phylocurve (version 2.1.1)

polynomial.fit: Fit polynomial curves to species data using stepwise regression

Description

Transforms raw data into regression curves using stepwise (AIC or BIC) polynomial regression. The maximum number of parameters (nterms), response data can be constrained between minima and maxima (for example, the default sets any negative predicted y value to 0).

Usage

polynomial.fit(data, x_variable, y_variable, method = "BIC",
  nterms = 2, min_x = -Inf, max_x = Inf, min_y = 0, max_y = Inf, eval_length = 30)

Arguments

data

Data frame with function-valued species data along with a column named "species"

x_variable

The name of the column in data corresponding to the predictor (x) variable

y_variable

The name of the column in data corresponding to the response (y) variable

method

Metric for stepwise polynomial regression. Either "AIC" or "BIC" (the default).

nterms

Maximum number of terms to allow in polynomial regression.

min_x

Lower range to remove from raw x data

max_x

Upper range to remove from raw x data

min_y

Lower range to remove from predicted response (for example, for response data in which only positive values are allowed, the min_y can be set to 0 (the default)).

max_y

Upper range to remove from predicted response.

eval_length

How many points to evaluate along the range of x.

Value

X

X (formatted for phylocurve.generalized)

Y

Y (formatted for phylocurve.generalized)

Examples

# NOT RUN {
x_length <- 50
nspecies <- 25
sim_data <- sim.curves(nspecies = nspecies,x_length = x_length)

# Fit polynomial curves to raw data
poly_data <- polynomial.fit(data = sim_data$data,x_variable = "x",y_variable = "y",min_y = 0)

# Align curve data using dynamic time warping
# NOT RUN (takes several minutes)
# P <- phylocurve.generalized(tree = sim_data$tree,X = poly_data$X,Y = poly_data$Y)
# }