CIplot
Illustration of the meaning of confidence levels.
Illustration of the meaning of confidence levels. Generate sets of confidence intervals for independent randomly generated sets of normally distributed numbers. Low confidence levels give narrow intervals that are less likely to bracket the true value. Higher confidence levels increase the probability of bracketing the true value, and are also much wider and therefore less precise. The shiny app can animate how the increase in confidence level and width leads to a consequent decrease in precision.
- Keywords
- shiny, hplot, dynamic , confidence
Usage
CIplot(n.intervals = 100,
n.per.row = 40,
pop.mean = 0,
pop.sd = 1,
conf.level = 0.95,
...)confintervaldata(n.intervals = 100,
n.per.row = 40,
pop.mean = 0,
pop.sd = 1,
conf.level = 0.95,
seed,
...)
confinterval.matrix(x,
conf.level = attr(x, "conf.level"),
...)
confintervalplot(x.ci,
n.intervals = nrow(x.ci),
pop.mean = attr(x.ci, "pop.mean"),
pop.sd = attr(x.ci, "pop.sd"),
n.per.row = attr(x.ci, "n.per.row"),
xlim, ylim, ...)
shiny.CIplot(height = "auto")
Arguments
- n.intervals
Number of sets of observations to generate. Each set leads to one confidence interval on the plot.
- n.per.row
Number of observations in each set.
- pop.mean, pop.sd
Population mean and standard deviation for generated set of
n.per.row
independent normally distributed random numbers.- conf.level
Confidence level of each of the
n.per.row
confidence intervals calculated from the generated datasets.- seed
Standard argument to
rnorm
.- x
Output matrix from
confintervaldata
.- x.ci
Output
data.frame
fromconfinterval.matrix
.- xlim, ylim
Standard
xyplot
arguments.- height
Height of graph on web page in pixels.
- …
Additional arguments. For
CIplot
,seed
will be forwarded toconfintervaldata
, andxlim
andylim
will be forwarded toconfintervalplot
. Any other additional arguments will be ignored.
Details
The shiny app has sliders for the n.intervals
, n.per.row
,
pop.mean
, pop.sd
, and conf.level
.
Changes in the conf.level
slider, either manually by animation,
use the same set of generated data to show how increasing the confidence
level increases the width of the confidence interval and consequently
decreases the precision of the interval estimator.
Value
CIplot
and confintervalplot
return a "trellis"
plot containing a plot of Confidence Intervals.
confintervaldata
returns a matrix of n.intervals
rows by
n.per.row
columns of independent normally distributed random
numbers.
The matrix has a set of attributes recording the arguments to the
function.
confinterval.matrix
returns a data.frame
of n.intervals
with three columns containing the lower bound, center, and upper bound
of the confidence interval for each row of its input matrix.
The data.frame
has a set of attributes recording the arguments to the
function.
shiny.CIplot
returns a shiny app object which, when printed,
runs a shiny app displaying the Confidence Interval plot and several
slider controls.
Examples
# NOT RUN {
## A. from the console
## example 1
CIplot()
## example 2
# }
# NOT RUN {
CIplot(n.intervals=100,
n.per.row=40,
pop.mean=0,
pop.sd=1,
conf.level=.95)
# }
# NOT RUN {
## example 3
# }
# NOT RUN {
tmp.data <- confintervaldata()
tmp.ci <- confinterval.matrix(tmp.data)
confintervalplot(tmp.ci)
# }
# NOT RUN {
## example 4
# }
# NOT RUN {
tmp.data <- confintervaldata(n.intervals=100,
n.per.row=40,
pop.mean=0,
pop.sd=1,
conf.level=.95)
tmp.ci <- confinterval.matrix(tmp.data)
confintervalplot(tmp.ci)
# }
# NOT RUN {
## B. shiny, initiated from the console
## example 5
# }
# NOT RUN {
if (interactive())
shiny.CIplot()
# }
# NOT RUN {
## example 6
# }
# NOT RUN {
if (interactive())
shiny.CIplot(height=800) ## px
## take control of the height of the graph in the web page
# }