#' Demonstrate binomials using manipulate controls that allow
#' you to set number of flips, probability of success, whether
#' the plot is counts or probabilities, and whether a Gaussian
#' distribution is superimposed. Also calculates areas to the
#' left and right of critical values for counts or probabilities.
#' @examples
#'\dontrun{
#' binomials1()
#'}
#' @export
run.binomials1 <- function(){
###################################################################
#manipulate binomials
###################################################################
par(mfrow=c(1,1))
manipulate(binomials1(n=n,p=p,plot.type=plot.type,x.axis.type=x.axis.type,y.axis.type=y.axis.type,
add.normal=add.normal,
add.values=add.values,
crit.value.c=crit.value.c,
crit.value.p=crit.value.p,
show.critical=show.critical),
p=slider(.01,.99,step=.01,initial=.5,label="Probability of Success (p)"),
n=slider(3,500,initial=3,label="Sample Size (N)",step=1),
crit.value.c=slider(0,200,initial=0,label="Critical Value (Counts)"),
crit.value.p=slider(0,1,step=.01,initial=.5,label="Critical Value (Proportions)"),
plot.type=picker("Counts","Proportions",label="Plot Type"),
x.axis.type=picker("Full Range","Gaussian",initial="Gaussian",label="X Axis Type"),
y.axis.type=picker("Full Range","Fixed",label="Y Axis Type"),
add.normal=checkbox(label="Add Normal Distribution"),
add.values=checkbox(label="Display probabilities"),
show.critical=checkbox(label="Show Critical Value")
)
}
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