R/202.CoverageProb_BASE_All_Graph.R
In RajeswaranV/proportion: Inference on Single Binomial Proportion and Bayesian Computations
Defines functions
PlotcovpTW
PlotcovpLT
PlotcovpSC
PlotcovpLR
PlotcovpAS
PlotcovpWD
PlotcovpAll
PlotcovpBA
PlotcovpEX
Documented in
PlotcovpAll
PlotcovpAS
PlotcovpBA
PlotcovpEX
PlotcovpLR
PlotcovpLT
PlotcovpSC
PlotcovpTW
PlotcovpWD
#############################################################################################################
#' Graphs of Coverage Probability - exact method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param e - Exact method indicator (1:Clop-Pear,0.5:MID-p)
#' @param a - Beta parameters for hypo "p"
#' @param b - Beta parameters for hypo "p"
#' @param t1 - Lower tolerance limit to check the spread of coverage Probability
#' @param t2 - Upper tolerance limit to check the spread of coverage Probability
#' @details The graphs of basic Coverage Probability methods
#' @family Basic coverage probability methods
#' @examples
#' \dontrun{
#' n= 10; alp=0.05; e=0.5; a=1;b=1; t1=0.93;t2=0.97 # Mid-p
#' PlotcovpEX(n,alp,e,a,b,t1,t2)
#' n= 10; alp=0.05; e=1; a=1;b=1; t1=0.93;t2=0.97 #Clop-Pear
#' PlotcovpEX(n,alp,e,a,b,t1,t2)
#' n=5; alp=0.05;
#' e=c(0.1,0.5,0.95,1) #Range including Mid-p and Clopper-Pearson
#' a=1;b=1; t1=0.93;t2=0.97
#' PlotcovpEX(n,alp,e,a,b,t1,t2)
#' }
#' @export
##### 7. EXACT METHOD - Coverage Probability
PlotcovpEX=function(n,alp,e,a,b,t1,t2)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(e)) stop("'e' is missing")
if (missing(a)) stop("'a' is missing")
if (missing(b)) stop("'b' is missing")
if (missing(t1)) stop("'t1' is missing")
if (missing(t2)) stop("'t2' is missing")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
if (alp>1 || alp<0 || length(alp) >1) stop("'alpha' has to be between 0 and 1")
if ((class(e) != "integer") & (class(e) != "numeric") || any(e>1) || any(e<0)) stop("'e' has to be between 0 and 1")
if (length(e)>10 ) stop("Plot of only 10 intervals of 'e' is possible")
if ((class(a) != "integer") & (class(a) != "numeric") || length(a)>1 || a<0 ) stop("'a' has to be greater than or equal to 0")
if ((class(b) != "integer") & (class(b) != "numeric") || length(b)>1 || b<0 ) stop("'b' has to be greater than or equal to 0")
if (t1>t2) stop(" t1 has to be lesser than t2")
if ((class(t1) != "integer") & (class(t1) != "numeric") || length(t1)>1 || t1<0 || t1>1 ) stop("'t1' has to be between 0 and 1")
if ((class(t2) != "integer") & (class(t2) != "numeric") || length(t2)>1 || t2<0 || t2>1 ) stop("'t2' has to be between 0 and 1")
method=Value=hp=cp=cpp=mcp=micp=NULL
if(length(e)>1){
dfex=gcovpEX(n,alp,e,a,b,t1,t2)
exdf=dfex[,c(1,2,5)]
exdf$e=as.factor(exdf$e)
exdf$t1=t1
exdf$t2=t2
exdf$alp=alp
ggplot2::ggplot(exdf, ggplot2::aes(x=hp, y=cpp))+
ggplot2::labs(y = "Coverage Probability") +
ggplot2::labs(title = "Coverage Probability for exact method for multiple e values") +
ggplot2::labs(x = "p") +
ggplot2::geom_hline(ggplot2::aes(yintercept=t1), color="red",linetype = 2) +
ggplot2::geom_hline(ggplot2::aes(yintercept=t2), color="blue",linetype = 2) +
ggplot2::geom_text(ggplot2::aes(y=t1, label="\nLower tolerance(t1)", x=.1), colour="red") +
ggplot2::geom_text(ggplot2::aes(y=t2, label="Higher tolerance(t2)", x=.1), colour="blue") +
ggplot2::geom_line(ggplot2::aes(color=e)) +
ggplot2::geom_hline(ggplot2::aes(yintercept=1-(alp)),linetype = 2)
}
else{
dfex=gcovpEX(n,alp,e,a,b,t1,t2)
dfex1=data.frame(micp=dfex$micpEX[1] ,mcp=dfex$mcpEX[1] )
dfex1$alp=alp
ggplot2::ggplot(dfex, ggplot2::aes(x=hp, y=cpp))+
ggplot2::labs(title = "Coverage Probability of exact method") +
ggplot2::labs(y = "Coverage Probability") +
ggplot2::labs(x = "p") +
ggplot2::geom_line(ggplot2::aes(color="Coverage Probability"))+
ggplot2::geom_point(ggplot2::aes(color="CP Values"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=1-(alp),color="Confidence Level"),linetype = 2)+
ggplot2::geom_hline(data=dfex1,ggplot2::aes(yintercept=micp,color="Minimum Coverage"))+
ggplot2::geom_hline(data=dfex1,ggplot2::aes(yintercept=mcp,color="Mean Coverage"))+
ggplot2::scale_colour_manual(name='Heading',
values=c('Coverage Probability'='red',
'CP Values'='red',
'Minimum Coverage'='black',
'Mean Coverage'='blue',
'Confidence Level'='brown'),
guide='legend') +
ggplot2::guides(colour = ggplot2::guide_legend(override.aes = list(linetype=c(2,1,1,1,1),
shape=c(NA, NA, 16,NA,NA))))
}
}
#############################################################################################################
#' Graphs of Coverage Probability of the Bayesian method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param a - Beta parameters for hypo "p"
#' @param b - Beta parameters for hypo "p"
#' @param t1 - Lower tolerance limit to check the spread of coverage Probability
#' @param t2 - Upper tolerance limit to check the spread of coverage Probability
#' @param a1 - Beta Prior Parameters for Bayesian estimation
#' @param a2 - Beta Prior Parameters for Bayesian estimation
#' @details The graphs of Coverage Probability of Bayesian method
#' @family Basic coverage probability methods
#' @examples
#' \dontrun{
#' n= 10; alp=0.05; a=1;b=1; t1=0.93;t2=0.97;a1=1;a2=1
#' PlotcovpBA(n,alp,a,b,t1,t2,a1,a2)
#' }
#' @export
#8.BAYESIAN
PlotcovpBA<-function(n,alp,a,b,t1,t2,a1,a2)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(a)) stop("'a' is missing")
if (missing(b)) stop("'b' is missing")
if (missing(t1)) stop("'t1' is missing")
if (missing(t2)) stop("'t2' is missing")
if (missing(a1)) stop("'a1' is missing")
if (missing(a2)) stop("'a2' is missing")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
if (alp>1 || alp<0 || length(alp) >1) stop("'alpha' has to be between 0 and 1")
if ((class(a) != "integer") & (class(a) != "numeric") || length(a)>1 || a<0 ) stop("'a' has to be greater than or equal to 0")
if ((class(b) != "integer") & (class(b) != "numeric") || length(b)>1 || b<0 ) stop("'b' has to be greater than or equal to 0")
if (t1>t2) stop(" t1 has to be lesser than t2")
if ((class(t1) != "integer") & (class(t1) != "numeric") || length(t1)>1 || t1<0 || t1>1 ) stop("'t1' has to be between 0 and 1")
