gridcv: Cross-validation

In mlesnoff/rchemo: Dimension reduction, Regression and Discrimination for Chemometrics

Cross-validation

Description

Functions for cross-validating predictive models.

The functions return "scores" (average error rates) of predictions for a given model and a grid of parameter values, calculated from a cross-validation process.

- gridcv: Can be used for any model.

- gridcvlv: Specific to models using regularization by latent variables (LVs) (e.g. PLSR). Much faster than gridcv.

- gridcvlb: Specific to models using ridge regularization (e.g. RR). Much faster than gridcv.

Argument pars (the grid) must be a list of named vectors, each vector corresponding to an argument of the model function and giving the parameter values to consider for this argument. This list can eventually be built with function mpars, which returns all the combinations of the input parameters, see the examples.

For gridcvlv, pars must not contain nlv (nb. LVs), and for gridcvlb, lb (regularization parameter lambda).

Usage

gridcv(X, Y, segm, score, fun, pars, verb = TRUE)

gridcvlv(X, Y, segm, score, fun, nlv, pars = NULL, verb = TRUE)

gridcvlb(X, Y, segm, score, fun, lb, pars = NULL, verb = TRUE)

Arguments

X	Training X-data (n, p).
Y	Training Y-data (n, q).
segm	CV segments, typically output of segmkf or segmts.
score	A function calculating a prediction score (e.g. msep).
fun	A function corresponding to the predictive model.
nlv	For gridcvlv. A vector of numbers of LVs.
lb	For gridcvlb. A vector of ridge regulariation parameters.
pars	A list of named vectors. Each vector must correspond to an argument of the model function and gives the parameter values to consider for this argument.
verb	Logical. If TRUE, fitting information are printed.

Value

Dataframes with the prediction scores for the grid.

Examples

######################## EXAMPLES WITH PLSR

n <- 50 ; p <- 8
X <- matrix(rnorm(n * p), ncol = p)
y <- rnorm(n)
Y <- cbind(y, 10 * rnorm(n))

K = 3
segm <- segmkf(n = n, K = K, nrep = 1)
segm

## Using gridcv
nlv <- 5
pars <- mpars(nlv = 1:nlv)
pars
gridcv(
    X, Y, segm,
    score = msep, 
    fun = plskern, 
    pars = pars, verb = TRUE)

## Using gridcvlv (much faster) 
gridcvlv(
    X, Y, segm, 
    score = msep, 
    fun = plskern, 
    nlv = 0:nlv, verb = TRUE)

######################## EXAMPLES WITH PLSLDA

n <- 50 ; p <- 8
X <- matrix(rnorm(n * p), ncol = p, byrow = TRUE)
y <- sample(c(1, 4, 10), size = n, replace = TRUE)
#y <- sample(c("a", "10", "d"), size = n, replace = TRUE)
#y <- as.factor(sample(c(1, 4, 10), size = n, replace = TRUE))
#y <- as.factor(sample(c("a", "10", "d"), size = n, replace = TRUE))

K = 3
segm <- segmkf(n = n, K = K, nrep = 1)
segm

## Using gridcv
nlv <- 5
pars <- mpars(nlv = 1:nlv, prior = c("unif", "prop"))
pars
gridcv(
    X, y, segm, 
    score = err, 
    fun = plslda,
    pars = pars, verb = TRUE)

## Using gridcvlv (much faster) 
pars <- mpars(prior = c("unif", "prop"))
pars
gridcvlv(
    X, y, segm, 
    score = err, 
    fun = plslda,
    nlv = 1:nlv, pars = pars, verb = TRUE)

######################## EXAMPLES WITH RR

n <- 50 ; p <- 8
X <- matrix(rnorm(n * p), ncol = p)
y <- rnorm(n)
Y <- cbind(y, 10 * rnorm(n))

K = 3
segm <- segmkf(n = n, K = K, nrep = 1)
segm

## Using gridcv
lb <- c(.1, 1)
pars <- mpars(lb = lb)
pars
gridcv(
    X, Y, segm, 
    score = msep, 
    fun = rr, 
    pars = pars, verb = TRUE)

## Using gridcvlb (much faster) 
gridcvlb(
    X, Y, segm, 
    score = msep, 
    fun = rr, 
    lb = lb, verb = TRUE)

######################## EXAMPLES WITH KRR

n <- 50 ; p <- 8
X <- matrix(rnorm(n * p), ncol = p)
y <- rnorm(n)
Y <- cbind(y, 10 * rnorm(n))

K = 3
segm <- segmkf(n = n, K = K, nrep = 1)
segm

## Using gridcv
lb <- c(.1, 1)
gamma <- 10^(-1:1)
pars <- mpars(lb = lb, gamma = gamma)
pars
gridcv(
    X, Y, segm, 
    score = msep, 
    fun = krr, 
    pars = pars, verb = TRUE)

## Using gridcvlb (much faster) 
pars <- mpars(gamma = gamma)
gridcvlb(
    X, Y, segm, 
    score = msep, 
    fun = krr, 
    lb = lb, pars = pars, verb = TRUE)

######################## EXAMPLES WITH LWPLSR

n <- 50 ; p <- 8
X <- matrix(rnorm(n * p), ncol = p)
y <- rnorm(n)
Y <- cbind(y, 10 * rnorm(n))

K = 3
segm <- segmkf(n = n, K = K, nrep = 1)
segm

nlvdis <- 5
h <- c(1, Inf)
k <- c(10, 20)
nlv <- 5
pars <- mpars(nlvdis = nlvdis, diss = "mahal",
              h = h, k = k)
pars
res <- gridcvlv(
    X, Y, segm, 
    score = msep, 
    fun = lwplsr, 
    nlv = 0:nlv, pars = pars, verb = TRUE)
res

mlesnoff/rchemo documentation built on April 15, 2023, 1:25 p.m.