Description Usage Arguments Examples

Provides the approximate cumulative time or cost required for units m through n (inclusive) using the Crawford unit model. Provides nearly the exact output as unit_cum_exact(), usually only off by 1-2 units but reduces computational time drastically if trying to calculate cumulative hours (costs) for over a million units.

1 | ```
unit_cum_appx(t, n, r, m = 1, na.rm = FALSE)
``` |

`t` |
time (or cost) required for the mth unit of production |

`n` |
The unit you wish to predict the cumulative time (or cost) to |

`r` |
learning curve rate |

`m` |
mth unit of production (default set to 1st production unit) |

`na.rm` |
Should |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
library(learningCurve)
# An estimator believes that the first unit of a product will
# require 100 labor hours. How many total hours will be required
# for 125 units given the organization has historically experienced
# an 85% learning curve?
unit_cum_appx(t = 100, n = 125, r = .85)
## [1] 5202.998
# Computational difference between unit_cum_exact() and unit_cum_appx()
# for 1 million units
system.time(unit_cum_exact(t = 100, n = 1000000, r = .85))
## user system elapsed
## 0.105 0.004 0.109
system.time(unit_cum_appx(t = 100, n = 1000000, r = .85))
## user system elapsed
## 0 0 0
``` |

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