if ((class(t2) != "integer") & (class(t2) != "numeric") || length(t2)>1 || t2<0 || t2>1 ) stop("'t2' has to be between 0 and 1")
if ((class(a1) != "integer") & (class(a1) != "numeric") || length(a1)>1 || a1<0 ) stop("'a1' has to be greater than or equal to 0")
if ((class(a2) != "integer") & (class(a2) != "numeric") || length(a2)>1 || a2<0 ) stop("'a2' has to be greater than or equal to 0")
ID=method=Value=hp=cp=cpp=mcpBAQ=micpBAQ=mcpBAH=micpBAH=NULL
####INPUT n
x=0:n
k=n+1
####INITIALIZATIONS
LBAQ=0
UBAQ=0
LBAH=0
UBAH=0
s=5000
cpBAQ=matrix(0,k,s)
ctBAQ=matrix(0,k,s) #Cover Pbty quantity in sum
cppBAQ=0 #Coverage probabilty
ctr=0
cpBAH=matrix(0,k,s)
ctBAH=matrix(0,k,s) #Cover Pbty quantity in sum
cppBAH=0 #Coverage probabilty
ctrH=0
##############
#library(TeachingDemos) #To get HPDs
for(i in 1:k)
{
#Quantile Based Intervals
LBAQ[i]=stats::qbeta(alp/2,x[i]+a1,n-x[i]+a2)
UBAQ[i]=stats::qbeta(1-(alp/2),x[i]+a1,n-x[i]+a2)
LBAH[i]=TeachingDemos::hpd(stats::qbeta,shape1=x[i]+a1,shape2=n-x[i]+a2,conf=1-alp)[1]
UBAH[i]=TeachingDemos::hpd(stats::qbeta,shape1=x[i]+a1,shape2=n-x[i]+a2,conf=1-alp)[2]
}
####COVERAGE PROBABILITIES
hp=sort(stats::rbeta(s,a,b),decreasing = FALSE) #HYPOTHETICAL "p"
for (j in 1:s)
{
for(i in 1:k)
{
if(hp[j] > LBAQ[i] && hp[j] < UBAQ[i])
{
cpBAQ[i,j]=stats::dbinom(i-1, n,hp[j])
ctBAQ[i,j]=1
}
if(hp[j] > LBAH[i] && hp[j] < UBAH[i])
{
cpBAH[i,j]=stats::dbinom(i-1, n,hp[j])
ctBAH[i,j]=1
}
}
cppBAQ[j]=sum(cpBAQ[,j]) #Coverage Probability
if(t1<cppBAQ[j]&&cppBAQ[j]<t2) ctr=ctr+1 #tolerance for cov prob - user defined
cppBAH[j]=sum(cpBAH[,j]) #Coverage Probability
if(t1<cppBAH[j]&&cppBAH[j]<t2) ctrH=ctrH+1 #tolerance for cov prob - user defined
}
CPBAQ=data.frame(hp,cpp=cppBAQ,method="Quantile")
CPBAH=data.frame(hp,cpp=cppBAH,method="HPD")
df.new=rbind(CPBAQ,CPBAH)
df.new$mcpBAQ=mean(cppBAQ)
df.new$micpBAQ=min(cppBAQ) #Mean Cov Prob
df.new$mcpBAH=mean(cppBAH)
df.new$micpBAH=min(cppBAH) #Mean Cov Prob
df.new$alp=alp #Mean Cov Prob
ggplot2::ggplot(df.new, ggplot2::aes(x=hp, y=cpp))+
ggplot2::labs(title = "Coverage Probability of Bayesian methods") +
ggplot2::labs(y = "Coverage Probability") +
ggplot2::labs(x = "p") +
ggplot2::geom_line(ggplot2::aes(color=method)) +
ggplot2::geom_hline(ggplot2::aes(yintercept=1-(alp),color="Confidence Level"),linetype = 2)+
ggplot2::geom_hline(ggplot2::aes(yintercept=micpBAQ,color="Minimum Coverage Quantile"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=mcpBAQ,color="Mean Coverage Quantile"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=micpBAH,color="Minimum Coverage HPD"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=mcpBAH,color="Mean Coverage HPD"))+
ggplot2::scale_colour_manual(name='Heading',
values=c('Quantile' ='black',
'HPD' = 'red',
'Minimum Coverage Quantile'='red',
'Mean Coverage Quantile'='blue',
'Minimum Coverage HPD'='black',
'Mean Coverage HPD'='cyan',
'Confidence Level'='brown'),
guide='legend') +
ggplot2::guides(colour = ggplot2::guide_legend(override.aes = list(linetype=c(2,1,1,1,1,1,1))))
}
#############################################################################################################
#' Graphs of basic Coverage Probability 6 base methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine)
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param a - Beta parameters for hypo "p"
#' @param b - Beta parameters for hypo "p"
#' @param t1 - Lower tolerance limit to check the spread of coverage Probability
#' @param t2 - Upper tolerance limit to check the spread of coverage Probability
#' @details The graphs of basic Coverage Probability methods
#' @family Basic coverage probability methods
#' @examples
#' \dontrun{
#' n= 10; alp=0.05; a=1; b=1; t1=0.93; t2=0.97
#' PlotcovpAll(n,alp,a,b,t1,t2)
#' }
#' @export
##### 9. Coverage Probability - Graph
PlotcovpAll<-function(n,alp,a,b,t1,t2)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(a)) stop("'a' is missing")
if (missing(b)) stop("'b' is missing")
if (missing(t1)) stop("'t1' is missing")
if (missing(t2)) stop("'t2' is missing")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
if (alp>1 || alp<0 || length(alp) >1) stop("'alpha' has to be between 0 and 1")
if ((class(a) != "integer") & (class(a) != "numeric") || length(a)>1 || a<0 ) stop("'a' has to be greater than or equal to 0")
if ((class(b) != "integer") & (class(b) != "numeric") || length(b)>1 || b<0 ) stop("'b' has to be greater than or equal to 0")
if (t1>t2) stop(" t1 has to be lesser than t2")
if ((class(t1) != "integer") & (class(t1) != "numeric") || length(t1)>1 || t1<0 || t1>1 ) stop("'t1' has to be between 0 and 1")
if ((class(t2) != "integer") & (class(t2) != "numeric") || length(t2)>1 || t2<0 || t2>1 ) stop("'t2' has to be between 0 and 1")
ID=method=Value=hp=cp=cpp=mcp=micp=NULL
####INPUT n
df1=gcovpW(n,alp,a,b,t1,t2)
df2=gcovpS(n,alp,a,b,t1,t2)
df3=gcovpA(n,alp,a,b,t1,t2)
df4=gcovpLT(n,alp,a,b,t1,t2)
df5=gcovpTW(n,alp,a,b,t1,t2)
df6=gcovpL(n,alp,a,b,t1,t2)
nndf= rbind(df1,df2,df3,df4,df5,df6)
nndf$t1=t1
nndf$t2=t2
nndf$alp=alp
ggplot2::ggplot(nndf, ggplot2::aes(x=hp, y=cp))+
ggplot2::labs(y = "Coverage Probability") +
ggplot2::labs(title = "Coverage Probability for 6 base methods") +
ggplot2::labs(x = "p") +
ggplot2::geom_hline(ggplot2::aes(yintercept=t1), color="red",linetype = 2) +
ggplot2::geom_hline(ggplot2::aes(yintercept=t2), color="blue",linetype = 2) +
ggplot2::geom_text(ggplot2::aes(y=t1, label="\nLower tolerance(t1)", x=.1), colour="red") +
ggplot2::geom_text(ggplot2::aes(y=t2, label="Higher tolerance(t2)", x=.1), colour="blue") +
ggplot2::geom_line(ggplot2::aes(color=method)) +
ggplot2::geom_hline(ggplot2::aes(yintercept=1-(alp)),linetype = 2)
}
#############################################################################################################
#' Plots Coverage Probability for base Wald method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param a - Beta parameters for hypo "p"
#' @param b - Beta parameters for hypo "p"
#' @param t1 - Lower tolerance limit to check the spread of coverage Probability
#' @param t2 - Upper tolerance limit to check the spread of coverage Probability
#' @details Plots Coverage Probability for base Wald method
