gridcv | R Documentation |
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
.
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)
X |
Training X-data ( |
Y |
Training Y-data ( |
segm |
CV segments, typically output of |
score |
A function calculating a prediction score (e.g. |
fun |
A function corresponding to the predictive model. |
nlv |
For |
lb |
For |
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. (see details) |
verb |
Logical. If |
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
).
Dataframes with the prediction scores for the grid.
Examples are given: - with PLSR, using gridcv and gridcvlv (much faster) - with PLSLDA, using gridcv and gridcvlv (much faster) - with RR, using gridcv and gridcvlb (much faster) - with KRR, using gridcv and gridcvlb (much faster) - with LWPLSR, using gridcvlv
## EXAMPLE 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
nlv <- 5
pars <- mpars(nlv = 1:nlv)
pars
gridcv(
X, Y, segm,
score = msep,
fun = plskern,
pars = pars, verb = TRUE)
gridcvlv(
X, Y, segm,
score = msep,
fun = plskern,
nlv = 0:nlv, verb = TRUE)
## EXAMPLE 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)
K = 3
segm <- segmkf(n = n, K = K, nrep = 1)
segm
nlv <- 5
pars <- mpars(nlv = 1:nlv, prior = c("unif", "prop"))
pars
gridcv(
X, y, segm,
score = err,
fun = plslda,
pars = pars, verb = TRUE)
pars <- mpars(prior = c("unif", "prop"))
pars
gridcvlv(
X, y, segm,
score = err,
fun = plslda,
nlv = 1:nlv, pars = pars, verb = TRUE)
## EXAMPLE 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
lb <- c(.1, 1)
pars <- mpars(lb = lb)
pars
gridcv(
X, Y, segm,
score = msep,
fun = rr,
pars = pars, verb = TRUE)
gridcvlb(
X, Y, segm,
score = msep,
fun = rr,
lb = lb, verb = TRUE)
## EXAMPLE 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
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)
pars <- mpars(gamma = gamma)
gridcvlb(
X, Y, segm,
score = msep,
fun = krr,
lb = lb, pars = pars, verb = TRUE)
## EXAMPLE 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
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