# norm.curve

From HH v1.5
0th

Percentile

##### plot a normal curve with both x and z axes.

Plot a normal curve with both x (with mean and se as specified) and z (mean=0, se=1) axes. Shade a region for rejection region, acceptance region, confidence interval. The density axis is marked in units appropriate for the z axis. The existence of any of the arguments se, sd, n forces dual x and z scales. When none of these arguments are used, the main title defaults to "Standard Normal Density N(0,1)" and only the z scale is printed. A second density curve, appropriate for an alternative hypothesis is displayed when the argument axis.name="z1" is specified.

Keywords
hplot, distribution, aplot
##### Usage
norm.setup(xlim.in=c(-2.5,2.5),
ylim.in = c(0, 0.4)/se,
mean=0,
main.in=ifelse(
!(missing(se) && missing(sd) && missing(n)),
paste("normal density:  se =", round(se,3)),
"Standard Normal Density N(0,1)"),
se=sd/sqrt(n), sd=1, n=1, ...)

norm.curve(mean=0, se=sd/sqrt(n),
critical.values=mean + se*c(-1.96, 1.96),
z=do.call("seq",
as.list(c((par()\$usr[1:2]-mean)/se, length=109))),
shade, col=par("col"), axis.name="z", sd=1, n=1, ...)
##### Arguments
xlim.in, ylim.in
xlim, ylim. Defaults to correct values for standard Normal(0,1). User must set values for other mean and standard error.
mean
Mean of the normal distribution in xbar-scale, used in calls to dnorm.
se
standard error of the normal distribution in xbar-scale, used in calls to dnorm.
sd, n
standard deviation and sample size of the normal distribution in x-scale. These may be used as an alternate way of specifying the standard error se.
critical.values
Critical values in xbar-scale. A scalar value implies a one-sided test. A vector of two values implies a two-sided test.
main.in
Main title.
z
z-values (standardized to N(0,1)) used as base of plot.
Valid values for shade are "right", "left", "inside", "outside". Default is "right" for one-sided critical.values and "outside" for two-sided critical values.
col
axis.name
"z" for the standard normal scale centered on the null hypothesis value of the mean. "z1" for the standard normal scale centered on the alternate hypothesis value of the mean.
...
Other arguments which are ignored.
• norm.setup
• norm.curve
##### Examples
old.par <- par(oma=c(4,0,2,5), mar=c(7,7,4,2)+.1)

norm.setup()
norm.curve()

norm.setup(xlim=c(75,125), mean=100, se=5)
norm.curve(100, 5, 100+5*(1.645))

norm.setup(xlim=c(75,125), mean=100, se=5)

norm.setup(xlim=c(75,125), mean=100, se=5)
norm.curve(mean=100, se=5, col=2)

norm.setup(xlim=c(75,125), mean=100, se=5)
norm.curve(100, 5, 100+5*c(-1.96, 1.96))

norm.setup(xlim=c(-3, 6))
norm.curve(crit=1.645, mean=1.645+1.281552, col=3,
norm.curve(crit=1.645, col=2)

norm.setup(xlim=c(-6, 12), se=2)
norm.curve(crit=2*1.645, se=2, mean=2*(1.645+1.281552),
norm.curve(crit=2*1.645, se=2, mean=0,

par(mfrow=c(2,1))
norm.setup()
norm.curve()
mtext("norm.setup(); norm.curve()", side=1,  line=5)
norm.setup(n=1)
norm.curve(n=1)
mtext("norm.setup(n=1); norm.curve(n=1)", side=1,  line=5)
par(mfrow=c(1,1))

par(mfrow=c(2,2))

## naively scaled,
## areas under the curve are numerically the same but visually different
norm.setup(n=1)
norm.curve(n=1)
norm.setup(n=2)
norm.curve(n=2)
norm.setup(n=4)
norm.curve(n=4)
norm.setup(n=10)
norm.curve(n=10)
mtext("areas under the curve are numerically the same but visually different",
side=3, outer=TRUE)

## scaled so all areas under the curve are numerically and visually the same
norm.setup(n=1, ylim=c(0,1.3))
norm.curve(n=1)
norm.setup(n=2, ylim=c(0,1.3))
norm.curve(n=2)
norm.setup(n=4, ylim=c(0,1.3))
norm.curve(n=4)
norm.setup(n=10, ylim=c(0,1.3))
norm.curve(n=10)
mtext("all areas under the curve are numerically and visually the same",
side=3, outer=TRUE)

par(mfrow=c(1,1))

par(old.par)
Documentation reproduced from package HH, version 1.5, License: GPL version 2 or newer

### Community examples

Looks like there are no examples yet.