# norm.curve

From HH v3.1-39
0th

Percentile

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

Plot a normal curve or a t-curve with both x (with mean and se as specified) and z or t (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 or t axis. The existence of any of the arguments se, sd, n forces dual x and (z or t) 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. The shaded area is printed on the plot.

When the optional argument df.t is specified, then a t-distribution with df.t degrees of freedom is plotted.

norm.observed plots a vertical line with arrowhead markers at the location of the observed xbar.

normal.and.t.dist is a driver function that uses all the others. It's primary function is drawing a plot. It returns an invisible list containing the values it calculated and displayed on the graph.

norm.curve draws the curves and filled areas as requested by the normal.and.t.dist function. Any out of bounds errors (for example, with normal.and.t.dist(deg.free=1)) are suppressed with par(err=-1) by this function and restored to the previous value when the norm.curve function completes.

Keywords
distribution, hplot, aplot
##### Usage
normal.and.t.dist(mu.H0          = 0,
mu.H1          = NA,
obs.mean       = 0,
std.dev        = 1,
n              = NA,
deg.freedom    = NA,
alpha.left     = alpha.right,
alpha.right    = .05,
Use.mu.H1      = FALSE,
Use.obs.mean   = FALSE,
Use.alpha.left = FALSE,
Use.alpha.right= TRUE,
hypoth.or.conf = 'Hypoth',
xmin           = NA,
xmax           = NA,
gxbar.min      = NA,
gxbar.max      = NA,
cex.crit       = 1.2,
polygon.density= -1,
polygon.lwd    = 4,
col.mean       = 'limegreen',
col.mean.label = 'limegreen',
col.alpha      = 'blue',
col.alpha.label= 'blue',
col.beta       = 'red',
col.beta.label = 'red',
col.conf       = 'palegreen',
col.conf.arrow = 'darkgreen',
col.conf.label = 'darkgreen'
)norm.setup(xlim=c(-2.5,2.5),
ylim = c(0, 0.4)/se,
mean=0,
main=main.calc,
se=sd/sqrt(n), sd=1, n=1,
df.t=NULL,
Use.obs.mean=TRUE,
...)norm.curve(mean=0, se=sd/sqrt(n),
critical.values=mean + se*c(-1, 1)*z.975,
z=if(se==0) 0 else
do.call("seq", as.list(c((par()$usr[1:2]-mean)/se, length=109))), shade, col="blue", axis.name=ifelse(is.null(df.t) || df.t==Inf, "z", "t"), second.axis.label.line=3, sd=1, n=1, df.t=NULL, axis.name.expr=axis.name, Use.obs.mean=TRUE, col.label=col, hypoth.or.conf="Hypoth", col.conf.arrow=par("col"), col.conf.label=par("col"), col.crit=ifelse(hypoth.or.conf=="Hypoth", 'blue', col.conf.arrow), cex.crit=1.2, polygon.density=-1, polygon.lwd=4, col.border=ifelse(is.na(polygon.density), FALSE, col), ...)norm.observed(xbar, t.xbar, t.xbar.H1=NULL, col="green", p.val=NULL, p.val.x=par()$usr[2]+ left.margin,
t.or.z=ifelse(is.null(deg.free) || deg.free==Inf, "z", "t"),
t.or.z.position=par()$usr[1]-left.margin, cex.small=par()$cex*.7, col.label=col,
xbar.negt=NULL, cex.large=par()$cex, left.margin=.15*diff(par()$usr[1:2]),
sided="", deg.free=NULL)norm.outline(dfunction, left, right, mu.H0, se, deg.free=NULL,
col.mean="green")
##### Arguments
xlim, ylim, xmin, xmax, gxbar.min, gxbar.max

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, std.dev, 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.

df.t, deg.freedom

Degrees of freedom for the t distribution. When df.t is NULL, the normal distribution is used.

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

Main title.

z

z-values (standardized to N(0,1)) used as base of plot.

Valid values for shade are "right", "left", "inside", "outside", "none". Default is "right" for one-sided critical.values and "outside" for two-sided critical values.

col

col.label, col.alpha, col.alpha.label

color of the area of the shaded rejection region and its label.

col.beta, col.beta.label

color of the area of the shaded region For Type II error and its label.

hypoth.or.conf

"Hypoth" or "Conf"

col.conf

Color of plot within confidence limits.

col.conf.arrow

Color of arrow denoting confidence limits.

col.conf.label

Color of label giving confidence level.

col.mean.label

Color of label for observed mean.

col.crit, cex.crit

Color and cex of critical values.

axis.name, axis.name.expr

defaults to "z" for the standard normal scale centered on the null hypothesis value of the mean or to "t" for the t distribution with df.t degrees of freedom. For alternative hypotheses, the user must specify either "z1" or "t1" for the standard normal scale, or t distibution with df.t degrees of freedom, centered on the alternate hypothesis value of the mean. The axis.name.expr allows R users to say expression(z[1]) to get real subscripts.

second.axis.label.line

Defaults to 3. Normally not needed. When two curves are drawn, one normal and one t, then the second curve needs a different label for the y-axis. Set this value to 4 to avoid overprinting.

xbar, obs.mean

xbar-value of the observed data.

t.xbar

t-value of the observed data under the null hypothesis.

Other arguments which are ignored.

Use.obs.mean

Logical. If TRUE, then include "mean" on the plot.

alpha.right, alpha.left

Area in tail of curve.

