These functions wrap ordination objects in the class tbl_ord, create tbl_ords directly from matrices, and test for the class and basic structure.
Usage
as_tbl_ord(x)
# S3 method for class '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)Value
A tbl_ord (as*(), make*()), an S3-class model object that can be
wrapped as one (un*()), or a logical value (is*(), value*()).
Details
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 ggplot2::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.
Examples
# 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': (47 x 3) x (6 x 3)'
#> # 3 coordinates: Factor1, Factor2, Factor3
#> #
#> # Rows (principal): [ 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 |
#> # ℹ 42 more rows |
#>
#> #
#> # Columns (principal): [ 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 |
#> # ℹ 42 more rows |
#>
#> #
#> # 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 -0.76689426 -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
#>