#' @family Basic coverage probability methods
#' @examples
#' \dontrun{
#' n= 10; alp=0.05; a=1; b=1; t1=0.93; t2=0.97
#' PlotcovpWD(n,alp,a,b,t1,t2)
#' }
#' @export
PlotcovpWD<-function(n,alp,a,b,t1,t2)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(a)) stop("'a' is missing")
if (missing(b)) stop("'b' is missing")
if (missing(t1)) stop("'t1' is missing")
if (missing(t2)) stop("'t2' is missing")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
if (alp>1 || alp<0 || length(alp) >1) stop("'alpha' has to be between 0 and 1")
if ((class(a) != "integer") & (class(a) != "numeric") || length(a)>1 || a<0 ) stop("'a' has to be greater than or equal to 0")
if ((class(b) != "integer") & (class(b) != "numeric") || length(b)>1 || b<0 ) stop("'b' has to be greater than or equal to 0")
if (t1>t2) stop(" t1 has to be lesser than t2")
if ((class(t1) != "integer") & (class(t1) != "numeric") || length(t1)>1 || t1<0 || t1>1 ) stop("'t1' has to be between 0 and 1")
if ((class(t2) != "integer") & (class(t2) != "numeric") || length(t2)>1 || t2<0 || t2>1 ) stop("'t2' has to be between 0 and 1")
ID=method=Value=hp=cp=cpp=mcp=micp=NULL
####INPUT n
Waldcovp.df = covpWD(n,alp,a,b,t1,t2)
nndf=gcovpW(n,alp,a,b,t1,t2)
nndf$mcp=Waldcovp.df$mcpW
nndf$micp=Waldcovp.df$micpW
nndf$t1=t1
nndf$t2=t2
nndf$alp=alp
ggplot2::ggplot(nndf, ggplot2::aes(x=hp, y=cp))+
ggplot2::labs(title = "Coverage Probability for Wald method") +
ggplot2::labs(y = "Coverage Probability") +
ggplot2::labs(x = "p") +
ggplot2::geom_hline(ggplot2::aes(yintercept=t1), color="red",linetype = 2) +
ggplot2::geom_hline(ggplot2::aes(yintercept=t2), color="blue",linetype = 2) +
ggplot2::geom_text(ggplot2::aes(y=t1, label="\nLower tolerance(t1)", x=.1), colour="red") +
ggplot2::geom_text(ggplot2::aes(y=t2, label="Higher tolerance(t2)", x=.1), colour="blue") +
ggplot2::geom_hline(ggplot2::aes(yintercept=micp,color="Minimum Coverage"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=mcp,color="Mean Coverage"))+
ggplot2::geom_line(ggplot2::aes(color=method)) +
ggplot2::guides(colour = ggplot2::guide_legend("Heading"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=1-(alp)),linetype = 2)
}
#############################################################################################################
#' Plots Coverage Probability for base ArcSine method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param a - Beta parameters for hypo "p"
#' @param b - Beta parameters for hypo "p"
#' @param t1 - Lower tolerance limit to check the spread of coverage Probability
#' @param t2 - Upper tolerance limit to check the spread of coverage Probability
#' @details Plots Coverage Probability for base ArcSine method
#' @family Basic coverage probability methods
#' @examples
#' \dontrun{
#' n= 10; alp=0.05; a=1; b=1; t1=0.93; t2=0.97
#' PlotcovpAS(n,alp,a,b,t1,t2)
#' }
#' @export
PlotcovpAS<-function(n,alp,a,b,t1,t2)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(a)) stop("'a' is missing")
if (missing(b)) stop("'b' is missing")
if (missing(t1)) stop("'t1' is missing")
if (missing(t2)) stop("'t2' is missing")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
if (alp>1 || alp<0 || length(alp) >1) stop("'alpha' has to be between 0 and 1")
if ((class(a) != "integer") & (class(a) != "numeric") || length(a)>1 || a<0 ) stop("'a' has to be greater than or equal to 0")
if ((class(b) != "integer") & (class(b) != "numeric") || length(b)>1 || b<0 ) stop("'b' has to be greater than or equal to 0")
if (t1>t2) stop(" t1 has to be lesser than t2")
if ((class(t1) != "integer") & (class(t1) != "numeric") || length(t1)>1 || t1<0 || t1>1 ) stop("'t1' has to be between 0 and 1")
if ((class(t2) != "integer") & (class(t2) != "numeric") || length(t2)>1 || t2<0 || t2>1 ) stop("'t2' has to be between 0 and 1")
ID=method=Value=hp=cp=cpp=mcp=micp=NULL
ArcSinecovp.df = covpAS(n,alp,a,b,t1,t2)
nndf=gcovpA(n,alp,a,b,t1,t2)
nndf$mcp=ArcSinecovp.df$mcpA
nndf$micp=ArcSinecovp.df$micpA
nndf$t1=t1
nndf$t2=t2
nndf$alp=alp
ggplot2::ggplot(nndf, ggplot2::aes(x=hp, y=cp))+
ggplot2::labs(title = "Coverage Probability for ArcSine method") +
ggplot2::labs(y = "Coverage Probability") +
ggplot2::labs(x = "p") +
ggplot2::geom_hline(ggplot2::aes(yintercept=t1), color="red",linetype = 2) +
ggplot2::geom_hline(ggplot2::aes(yintercept=t2), color="blue",linetype = 2) +
ggplot2::geom_text(ggplot2::aes(y=t1, label="\nLower tolerance(t1)", x=.1), colour="red") +
ggplot2::geom_text(ggplot2::aes(y=t2, label="Higher tolerance(t2)", x=.1), colour="blue") +
ggplot2::geom_hline(ggplot2::aes(yintercept=micp,color="Minimum Coverage"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=mcp,color="Mean Coverage"))+
ggplot2::geom_line(ggplot2::aes(color=method)) +
ggplot2::guides(colour = ggplot2::guide_legend("Heading"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=1-(alp)),linetype = 2)
}
#############################################################################################################
#' Plots Coverage Probability for base Likelihood Ratio method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param a - Beta parameters for hypo "p"
#' @param b - Beta parameters for hypo "p"
#' @param t1 - Lower tolerance limit to check the spread of coverage Probability
#' @param t2 - Upper tolerance limit to check the spread of coverage Probability
#' @details Plots Coverage Probability for base Likelihood Ratio method
#' @family Basic coverage probability methods
#' @examples
#' \dontrun{
#' n= 10; alp=0.05; a=1; b=1; t1=0.93; t2=0.97
#' PlotcovpLR(n,alp,a,b,t1,t2)
#' }
#' @export
PlotcovpLR<-function(n,alp,a,b,t1,t2)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(a)) stop("'a' is missing")
if (missing(b)) stop("'b' is missing")
if (missing(t1)) stop("'t1' is missing")
if (missing(t2)) stop("'t2' is missing")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
if (alp>1 || alp<0 || length(alp) >1) stop("'alpha' has to be between 0 and 1")
if ((class(a) != "integer") & (class(a) != "numeric") || length(a)>1 || a<0 ) stop("'a' has to be greater than or equal to 0")
if ((class(b) != "integer") & (class(b) != "numeric") || length(b)>1 || b<0 ) stop("'b' has to be greater than or equal to 0")
