plot.envcpt {EnvCpt} | R Documentation |
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.
## S3 method for class 'envcpt' plot(x,type=c('fit','bic','aic'),lwd=3,...,data=NA)
x |
A list produced as output from the |
type |
character vector. |
lwd |
Line width graphical parameter, see |
... |
Extra graphical parameters, passed to the original plot and the individual calls to |
data |
This argument is only required when |
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.
Returns the printed graphic to the active device.
Rebecca Killick & Claudie Beaulieu.
## 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)