plot.envcpt {EnvCpt}

Plots optionally either ("fit") the data and fits from each of the 8 models or ("aic") the aic for each of the 8 models.

Description

Uses the output of the envcpt function and plots optionally ("fit") the original data and the fit from each of the 8 models or ("aic") the aic for each of the 8 models.

Usage

## S3 method for class 'envcpt'
plot(x,type=c('fit','bic','aic'),lwd=3,colors=rainbow(12),...,data=NA)

Arguments

x

A list produced as output from the envcpt function. In essence a list with the named elements: "mean","meancpt","meanar1","meanar2","meanar1cpt","meanar2cpt","trend","trendcpt","trendar1","trendar2","trendar1cpt","trendar2cpt" where each element is the output from the appropriate function call.

type

character vector. fit returns a plot of the data and fitted models. aic returns a bar chart of the aic values for each of the 8 models

lwd

Line width graphical parameter, see par for further details.

colors

colors for the individual models to be passed to the plotting functions. Note that this must be of length 12 regardless of how many models were evaluated. Color validity is checked using col2rgb.

...

Extra graphical parameters, passed to the original plot and the individual calls to lines or barchar.

data

This argument is only required when x doesn't contain any fits that provide the data which is required for the "fit" type option. Default is NA as most of the time this is likely not required.

Details

If type="fit", the function plots the data at the bottom and stacks the different fits for the 8 models from the envcpt function on top. No scale is given as all data and fits are scaled to be in (0,1). This is designed as an initial visualization tool for the fits only. If type="aic" the function uses the AIC.envcpt function to calculate the AIC values for the envcpt output x. Then barcharts the AIC values in the same order as the type="fit" option. The minimum AIC is the preferred model and this is highlight by a solid block. This is designed as an initial visualization tool for the AIC values only. If type="bic" the function uses the BIC.envcpt function to calculate the BIC values for the envcpt output x. Then barcharts the BIC values in the same order as the type="fit" option. The minimum BIC is the preferred model and this is highlight by a solid block. This is designed as an initial visualization tool for the BIC values only.

For ease of use, if "colours" is specified instead of (or in addition to) colors then this will take precedence.

Value

Returns the printed graphic to the active device.

Author(s)

Rebecca Killick & Claudie Beaulieu.

See Also

envcpt

Examples

## Not run: 
set.seed(1)
x=c(rnorm(100,0,1),rnorm(100,5,1))
out=envcpt(x) # run all 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$meancpt # gives the model fit for the meancpt model.
AICweights(out) # gives the AIC weights for each model
BIC(out) # returns the BIC for each model.
which.min(BIC(out)) # gives meancpt (model 2) as the best model fit too.
plot(out,type='fit') # plots the fits
plot(out,type="aic") # plots the aic values
plot(out,type="bic") # plots the bic 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 all models with a minimum of 10 observations between changes
AIC(out) # returns the AIC for each model
which.min(AIC(out)) # gives trendcpt (model 8) as the best model fit.
out$trendcpt # gives the model fit for the trendcpt model.
AICweights(out) # gives the AIC weights for each model
BIC(out) # returns the BIC for each model.
which.min(BIC(out)) # gives trendcpt (model 8) as the best model fit too.
plot(out,type='fit') # plots the fits
plot(out,type="aic") # plots the aic values
plot(out,type="bic") # plots the bic values

set.seed(100)
x=arima.sim(model=list(ar=c(0.7,0.2)),n=500)+0.01*(1:500)
out=envcpt(x,models=c(3:6,9:12)) # runs a subset of models (those with AR components) 
AIC(out) # returns the AIC for each model
which.min(AIC(out)) # gives trendar2 (model 10) as the best model fit.
out$trendar2 # gives the model fit for the trendar2 model. Notice that the trend is tiny but does 
# produce a significantly better fit than the meanar2 model.
AICweights(out) # gives the AIC weights for each model
BIC(out) # returns the BIC for each model.
which.min(BIC(out)) # best fit is trendar2 (model 10) again.
plot(out,type='fit') # plots the fits
plot(out,type="aic") # plots the aic values
plot(out,type="bic") # plots the bic values

## End(Not run)