exp4p {berryFunctions} | R Documentation |
4-parametric exponential function
Description
Fits an exponential function of the form a*e^(b*(x+c))+d
Usage
exp4p(x, y, digits = 2, plot = FALSE, las = 1, col = 1:6, legarg = NULL, ...)
Arguments
x , y |
x and y Data |
digits |
significant digits for rounding R^2. DEFAULT: 2 |
plot |
plot data and fitted functions? DEFAULT: FALSE |
las |
label axis style, see |
col |
6 colors for lines and legend texts. DEFAULT: 1:6 |
legarg |
Arguments passed to |
... |
further graphical parameters passed to |
Details
This is mainly a building block for mReg
Value
Data.frame with the 4 parameters for each optim
method
Note
Optim can be slow! It refers to the functions rmse and rsquare, also in this package. L-BFGS-B needs finite values. In case it doesn't get any with the initial parameters (as in the first example Dataset), it tries again with the parameters optimized via Nelder Mead.
Author(s)
Berry Boessenkool, berry-b@gmx.de, 2012-2013, outsourced from mReg in July 2014
See Also
Examples
## Not run: ## Skip time consuming checks on CRAN
# exponential decline of temperature of a mug of hot chocolate
tfile <- system.file("extdata/Temp.txt", package="berryFunctions")
temp <- read.table(tfile, header=TRUE, dec=",")
head(temp)
plot(temp)
temp <- temp[-20,] # missing value - rmse would complain about it
x <- temp$Minuten
y <- temp$Temp
rm(tfile, temp)
exp4p(x,y, plot=TRUE)
# y=49*e^(-0.031*(x - 0 )) + 25 correct, judged from the model:
# Temp=T0 - Te *exp(k*t) + Te with T0=73.76, Tend=26.21, k=-0.031
# optmethod="Nelder-Mead" # y=52*e^(-0.031*(x + 3.4)) + 26 wrong
## End(Not run)