| methods-mca {ordr} | R Documentation |
Functionality for multiple correspondence analysis ('mca') objects
Description
These methods extract data from, and attribute new data to,
objects of class "mca" from the MASS package.
Usage
## S3 method for class 'mca'
as_tbl_ord(x)
## S3 method for class 'mca'
recover_rows(x)
## S3 method for class 'mca'
recover_cols(x)
## S3 method for class 'mca'
recover_inertia(x)
## S3 method for class 'mca'
recover_conference(x)
## S3 method for class 'mca'
recover_coord(x)
## S3 method for class 'mca'
recover_supp_rows(x)
## S3 method for class 'mca'
recover_aug_rows(x)
## S3 method for class 'mca'
recover_aug_cols(x)
## S3 method for class 'mca'
recover_aug_coord(x)
Arguments
x |
An ordination object. |
Details
Multiple correspondence analysis (MCA) relies on a singular value
decomposition of the indicator matrix X of a table of several
categorical variables, scaled by its column totals. MASS::mca() returns the
SVD factors UD and V as the row weights $fs, on which the
inertia is conferred, and the column coordinates $cs. The row coordinates
$rs are obtained as XV and accessible as supplementary elements.
Value
The recovery generics recover_*() return core model components, distribution of inertia,
supplementary elements, and intrinsic metadata; but they require methods for each model class to
tell them what these components are.
The generic as_tbl_ord() returns its input wrapped in the 'tbl_ord'
class. Its methods determine what model classes it is allowed to wrap. It
then provides 'tbl_ord' methods with access to the recoverers and hence to
the model components.
See Also
Other methods for singular value decomposition-based techniques:
methods-cancor,
methods-correspondence,
methods-lda,
methods-lra,
methods-prcomp,
methods-princomp,
methods-svd
Other models from the MASS package:
methods-correspondence,
methods-lda
Examples
# table of admissions and rejections from UC Berkeley
class(UCBAdmissions)
ucb_admissions <- as.data.frame(UCBAdmissions)
ucb_admissions <-
ucb_admissions[rep(seq(nrow(ucb_admissions)), ucb_admissions$Freq), -4L]
head(ucb_admissions)
# perform multiple correspondence analysis
ucb_admissions %>%
MASS::mca() %>%
as_tbl_ord() %>%
# augment profiles with names, masses, distances, and inertias
augment_ord() %>%
print() -> admissions_mca
# recover row and column coordinates and row weights
head(get_rows(admissions_mca, elements = "score"))
get_cols(admissions_mca)
head(get_rows(admissions_mca))
# column-standard biplot of factor levels
admissions_mca %>%
ggbiplot() +
theme_bw() + theme_biplot() +
geom_origin() +
#geom_rows_point(stat = "unique") +
geom_cols_point(aes(color = factor, shape = factor)) +
geom_cols_text_repel(aes(label = level, color = factor),
show.legend = FALSE) +
scale_color_brewer(palette = "Dark2") +
scale_size_area(guide = "none") +
labs(color = "Factor level", shape = "Factor level")