nlraa: predict_nls – R documentation

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predict_nls

Average predictions from several (non)linear models based on IC weights

Description

Computes weights based on AIC, AICc, or BIC and it generates weighted predictions by the relative value of the IC values

predict function for objects of class gam

Usage

predict_nls(
  ...,
  criteria = c("AIC", "AICc", "BIC"),
  interval = c("none", "confidence", "prediction"),
  level = 0.95,
  nsim = 1000,
  resid.type = c("none", "resample", "normal", "wild"),
  newdata = NULL
)

predict_gam(
  ...,
  criteria = c("AIC", "AICc", "BIC"),
  interval = c("none", "confidence", "prediction"),
  level = 0.95,
  nsim = 1000,
  resid.type = c("none", "resample", "normal", "wild"),
  newdata = NULL
)

Arguments

`...`	‘nls’ or ‘lm’ objects (‘glm’ and ‘gam’ objects inherit ‘lm’).
`criteria`	either ‘AIC’, ‘AICc’ or ‘BIC’.
`interval`	either ‘none’, ‘confidence’ or ‘prediction’.
`level`	probability level for the interval (default 0.95)
`nsim`	number of simulations to perform for intervals. Default 1000.
`resid.type`	either ‘none’, “resample”, “normal” or “wild”.
`newdata`	new data frame for predictions

Value

numeric vector of the same length as the fitted object.

Note

all the objects should be fitted to the same data. Weights are based on the chosen IC value (exp(-0.5 * IC)). For models of class gam there is very limited support.

Examples

## Example
require(ggplot2)
require(mgcv)
data(barley, package = "nlraa")

fm.L <- lm(yield ~ NF, data = barley)
fm.Q <- lm(yield ~ NF + I(NF^2), data = barley)
fm.A <- nls(yield ~ SSasymp(NF, Asym, R0, lrc), data = barley)
fm.LP <- nls(yield ~ SSlinp(NF, a, b, xs), data = barley)
fm.QP <- nls(yield ~ SSquadp3(NF, a, b, c), data = barley)
fm.BL <- nls(yield ~ SSblin(NF, a, b, xs, c), data = barley)
fm.G <- gam(yield ~ s(NF, k = 6), data = barley)

## Print the table with weights
IC_tab(fm.L, fm.Q, fm.A, fm.LP, fm.QP, fm.BL, fm.G)

## Each model prediction is weighted according to their AIC values
prd <- predict_nls(fm.L, fm.Q, fm.A, fm.LP, fm.QP, fm.BL, fm.G)

ggplot(data = barley, aes(x = NF, y = yield)) + 
  geom_point() + 
  geom_line(aes(y = fitted(fm.L), color = "Linear")) +
  geom_line(aes(y = fitted(fm.Q), color = "Quadratic")) +
  geom_line(aes(y = fitted(fm.A), color = "Asymptotic")) +  
  geom_line(aes(y = fitted(fm.LP), color = "Linear-plateau")) + 
  geom_line(aes(y = fitted(fm.QP), color = "Quadratic-plateau")) + 
  geom_line(aes(y = fitted(fm.BL), color = "Bi-linear")) + 
  geom_line(aes(y = fitted(fm.G), color = "GAM")) + 
  geom_line(aes(y = prd, color = "Avg. Model"), size = 1.2)

nlraa

Nonlinear Regression for Agricultural Applications

v0.89

GPL-3

Authors

Fernando Miguez [aut, cre] (<https://orcid.org/0000-0002-4627-8329>), José Pinheiro [ctb, cph] (author of nlme::nlsList, nlme::predict.gnls, nlme::predict.nlme), Douglas Bates [ctb, cph] (author of nlme::nlsList, nlme::predict.gnls, nlme::predict.nlme), R-core [ctb, cph]

Initial release