R/ord-recoverers.r
recoverers.RdThese functions return information about the matrix factorization underlying an ordination.
recover_rows(x)
recover_cols(x)
# Default S3 method
recover_rows(x)
# Default S3 method
recover_cols(x)
# S3 method for class 'data.frame'
recover_rows(x)
# S3 method for class 'data.frame'
recover_cols(x)
get_rows(x, elements = "all")
get_cols(x, elements = "all")
# S3 method for class 'tbl_ord'
as.matrix(x, ..., .matrix, elements = "all")
recover_inertia(x)
# Default S3 method
recover_inertia(x)
recover_coord(x)
# Default S3 method
recover_coord(x)
# S3 method for class 'data.frame'
recover_coord(x)
get_coord(x)
get_inertia(x)
# S3 method for class '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 partially matched (lowercase) to 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_*() functions are generics whose methods return base R
objects retrieved from the model wrapped in the 'tbl_ord' class:
rows: the row matrix as stored in the model
cols: the column matrix as stored in the model
inertia: the vector of eigen-values or squared singular values,
often known by other names depending on the model
coord: names for the artificial axes, from the model if available
The get_*() functions (which are not generics) return modifications of
these objects:
rows: the recovered rows,
adjusted according to any negation of axes or conference of inertia
cols: the recovered columns,
adjusted according to any negation of axes or conference of inertia
inertia: the recovered inertia, named by the recovered coordinates
coord: the recovered coordinates (unmodified)
dim() returns the dimensions of the decomposed matrix, i.e. the numbers of
rows of recover_rows() and of recover_cols().
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 recoverers:
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 dimensions of underlying matrix factorization (not of original data)
dim(arrests_lra)
#> [1] 50 3
# 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