Description Usage Arguments Details Value Examples
dc_cosine
is the cosine transformation.
dc_logistic
is the logistic transformation.
dc_zscore
is the zscore transformation.
dc_dist_canberra
computes the Canberra distance between 2 numeric vectors.
dc_dist_cosine
computes the cosine angle distance between 2 numeric vectors.
dc_dist_euclidean
compute the Euclidience distance between 2 numeric vectors.
dc_dist_pearson
compute the Pearson correlation distance between 2 numeric vectors.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | dc_cosine(x, max = 100)
dc_logistic(x, max = 100)
dc_zscore(x)
dc_dist_canberra(x, y)
dc_dist_cosine(x, y)
dc_dist_euclidean(x, y)
dc_dist_pearson(x, y)
dc_trim_outlier(x, fraction = 0.01)
dc_normalize_ptile(x, fraction = 0.01)
get_confidence_interval(x, level = 0.95)
dc_decile_band(x, n = NA)
dc_decile_ptile(x, band_ptile = c(seq(0, 0.95, 0.05)))
dc_rank_ptile(x, level_rank = c(1, 2, 3, 4, seq(5, 100, 5)))
dc_mode(x, na.rm = FALSE)
dc_ceiling(x, digits = 0, na.rm = FALSE)
|
x |
A numeric vector |
max |
A numeric value |
y |
A numeric vector |
fraction |
The percentile value (0 to 0.5) to trim out |
level |
The CI level (0.5 to 1.0) of observations to be measured. |
band_ptile |
The percentail band (0.0 to 1.0) |
level_rank |
The rank level (0.0 to 1.0) for calculating percentile |
na.rm |
A logical value indicating whether NA values should be stripped before the computation proceeds. |
digits |
similar to rbase::round() which is integer indicating the number of decimal places (round) or significant digits (signif) to be used. Negative values are allowed |
dc_ceiling
similar to rbase::ceiling() with support decimal round up
dc_mode
compute the stats mode
dc_rank_ptile
add columns with ranked percentiles
dc_decile_band
add columns with decile bands
dc_decile_ptile
add columns with decile percentiles
returns a numeric vector after normaliztion or distance between 2 vectors.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | library(dacol)
library(dplyr)
max = 30
dta1 = tibble(x1 = seq(-1.2*max, 1.2*max, length.out = 200),
x2 = seq(1, max, length.out = 200),
x3 = sample(200))
dta1 = mutate(dta1,
# Transformation
y_cosine = dc_cosine(x1, max),
y_logistic = dc_logistic(x2, max),
y_zcore = dc_zscore(x2),
# Distant between 2 vector columns
y_dist_canb = dc_dist_canberra(x2, x3),
y_dist_cos = dc_dist_cosine(x2, y_zcore),
y_dist_euc = dc_dist_euclidean(x2, y_zcore),
y_dist_pear = dc_dist_pearson(x2, y_zcore),
# Manage outliers
y_trim = dc_trim_outlier(x3, 0.01),
y_norm = dc_normalize_ptile(x3, 0.01),
# Stats measures
y_mode = dc_mode(x3),
y_ceil = dc_ceiling(x3, -1),
# Band segmentation
y_dec_band1 = dc_decile_band(x3),
y_dec_band2 = dc_decile_band(x3, c(seq(0, 0.9, 0.1))),
y_dec_ptile1 = dc_decile_ptile(x3),
y_dec_ptile2 = dc_decile_ptile(x3, c(seq(0, 0.9, 0.1)))
)
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