linear_filter()takes floating point errors into account when checking whether the alpha values sum to 1.
get_kernelmatrix. the function
tskrrHomogenousand dependent classes are now called
tskrrHomogeneous. The same correction is done for
tskrrHeterogeneous. This might affect code that uses
get_loo_funbased on the class name.
tskrrHeterogeneousImputewere renamed to
tskrrImputeHeterogeneousto follow the naming convention for the classes.
permtestclass now has getters that allow to extract the information from the test.
"vertices"for the settings
"both"respectively. These give the same results, and make it more clear what actually happens. This is adapted in functions
tune()and those dependent on it.
predict()now expects the new nodes to be on the rows.
permtestfunction is added.
Gfor the function
predict()have been renamed
loonow adds the labels to the output (except for linear filters)
tunenow allows for a one-dimensional grid search for heterogenous networks. Set
onedim = TRUEto avoid a full grid search.
has_onedimtells whether the grid search was one dimensional or not. This is a getter for the appropriate slote in the tskrrTune class.
plot_gridallows you to plot the loss in function of the searched grid after tuning a model. It deals with both 1D and 2D grids and can be used for quick evaluation of the optimal lambda values.
residualsallows you to calculate the residuals based on the predictions or on the loo values of choice.
plotmethod available now for
tskrrobjects. It allows to plot fitted values, residuals, original response and the results of different loo settings, together with dendrograms based on the kernel matrices.
predictdidn't give correct output when only
gwas passed. fixed.
colnamesdidn't get the correct labels for homogenous networks
impute_loois removed from the package.
eigen2matrixhad the second argument renamed from
val. The old name implied that the second argument took the vectors, which it doesn't!
tskrrImputevirtual class is added to represent imputed models.
is_symmetricdidn't take absolute values to compare. Fixed.
showmethods for objects are cleaned up.
predictgave nonsensical output. Fixed.
valid_labelsnow requires the K and G matrices to have the same ordering of row and column names. Otherwise the matrix wouldn't be symmetric and can't be used.
linear_filternow forces the alphas to sum up to 1.
tunenow returns an object of class
tskrrTuneprovides a more complete object with all information of tuning. It is a superclass with two real subclasses,
tunenow allows to pass the matrices directly so you don't have to create a model with
linear_filtergave totally wrong predictions due to a code error: fixed.
linear_filterreturned a matrix when NAs were present: fixed.
fittednow has an argument
labelswhich allows to add the labels to the returned object.
tskrrnow returns an error if the Y matrix is not symmetric or skewed when fitting a homogenous network.
labels now produces more informative errors and warnings.
In the testing procedures
input testing for
tskrr moved to its own function and is
also used by
has.orighas been removed as it doesn't make sense to keep the original kernel matrices. It is replaced by a slot
has.hatallowing to store the hat matrices.
g.orighave been replaced by the slots
Hgto store the hat matrices. These are more needed for fitting etc.
has_original has been removed and replaced by
keepof the function
tskrrnow stores the hat matrices instead of the original kernel matrices.
tskrrhas lost its argument
homogenous. It didn't make sense to set that by hand.
tskrrHomogenousImputeare added to allow for storing models with imputed predictions.
homogenousremoved in favor of
x. This allows for extension of the function based on either an object or the class of that object.
xbecomes the first argument.
linear_filterthat fits a linear filter over an adjacency matrix. This function comes with a class
tune()has a new argument
funthat allows to specify a function for optimization.
loss_auc()are provided for tuning.
update()allows to retrain the model with new lambdas.
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