0.036, 0.018, 0.009, 0.426, 0.394, 0.36, 0.347, 0.305,
0.255, 0.197, 0.157, 0.126, 0.009, 0.076, 0.055, 0.035,
0.017, 0.009, 0.42, 0.389, 0.355, 0.343, 0.301, 0.251,
0.194, 0.154, 0.124, 0.098, 0.075, 0.054, 0.035, 0.017,
0.009)
q20 <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 0.996, 0.992, 0.987, 0.983, 0.967,
0.935, 0.871, 0.807, 0.743, 0.676, 0.606, 0.529, 0.44,
0.321, 0.235, 0.95, 0.929, 0.901, 0.89, 0.845, 0.782,
0.694, 0.623, 0.56, 0.5, 0.44, 0.377, 0.306, 0.218, 0.155,
0.865, 0.836, 0.8, 0.786, 0.736, 0.67, 0.585, 0.52, 0.463,
0.411, 0.358, 0.305, 0.245, 0.172, 0.126, 0.814, 0.778,
0.732, 0.716, 0.661, 0.596, 0.516, 0.454, 0.402, 0.355,
0.306, 0.261, 0.208, 0.144, 0.099, 0.746, 0.71, 0.67,
0.657, 0.607, 0.545, 0.468, 0.41, 0.361, 0.317, 0.274,
0.23, 0.184, 0.125, 0.085, 0.7, 0.667, 0.627, 0.614,
0.565, 0.505, 0.432, 0.378, 0.331, 0.288, 0.25, 0.208,
0.166, 0.114, 0.077, 0.664, 0.632, 0.592, 0.579, 0.531,
0.474, 0.404, 0.354, 0.307, 0.268, 0.231, 0.192, 0.153,
0.104, 0.07, 0.627, 0.603, 0.564, 0.551, 0.504, 0.449,
0.381, 0.334, 0.29, 0.253, 0.217, 0.181, 0.143, 0.097,
0.065, 0.612, 0.579, 0.54, 0.527, 0.481, 0.429, 0.362,
0.216, 0.274, 0.239, 0.205, 0.172, 0.136, 0.091, 0.06,
0.59, 0.557, 0.52, 0.506, 0.461, 0.411, 0.345, 0.301,
0.261, 0.227, 0.195, 0.164, 0.129, 0.086, 0.057, 0.571,
0.538, 0.502, 0.489, 0.445, 0.395, 0.332, 0.288, 0.25,
0.217, 0.187, 0.157, 0.123, 0.082, 0.054, 0.554, 0.522,
0.486, 0.473, 0.43, 0.382, 0.32, 0.277, 0.241, 0.209,
0.179, 0.15, 0.118, 0.079, 0.052, 0.539, 0.508, 0.472,
0.46, 0.418, 0.37, 0.31, 0.268, 0.233, 0.202, 0.173,
0.144, 0.113, 0.076, 0.05, 0.526, 0.495, 0.46, 0.447,
0.406, 0.359, 0.301, 0.26, 0.226, 0.195, 0.167, 0.139,
0.109, 0.074, 0.049, 0.514, 0.484, 0.449, 0.437, 0.397,
0.35, 0.293, 0.252, 0.219, 0.189, 0.162, 0.134, 0.105,
0.071, 0.048, 0.503, 0.473, 0.439, 0.427, 0.387, 0.341,
0.286, 0.246, 0.213, 0.184, 0.157, 0.13, 0.101, 0.069,
0.047, 0.494, 0.464, 0.43, 0.418, 0.378, 0.333, 0.279,
0.24, 0.208, 0.179, 0.152, 0.126, 0.098, 0.067, 0.046,
0.485, 0.455, 0.422, 0.41, 0.371, 0.326, 0.273, 0.235,
0.203, 0.175, 0.148, 0.123, 0.096, 0.065, 0.045, 0.477,
0.447, 0.414, 0.402, 0.364, 0.32, 0.267, 0.23, 0.199,
0.171, 0.145, 0.12, 0.094, 0.064, 0.044, 0.469, 0.44,
0.407, 0.395, 0.358, 0.314, 0.262, 0.225, 0.195, 0.167,
