accessors.RdThese functions return information about the matrix factorization underlying an ordination.
recover_rows(x)
recover_cols(x)
# S3 method for default
recover_rows(x)
# S3 method for default
recover_cols(x)
# S3 method for data.frame
recover_rows(x)
# S3 method for data.frame
recover_cols(x)
get_rows(x, elements = "all")
get_cols(x, elements = "all")
# S3 method for tbl_ord
as.matrix(x, ..., .matrix, elements = "all")
recover_inertia(x)
# S3 method for default
recover_inertia(x)
recover_coord(x)
# S3 method for default
recover_coord(x)
# S3 method for data.frame
recover_coord(x)
get_coord(x)
get_inertia(x)
# S3 method for tbl_ord
dim(x)An object of class 'tbl_ord'.
Character vector; which elements of each factor for which to
render graphical elements. One of "all" (the default), "active", or any
supplementary element type defined by the specific class methods (e.g.
"score" for 'factanal', 'lda_ord', and 'cancord_ord' and "intraset" and
"interset" for 'cancor_ord').
Additional arguments from base::as.matrix(); ignored.
A character string matching one of several indicators for one
or both matrices in a matrix decomposition used for ordination. The
standard values are "rows", "cols", and "dims" (for both).
The recover_*() S3 methods extract one or both of the
row and column matrix factors that constitute the original ordination. These
are interpreted as the case scores (rows) and the variable loadings
(columns). The get_*() functions optionally (and by default) include any
supplemental observations (see supplementation).
The recover_*() functions are generics that require methods for each
ordination class. They are not intended to be called directly but are
exported so that users can query methods("recover_*").
get_coord() retrieves the names of the coordinates shared by the matrix
factors on which the original data were ordinated, and get_inertia()
retrieves a vector of the inertia with these names. dim() retrieves the
dimensions of the row and column factors, which reflect the dimensions of the
matrix they reconstruct---not the original data matrix. (This matters for
techniques that rely on eigendecomposition, for which the decomposed matrix
is square.)
Other generic accessors:
augmentation,
conference,
supplementation
# example ordination: LRA of U.S. arrests data
arrests_lra <- ordinate(USArrests, cols = c(Murder, Rape, Assault), lra)
# extract matrix factors
as.matrix(arrests_lra, .matrix = "rows")
#> LRSV1 LRSV2
#> Alabama -0.68001198 0.929601139
#> Alaska 0.92998988 -0.624577164
#> Arizona -0.32984955 -1.311581695
#> Arkansas -0.35134428 0.277323143
#> California 0.55165901 -1.004280086
#> Colorado 1.22910659 -0.638846890
#> Connecticut -0.43610804 -1.027017532
#> Delaware -1.54269183 -1.349322890
#> Florida -0.54682514 0.300298438
#> Georgia 0.15812112 1.916932033
#> Hawaii 3.51365819 2.150284964
#> Idaho -0.05250833 -2.109188891
#> Illinois -0.52429773 0.002652531
#> Indiana 1.22231804 0.881075829
#> Iowa 1.39095549 -0.632985189
#> Kansas 0.75715595 0.380674137
#> Kentucky 0.68864901 2.024832534
#> Louisiana -0.69015525 1.238509473
#> Maine -0.63716612 -1.499305459
#> Maryland -0.63629812 -0.286175559
#> Massachusetts -0.22762978 -1.124006923
#> Michigan 0.41414730 0.171745261
#> Minnesota 1.45222388 -0.789191960
#> Mississippi -1.47068651 1.436924829
#> Missouri 0.78566710 0.278289912
