Description Usage Arguments Value References See Also Examples
Function to evaluate the performance of the fitted the PLS models using various criteria. Evaluation is made for each component in the PLS object and can only be evaluated for regression PLS.
1 2 3 4 |
object |
Object of class inheriting from |
validation |
What kind of cross validation to use, matching one of |
folds |
Number of folds to use in the cross validation. |
BIC |
Return the BIC type criterion for large sample sizes (see paper for details). Note that this is not an actual BIC criterion. |
progressBar |
Logical to show a progress bar while computing the performances |
setseed |
optional double to set random seed for replication (default is no seed). |
scale_resp |
Logical to scale the responses. This is useful if comparing the fit on multiple responses. |
... |
additional arguments to be passed to fitting functions. |
perf
returns a list that contains the following performance measures:
MSEP |
A matrix of Mean Square Error of Prediction (MSEP) estimates by cross validation. The penalty is defined as: MSEP = 1/n ∑ ∑ (f_k(x_i) - y_i)^2 see the references for details. |
PRESS0 |
A vector of cross validated Predictive Residual Sum of Squares (PRESS) values. Each column corresponds to a response. Matches the PLS package. |
PRESS |
A matrix of cross validated Predictive Residual Sum of Squares (PRESS) values. Each row contains the values for a different component and each column corresponds to a response. |
R2 |
a matrix of R^2 values of the Y-variables with |
BIC |
A BIC type criterion for large samples (see references for details). Note that this is not an actual BIC criterion. |
cvPred |
an array with the cross-validated predictions. |
folds |
A list of the folds used in the cross validation. |
Mevik, Bjorn-Helge, and Henrik Rene Cederkvist. 2004. Mean Squared Error of Prediction (MSEP) Estimates for Principal Component Regression (PCR) and Partial Least Squares Regression (PLSR). Journal of Chemometrics 18 (9). John Wiley & Sons, Ltd.:422-29.
Tuning functions calc_pve
,
tune_sgspls
, tune_groups
.
Model performance and estimation predict.sgspls
, perf.sgspls
, coef.sgspls
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | set.seed(1)
n = 50; p = 500;
size.groups = 30; size.subgroups = 5
groupX <- ceiling(1:p / size.groups)
subgroupX <- ceiling(1:p / size.subgroups)
X = matrix(rnorm(n * p), ncol = p, nrow = n)
beta <- rep(0,p)
bSG <- -2:2; b0 <- rep(0,length(bSG))
betaG <- c(bSG, b0, bSG, b0, bSG, b0)
beta[1:size.groups] <- betaG
y = X %*% beta + 0.1*rnorm(n)
model <- sgspls(X, y, ncomp = 3, mode = "regression", keepX = 1,
groupX = groupX, subgroupX = subgroupX,
indiv_sparsity_x = 0.8, subgroup_sparsity_x = 0.15)
model_perf <- perf(model, folds = 5)
# Check model performance
model_perf$MSEP
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