plot.envcpt: Plots optionally either ("fit") the data and fits from each...

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/diagnostics.R

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

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

...

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.

Value

Returns the printed graphic to the active device.

Author(s)

Rebecca Killick & Claudie Beaulieu.

See Also

envcpt

Examples

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## 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)

Example output

Loading required package: changepoint
Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

Successfully loaded changepoint package version 2.2.2
 NOTE: Predefined penalty values changed in version 2.2.  Previous penalty values with a postfix 1 i.e. SIC1 are now without i.e. SIC and previous penalties without a postfix i.e. SIC are now with a postfix 0 i.e. SIC0. See NEWS and help files for further details.
Loading required package: MASS
Fitting 12 models

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           mean meancpt  meanar1  meanar2 meanar1cpt meanar2cpt    trend
logLik 949.2517 535.486 683.8642 640.5247   532.9724   530.5236 735.4193
nparam   2.0000   5.000   3.0000   4.0000     7.0000     9.0000   3.0000
       trendcpt trendar1 trendar2 trendar1cpt trendar2cpt
logLik  535.414  648.192 619.5107    532.7957    530.4183
nparam    7.000    4.000   5.0000      9.0000     11.0000
       mean     meancpt     meanar1     meanar2  meanar1cpt  meanar2cpt 
   953.2517    545.4860    689.8642    648.5247    546.9724    548.5236 
      trend    trendcpt    trendar1    trendar2 trendar1cpt trendar2cpt 
   741.4193    549.4140    656.1920    629.5107    550.7957    552.4183 
meancpt 
      2 
Class 'cpt' : Changepoint Object
       ~~   : S4 class containing 12 slots with names
              cpttype date version data.set method test.stat pen.type pen.value minseglen cpts ncpts.max param.est 

Created on  : Thu May  4 11:08:19 2017 

summary(.)  :
----------
Created Using changepoint version 2.2.2 
Changepoint type      : Change in mean and variance 
Method of analysis    : PELT 
Test Statistic  : Normal 
Type of penalty       : MBIC with value, 21.19327 
Minimum Segment Length : 5 
Maximum no. of cpts   : Inf 
Changepoint Locations : 100 
        mean      meancpt      meanar1      meanar2   meanar1cpt   meanar2cpt 
1.471637e-89 5.164235e-01 2.299732e-32 2.179893e-23 2.456047e-01 1.130823e-01 
       trend     trendcpt     trendar1     trendar2  trendar1cpt  trendar2cpt 
1.467638e-43 7.245090e-02 4.715216e-25 2.932684e-19 3.630777e-02 1.613078e-02 
       mean     meancpt     meanar1     meanar2  meanar1cpt  meanar2cpt 
   959.8483    561.9775    699.7591    661.7179    570.0606    578.2084 
      trend    trendcpt    trendar1    trendar2 trendar1cpt trendar2cpt 
   751.3143    572.5022    669.3852    646.0023    580.4806    588.6998 
meancpt 
      2 
Fitting 12 models

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       mean     meancpt     meanar1     meanar2  meanar1cpt  meanar2cpt 
  578.94707   -67.51376    27.29077   -29.55647    27.29077    29.29077 
      trend    trendcpt    trendar1    trendar2 trendar1cpt trendar2cpt 
  468.36581  -113.41437    20.92805   -33.70269  -109.94452  -105.29104 
trendcpt 
       8 
Class 'cpt.reg' : Changepoint Regression Object
       ~~   : S4 class containing 12 slots with names
              cpttype date version data.set method test.stat pen.type pen.value minseglen cpts ncpts.max param.est 

Created on  : Thu May  4 11:08:19 2017 

summary(.)  :
----------
Created Using changepoint version 2.2.2 
Changepoint type     : Change in regression 
Method of analysis   : PELT 
Test Statistic : Normal 
Type of penalty      : MBIC with value, 27.6073 
Maximum no. of cpts   : Inf 
Changepoint Locations : 100 
         mean       meancpt       meanar1       meanar2    meanar1cpt 
3.790989e-151  9.035235e-11  2.340974e-31  5.171508e-19  2.340974e-31 
   meanar2cpt         trend      trendcpt      trendar1      trendar2 
 8.611961e-32 3.900936e-127  8.377773e-01  5.636952e-30  4.111095e-18 
  trendar1cpt   trendar2cpt 
 1.477958e-01  1.442682e-02 
       mean     meancpt     meanar1     meanar2  meanar1cpt  meanar2cpt 
  585.98999    24.04422    37.85515   -15.47062    37.85515    43.37661 
      trend    trendcpt    trendar1    trendar2 trendar1cpt trendar2cpt 
  478.93019   -88.76414    35.01389   -16.09538   -78.25138   -66.55497 
trendcpt 
       8 
Fitting 8 models

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       mean     meancpt     meanar1     meanar2  meanar1cpt  meanar2cpt 
         NA          NA    1478.915    1455.634    1478.915    1480.915 
      trend    trendcpt    trendar1    trendar2 trendar1cpt trendar2cpt 
         NA          NA    1452.451    1430.101    1452.451    1430.101 
trendar2 
      10 

Call:
lm(formula = data[-c(1:2)] ~ c(1:(n - 2)) + data[2:(n - 1)] + 
    data[1:(n - 2)])

Coefficients:
    (Intercept)     c(1:(n - 2))  data[2:(n - 1)]  data[1:(n - 2)]  
      -0.069419         0.001703         0.661337         0.186613  

        mean      meancpt      meanar1      meanar2   meanar1cpt   meanar2cpt 
          NA           NA 1.256735e-11 1.428051e-06 1.256735e-11 4.623269e-12 
       trend     trendcpt     trendar1     trendar2  trendar1cpt  trendar2cpt 
          NA           NA 7.011284e-06 4.999923e-01 7.011284e-06 4.999923e-01 
       mean     meancpt     meanar1     meanar2  meanar1cpt  meanar2cpt 
         NA          NA    1491.553    1472.484    1491.553    1497.765 
      trend    trendcpt    trendar1    trendar2 trendar1cpt trendar2cpt 
         NA          NA    1469.302    1451.164    1469.302    1451.164 
trendar2 
      10 

EnvCpt documentation built on July 2, 2020, 12:52 a.m.

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