ImML {ImML} | R Documentation |
Tree volume models based on height and diameter
Description
Decision tree, random forest, support vector machine, and linear models for fitting tree volume to height and diameter.
Usage
ImML(data, plotit = TRUE, setseed = NULL, verbose = FALSE, ...)
Arguments
data |
The data frame to use.
Must contain the numeric variables
|
plotit |
If |
setseed |
If not |
verbose |
If |
... |
Additional arguments, currently not used. |
Details
Calculates mean absolute error, root mean square error, root relative squared error, and prediction error rate for train and test partitions of a data frame using decision tree, random forest, support vector machine, and linear models for fitting tree volume to height and diameter.
Value
A data frame consisting of mean absolute error, root mean square error, root relative squared error, and prediction error rate for train and test partitions using decision tree, random forest, support vector machine, and linear model.
Note
The data frame must contain the numeric variables
Volume
, Height
, and Diameter
.
Volume
is used as the dependent variable.
The gray line in the plot is a 1:1 line.
Author(s)
M. Iqbal Jeelani, jeelani.miqbal@gmail.com, Salvatore Mangiafico, mangiafico@njaes.rutgers.edu
References
Jeelani, M.I., Tabassum, A., Rather, K and Gul,M.2023. Neural Network Modeling of Height Diameter Relationships for Himalayan Pine through Back Propagation Approach. Journal of The Indian Society of Agricultural Statistics. 76(3): 169–178.
Examples
data(EastCirclePine)
ImML(EastCirclePine, plotit=FALSE, setseed=123)