nlraa: predict_nlme – R documentation

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predict_nlme

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 lme

Usage

predict_nlme(
  ...,
  criteria = c("AIC", "AICc", "BIC"),
  interval = c("none", "confidence", "prediction"),
  level = 0.95,
  nsim = 1000,
  plevel = 0,
  newdata = NULL
)

predict_lme(
  ...,
  criteria = c("AIC", "AICc", "BIC"),
  interval = c("none", "confidence", "prediction"),
  level = 0.95,
  nsim = 1000,
  plevel = 0,
  newdata = NULL
)

Arguments

`...`	nlme, lme, gls or gnls objects.
`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.
`plevel`	parameter level prediction to be passed to prediciton functions.
`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. The weights are based on the IC value.

Examples

## Example
require(ggplot2)
require(nlme)
data(Orange)

## All models should be fitted using Maximum Likelihood
fm.L <- nlme(circumference ~ SSlogis(age, Asym, xmid, scal), 
                random = pdDiag(Asym + xmid + scal ~ 1), 
                method = "ML", data = Orange)
fm.G <- nlme(circumference ~ SSgompertz(age, Asym, b2, b3), 
                random = pdDiag(Asym + b2 + b3 ~ 1), 
                method = "ML", data = Orange)
fm.F <- nlme(circumference ~ SSfpl(age, A, B, xmid, scal), 
                random = pdDiag(A + B + xmid + scal ~ 1), 
                method = "ML", data = Orange)
fm.B <- nlme(circumference ~ SSbg4rp(age, w.max, lt.e, ldtm, ldtb), 
                random = pdDiag(w.max + lt.e + ldtm + ldtb ~ 1), 
                method = "ML", data = Orange)

## Print the table with weights
IC_tab(fm.L, fm.G, fm.F, fm.B)

## Each model prediction is weighted according to their AIC values
prd <- predict_nlme(fm.L, fm.G, fm.F, fm.B)

ggplot(data = Orange, aes(x = age, y = circumference)) + 
  geom_point() + 
  geom_line(aes(y = predict(fm.L, level = 0), color = "Logistic")) +
  geom_line(aes(y = predict(fm.G, level = 0), color = "Gompertz")) +
  geom_line(aes(y = predict(fm.F, level = 0), color = "4P-Logistic")) +  
  geom_line(aes(y = predict(fm.B, level = 0), color = "Beta")) +
  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