0.142, 0.117, 0.092, 0.062, 0.043, 0.462, 0.434, 0.401,
0.39, 0.352, 0.309, 0.258, 0.221, 0.192, 0.164, 0.139,
0.114, 0.09, 0.061, 0.042, 0.456, 0.428, 0.395, 0.383,
0.346, 0.304, 0.254, 0.217, 0.189, 0.161, 0.136, 0.112,
0.089, 0.06, 0.041, 0.45, 0.422, 0.39, 0.379, 0.342,
0.3, 0.25, 0.214, 0.186, 0.158, 0.134, 0.11, 0.087, 0.059,
0.041, 0.444, 0.417, 0.385, 0.374, 0.338, 0.296, 0.246,
0.211, 0.183, 0.156, 0.132, 0.109, 0.086, 0.058, 0.04,
0.439, 0.412, 0.381, 0.37, 0.333, 0.292, 0.243, 0.208,
0.18, 0.154, 0.13, 0.107, 0.085, 0.058, 0.04, 0.434,
0.407, 0.376, 0.365, 0.329, 0.288, 0.239, 0.205, 0.177,
0.151, 0.128, 0.106, 0.083, 0.057, 0.039, 0.428, 0.402,
0.372, 0.361, 0.326, 0.285, 0.236, 0.202, 0.175, 0.149,
0.126, 0.104, 0.082, 0.056, 0.039)
q21 <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 0.998, 0.995, 0.99, 0.987, 0.976,
0.952, 0.902, 0.82, 0.795, 0.735, 0.669, 0.594, 0.504,
0.374, 0.273, 0.97, 0.951, 0.924, 0.913, 0.872, 0.821,
0.745, 0.68, 0.621, 0.563, 0.504, 0.439, 0.364, 0.268,
0.195, 0.919, 0.885, 0.842, 0.828, 0.78, 0.725, 0.637,
0.575, 0.517, 0.462, 0.408, 350, 0.285, 0.198, 0.138,
0.868, 0.829, 0.78, 0.763, 0.71, 0.65, 0.57, 0.509, 0.454,
0.402, 0.352, 0.298, 0.24, 0.166, 0.117, 0.816, 0.776,
0.725, 0.71, 0.657, 0.594, 0.516, 0.458, 0.407, 0.36,
0.313, 0.265, 0.212, 0.146, 0.103, 0.76, 0.726, 0.678,
0.664, 0.612, 0.551, 0.474, 0.42, 0.374, 0.329, 0.286,
0.24, 0.189, 0.13, 0.089, 0.713, 0.679, 0.638, 0.625,
0.576, 0.517, 0.442, 0.391, 0.348, 0.305, 0.265, 0.221,
0.173, 0.118, 0.08, 0.675, 0.642, 0.605, 0.592, 0.546,
0.49, 0.419, 0.37, 0.326, 0.285, 0.247, 0.206, 0.161,
0.11, 0.074, 0.649, 0.615, 0.578, 0.565, 0.521, 0.467,
0.399, 0.351, 0.308, 0.269, 0.232, 0.194, 0.152, 0.104,
0.07, 0.627, 0.593, 0.556, 0.544, 0.501, 0.448, 0.381,
0.334, 0.293, 0.256, 0.219, 0.184, 0.144, 0.099, 0.066,
0.607, 0.574, 0.537, 0.525, 0.483, 0.431, 0.366, 0.319,
0.28, 0.245, 0.208, 0.175, 0.138, 0.094, 0.062, 0.58,
0.557, 0.521, 0.509, 0.467, 0.416, 0.353, 0.307, 0.269,
0.235, 0.199, 0.167, 0.132, 0.09, 0.059, 0.573, 0.542,
0.507, 0.495, 0.453, 0.403, 0.341, 0.296, 0.259, 0.225,
0.192, 0.161, 0.127, 0.086, 0.057, 0.559, 0.529, 0.494,
0.482, 0.44, 0.391, 0.331, 0.287, 0.25, 0.218, 0.186,
0.155, 0.122, 0.082, 0.054, 0.547, 0.517, 0.482, 0.469,
