This is a convenience function to fit an ordination model to a data object, wrap the result as a tbl_ord, and annotate this output with metadata from the model and possibly from the data.
ordinate(x, model, ...)
# S3 method for default
ordinate(x, model, ...)
# S3 method for array
ordinate(x, model, ...)
# S3 method for table
ordinate(x, model, ...)
# S3 method for data.frame
ordinate(x, model, cols, augment, ...)
# S3 method for dist
ordinate(x, model, ...)A data object to be passed to the model, such as an array,
table, data.frame, or stats::dist.
An ordination function whose output is coercible to class
'tbl_ord', or a symbol or character string (handled by match.fun()).
Alternatively, a formula ~ fun(., ...) where fun is such a function and
other arguments are explicit, which will be evaluated with x in place of
..
Additional arguments passed to model.
<tidy-select> If x is a data
frame, columns to pass to model. If missing, all columns are used.
<tidy-select> If x is a data frame,
columns to augment to the row data of the ordination. If missing, all
columns not included in cols will be augmented.
An augmented tbl_ord.
The default method fits the specified model to the provided data object,
wraps the result as a tbl_ord, and augments this output with any intrinsic
metadata from the model via augment_ord().
The default method is used for most classes, though this may change in future. The data.frame method allows the user to specify what columns to include in the model and what columns with which to annotate the output.
# LRA of arrest data
ordinate(USArrests, cols = c(Murder, Rape, Assault), lra)
#> # A tbl_ord of class 'lra': (50 x 2) x (3 x 2)'
#> # 2 coordinates: LRSV1 and LRSV2
#> #
#> # Rows (standard): [ 50 x 2 | 3 ]
#> LRSV1 LRSV2 | name weight UrbanPop
#> | <chr> <dbl> <int>
#> 1 -0.680 0.930 | 1 Alabama 0.0271 58
#> 2 0.930 -0.625 | 2 Alaska 0.0318 48
#> 3 -0.330 -1.31 | 3 Arizona 0.0333 80
#> 4 -0.351 0.277 | 4 Arkansas 0.0219 50
#> 5 0.552 -1.00 | 5 California 0.0326 91
#> # … with 45 more rows
#> #
#> # Columns (standard): [ 3 x 2 | 2 ]
#> LRSV1 LRSV2 | name weight
#> | <chr> <dbl>
#> 1 0.283 4.96 | 1 Murder 0.0390
#> 2 2.88 -0.366 | 2 Rape 0.106
#> 3 -0.371 -0.181 | 3 Assault 0.855
# CMDS of inter-city distance data
ordinate(UScitiesD, cmdscale_ord, k = 3L)
#> # A tbl_ord of class 'cmds_ord': (10 x 3) x (10 x 3)'
#> # 3 coordinates: PCo1, PCo2, PCo3
#> #
#> # Rows (symmetric): [ 10 x 3 | 1 ]
#> PCo1 PCo2 PCo3 | name
#> | <chr>
#> 1 -719. 143. -35.1 | 1 Atlanta
#> 2 -382. -341. -29.6 | 2 Chicago
#> 3 482. -25.3 -53.4 | 3 Denver
#> 4 -161. 573. -1.45 | 4 Houston
#> 5 1204. 390. 18.6 | 5 LosAngeles
#> 6 -1134. 582. 32.3 | 6 Miami
#> 7 -1072. -519. 34.3 | 7 NewYork