Use.alpha.right, Use.alpha.left

Logical. If TRUE, then include the specified $\alpha$ on the plot.

t.xbar.H1

t-value under alternate hypothesis.

p.val

under specified hypothesis

p.val.x,t.or.z.position

location on x-axis to put label

t.or.z

label for axis.

cex.small

cex for left margin labels of axis.

xbar.negt

location in data scale of negative t- or z-value corresponding to observed x-value. Used for two-sided p-values.

cex.large

cex for labels in top margin.

left.margin

distance to the left of par()$usr[1]. sided type of test. deg.free degrees of freedom or NULL. dfunction "dnorm" or "dt" left left end of interval right right end of interval mu.H0, mu.H1 mean under the null hypothesis and alternative hypothesis. Use.mu.H1 Logical. If TRUE, then include mu.H1 on the plot. col.mean Color of outline. polygon.density, polygon.lwd, col.border density, lwd, border arguments to polygon. polygon.density is $-1$ by default to give a solid color filled region. Setting polygon.density to a positive value (we recommend 10) gives a diagonally-hatched area appropriate for printing the graph on a black and white printer. ##### Value An invisible list containing the calculated values of probabilities and critical values in the data scale, the null hypothesis z- or t-scale, and the alternative hypothesis z- or t-scale, as specified. The components are: beta.left, beta.middle, beta.right, crit.val, crit.val.H1, crit.val.H1.left, crit.val.left, crit.val.left.z, crit.val.z, obs.mean.H0.p.val, obs.mean.H0.side, obs.mean.H0.z, obs.mean.H1.z, obs.mean.x.neg, obs.mean.x.pos, obs.mean.z.pos, standard, standard.error, standard.normal ##### Aliases • norm.setup • norm.curve • norm.observed • norm.outline • normal.and.t.dist ##### Examples # NOT RUN { normal.and.t.dist() normal.and.t.dist(xmin=-4) normal.and.t.dist(std.dev=2) normal.and.t.dist(std.dev=2, Use.alpha.left=TRUE, deg.free=6) normal.and.t.dist(std.dev=2, Use.alpha.left=TRUE, deg.free=6, gxbar.max=.20) normal.and.t.dist(std.dev=2, Use.alpha.left=TRUE, deg.free=6, gxbar.max=.20, polygon.density=10) normal.and.t.dist(std.dev=2, Use.alpha.left=FALSE, deg.free=6, gxbar.max=.20, polygon.density=10, mu.H1=2, Use.mu.H1=TRUE, obs.mean=2.5, Use.obs.mean=TRUE, xmin=-7) normal.and.t.dist(std.dev=2, hypoth.or.conf="Conf") normal.and.t.dist(std.dev=2, hypoth.or.conf="Conf", deg.free=8) 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.observed(112, (112-100)/5) norm.outline("dnorm", 112, par()$usr[2], 100, 5)

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='red')

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(critical.values=1.645, mean=1.645+1.281552, col='green',
norm.curve(critical.values=1.645, col='red')

norm.setup(xlim=c(-6, 12), se=2)
norm.curve(critical.values=2*1.645, se=2, mean=2*(1.645+1.281552),
norm.curve(critical.values=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.observed(1.2, 1.2/(1/sqrt(1)))
norm.setup(n=2)
norm.curve(n=2)
norm.observed(1.2, 1.2/(1/sqrt(2)))
norm.setup(n=4)
norm.curve(n=4)
norm.observed(1.2, 1.2/(1/sqrt(4)))
norm.setup(n=10)
norm.curve(n=10)
norm.observed(1.2, 1.2/(1/sqrt(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.observed(1.2, 1.2/(1/sqrt(1)))
norm.setup(n=2, ylim=c(0,1.3))
norm.curve(n=2)
norm.observed(1.2, 1.2/(1/sqrt(2)))
norm.setup(n=4, ylim=c(0,1.3))
norm.curve(n=4)
norm.observed(1.2, 1.2/(1/sqrt(4)))
norm.setup(n=10, ylim=c(0,1.3))
norm.curve(n=10)
norm.observed(1.2, 1.2/(1/sqrt(10)))
mtext("all areas under the curve are numerically and visually the same",
side=3, outer=TRUE)

par(mfrow=c(1,1))

## t distribution
mu.H0 <- 16
se.val <- .4
df.val <- 10
crit.val <- mu.H0 - qt(.95, df.val) * se.val
mu.alt <- 15
obs.mean <- 14.8

alt.t <- (mu.alt - crit.val) / se.val
norm.setup(xlim=c(12, 19), se=se.val, df.t=df.val)
norm.curve(critical.values=crit.val, se=se.val, df.t=df.val, mean=mu.alt,
norm.curve(critical.values=crit.val, se=se.val, df.t=df.val, mean=mu.H0,
norm.observed(obs.mean, (obs.mean-mu.H0)/se.val)

## normal
norm.setup(xlim=c(12, 19), se=se.val)
norm.curve(critical.values=crit.val, se=se.val, mean=mu.alt,
norm.curve(critical.values=crit.val, se=se.val, mean=mu.H0,
norm.observed(obs.mean, (obs.mean-mu.H0)/se.val)

## normal and t
norm.setup(xlim=c(12, 19), se=se.val, main="t(6) and normal")
norm.curve(critical.values=15.5, se=se.val, mean=16.3,
norm.curve(critical.values=15.5, se.val, df.t=6, mean=14.7,
norm.curve(critical.values=15.5, se=se.val, mean=16.3,

norm.setup(xlim=c(12, 19), se=se.val, main="t(6) and normal")
norm.curve(critical.values=15.5, se=se.val, mean=15.5,
norm.curve(critical.values=15.5, se=se.val, df.t=6, mean=15.5,