multiblock (version 0.8.8.2)

sopls_results: Result functions for SO-PLS models

Description

Standard result functions for SO-PLS (sopls).

Usage

# S3 method for sopls
predict(
  object,
  newdata,
  ncomp = object$ncomp,
  type = c("response", "scores"),
  na.action = na.pass,
  ...
)
# S3 method for sopls
coef(object, ncomp = object$ncomp, intercept = FALSE, ...)
# S3 method for sopls
print(x, ...)
# S3 method for sopls
summary(
  object,
  what = c("all", "validation", "training"),
  digits = 4,
  print.gap = 2,
  ...
)
classify(object, ...)
# S3 method for sopls
classify(object, classes, newdata, ncomp, LQ = "LDA", ...)
# S3 method for sopls
R2(object, estimate, newdata, ncomp = "all", individual = FALSE, ...)
# S3 method for sopls
RMSEP(object, estimate, newdata, ncomp = "all", individual = FALSE, ...)
pcp(object, ...)
# S3 method for sopls
pcp(object, ncomp, ...)
# S3 method for default
pcp(object, X, ...)
cvanova(pred, ...)
# S3 method for default
cvanova(pred, true, absRes = TRUE, ...)
# S3 method for sopls
cvanova(pred, comps, absRes = TRUE, ...)
# S3 method for cvanova
print(x, ...)
# S3 method for cvanova
summary(object, ...)
# S3 method for cvanova
plot(x, ...)
# S3 method for sopls
residuals(object, ...)

Value

Returns depend on method used, e.g. predict.sopls returns predicted responses or scores depending on inputs, coef.sopls return regression coefficients, while print and summary methods return the object invisibly.

Arguments

object

A sopls object.

newdata

Optional new data with the same types of predictor blocks as the ones used for fitting the object.

ncomp

An integer vector giving the exact components to apply.

type

A character for predict indicating if responses or scores should be predicted (default = "response", or "scores"), for summary indicating which type of explained variance to compute (default = "train", alternative = "CV").

na.action

Function determining what to do with missing values in newdata.

...

Additional arguments. Currently not implemented.

intercept

A logical indicating if coefficients for the intercept should be included (default = FALSE).

x

A sopls object.

what

A character indicating if summary should include all, validation or training.

digits

The number of digits used for printing.

print.gap

Gap between columns when printing.

classes

A character vector of class labels.

LQ

A character indicating if 'max' (maximum score value), 'lda' or 'qda' should be used when classifying.

estimate

A character indicating if 'train', 'CV' or 'test' results should be displayed.

individual

A logical indicating if results for individual responses should be displayed.

X

A list of data blocks.

pred

An object holding the CV-predicted values (sopls, matrix or list of vectors)

true

A numeric of true response values for CVANOVA.

absRes

A logical indicating if absolute (TRUE) or squared (FALSE) residuals should be computed.

comps

An integer vector giving the exact components to apply.

Details

The parameter ncomp controls which components to apply/extract, resulting in the sequence of components leading up to the specific choice, i.e. ncomp = c(2,2,1) results in the sequence 1,0,0; 2,0,0; 2,1,0; 2,2,0; 2,2,1. Usage of the functions are shown using generics in the examples below. Prediction, regression coefficients, object printing and summary are available through: predict.sopls, coef.sopls, print.sopls and summary.sopls. Explained variances and RMSEP are available through R2.sopls and RMSEP.sopls. Principal components of predictions are available through pcp.sopls. Finally, there is work in progress on classifcation support through classify.sopls.

References

Jørgensen K, Mevik BH, Næs T. Combining designed experiments with several blocks of spectroscopic data. Chemometr Intell Lab Syst. 2007;88(2): 154–166.

See Also

Overviews of available methods, multiblock, and methods organised by main structure: basic, unsupervised, asca, supervised and complex. Common functions for plotting are found in sopls_plots.

Examples

data(potato)
mod <- sopls(Sensory[,1] ~ ., data = potato[c(1:3,9)], ncomp = 5, subset = 1:20)
testset <- potato[-(1:20),]; testset$Sensory <- testset$Sensory[,1,drop=FALSE]
predict(mod, testset, ncomp=c(2,1,2))
dim(coef(mod, ncomp=c(3,0,1))) # 
R2(mod, ncomp = c(4,1,2))
print(mod)
summary(mod)

# PCP from sopls object
modMulti <- sopls(Sensory ~ ., data = potato[c(1:3,9)], ncomp = 5, validation = "CV", segment = 5)
(PCP <- pcp(modMulti, c(2,1,2)))
scoreplot(PCP)

# PCP from matrices
preds <- modMulti$validation$Ypred[,,"2,1,2"]
PCP_default <- pcp(preds, potato[1:3])

# CVANOVA
modCV <- sopls(Sensory[,1] ~ ., data = potato[c(1:3,9)], ncomp = 5, validation = "CV", segment = 5)
summary(cva <- cvanova(modCV, "2,1,2"))
plot(cva)