ModelComponent {flowPloidy} | R Documentation

## An S4 class to represent model components

### Description

`ModelComponent` objects bundle the actual mathematical function for a particular component with various associated data necesarry to incorporate them into a complete NLS model.

### Details

To be included in the automatic processing of potential model components, a `ModelComponent` needs to be added to the variable `fhComponents`.

### Slots

`name`

  character, a convenient name with which to refer to the component

`desc`

  character, a short description of the component, for human readers

`color`

  character, the color to use when plotting the component

`includeTest`

  function, a function which takes a single argument, a `FlowHist` object, and returns `TRUE` if the component should be included in the model for that object.

`function`

  function, a single-line function that returns the value of the component. The function can take multiple arguments, which usually will include `xx`, the bin number (i.e., x value) of the histogram. The other arguments are model parameters, and should be included in the `initParams` function.

`initParams`

  function, a function with a single argument, a `FlowHist` object, which returns named list of model parameters and their initial estimates.

`specialParams`

  list, a named list. The names are variables to exclude from the default argument list, as they aren't parameters to fit in the NLS procedure, but are actually fixed values. The body of the list element is the object to insert into the model formula to account for that variable. Note that this slot is not set directly, but should be provided by the value returned by `specialParamSetter` (which by default is `list(xx = substitute(xx))`).

`specialParamSetter`

  function, a function with one argument, the `FlowHist` object, used to set the value of `specialParams`. This allows parameters to be declared 'special' based on values in the `FlowHist` object. The default value for this slot is a function which returns `list(xx = substitute(xx))`

`paramLimits`

  list, a named list with the upper and lower limits of each parameter in the function.

`doCounts`

  logical, should cell counts be evaluated for this component? Used to exclude the debris models, which don't work with R's Integrate function.

### Coding Concepts

See the source code file `models.R` for the actual code used in defining model components. Here are a few examples to illustrate different concepts.

We'll start with the G1 peaks. They are modelled by the components `fA1` and `fB1` (for the A and B samples). The `includeTest` for `fA1` is simply `function(fh) TRUE`, since there will always be at least one peak to fit. `fB1` is included if there is more than 1 detected peak, and the setting `samples` is more than 1, so the `includeTest` is

    function(fh) nrow(fhPeaks(fh)) > 1 && fhSamples(fh) > 1

The G1 component is defined by the function

    (a1 / (sqrt(2 * pi) * Sa) * exp(-((xx - Ma)^2)/(2 *
      Sa^2)))

with the arguments `a1, Ma, Sa, xx`. `xx` is treated specially, by default, and we don't need to deal with it here. The initial estimates for the other parameters are calculated in `initParams`:

    function(fh){
      Ma <- as.numeric(fhPeaks(fh)[1, "mean"])
      Sa <- as.numeric(Ma / 20)
      a1 <- as.numeric(fhPeaks(fh)[1, "height"] * Sa / 0.45)
      list(Ma = Ma, Sa = Sa, a1 = a1)
    }

`Ma` is the mean of the distribution, which should be very close to the peak. `Sa` is the standard distribution of the distribution. If we assume the CV is 5%, that means the `Sa` should be 5% of the distribution mean, which gives us a good first estimate. `a1` is a scaling parameter, and I came up with the initial estimate by trial-and-error. Given the other two values are going to be reasonably close, the starting value of `a1` doesn't seem to be that crucial.

The limits for these values are provided in `paramLimits`.

    paramLimits = list(Ma = c(0, Inf), Sa = c(0, Inf), a1 =
      c(0, Inf))

They're all bound between 0 and Infinity. The upper bound for `Ma` and `Sa` could be lowered to the number of bins, but I haven't had time or need to explore this yet.

