plot.fit: Plotting function for nls.predict and nlme.predict

In qPharmetra/qpToolkit: qPharmetra R Tools

Plotting function for nls.predict and nlme.predict

Description

Plots residual, predicted, or partial residial plots based on x generated by qP functions nlme.predict and nls.predict.

Usage

## S3 method for class 'fit'
plot(
  x,
  ...,
  newFunc,
  yLabel,
  xLabel,
  label.cex = 1.25,
  xLimits,
  yLimits,
  title,
  plot.title = TRUE,
  layout,
  aspect,
  mnplot = TRUE,
  seplot = TRUE,
  logX = FALSE,
  logY = FALSE,
  pointcol = gray[8],
  linecol = qp.blue,
  axis.lim.widener = 0.035,
  do.plot = TRUE,
  abline = NULL
)

Arguments

x	the output from nls.predict or nlme.predict, i.e. class 'fit'
...	any other arguments to be passed on to the lattice call
newFunc	the function to plot. If not suppliued will be taken from x
yLabel	same as ylab
xLabel	same as xlab
label.cex	fontmszie of axes labels
xLimits	same as xlim
yLimits	same as ylim
title	place a title above
plot.title	logical indicating if a plot title should be written
layout	will be passed on a lattice argument layout
aspect	lattice banking aspect
mnplot	logical indicating an average plot to be created as opposed to 'raw data
seplot	logical indicating is error bars need to be drawn
logX	logarithmic X axis?
logY	logarithmic Y axis?
pointcol	color of dots
linecol	color of
axis.lim.widener	scalar to stretch the x and y axis. Use in case data or predictions are not visible anymore
do.plot	Defaults to T (create the plot) if F it will output the prediction data
abline	will be passed on to the lattice call as is

Value

A plot

Examples

pkpdData = example.pkpdData()

DNase1 <- subset(DNase, Run == 1)

## using a selfStart model
fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1)
summary(fm1DNase1)

fm1DNase1.predict = nls.predict(density ~ conc,object = fm1DNase1)
plot(fm1DNase1.predict)
fm1DNase1.predict = nls.predict(density ~ conc,object = fm1DNase1)
plot(fm1DNase1.predict, logX = TRUE)

EFF.1comp.1abs <- function(dose, tob, cl, v, ka, keo)
{
  # Effect-site concentration for 1-compartment model, 1st-order absorption

  kel = cl / v

  # Define coefficients
  A = 1/(kel-ka) / (keo-ka)
  B = 1/(ka-kel) / (keo-kel)
  C = 1/(ka-keo) / (kel-keo)

  # Return effect-site concentration
  dose*ka*keo/v * (A*exp(-ka*tob) + B*exp(-kel*tob) + C*exp(-keo*tob))
}
fit.PD004.nlme = nlme.run(model = value ~ base +
 EFF.1comp.1abs(dose, time, cl * exp(cl.eta), v, ka, keo)
 , data = subset(pkpdData,type == "PD" & dose > 0 & value > 0.5),
                          fixed = base + cl + v + ka + keo ~ 1,
                          random = cl.eta ~ 1,
                          groups = ~ id,
                          start = c(base = 1, cl = 1, v = 10
                          , ka = 1, keo = 0.01),
                          problem = "True Model",
                          reference = 4)
summary(fit.PD004.nlme$object)
nlme.extract(fit.PD004.nlme$object)$table

# simple fit vs time
fit.PD004.pred.nlme = nlme.predict(func = value ~ time
   , fit.PD004.nlme$object)
plot(fit.PD004.pred.nlme)
fit.PD004.pred.nlme = nlme.predict(func = value ~ time
   , fit.PD004.nlme$object, method = "partial.residuals")
plot(fit.PD004.pred.nlme) ## this is the same: PARTIAL RESIDUALS
fit.PD004.pred.nlme = nlme.predict(func = value ~ time
   , fit.PD004.nlme$object, method = "prediction")
plot(fit.PD004.pred.nlme) ## 'simple' prediction for all xCovariate

## note that prediction and partial residual type of model fit
## plots are very different

# fit vs time by dose
fit.PD004.pred.nlme = nlme.predict(func = value ~ time | dose
   , fit.PD004.nlme$object)
plot(fit.PD004.pred.nlme)
fit.PD004.pred.nlme = nlme.predict(func = value ~ time | dose
   , fit.PD004.nlme$object, method = "residuals")
plot(fit.PD004.pred.nlme, yLimits = c(-1,1))
plot(fit.PD004.pred.nlme, yLimits = c(-1,1)
   , abline = list(h = 0, lty = 2))

qPharmetra/qpToolkit documentation built on May 24, 2023, 8:52 a.m.