Description Usage Arguments Details Value Author(s) References See Also Examples
Performs a careful evaluation by repeated doubleCV for multivariate regression methods, like PLS and PCR.
1 2 3 4 5 6  mvr_dcv(formula, ncomp, data, subset, na.action,
method = c("kernelpls", "widekernelpls", "simpls", "oscorespls", "svdpc"),
scale = FALSE, repl = 100, sdfact = 2,
segments0 = 4, segment0.type = c("random", "consecutive", "interleaved"),
length.seg0, segments = 10, segment.type = c("random", "consecutive", "interleaved"),
length.seg, trace = FALSE, plot.opt = FALSE, selstrat = "hastie", ...)

formula 
formula, like y~X, i.e., dependent~response variables 
ncomp 
number of PLS components 
data 
data frame to be analyzed 
subset 
optional vector to define a subset 
na.action 
a function which indicates what should happen when the data contain missing values 
method 
the multivariate regression method to be used, see

scale 
numeric vector, or logical. If numeric vector, X is scaled by dividing each variable with the corresponding element of 'scale'. If 'scale' is 'TRUE', X is scaled by dividing each variable by its sample standard deviation. If crossvalidation is selected, scaling by the standard deviation is done for every segment. 
repl 
Number of replicattion for the doubleCV 
sdfact 
factor for the multiplication of the standard deviation for the determination of the optimal number of components 
segments0 
the number of segments to use for splitting into training and test
data, or a list with segments (see 
segment0.type 
the type of segments to use. Ignored if 'segments0' is a list 
length.seg0 
Positive integer. The length of the segments to use. If specified, it overrides 'segments' unless 'segments0' is a list 
segments 
the number of segments to use for selecting the optimal number if
components, or a list with segments (see 
segment.type 
the type of segments to use. Ignored if 'segments' is a list 
length.seg 
Positive integer. The length of the segments to use. If specified, it overrides 'segments' unless 'segments' is a list 
trace 
logical; if 'TRUE', the segment number is printed for each segment 
plot.opt 
if TRUE a plot will be generated that shows the selection of the optimal number of components for each step of the CV 
selstrat 
method that defines how the optimal number of components is selected, should be one of "diffnext", "hastie", "relchange"; see details 
... 
additional parameters 
In this crossvalidation (CV) scheme, the optimal number of components is determined by an additional CV in the training set, and applied to the test set. The procedure is repeated repl times. There are different strategies for determining the optimal number of components (parameter selstrat): "diffnext" compares MSE+sdfact*sd(MSE) among the neighbors, and if the MSE falls outside this bound, this is the optimal number. "hastie" searches for the number of components with the minimum of the mean MSE's. The optimal number of components is the model with the smallest number of components which is still in the range of the MSE+sdfact*sd(MSE), where MSE and sd are taken from the minimum. "relchange" is a strategy where the relative change is combined with "hastie": First the minimum of the mean MSE's is searched, and MSE's of larger components are omitted. For this selection, the relative change in MSE compared to the min, and relative to the max, is computed. If this change is very small (e.g. smaller than 0.005), these components are omitted. Then the "hastie" strategy is applied for the remaining MSE's.
resopt 
array [nrow(Y) x ncol(Y) x repl] with residuals using optimum number of components 
predopt 
array [nrow(Y) x ncol(Y) x repl] with predicted Y using optimum number of components 
optcomp 
matrix [segments0 x repl] optimum number of components for each training set 
pred 
array [nrow(Y) x ncol(Y) x ncomp x repl] with predicted Y for all numbers of components 
SEPopt 
SEP over all residuals using optimal number of components 
sIQRopt 
spread of inner half of residuals as alternative robust spread measure to the SEPopt 
sMADopt 
MAD of residuals as alternative robust spread measure to the SEPopt 
MSEPopt 
MSEP over all residuals using optimal number of components 
afinal 
final optimal number of components 
SEPfinal 
vector of length ncomp with final SEP values; use the element afinal for the optimal SEP 
Peter Filzmoser <[email protected]>
K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.
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