Package 'QuanDA'

Example breast cancer data

Description

A list containing predictor matrix X and binary response y.

Usage

data(breast)

Value

This data frame contains the following:

x	gene expression levels.
y	Disease state that is coded as 1 and -1

Examples

data(breast)

---

Make Predictions from a 'quanda' Object

Description

Produces fitted values for new predictor data using a fitted 'quanda()' object.

Usage

## S3 method for class 'quanda'
predict(object, newx, type = c("class", "loss"), ...)

Arguments

object	Fitted 'quanda()' object from which predictions are to be derived.
newx	Matrix of new predictor values for which predictions are desired. This must be a matrix and is a required argument.
type	Type of prediction required. Type '"class"' produces the predicted binary class labels and type '"loss"' returns the fitted values. Default is "class".
...	Not used.

Value

Numeric vector of length n_new.

See Also

quanda

Examples

data(breast)
X <- as.matrix(X)
y <- as.numeric(as.character(y))
y[y==-1]=0
fit <- quanda(X, y)

---

Fit QuanDA for imbalanced binary classification

Description

QuanDA fits a quantile-regression-based discriminant with label jittering. For each candidate quantile level $\tau$, the binary labels are jittered (adding $U(0,1)$), a penalized quantile regression is fit multiple times, and the coefficient vectors are averaged. The best $\tau$ is selected by AUC.

Usage

quanda(
  x,
  y,
  lambda = 10^(seq(1, -4, length.out = 30)),
  lam2 = 0.01,
  n_rep = 10,
  tau_window = 0.05,
  nfolds = 5,
  maxit = 10000,
  eps = 1e-07,
  maxit_cv = 10000,
  eps_cv = 1e-05
)

Arguments

x	A numeric matrix of predictors with $n$ rows (observations) and $p$ columns (features).
y	A binary response vector of length $n$ with values 0 or 1.
lambda	Optional numeric vector of penalty values (largest lambda[1]). If NULL, a default sequence will be generated from the data.
lam2	Numeric, secondary penalty (ridge/elastic term) passed to hdqr. Default 0.01.
n_rep	Integer, number of jittering repetitions (averaged). Default 10.
tau_window	Width around the class rate to explore quantiles. Candidate $\tau$ are $b + \{-w,\ldots,w\}$ in steps of 0.01, clipped to $[0,1]$, where $b$ is the class rate and $w$ is tau_window. Default 0.1.
nfolds	Integer, number of CV folds used by cv_z(). Default 5.
maxit, maxit_cv, eps, eps_cv	Controls for inner optimizers and CV helper.

Details

We jitter labels via $z_i = y_i + U_i$, where $U_i \sim \mathrm{Unif}(0,1)$, fit penalized quantile regression at multiple $\tau$, average coefficients over n_rep jitters, compute AUCs on the original $(x,y)$, and pick the $\tau$ that maximizes AUC.

Value

An object of class "quanda" with elements:

beta

Numeric vector of length $p+1$ (intercept first).

tau_grid

Numeric vector of candidate $\tau$ values.

tau_best

Chosen $\tau$.

auc

Vector of AUCs across $\tau$.

call

The matched call.

Examples

data(breast)
X <- as.matrix(X)
y <- as.numeric(as.character(y))
y[y==-1]=0
fit <- quanda(X, y)
pred <- predict(fit, tail(X))

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