ttest_filter function

Univariate filters

A selection of simple univariate filters using t-test, Wilcoxon test, one-way ANOVA or correlation (Pearson or Spearman) for ranking variables. These filters are designed for speed. ttest_filter uses the Rfast package, wilcoxon_filter (Mann-Whitney) test uses matrixTests::row_wilcoxon_twosample , anova_filter uses matrixTests::col_oneway_welch (Welch's F-test) from the matrixTests

package. Can be applied to all or a subset of predictors. For mixed datasets (combined continuous & categorical) see stat_filter()

ttest_filter(
  y,
  x,
  force_vars = NULL,
  nfilter = NULL,
  p_cutoff = 0.05,
  rsq_cutoff = NULL,
  type = c("index", "names", "full"),
  keep_factors = TRUE,
  ...
)

anova_filter(
  y,
  x,
  force_vars = NULL,
  nfilter = NULL,
  p_cutoff = 0.05,
  rsq_cutoff = NULL,
  type = c("index", "names", "full"),
  keep_factors = TRUE,
  ...
)

wilcoxon_filter(
  y,
  x,
  force_vars = NULL,
  nfilter = NULL,
  p_cutoff = 0.05,
  rsq_cutoff = NULL,
  type = c("index", "names", "full"),
  exact = FALSE,
  keep_factors = TRUE,
  ...
)

correl_filter(
  y,
  x,
  method = "pearson",
  force_vars = NULL,
  nfilter = NULL,
  p_cutoff = 0.05,
  rsq_cutoff = NULL,
  type = c("index", "names", "full"),
  keep_factors = TRUE,
  ...
)

Arguments

• y: Response vector

• x: Matrix or dataframe of predictors

• force_vars: Vector of column names within x which are always retained in the model (i.e. not filtered). Default NULL means all predictors will be passed to filterFUN.

• nfilter: Number of predictors to return. If NULL all predictors with p-values < p_cutoff are returned.

• p_cutoff: p value cut-off

• rsq_cutoff: r^2 cutoff for removing predictors due to collinearity. Default NULL means no collinearity filtering. Predictors are ranked based on t-test. If 2 or more predictors are collinear, the first ranked predictor by t-test is retained, while the other collinear predictors are removed. See collinear().

• type: Type of vector returned. Default "index" returns indices, "names" returns predictor names, "full" returns a matrix of p values.

• keep_factors: Logical affecting factors with 3 or more levels. Dataframes are coerced to a matrix using data.matrix . Binary factors are converted to numeric values 0/1 and analysed as such. If keep_factors is TRUE (the default), factors with 3 or more levels are not filtered and are retained. If keep_factors is FALSE, they are removed.

• ...: optional arguments, including rsq_method passed to collinear()

  or arguments passed to matrixTests::row_wilcoxon_twosample in wilcoxon_filter().

• exact: Logical whether exact or approximate p-value is calculated. Default is FALSE for speed.

• method: Type of correlation, either "pearson" or "spearman".

Returns

Integer vector of indices of filtered parameters (type = "index") or character vector of names (type = "names") of filtered parameters in order of t-test p-value. If type is "full" full output from Rfast::ttests is returned.

Examples

## sigmoid function
sigmoid <- function(x) {1 / (1 + exp(-x))}

## load iris dataset and simulate a binary outcome
data(iris)
dt <- iris[, 1:4]
colnames(dt) <- c("marker1", "marker2", "marker3", "marker4")
dt <- as.data.frame(apply(dt, 2, scale))
y2 <- sigmoid(0.5 * dt$marker1 + 2 * dt$marker2) > runif(nrow(dt))
y2 <- factor(y2, labels = c("C1", "C2"))

ttest_filter(y2, dt)  # returns index of filtered predictors
ttest_filter(y2, dt, type = "name")  # shows names of predictors
ttest_filter(y2, dt, type = "full")  # full results table

data(iris)
dt <- iris[, 1:4]
y3 <- iris[, 5]
anova_filter(y3, dt)  # returns index of filtered predictors
anova_filter(y3, dt, type = "full")  # shows names of predictors
anova_filter(y3, dt, type = "name")  # full results table

See Also

lm_filter() stat_filter()