plotLZISD: Plot local inverse of z-score standard deviation 1/SD(Z)
| plotLZISD | R Documentation |
Plot local inverse of z-score standard deviation 1/SD(Z)
Description
Plot local inverse of z-score standard deviation 1/SD(Z)
Usage
plotLZISD(
X,
Z,
aux = NULL,
sdZ = 1,
logX = FALSE,
nBin = NULL,
intrv = NULL,
equiPop = TRUE,
popMin = 30,
logBin = TRUE,
plot = TRUE,
slide = FALSE,
method = c("cho", "bootstrap"),
BSmethod = c("bca", "perc", "basic"),
nBoot = 500,
xlab = "Calculated value",
xlim = NULL,
ylim = NULL,
title = "",
score = FALSE,
add = FALSE,
col = 5,
label = 0,
gPars = ErrViewLib::setgPars()
)
Arguments
X |
(vector) abscissae of the Z values |
Z |
(vector) set of z-score values to be tested |
aux |
(vector) auxilliary vector to resolve ties in X sorting |
sdZ |
(numeric) target value for 1/SD(Z) (default '1') |
logX |
(logical) log-transform X |
nBin |
(integer) number of intervals for local coverage stats |
intrv |
(object) intervals generated by 'genIntervals' (default: 'NULL') |
equiPop |
(logical) generate intervals with equal bin counts (default: 'TRUE') |
popMin |
(integer) minimal bin count in an interval |
logBin |
(logical) if 'equiPop = FALSE', one can choose between equal range intervals, or equal log-range intervals (default 'TRUE') |
plot |
(logical) plot the results |
slide |
(logical) use sliding window for subsetting (X,Z) |
method |
(string) method used to estimate 95 percent CI on 1/SD(Z) |
BSmethod |
(string) bootstrap variant |
nBoot |
(integer) number of bootstrap replicas |
xlab |
(string) X axis label |
xlim |
(vector) min and max values of X axis |
ylim |
(vector) limits of the y axis |
title |
(string) a title to display above the plot |
score |
(logical) estimate calibration stats (default: 'FALSE') |
add |
(logical) add to previous graph ? |
col |
(integer) color index of curve to add |
label |
(integer) index of letter for subplot tag |
gPars |
(list) graphical parameters |
Value
Invisibly returns a list of LZISD results. Mainly used for its plotting side effect.
Examples
uE = sqrt(rchisq(1000, df = 4)) # Re-scale uncertainty
E = rnorm(uE, mean=0, sd=uE) # Generate errors
plotLZISD(uE, E/uE, method = 'cho', ylim = c(0,2))
