I'm trying to fit a non-linear tree diameter height model (Max & Burkhart, 1976) to my data set (consists of D, breast height diameter (cm); H, total tree height (m); hi section height from ground level, di diameter at hi level etc.) in R.
I'm having trouble on fitting the model. I think it's because of the starting parameter values of the equation. I get "NaNs produced" errors. I tried to tweak the starting parameters. The number of errors decreased to 1 but not zero. So I need to find a way to estimate starting parameters for a non-linear regression model. I searched for Self starting models but could not apply to my equation because of complexity of the equation and my lack of knowledge. I will add all my data set here so you guys maybe show me a way.
By the way, I'm not sure if I can attach files to my question, so I will give a link to my dataset for anyone who wants to view or download. I uploaded my data to google drive and the link is https://drive.google.com/file/d/1q7W1bUcx4sK2G2QPte7ZtCudSLfBxpet/view?usp=sharing
# Function to compute Max & Burkhart (1976) equation
ComputeDi.MaxBurkhart <- function(hi, d, h, b1, b2, b3, b4, a1, a2){
x <- hi / h
x1 <- x - 1
x2 <- x ^ 2 - 1
di <- d * sqrt(b1 * x1 + b2 * x2 + b3 * (a1 - x) ^ 2 * ((a1 - x) >= 0.0) + b4 * (a2 - x) ^ 2 * ((a2 - x) >= 0.0))
return(di)
}
# Set the working directory
setwd("../Data")
# Load data and rename some variables
sylvestris <- read.csv("mydata.csv")
# Global fitting
nlmod.fp.di <- nls(di ~ ComputeDi.MaxBurkhart(hi, d, h, b1, b2, b3, b4, a1, a2), data = sylvestris, start = c(b1 = -2.53, b2 = 1.2, b3 = -1.5, b4 = 22, a1 = 0.72, a2 = 0.15
), control = nls.control(tol = 1e-07))
summary(nlmod.fp.di, correlation = T)
It's all OK until here. I'm getting Nan Errors after here!
# Set seed and select names of trees
trees <- unique(sylvestris$tree)
set.seed(15)
result.list <- list()
i <- 1
while(length(trees) > 0){
tree.smp <- sample(trees, 10, replace = F)
sylvestris.smp <- sylvestris[sylvestris$tree %in% tree.smp, ]
fitting.ols <- try(nls(di ~ ComputeDi.MaxBurkhart(hi, d, h, b1, b2, b3, b4, a1, a2), data = sylvestris.smp, start = c(b1 = -2.53, b2 = 1.2, b3 = -1.5, b4 = 22, a1 = 0.72, a2 = 0.15
), control = nls.control(tol = 1e-07)), silent = T)
if(class(fitting.ols)[1] == "try-error"){
fit.smp <- data.frame(trees = paste(tree.smp, collapse = "_"),
t(rep(NA, 8)))
names(fit.smp) <- c("trees", "b1", "b2", "b3", "b4", "a1",
"a2", "NS", "RSE")
} else {
nlmod.ols <- fitting.ols
fit.smp <- data.frame(trees = paste(tree.smp, collapse = "_"), t(coef(fitting.ols)), NS = sum(summary(fitting.ols)$parameters[, 4] > 0.05), RSE = summary(fitting.ols)$sigma)
}
result.list[[i]] <- fit.smp
i <- i + 1
trees <- trees[!trees %in% tree.smp]
}
I expect significant parameter estimations without any NaN errors. I'm sure the problem is about the starting values because this code block works perfect with another data set. When I changed the data, I get this errors. Thank you in advance.