if (t1>t2) stop(" t1 has to be lesser than t2")
if ((class(t1) != "integer") & (class(t1) != "numeric") || length(t1)>1 || t1<0 || t1>1 ) stop("'t1' has to be between 0 and 1")
if ((class(t2) != "integer") & (class(t2) != "numeric") || length(t2)>1 || t2<0 || t2>1 ) stop("'t2' has to be between 0 and 1")
ID=method=Value=hp=cp=cpp=mcp=micp=NULL
####INPUT n
LRcovp.df = covpLR(n,alp,a,b,t1,t2)
# ss1 = data.frame(method = LRcovp.df$method, MeanCP=LRcovp.df$mcpL, MinCP= LRcovp.df$micpL, RMSE_N=LRcovp.df$RMSE_N,RMSE_M=LRcovp.df$RMSE_M,RMSE_MI=LRcovp.df$RMSE_MI,tol=LRcovp.df$tol)
nndf=gcovpL(n,alp,a,b,t1,t2)
nndf$mcp=LRcovp.df$mcpL
nndf$micp=LRcovp.df$micpL
nndf$t1=t1
nndf$t2=t2
nndf$alp=alp
ggplot2::ggplot(nndf, ggplot2::aes(x=hp, y=cp))+
ggplot2::labs(title = "Coverage Probability for Likelihood Ratio method") +
ggplot2::labs(y = "Coverage Probability") +
ggplot2::labs(x = "p") +
ggplot2::geom_hline(ggplot2::aes(yintercept=t1), color="red",linetype = 2) +
ggplot2::geom_hline(ggplot2::aes(yintercept=t2), color="blue",linetype = 2) +
ggplot2::geom_text(ggplot2::aes(y=t1, label="\nLower tolerance(t1)", x=.1), colour="red") +
ggplot2::geom_text(ggplot2::aes(y=t2, label="Higher tolerance(t2)", x=.1), colour="blue") +
ggplot2::geom_hline(ggplot2::aes(yintercept=micp,color="Minimum Coverage"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=mcp,color="Mean Coverage"))+
ggplot2::geom_line(ggplot2::aes(color=method)) +
ggplot2::guides(colour = ggplot2::guide_legend("Heading"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=1-(alp)),linetype = 2)
}
#############################################################################################################
#' Plots Coverage Probability for base Score method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param a - Beta parameters for hypo "p"
#' @param b - Beta parameters for hypo "p"
#' @param t1 - Lower tolerance limit to check the spread of coverage Probability
#' @param t2 - Upper tolerance limit to check the spread of coverage Probability
#' @details Plots Coverage Probability for base Score method
#' @family Basic coverage probability methods
#' @examples
#' \dontrun{
#' n= 10; alp=0.05; a=1; b=1; t1=0.93; t2=0.97
#' PlotcovpSC(n,alp,a,b,t1,t2)
#' }
#' @export
PlotcovpSC<-function(n,alp,a,b,t1,t2)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(a)) stop("'a' is missing")
if (missing(b)) stop("'b' is missing")
if (missing(t1)) stop("'t1' is missing")
if (missing(t2)) stop("'t2' is missing")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
if (alp>1 || alp<0 || length(alp) >1) stop("'alpha' has to be between 0 and 1")
if ((class(a) != "integer") & (class(a) != "numeric") || length(a)>1 || a<0 ) stop("'a' has to be greater than or equal to 0")
if ((class(b) != "integer") & (class(b) != "numeric") || length(b)>1 || b<0 ) stop("'b' has to be greater than or equal to 0")
if (t1>t2) stop(" t1 has to be lesser than t2")
if ((class(t1) != "integer") & (class(t1) != "numeric") || length(t1)>1 || t1<0 || t1>1 ) stop("'t1' has to be between 0 and 1")
if ((class(t2) != "integer") & (class(t2) != "numeric") || length(t2)>1 || t2<0 || t2>1 ) stop("'t2' has to be between 0 and 1")
ID=method=Value=hp=cp=cpp=mcp=micp=NULL
####INPUT n
Scorecovp.df = covpSC(n,alp,a,b,t1,t2)
#ss1 = data.frame( MeanCP=Scorecovp.df$mcpS, MinCP= Scorecovp.df$micpS, RMSE_N=Scorecovp.df$RMSE_N,RMSE_M=Scorecovp.df$RMSE_M,RMSE_MI=Scorecovp.df$RMSE_MI,tol=Scorecovp.df$tol)
nndf=gcovpS(n,alp,a,b,t1,t2)
nndf$mcp=Scorecovp.df$mcpS
nndf$micp=Scorecovp.df$micpS
nndf$t1=t1
nndf$t2=t2
nndf$alp=alp
ggplot2::ggplot(nndf, ggplot2::aes(x=hp, y=cp))+
ggplot2::labs(title = "Coverage Probability for Score method") +
ggplot2::labs(y = "Coverage Probability") +
ggplot2::labs(x = "p") +
ggplot2::geom_hline(ggplot2::aes(yintercept=t1), color="red",linetype = 2) +
ggplot2::geom_hline(ggplot2::aes(yintercept=t2), color="blue",linetype = 2) +
ggplot2::geom_text(ggplot2::aes(y=t1, label="\nLower tolerance(t1)", x=.1), colour="red") +
ggplot2::geom_text(ggplot2::aes(y=t2, label="Higher tolerance(t2)", x=.1), colour="blue") +
ggplot2::geom_hline(ggplot2::aes(yintercept=micp,color="Minimum Coverage"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=mcp,color="Mean Coverage"))+
ggplot2::geom_line(ggplot2::aes(color=method)) +
ggplot2::guides(colour = ggplot2::guide_legend("Heading"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=1-(alp)),linetype = 2)
}
#############################################################################################################
#' Plots Coverage Probability for base Logit Wald method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param a - Beta parameters for hypo "p"
#' @param b - Beta parameters for hypo "p"
#' @param t1 - Lower tolerance limit to check the spread of coverage Probability
#' @param t2 - Upper tolerance limit to check the spread of coverage Probability
#' @details Plots Coverage Probability for base Logit Wald method
#' @family Basic coverage probability methods
#' @examples
#' \dontrun{
#' n= 10; alp=0.05; a=1; b=1; t1=0.93; t2=0.97
#' PlotcovpLT(n,alp,a,b,t1,t2)
#' }
#' @export
PlotcovpLT<-function(n,alp,a,b,t1,t2)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(a)) stop("'a' is missing")
if (missing(b)) stop("'b' is missing")
if (missing(t1)) stop("'t1' is missing")
if (missing(t2)) stop("'t2' is missing")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
if (alp>1 || alp<0 || length(alp) >1) stop("'alpha' has to be between 0 and 1")
if ((class(a) != "integer") & (class(a) != "numeric") || length(a)>1 || a<0 ) stop("'a' has to be greater than or equal to 0")
if ((class(b) != "integer") & (class(b) != "numeric") || length(b)>1 || b<0 ) stop("'b' has to be greater than or equal to 0")
if (t1>t2) stop(" t1 has to be lesser than t2")
if ((class(t1) != "integer") & (class(t1) != "numeric") || length(t1)>1 || t1<0 || t1>1 ) stop("'t1' has to be between 0 and 1")
if ((class(t2) != "integer") & (class(t2) != "numeric") || length(t2)>1 || t2<0 || t2>1 ) stop("'t2' has to be between 0 and 1")
ID=method=Value=hp=cp=cpp=mcp=micp=NULL
####INPUT n
WaldLcovp.df = covpLT(n,alp,a,b,t1,t2)
