Functionality for classical multidimensional scaling objects
Source:R/methods-stats-cmds.r
methods-cmds.RdThese methods extract data from, and attribute new data to,
objects of class "cmds_ord". This is a class introduced in this package
to identify objects returned by cmdscale_ord(), which wraps
stats::cmdscale().
Usage
# S3 method for class 'cmds_ord'
as_tbl_ord(x)
# S3 method for class 'cmds_ord'
recover_rows(x)
# S3 method for class 'cmds_ord'
recover_cols(x)
# S3 method for class 'cmds_ord'
recover_inertia(x)
# S3 method for class 'cmds_ord'
recover_coord(x)
# S3 method for class 'cmds_ord'
recover_conference(x)
# S3 method for class 'cmds_ord'
recover_aug_rows(x)
# S3 method for class 'cmds_ord'
recover_aug_cols(x)
# S3 method for class 'cmds_ord'
recover_aug_coord(x)Value
The recovery generics recover_*() return core model components, distribution of inertia,
supplementary elements, and intrinsic metadata; but they require methods for each model class to
tell them what these components are.
The generic as_tbl_ord() returns its input wrapped in the 'tbl_ord'
class. Its methods determine what model classes it is allowed to wrap. It
then provides 'tbl_ord' methods with access to the recoverers and hence to
the model components.
See also
Other methods for eigen-decomposition-based techniques:
methods-eigen,
methods-factanal,
methods-princomp
Other models from the stats package:
methods-cancor,
methods-factanal,
methods-kmeans,
methods-lm,
methods-prcomp,
methods-princomp
Examples
# 'dist' object (matrix of road distances) of large American cities
class(UScitiesD)
#> [1] "dist"
print(UScitiesD)
#> Atlanta Chicago Denver Houston LosAngeles Miami NewYork
#> Chicago 587
#> Denver 1212 920
#> Houston 701 940 879
#> LosAngeles 1936 1745 831 1374
#> Miami 604 1188 1726 968 2339
#> NewYork 748 713 1631 1420 2451 1092
#> SanFrancisco 2139 1858 949 1645 347 2594 2571
#> Seattle 2182 1737 1021 1891 959 2734 2408
#> Washington.DC 543 597 1494 1220 2300 923 205
#> SanFrancisco Seattle
#> Chicago
#> Denver
#> Houston
#> LosAngeles
#> Miami
#> NewYork
#> SanFrancisco
#> Seattle 678
#> Washington.DC 2442 2329
# use multidimensional scaling to infer artificial planar coordinates
UScitiesD %>%
cmdscale_ord(k = 2) %>%
as_tbl_ord() %>%
print() -> usa_mds
#> # A tbl_ord of class 'cmds_ord': (10 × 2) · (0 × 2)´
#> # 2 coordinates: PCo1 and PCo2
#> # Rows (principal, 100%): [ 10 × 2 | 0 ]
#> PCo1 PCo2 |
#> [9.2e+13][3e+12] |
#> 1 -719. 143. |
#> 2 -382. -341. |
#> 3 482. -25.3 |
#> 4 -161. 573. |
#> 5 1204. 390. |
#> 6 -1134. 582. |
#> 7 -1072. -519. |
#> 8 1421. 113. |
#> 9 1342. -580. |
#> 10 -980. -335. |
#> # Columns (standard, 0%): [ 0 × 2 | 0 ]
#> PCo1 PCo2 |
#> <dbl> <dbl> |
# recover (equivalent) matrices of row and column artificial coordinates
get_rows(usa_mds)
#> PCo1 PCo2
#> Atlanta -718.7594 142.99427
#> Chicago -382.0558 -340.83962
#> Denver 481.6023 -25.28504
#> Houston -161.4663 572.76991
#> LosAngeles 1203.7380 390.10029
#> Miami -1133.5271 581.90731
#> NewYork -1072.2357 -519.02423
#> SanFrancisco 1420.6033 112.58920
#> Seattle 1341.7225 -579.73928
#> Washington.DC -979.6220 -335.47281
get_cols(usa_mds)
#> PCo1 PCo2
# augment ordination with point names
(usa_mds <- augment_ord(usa_mds))
#> # A tbl_ord of class 'cmds_ord': (10 × 2) · (0 × 2)´
#> # 2 coordinates: PCo1 and PCo2
#> # Rows (principal, 100%): [ 10 × 2 | 2 ]
#> PCo1 PCo2 | name .element
#> [9.2e+13][3e+12] | <chr> <chr>
#> 1 -719. 143. | Atlanta active
#> 2 -382. -341. | Chicago active
#> 3 482. -25.3 | Denver active
#> 4 -161. 573. | Houston active
#> 5 1204. 390. | LosAngeles active
#> 6 -1134. 582. | Miami active
#> 7 -1072. -519. | NewYork active
#> 8 1421. 113. | SanFrancisco active
#> 9 1342. -580. | Seattle active
#> 10 -980. -335. | Washington.DC active
#> # Columns (standard, 0%): [ 0 × 2 | 1 ]
#> PCo1 PCo2 |
#> <dbl> <dbl> |
# reorient biplot to conventional compass
usa_mds %>%
negate_ord(c(1, 2)) %>%
ggbiplot() +
geom_cols_text(aes(label = name), size = 3) +
ggtitle("MDS biplot of distances between U.S. cities")
