PCA methods for mlxs_prcomp

predict.mlxs_prcomp() returns MLX scores. summary() and plot() only materialize standard deviations for base-style output; print() and biplot() materialize rotations and scores as needed for display.

Usage

# S3 method for class 'mlxs_prcomp'
predict(object, newdata, ...)

# S3 method for class 'mlxs_prcomp'
print(x, ...)

# S3 method for class 'mlxs_prcomp'
summary(object, ...)

# S3 method for class 'mlxs_prcomp'
plot(x, main = deparse1(substitute(x)), ...)

# S3 method for class 'mlxs_prcomp'
biplot(x, ...)

# S3 method for class 'mlxs_prcomp'
nobs(object, ...)

# S3 method for class 'mlxs_prcomp'
tidy(x, ...)

# S3 method for class 'mlxs_prcomp'
augment(x, data = NULL, newdata = NULL, output = c("data.frame", "mlx"), ...)

Arguments

object, x

A fitted mlxs_prcomp object.

newdata

Optional new observations to project.

...

Passed through to the corresponding base method.

data

Optional original data to append PCA scores to in augment.mlxs_prcomp().

output

Output format for augment.mlxs_prcomp(): either a data frame with appended score columns or the MLX score matrix directly.

Value

Method-specific output. predict.mlxs_prcomp() returns an MLX matrix. augment.mlxs_prcomp() returns either a data frame or MLX matrix.