nlraa (version 0.98)

R2M: R-squared for nonlinear mixed models

Description

R-squared ‘modified’ for nonlinear (mixed) models

Usage

R2M(x, ...)
# S3 method for nls
R2M(x, ...)
# S3 method for lm
R2M(x, ...)
# S3 method for gls
R2M(x, ...)
# S3 method for gnls
R2M(x, ...)
# S3 method for lme
R2M(x, ...)
# S3 method for nlme
R2M(x, ...)

Arguments

x

object of class ‘lm’, ‘nls’, ‘gls’, ‘gnls’, ‘lme’ or ‘nlme’ .

...

additional arguments (none use at the moment).

Value

it returns a list with the following structure: for an object of class ‘lm’, ‘nls’, ‘gls’ or ‘gnls’, R2: R-squared var.comp: variance components with var.fixed and var.resid var.perc: variance components percents (should add up to 100) for an object of class ‘lme’ or ‘nlme’ in addition it also returns: R2.marginal: marginal R2 which only includes the fixed effects R2.conditional: conditional R2 which includes both the fixed and random effects var.random: the variance contribution of the random effects

Details

I have read some papers about computing an R-squared for (non)linear (mixed) models and I am not sure that it makes sense at all. However, here they are and the method is general enough that it extends to nonlinear mixed models. What do these numbers mean and why would you want to compute them are good questions to ponder...

Recommended reading: Nakagawa and Schielzeth Methods in Ecology and Evolution 10.1111/j.2041-210x.2012.00261.x

https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faq-what-are-pseudo-r-squareds/

Spiess, AN., Neumeyer, N. An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: a Monte Carlo approach. BMC Pharmacol 10, 6 (2010). 10.1186/1471-2210-10-6

https://stat.ethz.ch/pipermail/r-sig-mixed-models/2010q1/003363.html

https://stats.stackexchange.com/questions/111150/calculating-r2-in-mixed-models-using-nakagawa-schielzeths-2013-r2glmm-me/225334#225334

See Also

IA_tab

Examples

# NOT RUN {
require(nlme)
data(barley, package = "nlraa")
fit2 <- lme(yield ~ NF + I(NF^2), random = ~ 1 | year, data = barley)
R2M(fit2)
## Nonlinear Mixed Model
barleyG <- groupedData(yield ~ NF | year, data = barley)
fit3L <- nlsLMList(yield ~ SSquadp3(NF, a, b, c), data = barleyG)
fit3 <- nlme(fit3L, random = pdDiag(a + b ~ 1))
R2M(fit3)
# }