These functions wrap ordination objects in the class tbl_ord, create tbl_ords directly from matrices, and test for the class and basic structure.
as_tbl_ord(x)
# S3 method for tbl_ord
as_tbl_ord(x)
make_tbl_ord(rows = NULL, cols = NULL, ...)
is_tbl_ord(x)
is.tbl_ord(x)
valid_tbl_ord(x)
un_tbl_ord(x)An ordination object.
Matrices to be used as factors of a tbl_ord.
Additional elements of a custom tbl_ord.
A tbl_ord (as*(), make*()), an S3-class model object that can be
wrapped as one (un*()), or a logical value (is*(), value*()).
The tbl_ord class wraps around a range of ordination classes, making
available a suite of ordination tools that specialize to each original object
class. These tools include format() and fortify() methods, which
facilitate the print() method and the ggbiplot() function.
No default method is provided for as_tbl_ord(), despite most defined
methods being equivalent (simply appending 'tbl_ord' to the vector of object
classes). This prevents objects for which other methods are not defined from
being re-classed as tbl_ords.
The function make_tbl_ord() creates a tbl_ord structured as a list of two
matrices, u and v, which must have the same number of columns and the
same column names.
is_tbl_ord() checks an object x for the tbl_ord class; valid_tbl_ord()
additionally checks for consistency between recover_coord(x) and the
columns of recover_rows(x) and recover_cols(x), using the recoverers.
un_tbl_ord() removes attributes associated with the tbl_ord class in order
to restore an object that was originally passed to as_tbl_ord.
# illustrative ordination: FA of Swiss social data
swiss_fa <- factanal(swiss, factors = 3L, scores = "regression")
print(swiss_fa)
#>
#> Call:
#> factanal(x = swiss, factors = 3L, scores = "regression")
#>
#> Uniquenesses:
#> Fertility Agriculture Examination Education
#> 0.005 0.286 0.213 0.114
#> Catholic Infant.Mortality
#> 0.083 0.743
#>
#> Loadings:
#> Factor1 Factor2 Factor3
#> Fertility -0.512 0.203 0.832
#> Agriculture -0.774 0.312 -0.129
#> Examination 0.751 -0.423 -0.211
#> Education 0.901 -0.262
#> Catholic -0.186 0.913 0.220
#> Infant.Mortality 0.500
#>
#> Factor1 Factor2 Factor3
#> SS loadings 2.273 1.164 1.120
#> Proportion Var 0.379 0.194 0.187
#> Cumulative Var 0.379 0.573 0.759
#>
#> The degrees of freedom for the model is 0 and the fit was 1e-04
# add the 'tbl_ord' wrapper
swiss_fa_ord <- as_tbl_ord(swiss_fa)
# inspect wrapped model
is_tbl_ord(swiss_fa_ord)
#> [1] TRUE
print(swiss_fa_ord)
#> # A tbl_ord of class 'factanal': (53 x 3) x (6 x 3)'
#> # 3 coordinates: Factor1, Factor2, Factor3
#> #
#> # Rows (symmetric): [ 53 x 3 | 0 ]
#> Factor1 Factor2 Factor3 |
#> |
#> 1 -0.512 0.203 0.832 |
#> 2 -0.774 0.312 -0.129 |
#> 3 0.751 -0.423 -0.211 |
#> 4 0.901 0.0784 -0.262 |
#> 5 -0.186 0.913 0.220 |
#>
#> #
#> # Columns (symmetric): [ 6 x 3 | 0 ]
#> Factor1 Factor2 Factor3 |
#> |
#> 1 -0.512 0.203 0.832 |
#> 2 -0.774 0.312 -0.129 |
#> 3 0.751 -0.423 -0.211 |
#> 4 0.901 0.0784 -0.262 |
#> 5 -0.186 0.913 0.220 |
#> 6 0.0289 0.0772 0.500 |
valid_tbl_ord(swiss_fa_ord)
#> [1] TRUE
# unwrap the model
un_tbl_ord(swiss_fa_ord)
#>
#> Call:
#> factanal(x = swiss, factors = 3L, scores = "regression")
#>
#> Uniquenesses:
#> Fertility Agriculture Examination Education
#> 0.005 0.286 0.213 0.114
#> Catholic Infant.Mortality
#> 0.083 0.743
#>
#> Loadings:
#> Factor1 Factor2 Factor3
#> Fertility -0.512 0.203 0.832
#> Agriculture -0.774 0.312 -0.129
#> Examination 0.751 -0.423 -0.211
#> Education 0.901 -0.262
#> Catholic -0.186 0.913 0.220
#> Infant.Mortality 0.500
#>
#> Factor1 Factor2 Factor3