nndf=gcovpLT(n,alp,a,b,t1,t2)
nndf$mcp=WaldLcovp.df$mcpLT
nndf$micp=WaldLcovp.df$micpLT
nndf$t1=t1
nndf$t2=t2
nndf$alp=alp
ggplot2::ggplot(nndf, ggplot2::aes(x=hp, y=cp))+
ggplot2::labs(title = "Coverage Probability for Logit Wald method") +
ggplot2::labs(y = "Coverage Probability") +
ggplot2::labs(x = "p") +
ggplot2::geom_hline(ggplot2::aes(yintercept=t1), color="red",linetype = 2) +
ggplot2::geom_hline(ggplot2::aes(yintercept=t2), color="blue",linetype = 2) +
ggplot2::geom_text(ggplot2::aes(y=t1, label="\nLower tolerance(t1)", x=.1), colour="red") +
ggplot2::geom_text(ggplot2::aes(y=t2, label="Higher tolerance(t2)", x=.1), colour="blue") +
ggplot2::geom_hline(ggplot2::aes(yintercept=micp,color="Minimum Coverage"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=mcp,color="Mean Coverage"))+
ggplot2::geom_line(ggplot2::aes(color=method)) +
ggplot2::guides(colour = ggplot2::guide_legend("Heading"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=1-(alp)),linetype = 2)
}
#############################################################################################################
#' Plots Coverage Probability for base Wald-T method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param a - Beta parameters for hypo "p"
#' @param b - Beta parameters for hypo "p"
#' @param t1 - Lower tolerance limit to check the spread of coverage Probability
#' @param t2 - Upper tolerance limit to check the spread of coverage Probability
#' @details Plots Coverage Probability for base Wald-T method
#' @family Basic coverage probability methods
#' @examples
#' \dontrun{
#' n= 10; alp=0.05; a=1; b=1; t1=0.93; t2=0.97
#' PlotcovpTW(n,alp,a,b,t1,t2)
#' }
#' @export
PlotcovpTW<-function(n,alp,a,b,t1,t2)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(a)) stop("'a' is missing")
if (missing(b)) stop("'b' is missing")
if (missing(t1)) stop("'t1' is missing")
if (missing(t2)) stop("'t2' is missing")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
if (alp>1 || alp<0 || length(alp) >1) stop("'alpha' has to be between 0 and 1")
if ((class(a) != "integer") & (class(a) != "numeric") || length(a)>1 || a<0 ) stop("'a' has to be greater than or equal to 0")
if ((class(b) != "integer") & (class(b) != "numeric") || length(b)>1 || b<0 ) stop("'b' has to be greater than or equal to 0")
if (t1>t2) stop(" t1 has to be lesser than t2")
if ((class(t1) != "integer") & (class(t1) != "numeric") || length(t1)>1 || t1<0 || t1>1 ) stop("'t1' has to be between 0 and 1")
if ((class(t2) != "integer") & (class(t2) != "numeric") || length(t2)>1 || t2<0 || t2>1 ) stop("'t2' has to be between 0 and 1")
ID=method=Value=hp=cp=cpp=mcp=micp=NULL
####INPUT n
AdWaldcovp.df = covpTW(n,alp,a,b,t1,t2)
nndf=gcovpTW(n,alp,a,b,t1,t2)
nndf$mcp=AdWaldcovp.df$mcpTW
nndf$micp=AdWaldcovp.df$micpTW
nndf$t1=t1
nndf$t2=t2
nndf$alp=alp
ggplot2::ggplot(nndf, ggplot2::aes(x=hp, y=cp))+
ggplot2::labs(title = "Coverage Probability for Wald-T method") +
ggplot2::labs(y = "Coverage Probability") +
ggplot2::labs(x = "p") +
ggplot2::geom_hline(ggplot2::aes(yintercept=t1), color="red",linetype = 2) +
ggplot2::geom_hline(ggplot2::aes(yintercept=t2), color="blue",linetype = 2) +
ggplot2::geom_text(ggplot2::aes(y=t1, label="\nLower tolerance(t1)", x=.1), colour="red") +
ggplot2::geom_text(ggplot2::aes(y=t2, label="Higher tolerance(t2)", x=.1), colour="blue") +
ggplot2::geom_hline(ggplot2::aes(yintercept=micp,color="Minimum Coverage"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=mcp,color="Mean Coverage"))+
ggplot2::geom_line(ggplot2::aes(color=method)) +
ggplot2::guides(colour = ggplot2::guide_legend("Heading"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=1-(alp)),linetype = 2)
}
RajeswaranV/proportion documentation built on June 17, 2022, 9:11 a.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions PlotcovpTW PlotcovpLT PlotcovpSC PlotcovpLR PlotcovpAS PlotcovpWD PlotcovpAll PlotcovpBA PlotcovpEX
Documented in PlotcovpAll PlotcovpAS PlotcovpBA PlotcovpEX PlotcovpLR PlotcovpLT PlotcovpSC PlotcovpTW PlotcovpWD
#############################################################################################################
#' Graphs of Coverage Probability - exact method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param e - Exact method indicator (1:Clop-Pear,0.5:MID-p)
#' @param a - Beta parameters for hypo "p"
#' @param b - Beta parameters for hypo "p"
#' @param t1 - Lower tolerance limit to check the spread of coverage Probability
#' @param t2 - Upper tolerance limit to check the spread of coverage Probability
#' @details The graphs of basic Coverage Probability methods
#' @family Basic coverage probability methods
#' @examples
#' \dontrun{
#' n= 10; alp=0.05; e=0.5; a=1;b=1; t1=0.93;t2=0.97 # Mid-p
#' PlotcovpEX(n,alp,e,a,b,t1,t2)
#' n= 10; alp=0.05; e=1; a=1;b=1; t1=0.93;t2=0.97 #Clop-Pear
#' PlotcovpEX(n,alp,e,a,b,t1,t2)
#' n=5; alp=0.05;
#' e=c(0.1,0.5,0.95,1) #Range including Mid-p and Clopper-Pearson
#' a=1;b=1; t1=0.93;t2=0.97
#' PlotcovpEX(n,alp,e,a,b,t1,t2)
#' }
#' @export
##### 7. EXACT METHOD - Coverage Probability
PlotcovpEX=function(n,alp,e,a,b,t1,t2)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(e)) stop("'e' is missing")
if (missing(a)) stop("'a' is missing")
if (missing(b)) stop("'b' is missing")
if (missing(t1)) stop("'t1' is missing")
if (missing(t2)) stop("'t2' is missing")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
if (alp>1 || alp<0 || length(alp) >1) stop("'alpha' has to be between 0 and 1")
if ((class(e) != "integer") & (class(e) != "numeric") || any(e>1) || any(e<0)) stop("'e' has to be between 0 and 1")
if (length(e)>10 ) stop("Plot of only 10 intervals of 'e' is possible")
if ((class(a) != "integer") & (class(a) != "numeric") || length(a)>1 || a<0 ) stop("'a' has to be greater than or equal to 0")
if ((class(b) != "integer") & (class(b) != "numeric") || length(b)>1 || b<0 ) stop("'b' has to be greater than or equal to 0")
if (t1>t2) stop(" t1 has to be lesser than t2")
if ((class(t1) != "integer") & (class(t1) != "numeric") || length(t1)>1 || t1<0 || t1>1 ) stop("'t1' has to be between 0 and 1")
