Builds an Opn object

In Momocs, Opn classes objects are lists of open outlines, with optionnal components, on which generic methods such as plotting methods (e.g. stack) and specific methods (e.g. npoly can be applied. Opn objects are primarily Coo objects.

Usage

Opn(x, fac = dplyr::tibble(), ldk = list())

Arguments

x

list of matrices of (x; y) coordinates, or an array, or a data.frame (and friends)

fac

(optionnal) a data.frame of factors and/or numerics specifying the grouping structure

ldk

(optionnal) list of landmarks as row number indices

Value

an Opn object

See also

Other classes: Coe(), Coo(), Ldk(), OpnCoe(), OutCoe(), Out(), TraCoe()

Examples

#Methods on Opn
methods(class=Opn)
#>  [1] add_ldk           combine           coo_bookstein     coo_sample       
#>  [5] coo_sample_prop   coo_slice         coo_smoothcurve   def_ldk          
#>  [9] def_ldk_angle     def_ldk_direction def_ldk_tips      dfourier         
#> [13] fgProcrustes      get_ldk           mosaic            npoly            
#> [17] opoly             panel             pile              rearrange_ldk    
#> see '?methods' for accessing help and source code
# we load some open outlines. See ?olea for credits
olea
#> Opn (curves)
#>   - 210 curves, 99 +/- 3 coords (in $coo)
#>   - 4 classifiers (in $fac): 
#> # A tibble: 210 × 4
#>   var   domes view  ind  
#>   <fct> <fct> <fct> <fct>
#> 1 Aglan cult  VD    O10  
#> 2 Aglan cult  VL    O10  
#> 3 Aglan cult  VD    O11  
#> 4 Aglan cult  VL    O11  
#> 5 Aglan cult  VD    O12  
#> 6 Aglan cult  VL    O12  
#> # ℹ 204 more rows
#>   - also: $ldk
panel(olea)

# orthogonal polynomials
op <- opoly(olea, degree=5)
#> 'nb.pts' missing and set to 91
# we print the Coe
op
#> An OpnCoe object [ opoly analysis ]
#> --------------------
#>  - $coe: 210 open outlines described
#>  - $baseline1: (-0.5; 0), $baseline2: (0.5; 0)
#> # A tibble: 210 × 4
#>   var   domes view  ind  
#>   <fct> <fct> <fct> <fct>
#> 1 Aglan cult  VD    O10  
#> 2 Aglan cult  VL    O10  
#> 3 Aglan cult  VD    O11  
#> 4 Aglan cult  VL    O11  
#> 5 Aglan cult  VD    O12  
#> 6 Aglan cult  VL    O12  
#> # ℹ 204 more rows
# Let's do a PCA on it
op.p <- PCA(op)
plot(op.p, 'domes')
#> will be deprecated soon, see ?plot_PCA

plot(op.p, 'var')
#> will be deprecated soon, see ?plot_PCA

# and now an LDA after a PCA
olda <- LDA(PCA(op), 'var')
#> 4 PC retained
# for CV table and others
olda
#>  * Cross-validation table ($CV.tab):
#>         classified
#> actual   Aglan Cypre MouBo1 PicMa
#>   Aglan     21     2     17    20
#>   Cypre     12     4     14     0
#>   MouBo1     4     2     54     0
#>   PicMa     22     1      2    35
#> 
#>  * Class accuracy ($CV.ce):
#>     Aglan     Cypre    MouBo1     PicMa 
#> 0.3500000 0.1333333 0.9000000 0.5833333 
#> 
#>  * Leave-one-out cross-validation ($CV.correct): (54.3% - 114/210): 
plot_LDA(olda)