Cosine model fitting with given initialization for two parameters.

Performs the updated nonlinear least squares (NLS) regression method for the cosine model proposed by Balasse et al. The method calculates with the proposed initial values for ampliitude and intercept, and then fits the NLS method as required.

Usage

sine_initial(data, amplitude, intercept)

Arguments

data: A data frame that contains the data for one individual. There should be two columns with names 'distance' and 'oxygen'.
amplitude: Initial value for the amplitude parameter.
intercept: Initial value for the intercept parameter.

Value

A fitted model object from the nls function in R:

m: an 'nlsModel' object incorporating the model.
convInfo: a list with convergence information
data: the expression that was passed to 'nls' as the data argument. The actual data values are present in the environment of the 'm' component.
call: the matched call with several components, notably 'algorithm'
dataClasses: the '"dataClasses"' attribute (if any) of the '"terms"' attribute of the model frame.
control: the control 'list' used

References

Florent Baty, Christian Ritz, Sandrine Charles, Martin Brutsche, Jean-Pierre Flandrois, Marie-Laure Delignette-Muller (2015). A Toolbox for Nonlinear Regression in R: The Package nlstools. Journal of Statistical Software, 66(5), 1-21. URL http://www.jstatsoft.org/v66/i05/.

Examples

armenia_split = split(armenia,f = armenia$ID)
amp = seq(1,10,by=0.5)
int = seq(-25,0,by=0.5)
sine_initial(armenia_split[[2]],amp[3],int[4])
#> Nonlinear regression model
#>   model: oxygen ~ intercept + amplitude * cos(frequency * distance + phase)
#>    data: data
#> intercept amplitude     phase frequency 
#>   -5.7143    5.9191   -0.1394    0.1776 
#>  residual sum-of-squares: 1.049
#> 
#> Number of iterations to convergence: 8 
#> Achieved convergence tolerance: 1.249e-06