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The data cumulative distribution function

Source: R/all_generics.R, R/dist_data_cdf_methods.R
dist_data_cdf-methods.Rd

This is generic function for distribution objects. This function calculates the data or empirical cdf.

The functions dist_data_all_cdf and dist_all_cdf are only available for discrete distributions. Their main purpose is to optimise the bootstrap procedure, where generating a vector xmin:xmax is very quick. Also, when bootstrapping very large values can be generated.

Usage

dist_all_cdf(m, lower_tail = TRUE, xmax = 1e+05)

dist_data_cdf(m, lower_tail = TRUE, xmax = 1e+05)

dist_data_all_cdf(m, lower_tail = TRUE, xmax = 1e+05)

# S4 method for class 'discrete_distribution'
dist_data_cdf(m, lower_tail = TRUE, xmax = 1e+05)

# S4 method for class 'discrete_distribution'
dist_data_all_cdf(m, lower_tail = TRUE, xmax = 1e+05)

# S4 method for class 'ctn_distribution'
dist_data_cdf(m, lower_tail = TRUE, xmax = 1e+05)

Arguments

m

a distribution object.

lower_tail

logical; if TRUE (default), probabilities are \(P[X \le x]\), otherwise, \(P[X > x]\).

xmax

default 1e5. The maximum x value calculated when working out the CDF.

Note

This method does not alter the internal state of the distribution objects.

Examples

##########################################
#Load data and create distribution object#
##########################################
data(moby_sample)
m = displ$new(moby_sample)
m$setXmin(7);m$setPars(2)

##########################################
# The data cdf                           #
##########################################
dist_data_cdf(m)
#>  [1] 0.1164179 0.2298507 0.3014925 0.3671642 0.4208955 0.4656716 0.5104478
#>  [8] 0.5373134 0.5761194 0.6000000 0.6208955 0.6388060 0.6656716 0.6716418
#> [15] 0.6865672 0.6955224 0.7134328 0.7164179 0.7313433 0.7462687 0.7552239
#> [22] 0.7641791 0.7820896 0.7880597 0.7910448 0.7940299 0.7970149 0.8029851
#> [29] 0.8119403 0.8268657 0.8328358 0.8358209 0.8388060 0.8417910 0.8477612
#> [36] 0.8507463 0.8626866 0.8656716 0.8686567 0.8716418 0.8776119 0.8805970
#> [43] 0.8835821 0.8865672 0.8895522 0.8925373 0.8955224 0.8985075 0.9014925
#> [50] 0.9074627 0.9104478 0.9134328 0.9164179 0.9194030 0.9223881 0.9253731
#> [57] 0.9313433 0.9343284 0.9373134 0.9402985 0.9432836 0.9462687 0.9522388
#> [64] 0.9552239 0.9582090 0.9611940 0.9641791 0.9671642 0.9701493 0.9731343
#> [71] 0.9761194 0.9791045 0.9820896 0.9850746 0.9880597 0.9910448 0.9940299
#> [78] 0.9970149 1.0000000