# rankLocReg.default: Ranking local linear regressions according to linearity In colin-olito/LoLinR: Local Linear Regression for Estimating Biological Rates From Time-Series & Trace Data

## Description

Ranking local linear regressions.

## Usage

 ```1 2 3``` ```## Default S3 method: rankLocReg(xall, yall, alpha, method = c("z", "eq", "pc"), verbose = TRUE) ```

## Arguments

 `xall` A numeric vector. `yall` A numeric vector. `alpha` Minimum window size, expressed as a proportion of total data. Must be higher than 0 and less-than or equal to 1. `method` Ranking method. See details. `verbose` Logical. Should progress be printed?

## Details

`rankLocReg` is the main function around which LoLinR is built. Given independent and dependent variables for a time series or trace data set, `rankLocReg` tries to find the 'most linear' ordered subset of the full data set. This is accomplished by fitting all possible local linear regressions with minimum window size `alpha`, and ranking them according to the combined linearity metric \$L\$. \$L\$ quantifies linearity from 1) the skewness of the standardized residuals, 2) the range of the 95 residuals (a modified Breusch-Godfrey \$R^2\$). These three components of \$L\$ can be weighted in 3 different ways: unweighted (`method="z"`), equal weights (`method="eq"`), and percentile ranks (`method="pc"`). If method is unspecified, default to `z`. For highly skewed, or otherwise ill-behaved data we strongly advise examining the relative behaviour of the different weighting methods using `plot.rankLocReg`.

For data sets with greater tha ~500 observations, we suggest thinning the number of observations using `thinData`, given that this does not compromise the resolution of the data for the question of interest.

## Value

A data frame with important output from all local regressions, ranked by metric `L` following raking method chosen by argument `method`.

## Author(s)

Colin Olito and Diego Barneche.

`locReg`, `thinData`, `plot.rankLocReg`.
 ```1 2 3 4``` ```# load sea urchin respirometry data data(UrchinData) # rank L metric by method 'eq' allRegs <- rankLocReg(xall=UrchinData\$time, yall=UrchinData\$D, alpha=0.3, method="eq", verbose=TRUE) ```