growthrates (version 0.8.4)

fit_easylinear: Fit Exponential Growth Model with a Heuristic Linear Method

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

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

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.

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 \(quota \cdot b_{max}\), 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.

References

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

See Also

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

Examples

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")