0.428, 0.38, 0.322, 0.279, 0.243, 0.211, 0.18, 0.15,
0.117, 0.078, 0.052, 0.536, 0.506, 0.472, 0.46, 0.419,
0.371, 0.314, 0.271, 0.236, 0.205, 0.174, 0.145, 0.113,
0.075, 0.05, 0.526, 0.496, 0.462, 0.45, 0.41, 0.363,
0.306, 0.264, 0.229, 0.199, 0.17, 0.141, 0.11, 0.073,
0.049, 0.517, 0.487, 0.453, 0.441, 0.402, 0.356, 0.299,
0.258, 0.223, 0.194, 0.165, 0.137, 0.107, 0.071, 0.048,
0.509, 0.479, 0.445, 0.434, 0.395, 0.349, 0.293, 0.252,
0.218, 0.189, 0.161, 0.133, 0.105, 0.069, 0.046, 0.501,
0.471, 0.438, 0.427, 0.388, 0.343, 0.287, 0.247, 0.214,
0.185, 0.158, 0.13, 0.103, 0.068, 0.045, 0.493, 0.464,
0.431, 0.42, 0.382, 0.337, 0.282, 0.242, 0.21, 0.181,
0.154, 0.127, 0.1, 0.067, 0.043, 0.486, 0.457, 0.424,
0.414, 0.376, 0.331, 0.277, 0.238, 0.206, 0.178, 0.151,
0.125, 0.098, 0.066, 0.042, 0.479, 0.45, 0.418, 0.407,
0.37, 0.325, 0.273, 0.234, 0.203, 0.175, 0.149, 0.123,
0.096, 0.064, 0.041, 0.472, 0.444, 0.412, 0.402, 0.365,
0.32, 0.269, 0.23, 0.2, 0.172, 0.146, 0.121, 0.094, 0.063,
0.041, 0.466, 0.438, 0.406, 0.396, 0.36, 0.316, 0.265,
0.227, 0.197, 0.17, 0.144, 0.119, 0.092, 0.062, 0.04,
0.46, 0.433, 0.401, 0.391, 0.355, 0.312, 0.261, 0.224,
0.194, 0.167, 0.142, 0.117, 0.091, 0.061, 0.04)
q22 <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 0.998, 0.995, 0.992, 0.99,
0.983, 0.965, 0.93, 0.88, 830, 0.78, 0.72, 0.64, 0.54,
0.41, 0.3, 0.97, 0.945, 0.919, 0.909, 0.881, 0.85, 0.78,
0.73, 0.67, 0.61, 0.54, 0.47, 0.39, 0.27, 0.2, 0.922,
0.89, 0.857, 0.846, 0.803, 0.745, 0.664, 0.602, 0.546,
0.49, 0.434, 0.375, 0.309, 0.218, 0.156, 0.873, 0.84,
0.8, 0.787, 0.737, 0.676, 0.592, 0.53, 0.478, 0.425,
0.373, 0.32, 0.261, 0.186, 0.128, 0.826, 0.791, 0.749,
0.734, 0.682, 0.62, 0.543, 0.483, 0.433, 0.384, 0.335,
0.285, 0.231, 0.15, 0.111, 0.781, 0.745, 0.703, 0.688,
0.637, 0.578, 0.503, 0.446, 0.397, 0.351, 0.305, 0.258,
0.208, 0.142, 0.099, 0.74, 0.704, 0.661, 0.648, 0.6,
0.543, 0.47, 0.416, 0.37, 0.325, 0.282, 0.238, 0.19,
0.13, 0.09, 0.705, 0.67, 0.628, 0.616, 0.57, 0.515, 0.443,
0.391, 0.347, 0.304, 0.263, 0.222, 0.177, 0.122, 0.084,
0.674, 0.641, 0.602, 0.59, 0.546, 0.492, 0.421, 0.37,
0.328, 0.287, 0.247, 0.208, 0.166, 0.115, 0.079, 0.647,
0.616, 0.579, 0.568, 0.525, 0.472, 0.402, 0.353, 0.312,
0.273, 0.234, 0.196, 0.156, 0.109, 0.075, 0.624, 0.595,
