Calculates and plots the AIC and eta-squared statistics for diferent layer models based on a changepoint analysis using the mean and variance.
cp_aic_eta(data, m = 10, nl = 3)
Arguments
| data | A data frame containing the location variable (depth or distance) in the first column, and the value of interest in the second column |
|---|---|
| m | The maximum number of breakpoints (# layers - 1) to look for |
| nl | The minimum number of points per layer to be considered |
Value
A ggplot and plotly objects showing the AIC and eta-squared statistics, and a data frame with all the data and possible layer models
Details
The example data given is intended to show the structure needed for input data. The user should follow this structure, which in general corresponds with a data frame with a sequence in the first column and the observed/measured values in the second column
Examples
cp_aic_eta(DPM_data, m = 10, nl = 3)#> $GGPLOT#> #> $PLOTLY #> #> $Data #> # A tibble: 101 × 12 #> Prof Blows B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 #> <dbl> <int> <fct> <fct> <fct> <fct> <fct> <fct> <fct> <fct> <fct> <fct> #> 1 0 0 [0,2.7] [0,2.7] [0,2.7] [0,2… [0,2… [0,2… [0,2… [0,2… [0,2… [0,2… #> 2 0.1 10 [0,2.7] [0,2.7] [0,2.7] [0,2… [0,2… [0,2… [0,2… [0,2… [0,2… [0,2… #> 3 0.2 8 [0,2.7] [0,2.7] [0,2.7] [0,2… [0,2… [0,2… [0,2… [0,2… [0,2… [0,2… #> 4 0.3 5 [0,2.7] [0,2.7] [0,2.7] [0,2… [0,2… [0,2… [0,2… [0,2… [0,2… [0,2… #> 5 0.4 5 [0,2.7] [0,2.7] [0,2.7] [0,2… [0,2… [0,2… [0,2… [0,2… [0,2… [0,2… #> 6 0.5 9 [0,2.7] [0,2.7] [0,2.7] [0,2… [0,2… [0,2… [0,2… [0,2… [0,2… [0,2… #> 7 0.6 6 [0,2.7] [0,2.7] [0,2.7] [0,2… [0,2… [0,2… [0,2… [0,2… [0,2… [0,2… #> 8 0.7 3 [0,2.7] [0,2.7] [0,2.7] [0,2… [0,2… [0,2… [0,2… [0,2… [0,2… [0,2… #> 9 0.8 3 [0,2.7] [0,2.7] [0,2.7] [0,2… [0,2… [0,2… [0,2… [0,2… [0,2… [0,2… #> 10 0.9 3 [0,2.7] [0,2.7] [0,2.7] [0,2… [0,2… [0,2… [0,2… [0,2… [0,2… [0,2… #> # … with 91 more rows #>
