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.

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: 1.426e-06