predict.nls: predict.nls

In xgxr: Exploratory Graphics for Pharmacometrics

predict.nls

Description

predict.nls

Usage

## S3 method for class 'nls'
predict(
  object,
  newdata = NULL,
  se.fit = FALSE,
  interval = "none",
  level = 0.95,
  ...
)

Arguments

object	Object of class inheriting from "nls"
newdata	An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
se.fit	A switch indicating if standard errors are required.
interval	Type of interval calculation, "none" or "confidence"
level	Level of confidence interval to use
...	additional arguments affecting the predictions produced.

Value

predict.nls produces a vector of predictions or a matrix of predictions and bounds with column names fit, lwr, and upr if interval is set.

If se.fit is TRUE, a list with the following components is returned:

fit	vector or matrix as above
se.fit	standard error of predicted means
residual.scale	residual standard deviations
df	degrees of freedom for residual

Examples

set.seed(12345)
data_to_plot <- data.frame(x1 = rep(c(0, 25, 50, 100, 200, 400, 600), 10)) %>%
  dplyr::mutate(AUC = x1*rlnorm(length(x1), 0, 0.3),
         x2 = x1*stats::rlnorm(length(x1), 0, 0.3),
         Response = (15 + 50*x2/(20+x2))*stats::rlnorm(length(x2), 0, 0.3))


gg <- ggplot2::ggplot(data = data_to_plot, ggplot2::aes(x = AUC, y = Response)) + 
  ggplot2::geom_point() + 
  xgx_geom_smooth(method = "nls",  
                  method.args = list(formula = y ~ E0 + Emax* x / (EC50 + x),
                                     start = list(E0 = 15, Emax = 50, EC50 = 20) ), 
                  color = "black", size = 0.5, alpha = 0.25)
gg

mod <- stats::nls(formula = Response ~ E0 + Emax * AUC / (EC50 + AUC), 
data = data_to_plot, 
start = list(E0 = 15, Emax = 50, EC50 = 20))

predict.nls(mod)

predict.nls(mod, se.fit = TRUE)

predict.nls(mod, 
            newdata = data.frame(AUC = c(0, 25, 50, 100, 200, 400, 600)), 
            se.fit = TRUE)
            
predict.nls(mod, 
            newdata = data.frame(AUC = c(0, 25, 50, 100, 200, 400, 600)), 
            se.fit = TRUE, interval = "confidence", level = 0.95)
            
predict(mod, 
            newdata = data.frame(AUC = c(0, 25, 50, 100, 200, 400, 600)), 
            se.fit = TRUE, interval = "confidence", level = 0.95)

xgxr documentation built on March 31, 2023, 11:46 p.m.