Perform leave-one-out fitting + validation of PARAFAC models on a given FEEM cube.
feemjackknife(cube, ..., progress = TRUE)
# S3 method for feemjackknife
plot(
x, kind = c('estimations', 'RIP', 'IMP'), ...
)
# S3 method for feemjackknife
coef(
object, kind = c('estimations', 'RIP', 'IMP'), ...
)A list of class feemjackknife containing the following
entries:
Result of fitting the overall cube with
feemparafac.
A list of length dim(cube)[3] containing the reduced dataset
components. Every feemparafac object in the list has
an additional Chat attribute containing the result of
fitting the excluded spectrum back to the loadings of the reduced
model.
A lattice plot object. Its print or plot method
will draw the plot on an appropriate plotting device.
A data.frame containing various columns,
depending on the value of the kind argument:
Values of the loadings.
The axis of the loadings, “Emission” or “Excitation”.
Emission or excitation wavelength the loading values correspond to.
The component number.
The sample (name if cube had names, integer if it didn't)
that was omitted to get the resulting loading values.
Mean squared residual value for the model with a given sample omitted.
Mean squared difference in emission mode loadings between the overall model and the model with a given sample omitted.
Mean squared difference in excitation mode loadings between the overall model and the model with a given sample omitted.
The sample (name if cube had names, integer if it didn't)
that was omitted from a given model.
Score values for the overall model.
Score values estimated from the loadings of the model missing a given sample.
The component number.
The sample (name if cube had names, integer if it didn't)
that was omitted from a given model.
A feemcube object.
Set to FALSE to disable the progress bar.
An object returned by feemjackknife.
Chooses what to plot (when called as plot(...)) or return
as a data.frame (when called as
coef(...)):
Produce the loadings from every leave-one-out model.
Produce a Resample Influence Plot, i.e. mean squared difference between loadings in overall and leave-one-out models plotted against mean squared residuals in leave-one-out models.
Produce an Identity Match Plot, i.e. scores in leave-one-out models plotted against scores in the overall model.
Passed as-is to feemparafac and, eventually, to
multiway function parafac.
When kind is “RIP” or “IMP”, pass a q
argument to specify the quantile of residual values (for RIP)
or absolute score differences (IMP) above which sample names (or
numbers) should be plotted. Default value for q is \(0.9\).
Remaining arguments are passed as-is to
xyplot.
No further parameters are allowed.
The function takes each sample out of the dataset, fits a PARAFAC model without it, then fits the outstanding sample to the model with emission and excitation factors fixed:
$$ \hat{\mathbf{c}} = (\mathbf{A} \ast \mathbf{B})^{+} \times \mathrm{vec}(\mathbf{X}) $$
The individual leave-one-out models (fitted loadings \(\mathbf A\), \(\mathbf B\) and scores \(\mathbf C\)) are reordered according to best Tucker's congruence coefficient match and rescaled by minimising \( || \mathbf A \, \mathrm{diag}(\mathbf s_\mathrm A) - \mathbf A^\mathrm{orig} ||^2 \) and \( || \mathbf{B} \, \mathrm{diag}(\mathbf s_\mathrm B) - \mathbf B^\mathrm{orig} ||^2 \) over \(\mathbf s_\mathrm A\) and \(\mathbf s_\mathrm B\), subject to \( \mathrm{diag}(\mathbf s_\mathrm A) \times \mathrm{diag}(\mathbf s_\mathrm B) \times \mathrm{diag}(\mathbf s_\mathrm C) = \mathbf I \), to make them comparable.
Once the models are fitted, resample influence plots and identity match plots can be produced from resulting data to detect outliers.
To conserve memory, feemjackknife puts the user-provided
cube in an environment and passes it via envir and
subset options of feemparafac. This means that,
unlike in feemparafac, the cube argument has
to be a feemcube object and passing envir and
subset options to feemjackknife is not supported. It
is recommended to fully name the parameters to be passed to
feemparafac to avoid problems.
plot.feemjackknife provides sane defaults for
xyplot parameters xlab, ylab,
scales, as.table, but they can be overridden.
albatross:::.Rdreference('Riu2003')
feemparafac, feemcube
# \donttest{
data(feems)
cube <- feemscale(feemscatter(cube, rep(14, 4)), na.rm = TRUE)
# takes a long time; the stopping criterion is weaked for speed
jk <- feemjackknife(cube, nfac = 3, ctol = 1e-4)
# feemparafac methods should be able to use the environment and subset
plot(jk$leaveone[[1]])
plot(jk)
plot(jk, 'IMP')
plot(jk, 'RIP')
head(coef(jk))
# }Run the code above in your browser using DataLab