if ((class(t2) != "integer") & (class(t2) != "numeric") || length(t2)>1 || t2<0 || t2>1 ) stop("'t2' has to be between 0 and 1")
method=Value=hp=cp=cpp=mcp=micp=NULL
if(length(e)>1){
dfex=gcovpEX(n,alp,e,a,b,t1,t2)
exdf=dfex[,c(1,2,5)]
exdf$e=as.factor(exdf$e)
exdf$t1=t1
exdf$t2=t2
exdf$alp=alp
ggplot2::ggplot(exdf, ggplot2::aes(x=hp, y=cpp))+
ggplot2::labs(y = "Coverage Probability") +
ggplot2::labs(title = "Coverage Probability for exact method for multiple e values") +
ggplot2::labs(x = "p") +
ggplot2::geom_hline(ggplot2::aes(yintercept=t1), color="red",linetype = 2) +
ggplot2::geom_hline(ggplot2::aes(yintercept=t2), color="blue",linetype = 2) +
ggplot2::geom_text(ggplot2::aes(y=t1, label="\nLower tolerance(t1)", x=.1), colour="red") +
ggplot2::geom_text(ggplot2::aes(y=t2, label="Higher tolerance(t2)", x=.1), colour="blue") +
ggplot2::geom_line(ggplot2::aes(color=e)) +
ggplot2::geom_hline(ggplot2::aes(yintercept=1-(alp)),linetype = 2)
}
else{
dfex=gcovpEX(n,alp,e,a,b,t1,t2)
dfex1=data.frame(micp=dfex$micpEX[1] ,mcp=dfex$mcpEX[1] )
dfex1$alp=alp
ggplot2::ggplot(dfex, ggplot2::aes(x=hp, y=cpp))+
ggplot2::labs(title = "Coverage Probability of exact method") +
ggplot2::labs(y = "Coverage Probability") +
ggplot2::labs(x = "p") +
ggplot2::geom_line(ggplot2::aes(color="Coverage Probability"))+
ggplot2::geom_point(ggplot2::aes(color="CP Values"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=1-(alp),color="Confidence Level"),linetype = 2)+
ggplot2::geom_hline(data=dfex1,ggplot2::aes(yintercept=micp,color="Minimum Coverage"))+
ggplot2::geom_hline(data=dfex1,ggplot2::aes(yintercept=mcp,color="Mean Coverage"))+
ggplot2::scale_colour_manual(name='Heading',
values=c('Coverage Probability'='red',
'CP Values'='red',
'Minimum Coverage'='black',
'Mean Coverage'='blue',
'Confidence Level'='brown'),
guide='legend') +
ggplot2::guides(colour = ggplot2::guide_legend(override.aes = list(linetype=c(2,1,1,1,1),
shape=c(NA, NA, 16,NA,NA))))
}
}
#############################################################################################################
#' Graphs of Coverage Probability of the Bayesian method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param a - Beta parameters for hypo "p"
#' @param b - Beta parameters for hypo "p"
#' @param t1 - Lower tolerance limit to check the spread of coverage Probability
#' @param t2 - Upper tolerance limit to check the spread of coverage Probability
#' @param a1 - Beta Prior Parameters for Bayesian estimation
#' @param a2 - Beta Prior Parameters for Bayesian estimation
#' @details The graphs of Coverage Probability of Bayesian method
#' @family Basic coverage probability methods
#' @examples
#' \dontrun{
#' n= 10; alp=0.05; a=1;b=1; t1=0.93;t2=0.97;a1=1;a2=1
#' PlotcovpBA(n,alp,a,b,t1,t2,a1,a2)
#' }
#' @export
#8.BAYESIAN
PlotcovpBA<-function(n,alp,a,b,t1,t2,a1,a2)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(a)) stop("'a' is missing")
if (missing(b)) stop("'b' is missing")
if (missing(t1)) stop("'t1' is missing")
if (missing(t2)) stop("'t2' is missing")
if (missing(a1)) stop("'a1' is missing")
if (missing(a2)) stop("'a2' is missing")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
if (alp>1 || alp<0 || length(alp) >1) stop("'alpha' has to be between 0 and 1")
if ((class(a) != "integer") & (class(a) != "numeric") || length(a)>1 || a<0 ) stop("'a' has to be greater than or equal to 0")
if ((class(b) != "integer") & (class(b) != "numeric") || length(b)>1 || b<0 ) stop("'b' has to be greater than or equal to 0")
if (t1>t2) stop(" t1 has to be lesser than t2")
if ((class(t1) != "integer") & (class(t1) != "numeric") || length(t1)>1 || t1<0 || t1>1 ) stop("'t1' has to be between 0 and 1")
if ((class(t2) != "integer") & (class(t2) != "numeric") || length(t2)>1 || t2<0 || t2>1 ) stop("'t2' has to be between 0 and 1")
if ((class(a1) != "integer") & (class(a1) != "numeric") || length(a1)>1 || a1<0 ) stop("'a1' has to be greater than or equal to 0")
if ((class(a2) != "integer") & (class(a2) != "numeric") || length(a2)>1 || a2<0 ) stop("'a2' has to be greater than or equal to 0")
ID=method=Value=hp=cp=cpp=mcpBAQ=micpBAQ=mcpBAH=micpBAH=NULL
####INPUT n
x=0:n
k=n+1
####INITIALIZATIONS
LBAQ=0
UBAQ=0
LBAH=0
UBAH=0
s=5000
cpBAQ=matrix(0,k,s)
ctBAQ=matrix(0,k,s) #Cover Pbty quantity in sum
cppBAQ=0 #Coverage probabilty
ctr=0
cpBAH=matrix(0,k,s)
ctBAH=matrix(0,k,s) #Cover Pbty quantity in sum
cppBAH=0 #Coverage probabilty
ctrH=0
##############
#library(TeachingDemos) #To get HPDs
for(i in 1:k)
{
#Quantile Based Intervals
LBAQ[i]=stats::qbeta(alp/2,x[i]+a1,n-x[i]+a2)
UBAQ[i]=stats::qbeta(1-(alp/2),x[i]+a1,n-x[i]+a2)
LBAH[i]=TeachingDemos::hpd(stats::qbeta,shape1=x[i]+a1,shape2=n-x[i]+a2,conf=1-alp)[1]
UBAH[i]=TeachingDemos::hpd(stats::qbeta,shape1=x[i]+a1,shape2=n-x[i]+a2,conf=1-alp)[2]
}
####COVERAGE PROBABILITIES
hp=sort(stats::rbeta(s,a,b),decreasing = FALSE) #HYPOTHETICAL "p"
for (j in 1:s)
{
for(i in 1:k)
{
if(hp[j] > LBAQ[i] && hp[j] < UBAQ[i])
{
cpBAQ[i,j]=stats::dbinom(i-1, n,hp[j])
ctBAQ[i,j]=1
}
if(hp[j] > LBAH[i] && hp[j] < UBAH[i])
{
cpBAH[i,j]=stats::dbinom(i-1, n,hp[j])
ctBAH[i,j]=1
}
}
cppBAQ[j]=sum(cpBAQ[,j]) #Coverage Probability
if(t1<cppBAQ[j]&&cppBAQ[j]<t2) ctr=ctr+1 #tolerance for cov prob - user defined
cppBAH[j]=sum(cpBAH[,j]) #Coverage Probability
if(t1<cppBAH[j]&&cppBAH[j]<t2) ctrH=ctrH+1 #tolerance for cov prob - user defined
}
CPBAQ=data.frame(hp,cpp=cppBAQ,method="Quantile")
CPBAH=data.frame(hp,cpp=cppBAH,method="HPD")
df.new=rbind(CPBAQ,CPBAH)
df.new$mcpBAQ=mean(cppBAQ)
df.new$micpBAQ=min(cppBAQ) #Mean Cov Prob
df.new$mcpBAH=mean(cppBAH)
df.new$micpBAH=min(cppBAH) #Mean Cov Prob
df.new$alp=alp #Mean Cov Prob
ggplot2::ggplot(df.new, ggplot2::aes(x=hp, y=cpp))+
ggplot2::labs(title = "Coverage Probability of Bayesian methods") +
ggplot2::labs(y = "Coverage Probability") +
ggplot2::labs(x = "p") +
ggplot2::geom_line(ggplot2::aes(color=method)) +
ggplot2::geom_hline(ggplot2::aes(yintercept=1-(alp),color="Confidence Level"),linetype = 2)+
ggplot2::geom_hline(ggplot2::aes(yintercept=micpBAQ,color="Minimum Coverage Quantile"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=mcpBAQ,color="Mean Coverage Quantile"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=micpBAH,color="Minimum Coverage HPD"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=mcpBAH,color="Mean Coverage HPD"))+
