Standard result computation and extraction functions for ROSA (rosa).
# S3 method for rosa
predict(
object,
newdata,
ncomp = 1:object$ncomp,
comps,
type = c("response", "scores"),
na.action = na.pass,
...
)
# S3 method for rosa
coef(object, ncomp = object$ncomp, comps, intercept = FALSE, ...)
# S3 method for rosa
print(x, ...)
# S3 method for rosa
summary(
object,
what = c("all", "validation", "training"),
digits = 4,
print.gap = 2,
...
)
blockexpl(object, ncomp = object$ncomp, type = c("train", "CV"))
# S3 method for rosaexpl
print(x, digits = 3, compwise = FALSE, ...)
rosa.classify(object, classes, newdata, ncomp, LQ)
# S3 method for rosa
scores(object, ...)
# S3 method for rosa
loadings(object, ...)A rosa object.
Optional new data with the same types of predictor blocks as the ones used for fitting the object.
An integer giving the number of components to apply (cummulative).
An integer vector giving the exact components to apply (subset).
For blockexpl: Character indicating which type of explained variance to compute (default = "train", alternative = "CV").
Function determining what to do with missing values in newdata.
Additional arguments. Currently not implemented.
A logical indicating if coefficients for the intercept should be included (default = FALSE).
A rosa object.
A character indicating if summary should include all, validation or training.
The number of digits used for printing.
Gap between columns when printing.
Logical indicating if block-wise (default/FALSE) or component-wise (TRUE) explained variance should be printed.
A character vector of class labels.
A character indicating if 'max' (maximum score value), 'lda' or 'qda' should be used when classifying.
Returns depend on method used, e.g. predict.rosa returns predicted responses
or scores depending on inputs, coef.rosa returns regression coefficients, blockexpl
returns an object of class rosaexpl containing block-wise and component-wise explained variance contained in a matrix with attributes.
Usage of the functions are shown using generics in the examples below.
Prediction, regression coefficients, object printing and summary are available through:
predict.rosa, coef.rosa, print.rosa and summary.rosa.
Explained variances are available (block-wise and global) through blockexpl and print.rosaexpl.
Scores and loadings have their own extensions of scores() and loadings() throught
scores.rosa and loadings.rosa. Finally, there is work in progress on classifcation
support through rosa.classify.
When type is "response" (default), predicted response values
are returned. If comps is missing (or is NULL), predictions
for length(ncomp) models with ncomp[1] components,
ncomp[2] components, etc., are returned. Otherwise, predictions for
a single model with the exact components in comps are returned.
(Note that in both cases, the intercept is always included in the
predictions. It can be removed by subtracting the Ymeans component
of the fitted model.)
If comps is missing (or is NULL), coef()[,,ncomp[i]]
are the coefficients for models with ncomp[i] components, for \(i
= 1, \ldots, length(ncomp)\). Also, if intercept = TRUE, the first
dimension is \(nxvar + 1\), with the intercept coefficients as the first
row.
If comps is given, however, coef()[,,comps[i]] are the
coefficients for a model with only the component comps[i], i.e., the
contribution of the component comps[i] on the regression
coefficients.
Liland, K.H., Næs, T., and Indahl, U.G. (2016). ROSA - a fast extension of partial least squares regression for multiblock data analysis. Journal of Chemometrics, 30, 651–662, doi:10.1002/cem.2824.
Overviews of available methods, multiblock, and methods organised by main structure: basic, unsupervised, asca, supervised and complex.
Common functions for computation and extraction of results and plotting are found in rosa_results and rosa_plots, respectively.
data(potato)
mod <- rosa(Sensory[,1] ~ ., data = potato, ncomp = 5, subset = 1:20)
testset <- potato[-(1:20),]; testset$Sensory <- testset$Sensory[,1,drop=FALSE]
predict(mod, testset, ncomp=5)
#> , , 5 comps
#>
#> Sensory[, 1]
#> 21 4.892899
#> 22 4.454407
#> 23 8.177141
#> 24 3.087408
#> 25 4.671565
#> 26 3.926871
#>
dim(coef(mod, ncomp=5)) # <variables x responses x components>
#> [1] 3946 1 1
print(mod)
#> Response Orinented Sequential Alternation , fitted with the CPPLS algorithm.
#> Call:
#> rosa(formula = Sensory[, 1] ~ ., ncomp = 5, data = potato, subset = 1:20)
summary(mod)
#> Data: X dimension: 20 3946
#> Y dimension: 20 1
#> Fit method:
#> Number of components considered: 5
#> TRAINING: % variance explained
#> 1 comps 2 comps 3 comps 4 comps 5 comps
#> X 28.09 45.00 49.16 62.24 72.58
#> Sensory[, 1] 72.84 91.32 93.68 95.15 96.73
blockexpl(mod)
#> Block-wise explained variance
#>
#> Chemical Compression NIRraw NIRcooked CPMGraw CPMGcooked FIDraw FIDcooked
#> X 0.453 0.103 0.169 0 0 0 0 0
#> Y 0.767 0.016 0.185 0 0 0 0 0
#> residual
#> X 0.274
#> Y 0.033
print(blockexpl(mod), compwise=TRUE)
#> Component-wise explained variance
#>
#> comp.1 (Chemical) comp.2 (NIRraw) comp.3 (Chemical) comp.4 (Chemical)
#> X 0.281 0.169 0.042 0.131
#> Y 0.728 0.185 0.024 0.015
#> comp.5 (Compression) residual
#> X 0.103 0.033
#> Y 0.016 0.033