Description Usage Arguments Details Value Examples

Returns identifying information for the compounds in the order in which the corresponding regression coefficient for a given compound first becomes nonzero as part of the Elastic Net path

1 2 |

`msObj` |
An object of class |

`bioact` |
Either a numeric vector or matrix, or a data frame providing bioactivity data. If a numeric vector, then it is assumed that each entry corresponds to a particular fraction. If the data is 2-dimensional, then it is assumed that each column corresponds to a particular fraction, and that each row corresponds to a particular bioactivity replicate. |

`region_ms` |
Either |

`region_bio` |
Either |

`lambda` |
A single nonnegative numeric value providing the quadratic penalty mixture parameter argument for the elastic net model. The elastic net fits the least squares model with penalty function
where |

`pos_only` |
Either |

`ncomp` |
Either |

`rankEN`

prepares the data by extracting the region of interest
from the mass spectrometry abundance data and from the bioactivity data.
If bioactivity replicates are present, then the within-fraction
replicates are averaged. Once the data has been converted into the
appropriate form, then an elastic net model is fitted by invoking the
`enet`

function from the `elasticnet`

package, and an ordered
list of candidate compounds is constructed such that compounds are ranked
by the order in which they first enter the model. The list may be
filtered and / or pruned before being returned to the user, as determined
by the arguments to `pos_only`

and `ncomp`

.

Returns an object of class `rankEN`

. This object is a
`list`

with elements described below. The class is equipped with a
`print`

, `summary`

, and `extract_ranked`

function.

`mtoz`

A vector providing the mass-to-charge values of the candidate compounds, such that the

`k`

-th element of the vector provides the mass-to-charge value of the`k`

-th compound to enter the elastic net model, possibly after removing compounds nonpositively correlated with bioactivity levels.`charge`

A vector providing the charge state of the candidate compounds, such that the

`k`

-th element of the vector provides the charge state of the`k`

-th compound to enter the elastic net model, possibly after removing compounds nonpositively correlated with bioactivity levels.`comp_cor`

A vector providing the correlation between each of the candidate compounds and the bioactivity levels, such that the

`k`

-th element of the vector provides the correlation between the`k`

-th compound to enter the elastic net model and the bioactivity levels, possibly after removing compounds nonpositively correlated with bioactivity levels.`enet_fit`

The fitted model object produced by

`rankEN`

's internal invokation of the`enet`

function from the`elasticnet`

package.`summ_info`

A list containing information related to the data used to fit the elastic net model; used by the summary function.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | ```
# Load mass spectrometry data
data(mass_spec)
# Convert mass_spec from a data.frame to an msDat object
ms <- msDat(mass_spec = mass_spec,
mtoz = "m/z",
charge = "Charge",
ms_inten = c(paste0("_", 11:43), "_47"))
# Load growth inhibition bioactivity data. Each element in bioact is a
# stand-alone dataset for a species of virus or bacteria.
data(bioact)
# Perform the candidate ranking procedure with fractions 21-24 as the region
# of interest. Note that it is not advisable to calculate the elastic net
# estimates with 30,799 candidate compounds on 4 data points!
## Not run:
rank_out <- rankEN(msObj = ms,
bioact = bioact$ec,
region_ms = paste0("_", 21:24),
region_bio = paste0("_", 21:24),
lambda = 0.001,
pos_only = TRUE,
ncomp = NULL)
# print, summary function
rank_out
summary(rank_out)
# Extract ranked compounds as a data.frame
ranked_candidates <- extract_ranked(rank_out)
## End(Not run)
``` |

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