likelihood (version 1.9)

predicted_results: Calculate Model Predicted Results

Description

Calculate predicted results of the dependent variable from a model with parameters set up as for the likeli and anneal functions. These predicted results are useful for various statistical calculations when compared to observed results from a dataset.

Usage

predicted_results(model, par, var, source_data, ...)

Value

A vector of predicted results, one for each observation in

source_data.

Arguments

model

Model function to use to calculate predicted results.

par

List of parameters for which likelihood is being estimated. All elements in par must be numeric vectors.

var

List object with the source for all other non-parameter arguments and data used by model, pdf, or any sub-functions.

source_data

Data frame containing any needed source data.

...

Any other data that may be needed by the model or any of its sub-functions. This is an alternative to providing the data in var; however, passing values in var is strongly recommended.

Details

The parameters for this function are set up exactly as they are in anneal and likeli. See those pages for details on how to do this.

Extra list members in var are ignored, so if you have set up a var list for use with likeli or anneal, you can use that list with predicted_results without removing arguments for the PDF.

Examples

## Use the included crown_rad dataset
data( crown_rad )

## Create our model function - crown radius is a linear function of DBH.
## DBH is a column of data from the crown_rad dataset; a and b are single
## parameter values.
model <- function (a, b, DBH) {a + b * DBH}

## Create our parameters list and set values for a and b
par <- list(a = 1.12, b = 0.07)

## Create a place to put all the other data needed by
## the model and PDF, and indicate that DBH comes from 
## the column marked "DBH" in the dataset
var <- list(DBH = "DBH")

predicted <- predicted_results(model, par, var, crown_rad)

## Calculate R2 - proportion of variance explained by the model relative to 
## that explained by the simple mean of the data
meanrad <- mean(crown_rad$Radius)
sse <- (crown_rad$Radius - predicted)^2
sst <- (crown_rad$Radius - meanrad)^2
R2 <- 1 - (sum(sse)/sum(sst))