delta: Crawford vs. Wright Unit Difference

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

Description

Computes the difference between the unit or cumulative prediction estimates provided by the Crawford and Wright models.

Usage

delta(t, m, n, r, level = "u")

Arguments

t	time (or cost) required to produce the first unit
m	mth unit for which you have time (or cost) information (default is m = 1)
n	the nth unit you wish to predict the time (or cost) for when comparing unit predictions or the last unit in the block when comparing cumulative time (or costs)
r	learning curve rate
level	calculate unit ("u") versus cumulative ("c") differences (default = "u")

Examples

# The first unit of production is expected to require 50 hours and
# the learning rate is expected to be 88.5%. However, the estimator
# is not sure whether the learning rate is based on the unit model
# or cumulative average model and wants to understand the difference
# between potential outcomes for each unit.

# differences between per unit time requirements
delta(t = 50, m = 1, n = 25, r = .885)
## [1] 0.000000 5.750000 6.103821 6.110519 6.041146 5.953271 5.863560 5.777401 5.696436
## [10] 5.620942 5.550687 5.485263 5.424223 5.367136 5.313606 5.263280 5.215844 5.171025
## [19] 5.128579 5.088293 5.049980 5.013473 4.978624 4.945304 4.913395

# differences between cumulative unit time requirements
delta(t = 50, m = 1, n = 25, r = .885, level = "c")
## [1]   0.00000   5.75000  11.85382  17.96434  24.00549  29.95876  35.82232  41.59972
## [9]  47.29615  52.91710  58.46778  63.95305  69.37727  74.74440  80.05801  85.32129
## [17]  90.53713  95.70816 100.83674 105.92503 110.97501 115.98848 120.96711 125.91241
## [25] 130.82581

learningCurve documentation built on May 2, 2019, 2:13 p.m.