Skip to contents

Plots a layered model and summarizes the statistics for each layer

Source: R/layers_window.R
layers_window.Rd

Given a set of breakpoints (depths/distances), plots a layered model of the data against distance, plots the confidence intervals for each layer, and gives a summary table.

Usage

layers_window(x, breaks, conf.level = 0.95)

Arguments

x

A data frame containing the location variable (depth or distance) in the first column, and the value(s) of interest in the rest of the columns

breaks

A vector containing the breakpoints (from 'RI', 'Cohen d' or 'Mahalanobis D2')

conf.level

Confidence level to use for plot and summary statistics (Default is 0.95)

Value

A ggplot and plotly objects showing the layered model, another showing the confidence intervals, and a summary table

See also

Other layer detection: RI(), T2_stat(), T_stat(), cp_aic_eta(), cp_layers(), sc_bic(), sc_layers()

Examples

layers_window(DPM_data, breaks = c(3.8,8.9))
#> Warning: X11 error: GLXBadContext
#> Warning: 'rgl.init' failed, will use the null device.
#> See '?rgl.useNULL' for ways to avoid this warning.
#> $LayersGG

#> 
#> $LayersLY
#> 
#> $StatsGG

#> 
#> $StatsLY
#> 
#> $Summary
#>   boundaries Property Obs  Mean    SD Min Max CI.lwr CI.upr   MoE
#> 1    [0,3.8]    Blows  39  5.85 2.370   0  10   5.08   6.61 0.768
#> 2  (3.8,8.9]    Blows  51  8.16 1.250   6  11   7.80   8.51 0.352
#> 3   (8.9,10]    Blows  11 10.80 0.874  10  12  10.20  11.40 0.587
#> 
#> $ES
#> [1] 0.454
#> 
layers_window(CPTu_data, breaks = c(1.2,3.8,5.1))
#> $LayersGG

#> 
#> $LayersLY
#> 
#> $StatsGG

#> 
#> $StatsLY
#> 
#> $Summary
#>    boundaries Property Obs    Mean      SD    Min     Max CI.lwr CI.upr     MoE
#> 1  [0.12,1.2]       qc  55   1.530   0.849   0.75    3.47   1.30   1.76   0.230
#> 2  [0.12,1.2]       fs  55  51.400  20.000   0.53  101.00  46.00  56.80   5.410
#> 3  [0.12,1.2]        u  55  -0.263  20.000 -37.30   39.20  -5.66   5.14   5.410
#> 4   (1.2,3.8]       qc 130   2.230   0.804   1.17    5.06   2.09   2.37   0.140
#> 5   (1.2,3.8]       fs 130  97.100  35.800  23.90  181.00  90.90 103.00   6.210
#> 6   (1.2,3.8]        u 130 131.000  94.200  -5.99  400.00 114.00 147.00  16.300
#> 7   (3.8,5.1]       qc  65   7.310   1.210   4.69   10.60   7.01   7.61   0.300
#> 8   (3.8,5.1]       fs  65 347.000  70.700 210.00  509.00 330.00 365.00  17.500
#> 9   (3.8,5.1]        u  65 707.000 458.000  72.60 2040.00 594.00 821.00 113.000
#> 10 (5.1,11.6]       qc 323   4.310   2.140   2.38   22.60   4.07   4.54   0.234
#> 11 (5.1,11.6]       fs 323 167.000  76.300 -22.20  467.00 159.00 176.00   8.350
#> 12 (5.1,11.6]        u 323 752.000 688.000 -54.80 2530.00 677.00 828.00  75.300
#> 
#> $ES
#> [1] 0.282
#>