generate simulated data set with nonlinear function

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Description

creates a data set with Y ~ 30+cos(X) for 10 subjects, to compare restricted cubic spline in lmer with the spline of ols.

Usage

1

Arguments

intr

integer denoting type of data set: with 0 a data set with simple spline is made, with 1 a data set with a parallel interaction, and with 2 a data set with a crossed interaction.

Details

Requires rms package to be attached.

Value

A data frame with as values:

y

y = 30 + cos(X)

X

ranges from 2 to 8.28

Subject

random-effects factor with 10 levels

Ranef

subjects-specific changes to intercept

Error

by-observation noise

Y

the dependent variable, y+Ranef+Error

Note

intended for illustration only

Author(s)

R. H. Baayen

See Also

See Also as plotLMER.fnc

Examples

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## Not run: 
require("rms")
require("optimx")
require("lmerTest")
dfr = makeSplineData.fnc()
table(dfr$Subject)
xylowess.fnc(Y ~ X | Subject, data = dfr)

dfr.lmer = lmer(Y ~ rcs(X, 5) + (1|Subject), data = dfr,
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
dfr$fittedLMER = as.vector(dfr.lmer@X %*% fixef(dfr.lmer))

dfr.dd = datadist(dfr)
options(datadist='dfr.dd')
dfr.ols = ols(Y~Subject+rcs(X), data=dfr, x=T, y=T)
dfr$fittedOLS = fitted(dfr.ols)

# we plot the lmer() fit in blue, the ols() fit in red (both adjusted for
# subject S1), and plot the underlying model in green
plot(dfr[dfr$Subject=="S1",]$X, dfr[dfr$Subject=="S1",]$fittedLMER +
  ranef(dfr.lmer)[[1]]["S1",], type="l", col="blue",
  ylim = range(dfr$y + ranef(dfr.lmer)[[1]]["S1",],
  dfr[dfr$Subject == "S1",]$fittedLMER,
  dfr[dfr$Subject == "S1",]$fittedOLS), xlab="X", ylab="Y")   
lines(dfr[dfr$Subject=="S1",]$X, dfr[dfr$Subject=="S1",]$fittedOLS, col="red")
lines(dfr[dfr$Subject=="S1",]$X, dfr[dfr$Subject=="S1",]$y+ranef(dfr.lmer)[[1]]["S1",], 
  col="green")
legend(2,29,c("30+cos(x)", "lmer (S1)", "ols (S1)"), lty=rep(1,3), 
col=c("green", "blue", "red"))

## End(Not run)

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