The G2 peaks include the `d` argument, which is the ratio of the G2 peak to the G1 peak. That is, the linearity parameter:

    func = function(a2, Ma, Sa, d, xx){
      (a2 / (sqrt(2 * pi) * Sa * 2) *
        exp(-((xx - Ma * d)^2)/(2 * (Sa * 2)^2)))
    }

`d` is the ratio between the G2 and G1 peaks. If `linearity = "fixed"`, it is set to 2. Otherwise, it is fit as a model parameter. This requires special handling. First, we check the `linearity` value in `initParams`, and provide a value for `d` if needed:

    res <- list(a2 = a2)
    if(fhLinearity(fh) == "variable")
        res <- c(res, d = 2)

Here, `a2` is always treated as a parameter, and `d` is appended to the initial paramter list only if needed.

We also need to use the `specialParamSetter` function, in this case calling the helper function `setLinearity(fh)`. This function checks the value of `linearity`, and returns the appropriate object depending on the result.

Note that we use the arguments `Ma` and `Sa` appear in the `function` slot for `fA2`, but we don't need to provide their initial values or limits. These values are already supplied in the definition of `fA1`, which is always present when `fA2` is.

NB.: This isn't checked in the code! I know `fA1` is always present, but there is no automated checking of this fact. If you create a `ModelComponent` that has parameters that are not defined in that component, and are not defined in other components (like `Ma` is in this case), you will cause problems. There is also nothing to stop you from defining a parameter multiple times. That is, you could define initial estimates and limits for `Ma` in `fA1` and `fA2`. This may also cause problems. It would be nice to do some sanity-checking to protect against using parameters without defining initial estimates or limits, or providing multiple/conflicting definitions.

The Single-Cut Debris component is unusual in two ways. It doesn't include the argument `xx`, but it uses the pre-computed values `SCvals`. Consequently, we must provide a function for `specialParamSetter` to deal with this:

    specialParamSetter = function(fh){ list(SCvals =
    substitute(SCvals)) }

The Multi-Cut Debris component `MC` is similar, but it needs to include `xx` as a special parameter. The aggregate component `AG` also includes several special parameters.

For more discussion of the debris components, see `DebrisModels`.

The code responsible for this is in the file `models.R`. Accessor functions are provided (but not exported) for getting and setting `ModelComponent` slots. These functions are named `mcSLOT`, and include `mcFunc`, `mcColor`, `mcName`, `mcDesc`, `mcSpecialParams`, `mcSpecialParamSetter`, `mcIncludeTest`, `mcInitParams`.

### Examples

    ## The 'master list' of components is stored in fhComponents:
    flowPloidy:::fhComponents ## outputs a list of component summaries

    ## adding a new component to the list:
    ## Not run:
    fhComponents$pois <-
      new("ModelComponent", name = "pois", color = "bisque",
          desc = "A poisson component, as a silly example",
          includeTest = function(fh){
              ## in this case, we check for a flag in the opt slot
              ## We could also base the test on some feature of the
              ## data, perhaps something in the peaks or histData slots
              "pois" %in% fh@opt
          },
          func = function(xx, plam){
              ## The function needs to be complete on a single line, as it
              ## will be 'stitched' together with other functions to make
              ## the complete model.
              exp(-plam)*plam^xx/factorial(xx)
          },
          initParams = function(fh){
              ## If we were to use this function for one of our peaks, we
              ## could use the peak position as our initial estimate of
              ## the Poisson rate parameter:
              plam <- as.numeric(fhPeaks(fh)[1, "mean"])
          },
          ## bound the search for plam between 0 and infinity. Tighter
          ## bounds might be useful, if possible, in speeding up model
          ## fitting and avoiding local minima in extremes.
          paramLimits = list(plam = c(0, Inf))
      )

      ## specialParamSetter is not needed here - it will default to a
      ## function that returns "xx = xx", indicating that all other
      ## parameters will be fit. That is what we need for this example. If
      ## the component doesn't include xx, or includes other fixed
      ## parameters, then specialParamSetter will need to be provided.

      ## Note that if our intention is to replace an existing component with
      ## a new one, we either need to explicitly change the includeTest for
      ## the existing component to account for situations when the new one
      ## is used instead. As a temporary hack, you could add both and then
      ## manually remove one with \code{dropComponents}.

    ## End(Not run)

[Package flowPloidy version 1.18.0 Index]