Description Details Author(s) References See Also Examples

Tools for automatic model selection and diagnostics for Climate and Environmental data. In particular the `envcpt()`

function does automatic model selection between a variety of trend, changepoint and autocorrelation models. The `envcpt()`

function should be your first port of call.

Package: | EnvCpt |

Type: | Package |

Version: | 0.1.1 |

Date: | 2016-10-11 |

License: | GPL |

LazyLoad: | yes |

Rebecca Killick <[email protected]>, Claudie Beaulieu <[email protected]>, Simon Taylor <[email protected]>

Maintainer: Rebecca Killick <[email protected]>

PELT Algorithm: Killick R, Fearnhead P, Eckley IA (2012) Optimal detection of changepoints with a linear computational cost, *JASA* **107(500)**, 1590–1598

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ```
## Not run:
set.seed(1)
x=c(rnorm(100,0,1),rnorm(100,5,1))
out=envcpt(x) # run the 8 models with default values
out[[1]] # first row is twice the negative log-likelihood for each model
# second row is the number of parameters
AIC(out) # returns AIC for each model.
which.min(AIC(out)) # gives meancpt (model 2) as the best model fit.
out[[3]] # gives the model fit for the meancpt model.
plot(out,type='fit') # plots the fits
plot(out,type="aic") # plots the aic values
set.seed(10)
x=c(0.01*(1:100),1.5-0.02*((101:250)-101))+rnorm(250,0,0.2)
out=envcpt(x,minseglen=10) # run the 8 models with a minimum of 10 observations between changes
AIC(out) # returns the AIC for each model
which.min(AIC(out)) # gives trendcpt (model 6) as the best model fit.
out[[7]] # gives the model fit for the trendcpt model.
plot(out,type='fit') # plots the fits
plot(out,type="aic") # plots the aic values
set.seed(100)
x=arima.sim(model=list(ar=0.8),n=100)+5
out=envcpt(x) # run the 8 models with
AIC(out) # returns the AIC for each model
which.min(AIC(out)) # gives trendar (model 7) as the best model fit.
out[[7]] # gives the model fit for the trendar model. Notice that the trend is tiny but does
# produce a significantly better fit than the meanar model.
plot(out,type='fit') # plots the fits
plot(out,type="aic") # plots the aic values
## End(Not run)
``` |

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