ggplot2::scale_colour_manual(name='Heading',
values=c('Quantile' ='black',
'HPD' = 'red',
'Minimum Coverage Quantile'='red',
'Mean Coverage Quantile'='blue',
'Minimum Coverage HPD'='black',
'Mean Coverage HPD'='cyan',
'Confidence Level'='brown'),
guide='legend') +
ggplot2::guides(colour = ggplot2::guide_legend(override.aes = list(linetype=c(2,1,1,1,1,1,1))))
}
#############################################################################################################
#' Graphs of basic Coverage Probability 6 base methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine)
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param a - Beta parameters for hypo "p"
#' @param b - Beta parameters for hypo "p"
#' @param t1 - Lower tolerance limit to check the spread of coverage Probability
#' @param t2 - Upper tolerance limit to check the spread of coverage Probability
#' @details The graphs of basic Coverage Probability methods
#' @family Basic coverage probability methods
#' @examples
#' \dontrun{
#' n= 10; alp=0.05; a=1; b=1; t1=0.93; t2=0.97
#' PlotcovpAll(n,alp,a,b,t1,t2)
#' }
#' @export
##### 9. Coverage Probability - Graph
PlotcovpAll<-function(n,alp,a,b,t1,t2)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(a)) stop("'a' is missing")
if (missing(b)) stop("'b' is missing")
if (missing(t1)) stop("'t1' is missing")
if (missing(t2)) stop("'t2' is missing")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
if (alp>1 || alp<0 || length(alp) >1) stop("'alpha' has to be between 0 and 1")
if ((class(a) != "integer") & (class(a) != "numeric") || length(a)>1 || a<0 ) stop("'a' has to be greater than or equal to 0")
if ((class(b) != "integer") & (class(b) != "numeric") || length(b)>1 || b<0 ) stop("'b' has to be greater than or equal to 0")
if (t1>t2) stop(" t1 has to be lesser than t2")
if ((class(t1) != "integer") & (class(t1) != "numeric") || length(t1)>1 || t1<0 || t1>1 ) stop("'t1' has to be between 0 and 1")
if ((class(t2) != "integer") & (class(t2) != "numeric") || length(t2)>1 || t2<0 || t2>1 ) stop("'t2' has to be between 0 and 1")
ID=method=Value=hp=cp=cpp=mcp=micp=NULL
####INPUT n
df1=gcovpW(n,alp,a,b,t1,t2)
df2=gcovpS(n,alp,a,b,t1,t2)
df3=gcovpA(n,alp,a,b,t1,t2)
df4=gcovpLT(n,alp,a,b,t1,t2)
df5=gcovpTW(n,alp,a,b,t1,t2)
df6=gcovpL(n,alp,a,b,t1,t2)
nndf= rbind(df1,df2,df3,df4,df5,df6)
nndf$t1=t1
nndf$t2=t2
nndf$alp=alp
ggplot2::ggplot(nndf, ggplot2::aes(x=hp, y=cp))+
ggplot2::labs(y = "Coverage Probability") +
ggplot2::labs(title = "Coverage Probability for 6 base methods") +
ggplot2::labs(x = "p") +
ggplot2::geom_hline(ggplot2::aes(yintercept=t1), color="red",linetype = 2) +
ggplot2::geom_hline(ggplot2::aes(yintercept=t2), color="blue",linetype = 2) +
ggplot2::geom_text(ggplot2::aes(y=t1, label="\nLower tolerance(t1)", x=.1), colour="red") +
ggplot2::geom_text(ggplot2::aes(y=t2, label="Higher tolerance(t2)", x=.1), colour="blue") +
ggplot2::geom_line(ggplot2::aes(color=method)) +
ggplot2::geom_hline(ggplot2::aes(yintercept=1-(alp)),linetype = 2)
}
#############################################################################################################
#' Plots Coverage Probability for base Wald method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param a - Beta parameters for hypo "p"
#' @param b - Beta parameters for hypo "p"
#' @param t1 - Lower tolerance limit to check the spread of coverage Probability
#' @param t2 - Upper tolerance limit to check the spread of coverage Probability
#' @details Plots Coverage Probability for base Wald method
#' @family Basic coverage probability methods
#' @examples
#' \dontrun{
#' n= 10; alp=0.05; a=1; b=1; t1=0.93; t2=0.97
#' PlotcovpWD(n,alp,a,b,t1,t2)
#' }
#' @export
PlotcovpWD<-function(n,alp,a,b,t1,t2)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(a)) stop("'a' is missing")
if (missing(b)) stop("'b' is missing")
if (missing(t1)) stop("'t1' is missing")
if (missing(t2)) stop("'t2' is missing")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
if (alp>1 || alp<0 || length(alp) >1) stop("'alpha' has to be between 0 and 1")
if ((class(a) != "integer") & (class(a) != "numeric") || length(a)>1 || a<0 ) stop("'a' has to be greater than or equal to 0")
if ((class(b) != "integer") & (class(b) != "numeric") || length(b)>1 || b<0 ) stop("'b' has to be greater than or equal to 0")
if (t1>t2) stop(" t1 has to be lesser than t2")
if ((class(t1) != "integer") & (class(t1) != "numeric") || length(t1)>1 || t1<0 || t1>1 ) stop("'t1' has to be between 0 and 1")
if ((class(t2) != "integer") & (class(t2) != "numeric") || length(t2)>1 || t2<0 || t2>1 ) stop("'t2' has to be between 0 and 1")
ID=method=Value=hp=cp=cpp=mcp=micp=NULL
####INPUT n
Waldcovp.df = covpWD(n,alp,a,b,t1,t2)
nndf=gcovpW(n,alp,a,b,t1,t2)
nndf$mcp=Waldcovp.df$mcpW
nndf$micp=Waldcovp.df$micpW
nndf$t1=t1
nndf$t2=t2
nndf$alp=alp
ggplot2::ggplot(nndf, ggplot2::aes(x=hp, y=cp))+
ggplot2::labs(title = "Coverage Probability for Wald method") +
ggplot2::labs(y = "Coverage Probability") +
ggplot2::labs(x = "p") +
ggplot2::geom_hline(ggplot2::aes(yintercept=t1), color="red",linetype = 2) +
ggplot2::geom_hline(ggplot2::aes(yintercept=t2), color="blue",linetype = 2) +
ggplot2::geom_text(ggplot2::aes(y=t1, label="\nLower tolerance(t1)", x=.1), colour="red") +
ggplot2::geom_text(ggplot2::aes(y=t2, label="Higher tolerance(t2)", x=.1), colour="blue") +
ggplot2::geom_hline(ggplot2::aes(yintercept=micp,color="Minimum Coverage"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=mcp,color="Mean Coverage"))+
ggplot2::geom_line(ggplot2::aes(color=method)) +
ggplot2::guides(colour = ggplot2::guide_legend("Heading"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=1-(alp)),linetype = 2)
}
#############################################################################################################
#' Plots Coverage Probability for base ArcSine method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param a - Beta parameters for hypo "p"
#' @param b - Beta parameters for hypo "p"