#> 8 1421. 113. 7.75 | 8 SanFrancisco
#> 9 1342. -580. 23.7 | 9 Seattle
#> 10 -980. -335. 2.90 | 10 Washington.DC
#> #
#> # Columns (symmetric): [ 10 x 3 | 1 ]
#> PCo1 PCo2 PCo3 | name
#> | <chr>
#> 1 -719. 143. -35.1 | 1 Atlanta
#> 2 -382. -341. -29.6 | 2 Chicago
#> 3 482. -25.3 -53.4 | 3 Denver
#> 4 -161. 573. -1.45 | 4 Houston
#> 5 1204. 390. 18.6 | 5 LosAngeles
#> 6 -1134. 582. 32.3 | 6 Miami
#> 7 -1072. -519. 34.3 | 7 NewYork
#> 8 1421. 113. 7.75 | 8 SanFrancisco
#> 9 1342. -580. 23.7 | 9 Seattle
#> 10 -980. -335. 2.90 | 10 Washington.DC
# PCA of iris data
ordinate(iris, princomp, cols = -Species, augment = c(Sepal.Width, Species))
#> # A tbl_ord of class 'princomp': (150 x 4) x (4 x 4)'
#> # 4 coordinates: Comp.1, Comp.2, ..., Comp.4
#> #
#> # Rows (principal): [ 150 x 4 | 2 ]
#> Comp.1 Comp.2 Comp.3 ... | Sepal.Width Species
#> | <dbl> <fct>
#> 1 -2.68 0.319 0.0279 | 1 3.5 setosa
#> 2 -2.71 -0.177 0.210 ... | 2 3 setosa
#> 3 -2.89 -0.145 -0.0179 | 3 3.2 setosa
#> 4 -2.75 -0.318 -0.0316 | 4 3.1 setosa
#> 5 -2.73 0.327 -0.0901 | 5 3.6 setosa
#> # … with 145 more rows
#> #
#> # Columns (standard): [ 4 x 4 | 3 ]
#> Comp.1 Comp.2 Comp.3 ... | name center scale
#> | <chr> <dbl> <dbl>
#> 1 0.361 0.657 0.582 | 1 Sepal.Length 5.84 1
#> 2 -0.0845 0.730 -0.598 ... | 2 Sepal.Width 3.06 1
#> 3 0.857 -0.173 -0.0762 | 3 Petal.Length 3.76 1
#> 4 0.358 -0.0755 -0.546 | 4 Petal.Width 1.20 1
ordinate(iris, cols = 1:4, ~ prcomp(., center = TRUE, scale. = TRUE))
#> # A tbl_ord of class 'prcomp': (150 x 4) x (4 x 4)'
#> # 4 coordinates: PC1, PC2, ..., PC4
#> #
#> # Rows (principal): [ 150 x 4 | 1 ]
#> PC1 PC2 PC3 ... | Species
#> | <fct>
#> 1 -2.26 -0.478 0.127 | 1 setosa
#> 2 -2.07 0.672 0.234 ... | 2 setosa
#> 3 -2.36 0.341 -0.0441 | 3 setosa
#> 4 -2.29 0.595 -0.0910 | 4 setosa
#> 5 -2.38 -0.645 -0.0157 | 5 setosa
#> # … with 145 more rows
#> #
#> # Columns (standard): [ 4 x 4 | 3 ]
#> PC1 PC2 PC3 ... | name center scale
#> | <chr> <dbl> <dbl>
#> 1 0.521 -0.377 0.720 | 1 Sepal.Length 5.84 0.828
#> 2 -0.269 -0.923 -0.244 ... | 2 Sepal.Width 3.06 0.436
#> 3 0.580 -0.0245 -0.142 | 3 Petal.Length 3.76 1.77
#> 4 0.565 -0.0669 -0.634 | 4 Petal.Width 1.20 0.762
# CA of hair & eye color data
haireye <- as.data.frame(rowSums(HairEyeColor, dims = 2L))
ordinate(haireye, MASS::corresp, cols = everything())
#> # A tbl_ord of class 'correspondence': (4 x 1) x (4 x 1)'
#> # 1 coordinate: Can1
#> #
#> # Rows (standard): [ 4 x 1 | 1 ]
#> Can1 | name
#> | <chr>
#> 1 -1.10 | 1 Black
#> 2 -0.324 | 2 Brown
#> 3 -0.283 | 3 Red
#> 4 1.83 | 4 Blond
#> #
#> # Columns (standard): [ 4 x 1 | 1 ]
#> Can1 | name
#> | <chr>
#> 1 -1.08 | 1 Brown
#> 2 1.20 | 2 Blue
#> 3 -0.465 | 3 Hazel
#> 4 0.354 | 4 Green
