ssq {MQMF}

ssq a generalized function for summing squared residuals

Description

ssq is a generalized function for summing squared residuals which is designed for ease of use in nlm (or optim, or nlminb). NAs are removed automatically. It assumes the input of a predefined function 'funk' that will calculate predicted values of a 'dependent' variable from a vector of 'independent' or observed values, for which one has observations. The dependent or predicted values are compared with the observed or 'independent' data, and the resulting SSQ returned. The use of ... means this is a very general function but it does mean you need to be very careful with placement and spelling of the input variables required by whatever funk you are using. It is always best to explicitly name the arguments although the correct order will also work correctly.

Usage

ssq(funk, observed, ...)

Arguments

funk

a function that uses a parameter vector and vector of observations to calculate predicted values, which are compared with the observed values to give the ssq and be returned

observed

a vector containing the observed data (y-axis)

...

required to allow funk to access its parameters and data without having to explicitly declare them in ssq. Note that this means inside the ssq function the call to funk also needs to have the ellipsis, otherwise it will not be able to see those other arguments. It is vital to spell the names of funk's arguments correctly as errors are not always announced and will obviously lead to misleading outputs.

Value

a single number (scaler) that is the sum of squared residuals between the dep values and those calculated by funk

Examples

  data(minnow)  # remember -13 is only 3+ months
  pars <- c(89, 0.1,-13)  # ssq = 83477.84
  ssq(funk=vB,observed=minnow$length,p=pars,ages=minnow$week)