0.559, 0.548, 0.507, 0.454, 0.386, 0.338, 0.298, 0.261,
0.223, 0.186, 0.148, 0.104, 0.071, 0.605, 0.577, 0.542,
0.531, 0.49, 0.438, 0.373, 0.325, 0.286, 0.25, 0.214,
0.178, 0.142, 0.099, 0.067, 0.589, 0.561, 0.527, 0.516,
0.475, 0.424, 0.361, 0.314, 0.276, 0.241, 0.206, 0.171,
0.135, 0.094, 0.063, 0.575, 0.547, 0.514, 0.503, 0.462,
0.412, 0.35, 0.304, 0.268, 0.233, 0.199, 0.165, 0.103,
0.09, 0.06, 0.562, 0.535, 0.502, 0.491, 0.45, 0.401,
0.34, 0.295, 0.26, 0.226, 0.193, 0.16, 0.125, 0.086,
0.057, 0.551, 0.524, 0.491, 0.48, 0.44, 0.391, 0.331,
0.287, 0.252, 0.22, 0.187, 0.155, 0.12, 0.082, 0.054,
0.541, 0.514, 0.481, 0.47, 0.43, 0.382, 0.323, 0.28,
0.245, 0.213, 0.182, 0.15, 0.116, 0.078, 0.051, 0.532,
0.505, 0.472, 0.461, 0.421, 0.374, 0.316, 0.274, 0.239,
0.207, 0.177, 0.146, 0.113, 0.075, 0.049, 0.524, 0.497,
0.464, 0.452, 0.413, 0.367, 0.31, 0.268, 0.232, 0.201,
0.172, 0.142, 0.111, 0.074, 0.047, 0.516, 0.489, 0.457,
0.445, 0.406, 0.36, 0.304, 0.262, 0.227, 0.196, 0.168,
0.138, 0.108, 0.073, 0.045, 0.508, 0.482, 0.45, 0.438,
0.399, 0.354, 0.298, 0.257, 0.222, 0.192, 0.164, 0.135,
0.106, 0.072, 0.044, 0.501, 0.475, 0.443, 0.432, 0.393,
0.348, 0.292, 0.252, 0.218, 0.189, 0.161, 0.132, 0.104,
0.071, 0.043, 0.495, 0.469, 0.437, 0.426, 0.387, 0.342,
0.287, 0.247, 0.215, 0.186, 0.158, 0.13, 0.102, 0.069,
0.042, 0.489, 0.463, 0.431, 0.419, 0.381, 0.337, 0.282,
0.243, 0.211, 0.183, 0.155, 0.128, 0.1, 0.068, 0.041,
0.483, 0.457, 0.425, 0.414, 0.376, 0.332, 0.278, 0.239,
0.208, 0.18, 0.153, 0.126, 0.098, 0.067, 0.041)
dim(q10) <- c(15, 30)
dim(q11) <- c(15, 30)
dim(q12) <- c(15, 30)
dim(q20) <- c(15, 30)
dim(q21) <- c(15, 30)
dim(q22) <- c(15, 30)
if (type == 10)
q0 <- q10[, n]
else if (type == 11)
q0 <- q11[, n]
else if (type == 12)
q0 <- q12[, n]
else if (type == 20)
q0 <- q20[, n]
else if (type == 21)
q0 <- q21[, n]
else
q0 <- q22[, n]
if (rev) {
res <- q_table(p, q0, pp)
}
else {
res <- q_table(p, pp, q0)
}
res[res < 0] <- 0
res[res > 1] <- 1
invisible(res)
}
#' Outlier removals with Dixon's method
#'
#' @param q Vector of quantiles.
#' @inheritParams q_dixon
p_dixon <- function (q, n, type = 10)
{
q_dixon(q, n, type, rev = TRUE)
}
#' Distribution table
#'
#' @param probs Vector of given probabilities.
#' @param quants Vector of given corresponding quantiles.
