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 `par`. DEFAULT: 1 `col` 6 colors for lines and legend texts. DEFAULT: 1:6 `legarg` Arguments passed to `legend`. DEFAULT: NULL `...` further graphical parameters passed to `plot`

### 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

`mReg`, `lm`

### 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)

```

[Package berryFunctions version 1.20.1 Index]