norm.curve {HH} | R Documentation |
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.
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, ...)
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. |
shade |
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 |
color of the shaded region. |
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. |
Richard M. Heiberger <rmh@temple.edu>
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.curve(100, 5, 100+5*(-1.645), shade="left") 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, shade="left", axis.name="z1") 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), col=3, shade="left", axis.name="z1") norm.curve(crit=2*1.645, se=2, mean=0, col=2, shade="right") 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)