View source: R/plot_classif_gen.r
plot_classifX_indepPred | R Documentation |
Calculate and plot predictions from independent, manually provided
data. One or more of the implemented classifier-models (see e.g.
calc_discrimAnalysis_args
and the links to other classifier
functions there) have to be present in the cube. The manually provided data
in indepDataset
are then projected into each single classification
model present in the cube, and the results are validated using either the class
variable present in the independent dataset that has the exactly same name as the
class variable used to generate the models, or a user-defined class variable
(parameter icv
) can be used for validation.
plot_classifX_indepPred(
indepDataset,
cube,
ccv = NULL,
icv = NULL,
pl = TRUE,
toxls = "def",
info = "def",
confirm = "def",
predList = NULL,
apPlot = "def",
...
)
indepDataset |
The dataset containing the independent data. An object
of class 'aquap_data' as produced by |
cube |
An object of class 'aquap_cube' as produced by |
ccv |
"Cube class variable", character vector or NULL. The names of
one or more class variables in the cube on which classification models have
been calculated. Leave at the default NULL to use all of the class
variables on which a classification model has been calculated, or provide a
character vector with valid variable names for a sub-selection. For the selected
variables, predictions from the data in the independent dataset will be made.
If argument |
icv |
"Independent class variable", character vector or NULL. The names
of class variables in the independent dataset. If left at the default
NULL, class variables in the independent dataset with exactly the same
name(s) as specified in argument |
pl |
Logical, defaults to TRUE. If predicted data should be plotted at all. If FALSE, only the calculation and the (possible) export to an excel file (see details) will be performed. |
toxls |
'def' or logical. If left at the default 'def' the value from
|
info |
'def' or logical. If left at the default 'def' the value from
|
confirm |
'def' or logical. If left at the default 'def' the value from
|
predList |
NULL or list. If left at the default null, the independent
dataset is used to make predictions on all available classification models,
resulting in a list containing the predictions. If this list is in turn
provided to |
apPlot |
The analysis procedure used for plotting. |
... |
General plotting parameters, see XXX. |
For every single element in the cube, i.e. for every split-variation
of the original dataset (as produced in gdmm
the according
subgroups within the independent dataset are constructed. In case of a dataset
resulting in no observations the process is aborted. Also, the data pre- and
post- treatments (see dpt_modules
) as defined in the analysis
procedure used to produce the cube are applied to the independent dataset resp.
to its subgroups as defined by the application of possible split-variables
(see above).
If toXls
is TRUE, the results of the predictions will be exported to Excel.
An (invisible) list containing the numerical results of the
predictions, and if parameter toXls
is TRUE, these data are exported
to an excel file in the results folder as well.
Other Classification functions:
calc_NNET_args
,
calc_SVM_args
,
calc_discrimAnalysis_args
,
calc_randomForest_args
Other Plot functions:
plot,aquap_cube,missing-method
,
plot,aquap_data,missing-method
,
plot_aqg()
,
plot_da,aquap_cube-method
,
plot_nnet,aquap_cube-method
,
plot_pca,aquap_cube-method
,
plot_pls,aquap_cube-method
,
plot_pls_indepPred()
,
plot_rnf,aquap_cube-method
,
plot_simca,aquap_cube-method
,
plot_svm,aquap_cube-method
## Not run:
fd <- gfd() # loading or importing the rawdata
fd1 <- ssc(fd, C_Foo!="bar") # manually splitting up the dataset
fd2 <- ssc(fd, C_Foo=="bar")
cube <- gdmm(fd1) # this is assuming that the standard analysis procedure is set
# up to perform a classifier method
# we are using `fd1` to produce the cube, and then `fd2` as independent dataset
# to perform independent predictions on all the models in the cube
predictions <- plot_classifX_indepPred(fd2, cube)
predictions <- plot_classifX_indepPred(fd2, cube, icv="C_blabla", pl=FALSE)
# to redirect the original validation to class-variable "C_blabla"
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.