#' @inheritParams q_dixon
q_table <- function (p, probs, quants)
{
quants <- quants[order(probs)]
probs <- sort(probs)
res <- vector()
for (n in 1:length(p)) {
pp <- p[n]
if (pp <= probs[1]) {
q0 <- quants[c(1, 2)]
p0 <- probs[c(1, 2)]
fit <- lm(q0 ~ p0)
}
else if (pp >= probs[length(probs)]) {
q0 <- quants[c(length(quants) - 1, length(quants))]
p0 <- probs[c(length(probs) - 1, length(probs))]
fit <- lm(q0 ~ p0)
}
else {
x0 <- which(abs(pp - probs) == min(abs(pp - probs)))
x1 <- which(abs(pp - probs) == sort(abs(pp - probs))[2])
x <- min(c(x0, x1))
if (x == 1)
x <- 2
if (x > length(probs) - 2)
x <- length(probs) - 2
i <- c(x - 1, x, x + 1, x + 2)
q0 <- quants[i]
p0 <- probs[i]
fit <- lm(q0 ~ poly(p0, 3))
}
res <- c(res, predict(fit, newdata = list(p0 = pp)))
}
invisible(res)
}
#' Outlier removals with Dixon's method
#'
#' @param x A numeric vector.
Dixon_outliers <- function(x)
{
# x = c(0.0000000, 0.0000000, 1.0271542, 0.0000000, 0.2080097)
# x = c(0.0000000, 0.0000000, NA, 0.0000000, 0.2080097)
# x = c(0.0000000, 0.0000000, 0.0000000, 0.2080097)
# x = c(NA, NA, NA, 0.2080097)
x2 <- x[!is.na(x)]
if (length(x2) > 2L && length(unique(x2)) > 1L) {
out <- dixon_test(as.numeric(x), type = 0)
while(out$p.value < 0.05) {
if (out$alternative == "high") {
x[which.max(x)] <- NA
} else {
x[which.min(x)] <- NA
}
x2 <- x[!is.na(x)]
if (length(x2) > 2 && length(unique(x2)) > 1) {
out <- dixon_test(as.numeric(x), type = 0)
} else {
out$p.value <- 1
}
}
}
invisible(x)
}
#' Outlier removals with Grubbs's method
#'
#' @param x A data frame.
#' @param type Type for grubbs.test.
Grubbs_outliers <- function(x, type = 10)
{
x2 <- x[!is.na(x)]
if (length(x2) > 2 && length(unique(x2)) > 1) {
out <- grubbs.test(as.numeric(x, type))
while(out$p.value < 0.05) {
if (grepl("^highest", out$alternative)) {
x[which.max(x)] <- NA
}
else {
x[which.min(x)] <- NA
}
x2 <- x[!is.na(x)]
if (length(x2) > 2 && length(unique(x2)) > 1) {
out <- grubbs.test(as.numeric(x), type = type)
}
else {
out$p.value <- 1
}
}
}
invisible(x)
}
#' Outlier removals with Grubbs' method
#'
#' See also outliers::grubbs.test.
#'
#' @inheritParams dixon_test
grubbs_test <- function (x, type = 10, opposite = FALSE, two.sided = FALSE)
{
if (sum(c(10, 11, 20) == type) == 0) {
stop("Incorrect type", call. = FALSE)
}
DNAME <- deparse(substitute(x))
x <- sort(x[complete.cases(x)])
n <- length(x)
if (type == 11) {
g <- (x[n] - x[1])/sd(x)
u <- var(x[2:(n - 1)])/var(x) * (n - 3)/(n - 1)
pval = 1 - p_grubbs(g, n, type = 11)
method <- "Grubbs test for two opposite outliers"
alt = paste(x[1], "and", x[n], "are outliers")
}
else if (type == 10) {
if (xor(((x[n] - mean(x)) < (mean(x) - x[1])), opposite)) {
alt = paste("lowest value", x[1], "is an outlier")
o <- x[1]
d <- x[2:n]
}
else {
alt = paste("highest value", x[n], "is an outlier")
o <- x[n]
d <- x[1:(n - 1)]
}
g <- abs(o - mean(x))/sd(x)
u <- var(d)/var(x) * (n - 2)/(n - 1)
pval <- 1 - p_grubbs(g, n, type = 10)
method <- "Grubbs test for one outlier"
}
else {
if (xor(((x[n] - mean(x)) < (mean(x) - x[1])), opposite)) {
alt = paste("lowest values", x[1], ",",
x[2], "are outliers")
u <- var(x[3:n])/var(x) * (n - 3)/(n - 1)
}
else {
alt = paste("highest values", x[n - 1], ",",
x[n], "are outliers")
u <- var(x[1:(n - 2)])/var(x) * (n - 3)/(n - 1)
}
g <- NULL
pval <- p_grubbs(u, n, type = 20)
method <- "Grubbs test for two outliers"
}
if (two.sided) {
pval <- 2 * pval
if (pval > 1)
pval <- 2 - pval
}
RVAL <- list(statistic = c(G = g, U = u), alternative = alt,
p.value = pval, method = method, data.name = DNAME)
class(RVAL) <- "htest"
invisible(RVAL)
}
#' Distribution function
#'
#' @param p Vector of probabilities. See also outliers::dixon.test.
