tor_calculate_label_rate: Calculate protein or peptide labeling rate
In KopfLab/turnoveR: Processing tools for protein degradation experiments.

Description Usage Arguments Value

Calculate protein or peptide labeling rate by non-linear least squares fitting to exponential equation f_{labeled} = 1 - e^{-k\cdot t} where k is the protein/peptide labeling rate (in units of reciprocal time, depending on what the time column is recorded in). Forces fit through the origin (i.e. assumes no labeling at time point 0) and thus explicitly ignores 0 time points.

1 2	tor_calculate_label_rate(data, time_col = "hours", min_num_timepoints = 2, combine_peptides = TRUE, quiet = FALSE)

`data`	the data with frac_lab/frac_ulab calculated (tor_calculate_labeled_fraction)
`time_col`	the name of the column that holds the time, assumes "hours" by default.
`min_num_timepoints`	what is the minimum number of time points to try to fit a curve? 0 time point do not count towards making this minimum
`combine_peptides`	whether to combine all peptides for a fit (i.e. fit the whole protein), or to calculate a fit for each peptide. Default is to combine across peptides.

data frame with one line for each peptide or protein (depending on combine_peptides) and the following added columns:

nested_data: all the data for the protein or peptide
num_peptides: the number of peptides combined for each protein (always 1 if combine_peptides = FALSE)
num_timepoints: the number of unique time points that went into each fit - does not include any 0 time points
num_datapoints: the number of individual data points that went into each fit
enough_data: whether there was enough data to fit a curve at all - i.e. whether there were at least 2 time points
fit_error: whether the non-linear squares fit succeeded or failed
label_rate: the estimated label rate, in units of reciprocal time depending on the time_col
label_rate_se: the standard error of the estimated label rate based on the least squares fit
fit_rse: the residual standard error of the fit
fit: the actual regression model fit