predict,proDAFit-method {proDA} | R Documentation

## Predict the parameters or values of additional proteins

### Description

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"`).

### Usage

    ## S4 method for signature 'proDAFit'
    predict(object, newdata, newdesign,
      type = c("response", "feature_parameters"), ...)

### Arguments

`object` | an 'proDAFit' object that is produced by `proDA()`.
`newdata` | a matrix or a SummarizedExperiment which contains the new abundances for which values are predicted.
`newdesign` | a formula or design matrix that specifies the new structure that will be fitted
`type` | either "response" or "feature_parameters". Default: `"response"`
`...` | additional parameters for the construction of the 'proDAFit' object.

### Details

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 classical `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.

### Value

If `type = "response"` a matrix with the same dimensions as `object`. Or, if `type = "feature_parameters"` a 'proDAFit' object with the same hyper-parameters and column data as `object`, but new fitted `rowData()`.

[Package proDA version 1.0.0 Index]