These 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)

Arguments

x

An object of class 'tbl_ord'.

elements

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.

.matrix

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).

Value

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().

Details

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.)

See also

Other generic recoverers: augmentation, conference, supplementation

Examples

# 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