# fit_easylinear: Fit Exponential Growth Model with a Heuristic Linear Method In growthrates: Estimate Growth Rates from Experimental Data

## Description

Determine maximum growth rates from the log-linear part of a growth curve using a heuristic approach similar to the “growth rates made easy”-method of Hall et al. (2013).

## Usage

 `1` ```fit_easylinear(time, y, h = 5, quota = 0.95) ```

## Arguments

 `time` vector of independent variable. `y` vector of dependent variable (concentration of organisms). `h` width of the window (number of data). `quota` part of window fits considered for the overall linear fit (relative to max. growth rate)

## Details

The algorithm works as follows:

1. Fit linear regressions to all subsets of `h` consecutive data points. If for example h=5, fit a linear regression to points 1 ... 5, 2 ... 6, 3... 7 and so on. The method seeks the highest rate of exponential growth, so the dependent variable is of course log-transformed.

2. Find the subset with the highest slope b_max and include also the data points of adjacent subsets that have a slope of at least quota * b_max, e.g. all data sets that have at least 95% of the maximum slope.

3. Fit a new linear model to the extended data window identified in step 2.

## Value

object with parameters of the fit. The lag time is currently estimated as the intersection between the fit and the horizontal line with y=y_0, where `y0` is the first value of the dependent variable. The intersection of the fit with the abscissa is indicated as `y0_lm` (lm for linear model). These identifieres and their assumptions may change in future versions.

## References

Hall, BG., Acar, H, Nandipati, A and Barlow, M (2014) Growth Rates Made Easy. Mol. Biol. Evol. 31: 232-38, doi: 10.1093/molbev/mst187

## See Also

Other fitting functions: `all_easylinear()`, `all_growthmodels()`, `all_splines()`, `fit_growthmodel()`, `fit_spline()`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19``` ```data(bactgrowth) splitted.data <- multisplit(bactgrowth, c("strain", "conc", "replicate")) dat <- splitted.data[[1]] plot(value ~ time, data=dat) fit <- fit_easylinear(dat\$time, dat\$value) plot(fit) plot(fit, log="y") plot(fit, which="diagnostics") fitx <- fit_easylinear(dat\$time, dat\$value, h=8, quota=0.95) plot(fit, log="y") lines(fitx, pch="+", col="blue") plot(fit) lines(fitx, pch="+", col="blue") ```

growthrates documentation built on Nov. 3, 2020, 1:07 a.m.