#' @param n Length of sample Length of sample.
#' @param rev Logical; if TRUE, acts as p_dixon.
#' @inheritParams dixon_test
q_grubbs <- function (p, n, type = 10, rev = FALSE)
{
if (type == 10) {
if (!rev) {
return(((n - 1)/sqrt(n)) *
sqrt(qt((1 - p)/n, n - 2)^2/(n - 2 + qt((1 - p)/n, n - 2)^2)))
}
else {
s <- (p^2 * n * (2 - n))/(p^2 * n - (n - 1)^2)
t <- sqrt(s)
if (is.nan(t)) {
res <- 0
}
else {
res <- n * (1 - pt(t, n - 2))
res[res > 1] <- 1
}
return(1 - res)
}
}
else if (type == 11) {
if (!rev) {
return(sqrt((2 * (n - 1) * qt((1 - p)/(n * (n - 1)),
n - 2)^2)/(n - 2 + qt((1 - p)/(n * (n - 1)),
n - 2)^2)))
}
else {
q <- p
p <- vector()
for (i in 1:length(q)) {
if (q[i] > qgrubbs(0.9999, n, type = 11)) {
pp <- 1
}
else if (q[i] < qgrubbs(2e-16, n, type = 11)) {
pp <- 0
}
else {
f <- function(x, q, n) {
qgrubbs(x, n, type = 11) - q
}
pp <- uniroot(f, c(0.001, 0.9999), q = q[i],
n = n)$root
}
p <- c(p, pp)
}
return(p)
}
}
else {
if (n > 30)
stop("n must be in range 3-30")
pp <- c(0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 0.4, 0.6,
0.8, 0.9, 0.95, 0.975, 0.99)
gtwo <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 1e-05, 2e-04, 8e-04, 0.0031, 0.007, 0.013,
0.055, 0.138, 0.283, 0.399, 0.482, 0.54, 0.589, 0.0035,
0.009, 0.0183, 0.0376, 0.058, 0.078, 0.169, 0.276,
0.41, 0.502, 0.571, 0.63, 0.689, 0.0186, 0.0349,
0.0565, 0.0921, 0.124, 0.153, 0.257, 0.361, 0.478,
0.562, 0.626, 0.674, 0.724, 0.044, 0.0708, 0.102,
0.1479, 0.186, 0.217, 0.325, 0.423, 0.53, 0.605,
0.659, 0.701, 0.743, 0.075, 0.1101, 0.1478, 0.1994,
0.238, 0.271, 0.376, 0.468, 0.568, 0.635, 0.684,
0.722, 0.763, 0.1082, 0.1492, 0.1909, 0.2454, 0.285,
0.319, 0.42, 0.507, 0.598, 0.658, 0.703, 0.738, 0.776,
0.1415, 0.1865, 0.2305, 0.2863, 0.326, 0.358, 0.455,
0.537, 0.622, 0.678, 0.721, 0.753, 0.787, 0.1736,
0.2212, 0.2666, 0.3226, 0.363, 0.394, 0.487, 0.564,
0.624, 0.695, 0.734, 0.765, 0.797, 0.2044, 0.2536,
0.2996, 0.3552, 0.393, 0.424, 0.514, 0.585, 0.66,
0.709, 0.745, 0.773, 0.802, 0.2333, 0.2836, 0.3295,
0.3843, 0.421, 0.451, 0.537, 0.605, 0.676, 0.721,
0.755, 0.782, 0.809, 0.2605, 0.3112, 0.3568, 0.4106,
0.447, 0.477, 0.558, 0.622, 0.689, 0.733, 0.765,
0.789, 0.817, 0.2859, 0.3367, 0.3818, 0.4345, 0.469,
