## quantile functions: mileage may vary

**W**hen experimenting with various quantiles functions in R, I was shocked *[ok this is a bit excessive, let us say surprised]* by how widely the execution times would vary. To the point of blaming a completely different feature of R. Borrowing from Charlie Geyer’s webpage on the topic of probability distributions in R, here is a table for some standard distributions: I ran

u=runif(1e7) system.time(x<-qcauchy(u))

choosing an arbitrary parameter whenever needed.

Distribution | Function | Time |
---|---|---|

Cauchy | `qcauchy` |
2.2 |

Chi-Square | `qchisq` |
43.8 |

Exponential | `qexp` |
0.95 |

F | `qf` |
34.2 |

Gamma | `qgamma` |
37.2 |

Logistic | `qlogis` |
1.7 |

Log Normal | `qlnorm` |
2.2 |

Normal | `qnorm` |
1.4 |

Student t | `qt` |
31.7 |

Uniform | `qunif` |
0.86 |

Weibull | `qweibull` |
2.9 |

Of course, it does not mean much in that all the slow distributions (except for Weibull) are parameterised. Nonetheless, that a chi-square inversion take 50 times longer than a uniform inversion remains puzzling as to why it is not coded more efficiently. In particular, I was wondering why the chi-square inversion was slower than the Gamma inversion. Rerunning both inversions showed that they are equivalent:

> u=runif(1e7) > system.time(x<-qgamma(u,sha=1.5)) utilisateur système écoulé 21.534 0.016 21.532 > system.time(x<-qchisq(u,df=3)) utilisateur système écoulé 21.372 0.008 21.361

Which also shows how variable *system.time* can be.

May 12, 2015 at 10:11 am

Hi,

you might want to look at microbenchmark to at least reduce the variability due to whatever the rest of the system is up to.

At least here this suggests that qchisq depends on the degrees of freedom with 3df being the worst offender.

library(microbenchmark)

u<-runif(1e4)

microbenchmark(

qcauchy(u),

qchisq(u, 2),

qchisq(u, 3),

qchisq(u, 4),

qchisq(u, 10),

qgamma(u, shape = 1.5)

)

May 12, 2015 at 9:19 pm

Thank you. This is a most useful alternative to the system.time() function. Indeed, df=3 is strangely costly:

expr min lq mean median uq

qcauchy(u) 1.04 1.10 1.65 1.48 2.19

qchisq(u,2) 12.8 13.28 19.53 14.40 26.66

qchisq(u,3) 20.9 21.89 32.13 23.39 43.68

qchisq(u,4) 10.9 11.41 15.89 11.64 22.78

qchisq(u,10) 11.5 11.92 16.79 12.29 23.88

qgamma(u,1.5)21.07 21.89 30.76 22.47 43.83