dparetotol.int {tolerance} | R Documentation |
Discrete Pareto Tolerance Intervals
Description
Provides 1-sided or 2-sided tolerance intervals for data distributed according to the discrete Pareto distribution.
Usage
dparetotol.int(x, m = NULL, alpha = 0.05, P = 0.99, side = 1,
...)
Arguments
x |
A vector of raw data which is distributed according to a discrete Pareto distribution. |
m |
The number of observations in a future sample for which the tolerance limits will be calculated. By default, |
alpha |
The level chosen such that 1-alpha is the confidence level. |
P |
The proportion of the population to be covered by this tolerance interval. |
side |
Whether a 1-sided or 2-sided tolerance interval is required (determined by |
... |
Additional arguments passed to the |
Details
The discrete Pareto is a discretized of the continuous Type II Pareto distribution (also called the Lomax distribution). Discrete Pareto distributions are heavily right-skewed distributions and potentially good models for discrete lifetime data and extremes in count data. For most practical applications, one will typically be interested in 1-sided upper bounds.
Value
dparetotol.int
returns a data frame with the following items:
alpha |
The specified significance level. |
P |
The proportion of the population covered by this tolerance interval. |
theta |
MLE for the shape parameter |
1-sided.lower |
The 1-sided lower tolerance bound. This is given only if |
1-sided.upper |
The 1-sided upper tolerance bound. This is given only if |
2-sided.lower |
The 2-sided lower tolerance bound. This is given only if |
2-sided.upper |
The 2-sided upper tolerance bound. This is given only if |
References
Young, D. S., Naghizadeh Qomi, M., and Kiapour, A. (2019), Approximate Discrete Pareto Tolerance Limits for Characterizing Extremes in Count Data, Statistica Neerlandica, 73, 4–21.
See Also
Examples
## 95%/95% 1-sided tolerance intervals for data assuming
## the discrete Pareto distribution.
set.seed(100)
x <- rdpareto(n = 500, theta = 0.5)
out <- dparetotol.int(x, alpha = 0.05, P = 0.95, side = 1)
out