0.497, 0.576, 0.639, 0.701, 0.742, 0.773, 0.797,
0.822, 0.3098, 0.3603, 0.4048, 0.4562, 0.491, 0.518,
0.593, 0.653, 0.713, 0.751, 0.78, 0.803, 0.826, 0.3321,
0.3822, 0.4259, 0.4761, 0.511, 0.536, 0.609, 0.666,
0.723, 0.76, 0.787, 0.808, 0.831, 0.353, 0.4025,
0.4455, 0.4944, 0.526, 0.552, 0.622, 0.676, 0.732,
0.767, 0.793, 0.814, 0.835, 0.3725, 0.4214, 0.4636,
0.5113, 0.543, 0.567, 0.635, 0.688, 0.74, 0.774,
0.799, 0.818, 0.838, 0.3909, 0.4391, 0.4804, 0.5269,
0.559, 0.582, 0.647, 0.697, 0.748, 0.781, 0.805,
0.823, 0.843, 0.408, 0.457, 0.496, 0.542, 0.571,
0.594, 0.657, 0.706, 0.755, 0.786, 0.81, 0.828, 0.847,
0.425, 0.474, 0.512, 0.556, 0.584, 0.606, 0.668,
0.715, 0.762, 0.792, 0.815, 0.834, 0.85, 0.442, 0.486,
0.524, 0.568, 0.596, 0.618, 0.677, 0.723, 0.769,
0.797, 0.819, 0.836, 0.853, 0.453, 0.5, 0.538, 0.581,
0.608, 0.628, 0.686, 0.73, 0.774, 0.802, 0.823, 0.84,
0.857, 0.466, 0.511, 0.547, 0.589, 0.616, 0.637,
0.693, 0.736, 0.779, 0.807, 0.827, 0.843, 0.86, 0.482,
0.525, 0.561, 0.601, 0.627, 0.647, 0.701, 0.743,
0.784, 0.811, 0.83, 0.845, 0.861, 0.492, 0.536, 0.572,
0.611, 0.636, 0.655, 0.709, 0.749, 0.789, 0.815,
0.834, 0.849, 0.864, 0.505, 0.548, 0.583, 0.621,
0.646, 0.664, 0.716, 0.755, 0.794, 0.819, 0.837,
0.851, 0.866, 0.516, 0.558, 0.592, 0.629, 0.654,
0.672, 0.722, 0.76, 0.798, 0.822, 0.84, 0.854, 0.869,
0.528, 0.568, 0.602, 0.638, 0.661, 0.679, 0.728,
0.765, 0.802, 0.826, 0.842, 0.856, 0.87)
dim(gtwo) <- c(13, 30)
if (!rev)
res <- qtable(p, pp, gtwo[, n])
else
res <- qtable(p, gtwo[, n], pp)
res[res < 0] <- 0
res[res > 1] <- 1
invisible(res)
}
}
#' Outlier removals with Grubbs' method
#'
#' @param q Vector of quantiles.
#' @inheritParams q_dixon
p_grubbs <- function (q, n, type = 10)
{
q_grubbs(q, n, type, rev = TRUE)
}
#' Outlier removals with Rosner's method
#'
#' @param x A matrix or data.frame.
Rosner_outliers <- function(x)
{
if (length(unique(x)) < 5)
return(x)
# up to 9-number outliers; may get warnings with NA being an outlier
suppressWarnings(gofOutlier_obj <- EnvStats::rosnerTest(as.numeric(x), 9))
if (gofOutlier_obj$n.outliers > 0) {
Index <- with(gofOutlier_obj$all.stat, Obs.Num[Outlier == TRUE])
x[Index] <- NA
}
invisible(x)
}
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