#> SS loadings 2.273 1.164 1.120
#> Proportion Var 0.379 0.194 0.187
#> Cumulative Var 0.379 0.573 0.759
#>
#> The degrees of freedom for the model is 0 and the fit was 1e-04
# create a 'tbl_ord' directly from row and column factors
# (missing inertia & other attributes)
swiss_fa_ord2 <- make_tbl_ord(rows = swiss_fa$scores, cols = swiss_fa$loadings)
# inspect wrapped factors
is_tbl_ord(swiss_fa_ord2)
#> [1] TRUE
print(swiss_fa_ord2)
#> # A tbl_ord: (47 x 3) x (6 x 3)'
#> # 3 coordinates: Factor1, Factor2, Factor3
#> #
#> # Rows: [ 47 x 3 | 0 ]
#> Factor1 Factor2 Factor3 |
#> |
#> 1 0.453 -0.900 1.46 |
#> 2 -0.143 0.886 0.942 |
#> 3 -0.308 0.848 1.75 |
#> 4 -0.125 -0.463 1.53 |
#> 5 0.341 -0.846 1.05 |
#>
#> #
#> # Columns: [ 6 x 3 | 0 ]
#> Factor1 Factor2 Factor3 |
#> |
#> 1 -0.512 0.203 0.832 |
#> 2 -0.774 0.312 -0.129 |
#> 3 0.751 -0.423 -0.211 |
#> 4 0.901 0.0784 -0.262 |
#> 5 -0.186 0.913 0.220 |
#> 6 0.0289 0.0772 0.500 |
valid_tbl_ord(swiss_fa_ord2)
#> [1] TRUE
# unwrap factors
un_tbl_ord(swiss_fa_ord2)
#> $rows
#> Factor1 Factor2 Factor3
#> Courtelary 0.45318326 -0.8996943 1.45754866
#> Delemont -0.14322063 0.8855428 0.94227871
#> Franches-Mnt -0.30797336 0.8477030 1.74514043
#> Moutier -0.12534083 -0.4629021 1.52823000
#> Neuveville 0.34109514 -0.8458178 1.04687165
#> Porrentruy -0.04473114 1.0029812 0.32499084
#> Broye -0.20957031 0.9388742 0.95013743
#> Glane -0.07584684 0.9555256 1.84540889
#> Gruyere -0.17406590 1.0769276 0.80852222
#> Sarine 0.48706293 0.9876863 1.28186031
#> Veveyse -0.21675238 1.0104124 1.24699185
#> Aigle -0.10911693 -0.6583181 -0.49672411
#> Aubonne -0.68437504 -0.8402563 -0.52979298
#> Avenches -0.03638406 -0.7941117 0.04684235
#> Cossonay -0.63417597 -0.9062500 -0.97644764
#> Echallens -0.85262069 -0.5223686 -0.56695418
#> Grandson -0.07196915 -0.9996237 0.34908713
#> Lausanne 1.61585928 -0.2740672 -0.32842180
#> La Vallee 1.11176971 -0.7484136 -0.66355682
#> Lavaux -0.45759227 -0.8101884 -0.57253943
#> Morges -0.16023764 -0.8189392 -0.35143678
#> Moudon -0.76689425 -0.8871886 -0.73359873
#> Nyone 0.01148304 -0.4441114 -1.18020392
#> Orbe -0.53346789 -0.8042945 -1.34826105
#> Oron -1.11777160 -1.0181496 -0.21175864
#> Payerne -0.36904017 -0.8817496 0.37541143
#> Paysd'enhaut -1.13077900 -0.8986374 -0.29968480
#> Rolle -0.42160714 -0.6023891 -1.03877760
#> Vevey 0.94345222 -0.3409626 -0.46681372
#> Yverdon -0.32587490 -0.7719262 -0.45767841
#> Conthey -1.31619000 1.3250097 -0.61688868
#> Entremont -0.89250420 1.4492843 -0.97170425
#> Herens -1.23489054 1.2885602 -0.38378811
#> Martigwy -0.65337979 1.3258772 -0.68175988
#> Monthey -0.77161554 1.1439436 0.14336145
#> St Maurice -0.58929096 1.5132175 -1.21362340
#> Sierre -1.07854088 1.0949088 1.16699564
#> Sion 0.07266005 1.2536309 0.60709606
#> Boudry 0.42781724 -0.9511974 0.51240946
#> La Chauxdfnd 0.80305682 -0.8488623 0.28442301
#> Le Locle 0.63722154 -0.8395794 0.83668414
#> Neuchatel 2.31502030 -0.3320992 0.93658497
#> Val de Ruz -0.19124613 -1.0328917 0.84358034
#> ValdeTravers 0.26693510 -0.9979172 0.17191080
#> V. De Geneve 3.72546960 1.0799755 -1.35159069
#> Rive Droite 1.02570325 0.9919321 -2.04348421
#> Rive Gauche 1.45927665 1.0609142 -1.96687793
#>
#> $cols
#>
#> Loadings:
#> Factor1 Factor2 Factor3
#> Fertility -0.512 0.203 0.832
#> Agriculture -0.774 0.312 -0.129
#> Examination 0.751 -0.423 -0.211
#> Education 0.901 -0.262
#> Catholic -0.186 0.913 0.220
#> Infant.Mortality 0.500
#>
#> Factor1 Factor2 Factor3
#> SS loadings 2.273 1.164 1.120
#> Proportion Var 0.379 0.194 0.187
#> Cumulative Var 0.379 0.573 0.759
#>