View source: R/variable_importance.R
variable_importance | R Documentation |
variable_importance
measures importance of variables based on
specified methods.
variable_importance(
sample,
variables,
operation = "replicate_correlation",
...
)
sample |
tbl containing sample used to estimate parameters. |
variables |
character vector specifying observation variables. |
operation |
optional character string specifying method for computing
variable importance. This must be one of the strings
|
... |
arguments passed to variable importance operation. |
data frame containing variable importance measures.
set.seed(123)
x1 <- rnorm(10)
x2 <- x1 + rnorm(10) / 100
y1 <- rnorm(10)
y2 <- y1 + rnorm(10) / 10
z1 <- rnorm(10)
z2 <- z1 + rnorm(10) / 1
batch <- rep(rep(1:2, each = 5), 2)
treatment <- rep(1:10, 2)
replicate_id <- rep(1:2, each = 10)
sample <-
tibble::tibble(
x = c(x1, x2), y = c(y1, y2), z = c(z1, z2),
Metadata_treatment = treatment,
Metadata_replicate_id = replicate_id,
Metadata_batch = batch
)
head(sample)
# `replicate_correlation`` returns the median, min, and max
# replicate correlation (across batches) per variable
variable_importance(
sample = sample,
variables = c("x", "y", "z"),
operation = "replicate_correlation",
strata = c("Metadata_treatment"),
replicates = 2,
split_by = "Metadata_batch",
replicate_by = "Metadata_replicate_id",
cores = 1
)
# `svd_entropy`` measures the contribution of each variable in decreasing
# the data entropy.
variable_importance(
sample = sample,
variables = c("x", "y", "z"),
operation = "svd_entropy",
cores = 1
)
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