#> Montana 0.65946921 0.566956397
#> Nebraska 0.82281297 -0.284750183
#> Nevada 1.14994121 0.060475841
#> New Hampshire 0.88778358 -0.710870888
#> New Jersey 0.01717089 0.205735676
#> New Mexico -0.12336523 -0.223126918
#> New York -0.35365080 0.100656157
#> North Carolina -2.35659680 0.189728037
#> North Dakota 0.74912231 -2.900272794
#> Ohio 1.11076020 0.766013515
#> Oklahoma 0.30637650 -0.053491161
#> Oregon 1.13217636 -1.312593507
#> Pennsylvania 0.48983869 0.840580871
#> Rhode Island -2.42442178 -1.868251942
#> South Carolina -0.96800438 0.752042083
#> South Dakota 0.61067278 -0.091684230
#> Tennessee 0.55166149 1.334277800
#> Texas 0.22895595 1.088292623
#> Utah 1.20949943 -1.771844197
#> Vermont 1.78310111 -0.254863377
#> Virginia 0.33180838 0.612645656
#> Washington 1.07067103 -1.635935144
#> West Virginia -0.02043499 1.475198849
#> Wisconsin 1.43728882 0.033416687
#> Wyoming -0.50955934 0.033243165
as.matrix(arrests_lra, .matrix = "cols")
#> LRSV1 LRSV2
#> Murder 0.283086 4.9570302
#> Rape 2.876702 -0.3660163
#> Assault -0.370595 -0.1805698
# special named functions
get_rows(arrests_lra)
#> LRSV1 LRSV2
#> Alabama -0.68001198 0.929601139
#> Alaska 0.92998988 -0.624577164
#> Arizona -0.32984955 -1.311581695
#> Arkansas -0.35134428 0.277323143
#> California 0.55165901 -1.004280086
#> Colorado 1.22910659 -0.638846890
#> Connecticut -0.43610804 -1.027017532
#> Delaware -1.54269183 -1.349322890
#> Florida -0.54682514 0.300298438
#> Georgia 0.15812112 1.916932033
#> Hawaii 3.51365819 2.150284964
#> Idaho -0.05250833 -2.109188891
#> Illinois -0.52429773 0.002652531
#> Indiana 1.22231804 0.881075829
#> Iowa 1.39095549 -0.632985189
#> Kansas 0.75715595 0.380674137
#> Kentucky 0.68864901 2.024832534
#> Louisiana -0.69015525 1.238509473
#> Maine -0.63716612 -1.499305459
#> Maryland -0.63629812 -0.286175559
#> Massachusetts -0.22762978 -1.124006923
#> Michigan 0.41414730 0.171745261
#> Minnesota 1.45222388 -0.789191960
#> Mississippi -1.47068651 1.436924829
#> Missouri 0.78566710 0.278289912
#> Montana 0.65946921 0.566956397
#> Nebraska 0.82281297 -0.284750183
#> Nevada 1.14994121 0.060475841
#> New Hampshire 0.88778358 -0.710870888
#> New Jersey 0.01717089 0.205735676
#> New Mexico -0.12336523 -0.223126918
#> New York -0.35365080 0.100656157
#> North Carolina -2.35659680 0.189728037
#> North Dakota 0.74912231 -2.900272794
#> Ohio 1.11076020 0.766013515
#> Oklahoma 0.30637650 -0.053491161
#> Oregon 1.13217636 -1.312593507
#> Pennsylvania 0.48983869 0.840580871
#> Rhode Island -2.42442178 -1.868251942
#> South Carolina -0.96800438 0.752042083
#> South Dakota 0.61067278 -0.091684230
#> Tennessee 0.55166149 1.334277800
#> Texas 0.22895595 1.088292623
#> Utah 1.20949943 -1.771844197
#> Vermont 1.78310111 -0.254863377
#> Virginia 0.33180838 0.612645656
#> Washington 1.07067103 -1.635935144
#> West Virginia -0.02043499 1.475198849
#> Wisconsin 1.43728882 0.033416687
#> Wyoming -0.50955934 0.033243165
get_cols(arrests_lra)
#> LRSV1 LRSV2
#> Murder 0.283086 4.9570302
#> Rape 2.876702 -0.3660163
#> Assault -0.370595 -0.1805698
# get names of artificial / latent coordinates
get_coord(arrests_lra)
#> [1] "LRSV1" "LRSV2"
# get distribution of inertia
get_inertia(arrests_lra)
#> LRSV1 LRSV2
#> 0.013826903 0.004074913
# get dimensions of underlying matrix factorization (not of original data)
dim(arrests_lra)
#> [1] 50 3