ImFoR {ImFoR}

Non-Linear Height Diameter Models for Forestry

Description

Non-Linear Height Diameter Models for Forestry

Usage

ImFoR(data, train_frac = 0.8)

Arguments

data

Datasets

train_frac

Train-Test fraction

Value

• metrics: Metrics of all applied models

• plot: Plot

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

• Tabassum, A., Jeelani, M.I., Sharma,M., Rather, K R ., Rashid, I and Gul,M.2022. Predictive Modelling of Height and Diameter Relationships of Himalayan Chir Pine . Agricultural Science Digest - A Research Journal. DOI:10.18805/ag.D-5555

• Huang, S., Titus, S.J., and Wiens, D.P. 1992. Comparison of nonlinear height-diameter functiond for major Alberta tree species. Can J. For. Res. 22: 1297-1304. DOI : 10.1139/x92-172

• - Zeide, B. 1993. Analysis of growth equations. Forest Science 39(3):594-616. doi:10.1093/forestscience/39.3.594

Examples

library("ImFoR")
data <- system.file("extdata", "data_test.csv", package = "ImFoR")
data_test <- read.csv(data)
Model<-ImFoR(data =data_test)