# FA of Swiss social data
ordinate(swiss, model = factanal, factors = 2L, scores = "Bartlett")
#> # A tbl_ord of class 'factanal': (53 x 2) x (6 x 2)'
#> # 2 coordinates: Factor1 and Factor2
#> #
#> # Rows (symmetric): [ 53 x 2 | 2 ]
#> Factor1 Factor2 | name .element
#> | <chr> <chr>
#> 1 -0.652 0.393 | 1 Fertility active
#> 2 -0.631 0.333 | 2 Agriculture active
#> 3 0.685 -0.510 | 3 Examination active
#> 4 0.997 -0.0313 | 4 Education active
#> 5 -0.124 0.961 | 5 Catholic active
#> # … with 48 more rows
#> #
#> # Columns (symmetric): [ 6 x 2 | 3 ]
#> Factor1 Factor2 | name uniquen…¹ .elem…²
#> | <chr> <dbl> <chr>
#> 1 -0.652 0.393 | 1 Fertility 0.420 active
#> 2 -0.631 0.333 | 2 Agriculture 0.492 active
#> 3 0.685 -0.510 | 3 Examination 0.270 active
#> 4 0.997 -0.0313 | 4 Education 0.005 active
#> 5 -0.124 0.961 | 5 Catholic 0.0607 active
#> 6 -0.0947 0.175 | 6 Infant.Mortality 0.960 active
#> # … with abbreviated variable names
#> # ¹uniqueness, ².element
# LDA of iris data
ordinate(iris, ~ lda_ord(.[, 1:4], .[, 5], ret.x = TRUE))
#> # A tbl_ord of class 'lda_ord': (153 x 2) x (4 x 2)'
#> # 2 coordinates: LD1 and LD2
#> #
#> # Rows (principal): [ 153 x 2 | 4 ]
#> LD1 LD2 | name prior counts .element
#> | <chr> <dbl> <int> <chr>
#> 1 7.61 0.215 | 1 setosa 0.333 50 active
#> 2 -1.83 -0.728 | 2 versicolor 0.333 50 active
#> 3 -5.78 0.513 | 3 virginica 0.333 50 active
#> 4 8.06 0.300 | 4 NA NA NA score
#> 5 7.13 -0.787 | 5 NA NA NA score
#> # … with 148 more rows
#> #
#> # Columns (standard): [ 4 x 2 | 2 ]
#> LD1 LD2 | name .element
#> | <chr> <chr>
#> 1 0.829 0.0241 | 1 Sepal.Length active
#> 2 1.53 2.16 | 2 Sepal.Width active
#> 3 -2.20 -0.932 | 3 Petal.Length active
#> 4 -2.81 2.84 | 4 Petal.Width active
# CCA of savings data
ordinate(
LifeCycleSavings[, c("pop15", "pop75")],
# second data set must be handled as an additional parameter to `model`
y = LifeCycleSavings[, c("sr", "dpi", "ddpi")],
model = cancor_ord, scores = TRUE
)
#> # A tbl_ord of class 'cancor_ord': (54 x 2) x (56 x 2)'
#> # 2 coordinates: CanCor1 and CanCor2
#> #
#> # Rows (standard): [ 54 x 2 | 3 ]
#> CanCor1 CanCor2 | name center .element
#> | <chr> <dbl> <chr>
#> 1 -0.00911 -0.0362 | 1 pop15 35.1 active
#> 2 0.0486 -0.260 | 2 pop75 2.29 active
#> 3 0.0804 0.0577 | 3 Australia NA score
#> 4 0.210 -0.125 | 4 Austria NA score
#> 5 0.207 -0.147 | 5 Belgium NA score
#> # … with 49 more rows
#> #
#> # Columns (standard): [ 56 x 2 | 3 ]
#> CanCor1 CanCor2 | name center .element
#> | <chr> <dbl> <chr>
#> 1 0.00847 0.0334 | 1 sr 9.67 active
#> 2 0.000131 -0.0000759 | 2 dpi 1107. active
#> 3 0.00417 -0.0123 | 3 ddpi 3.76 active
#> 4 0.171 -0.0232 | 4 Australia NA score
#> 5 0.0735 0.0475 | 5 Austria NA score
#> # … with 51 more rows