agg_curve: Aggregate Learning Curve

In Auburngrads/learningCurve: An Implementation of Crawford's and Wright's Learning Curve Production Functions

Description

Computes the approximate aggregate cumulative learning curve formula by calculating the sum of all contributing hours from all departments for all production units 1 through n.

Usage

agg_curve(t, r, n, na.rm = FALSE)

Arguments

t	vector of hours (or costs) for the first unit from departments 1 through m
r	vector of historical learning rates for departments 1 through m
n	total units to be produced across all departments
na.rm	Should NA values be removed?

Examples

## Not run: 
# A project is expected to get underway soon to produce 300
# widgets. Three departments will be involved. Historically,
# these departments have had learning curves of 85%, 87%, and
# 80% respectively. The first unit hours for these departments
# have been estimated at 70, 45, and 25 respectively. What is 
# the total predicted hours required for the entire effort?

t <- c(70, 45, 25)
r <- c(.85, .87, .8)

agg_curve(t = t, r = r, n = 300)
## [1] 11000.96

## End(Not run)

Auburngrads/learningCurve documentation built on May 5, 2019, 8:13 a.m.