plot_id_nlsLoop: Creates a graph of one of the levels of id_col with the data...

In padpadpadpad/nlsTools: Methods for Finding the Best Estimated Fits for Non-Least Squares Regression over the Level of a Factor

Description

Creates a graph of one of the levels of id_col with the data points and corresponding predictions.

Usage

plot_id_nlsLoop(data, param_data, id)

Arguments

data	the data frame containing the data used in the nlsLoop call.
param_data	the nlsLoop object.
id	the level of id_col from which the plot is desired.

Value

a plot using ggplot2 of the points and predictions of a single level of id_col

Author(s)

Daniel Padfield

Examples

# load in data

data("Chlorella_TRC")
Chlorella_TRC_test <- Chlorella_TRC[Chlorella_TRC$curve_id %in% c(1:10),]

# run nlsLoop()

# define the Sharpe-Schoolfield equation
schoolfield_high <- function(lnc, E, Eh, Th, temp, Tc) {
 Tc <- 273.15 + Tc
 k <- 8.62e-5
 boltzmann.term <- lnc + log(exp(E/k*(1/Tc - 1/temp)))
 inactivation.term <- log(1/(1 + exp(Eh/k*(1/Th - 1/temp))))
 return(boltzmann.term + inactivation.term)
}

fits <- nlsLoop(ln.rate ~ schoolfield_high(lnc, E, Eh, Th, temp = K, Tc = 20),
                data = Chlorella_TRC_test,
                tries = 500,
                id_col = 'curve_id',
                param_bds = c(-10, 10, 0.1, 2, 0.5, 5, 285, 330),
                lower = c(lnc=-10, E=0, Eh=0, Th=0))

plot_id_nlsLoop(data = Chlorella_TRC_test, param_data = fits, id = '1')

padpadpadpad/nlsTools documentation built on May 24, 2019, 5:59 p.m.