errorsINx | R Documentation |
Simulates $y-$ and $x-$values for the straight line regression model, but with $x-$values subject to random measurement error, following the classical “errors in x” model. Optionally, the x-values can be split into two groups, with one group shifted relative to the other
errorsINx(mu = 12.5, n = 200, a = 15, b = 1.5, SDx=2, SDyerr = 1.5,
timesSDx=(1:5)/2.5, gpfactor=if(missing(gpdiff))FALSE else TRUE,
gpdiff=if(gpfactor) 1.5 else 0, layout=NULL,
parset = simpleTheme(alpha = 0.75, col = c("black","gray45"),
col.line = c("black","gray45"), lwd=c(1,1.5), pch=c(1,2),
lty=c(1,2)), print.summary=TRUE, plotit=TRUE, xrelation="same")
mu |
Mean of $z$ |
n |
Number of points |
a |
Intercept in model where $z$ is measured without error |
b |
Slope in model where $z$ is measured without error |
SDx |
SD of $z$-values, measured without error |
SDyerr |
SD of error term in |
timesSDx |
SD of measurement error is |
gpfactor |
Should x-values be split into two groups, with one shifted relative to the other? |
gpdiff |
Amount of shift of one group of z-values relative to the other |
layout |
Layout for lattice graph, if requested |
parset |
Parameters to be supplied to the lattice plot, if any |
print.summary |
Print summary information on fits? |
plotit |
logical: plot the data? |
xrelation |
character: sets the x-axis |
The argument timesSDx
can be a numeric vector.
One set of $x$-values that are contaminated with measurement error
is simulated for each element of timesSDx
.
gph |
the trellis graphics object |
mat |
A matrix, with |
John Maindonald
Data Analysis and Graphics Using R, 3rd edn, Section 6.7
library(lattice)
errorsINx()
errorsINx(gpdiff=2, timesSDx=1.25, SDyerr=2.5, n=80)
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