This function can either predict the abundance matrix for proteins
type = "response") without missing values according to the
linear probabilistic dropout model, fitted with
proDA(). Or, it
can predict the feature parameters for additional proteins given their
abundances including missing values after estimating the hyper-parameters
on a dataset with the same sample structure
type = "feature_parameters").
# S4 method for proDAFit predict(object, newdata, newdesign, type = c("response", "feature_parameters"), ...)
an 'proDAFit' object that is produced by
a matrix or a SummarizedExperiment which contains the new abundances for which values are predicted.
a formula or design matrix that specifies the new structure that will be fitted
either "response" or "feature_parameters". Default:
additional parameters for the construction of the 'proDAFit' object.
type = "response" a matrix with the same dimensions
object. Or, if
type = "feature_parameters" a
'proDAFit' object with the same hyper-parameters and column data
object, but new fitted
Note: this method behaves a little different from what one might
expect from the classical
predict.lm() function, because
object is not just a single set of coefficients for one fit, but
many fits (ie. one for each protein) with some more hyper-parameters. The
predict function predicts the response for new samples.
This function does not support this, instead it is useful for getting a
matrix without missing values for additional proteins.