#' @param t1 - Lower tolerance limit to check the spread of coverage Probability
#' @param t2 - Upper tolerance limit to check the spread of coverage Probability
#' @details Plots Coverage Probability for base ArcSine method
#' @family Basic coverage probability methods
#' @examples
#' \dontrun{
#' n= 10; alp=0.05; a=1; b=1; t1=0.93; t2=0.97
#' PlotcovpAS(n,alp,a,b,t1,t2)
#' }
#' @export
PlotcovpAS<-function(n,alp,a,b,t1,t2)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(a)) stop("'a' is missing")
if (missing(b)) stop("'b' is missing")
if (missing(t1)) stop("'t1' is missing")
if (missing(t2)) stop("'t2' is missing")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
if (alp>1 || alp<0 || length(alp) >1) stop("'alpha' has to be between 0 and 1")
if ((class(a) != "integer") & (class(a) != "numeric") || length(a)>1 || a<0 ) stop("'a' has to be greater than or equal to 0")
if ((class(b) != "integer") & (class(b) != "numeric") || length(b)>1 || b<0 ) stop("'b' has to be greater than or equal to 0")
if (t1>t2) stop(" t1 has to be lesser than t2")
if ((class(t1) != "integer") & (class(t1) != "numeric") || length(t1)>1 || t1<0 || t1>1 ) stop("'t1' has to be between 0 and 1")
if ((class(t2) != "integer") & (class(t2) != "numeric") || length(t2)>1 || t2<0 || t2>1 ) stop("'t2' has to be between 0 and 1")
ID=method=Value=hp=cp=cpp=mcp=micp=NULL
ArcSinecovp.df = covpAS(n,alp,a,b,t1,t2)
nndf=gcovpA(n,alp,a,b,t1,t2)
nndf$mcp=ArcSinecovp.df$mcpA
nndf$micp=ArcSinecovp.df$micpA
nndf$t1=t1
nndf$t2=t2
nndf$alp=alp
ggplot2::ggplot(nndf, ggplot2::aes(x=hp, y=cp))+
ggplot2::labs(title = "Coverage Probability for ArcSine method") +
ggplot2::labs(y = "Coverage Probability") +
ggplot2::labs(x = "p") +
ggplot2::geom_hline(ggplot2::aes(yintercept=t1), color="red",linetype = 2) +
ggplot2::geom_hline(ggplot2::aes(yintercept=t2), color="blue",linetype = 2) +
ggplot2::geom_text(ggplot2::aes(y=t1, label="\nLower tolerance(t1)", x=.1), colour="red") +
ggplot2::geom_text(ggplot2::aes(y=t2, label="Higher tolerance(t2)", x=.1), colour="blue") +
ggplot2::geom_hline(ggplot2::aes(yintercept=micp,color="Minimum Coverage"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=mcp,color="Mean Coverage"))+
ggplot2::geom_line(ggplot2::aes(color=method)) +
ggplot2::guides(colour = ggplot2::guide_legend("Heading"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=1-(alp)),linetype = 2)
}
#############################################################################################################
#' Plots Coverage Probability for base Likelihood Ratio method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param a - Beta parameters for hypo "p"
#' @param b - Beta parameters for hypo "p"
#' @param t1 - Lower tolerance limit to check the spread of coverage Probability
#' @param t2 - Upper tolerance limit to check the spread of coverage Probability
#' @details Plots Coverage Probability for base Likelihood Ratio method
#' @family Basic coverage probability methods
#' @examples
#' \dontrun{
#' n= 10; alp=0.05; a=1; b=1; t1=0.93; t2=0.97
#' PlotcovpLR(n,alp,a,b,t1,t2)
#' }
#' @export
PlotcovpLR<-function(n,alp,a,b,t1,t2)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(a)) stop("'a' is missing")
if (missing(b)) stop("'b' is missing")
if (missing(t1)) stop("'t1' is missing")
if (missing(t2)) stop("'t2' is missing")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
if (alp>1 || alp<0 || length(alp) >1) stop("'alpha' has to be between 0 and 1")
if ((class(a) != "integer") & (class(a) != "numeric") || length(a)>1 || a<0 ) stop("'a' has to be greater than or equal to 0")
if ((class(b) != "integer") & (class(b) != "numeric") || length(b)>1 || b<0 ) stop("'b' has to be greater than or equal to 0")
if (t1>t2) stop(" t1 has to be lesser than t2")
if ((class(t1) != "integer") & (class(t1) != "numeric") || length(t1)>1 || t1<0 || t1>1 ) stop("'t1' has to be between 0 and 1")
if ((class(t2) != "integer") & (class(t2) != "numeric") || length(t2)>1 || t2<0 || t2>1 ) stop("'t2' has to be between 0 and 1")
ID=method=Value=hp=cp=cpp=mcp=micp=NULL
####INPUT n
LRcovp.df = covpLR(n,alp,a,b,t1,t2)
# ss1 = data.frame(method = LRcovp.df$method, MeanCP=LRcovp.df$mcpL, MinCP= LRcovp.df$micpL, RMSE_N=LRcovp.df$RMSE_N,RMSE_M=LRcovp.df$RMSE_M,RMSE_MI=LRcovp.df$RMSE_MI,tol=LRcovp.df$tol)
nndf=gcovpL(n,alp,a,b,t1,t2)
nndf$mcp=LRcovp.df$mcpL
nndf$micp=LRcovp.df$micpL
nndf$t1=t1
nndf$t2=t2
nndf$alp=alp
ggplot2::ggplot(nndf, ggplot2::aes(x=hp, y=cp))+
ggplot2::labs(title = "Coverage Probability for Likelihood Ratio method") +
ggplot2::labs(y = "Coverage Probability") +
ggplot2::labs(x = "p") +
ggplot2::geom_hline(ggplot2::aes(yintercept=t1), color="red",linetype = 2) +
ggplot2::geom_hline(ggplot2::aes(yintercept=t2), color="blue",linetype = 2) +
ggplot2::geom_text(ggplot2::aes(y=t1, label="\nLower tolerance(t1)", x=.1), colour="red") +
ggplot2::geom_text(ggplot2::aes(y=t2, label="Higher tolerance(t2)", x=.1), colour="blue") +
ggplot2::geom_hline(ggplot2::aes(yintercept=micp,color="Minimum Coverage"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=mcp,color="Mean Coverage"))+
ggplot2::geom_line(ggplot2::aes(color=method)) +
ggplot2::guides(colour = ggplot2::guide_legend("Heading"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=1-(alp)),linetype = 2)
}
#############################################################################################################
#' Plots Coverage Probability for base Score method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param a - Beta parameters for hypo "p"
#' @param b - Beta parameters for hypo "p"
#' @param t1 - Lower tolerance limit to check the spread of coverage Probability
#' @param t2 - Upper tolerance limit to check the spread of coverage Probability
#' @details Plots Coverage Probability for base Score method
#' @family Basic coverage probability methods
#' @examples
#' \dontrun{
#' n= 10; alp=0.05; a=1; b=1; t1=0.93; t2=0.97
#' PlotcovpSC(n,alp,a,b,t1,t2)
#' }
#' @export
PlotcovpSC<-function(n,alp,a,b,t1,t2)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(a)) stop("'a' is missing")
if (missing(b)) stop("'b' is missing")
if (missing(t1)) stop("'t1' is missing")
if (missing(t2)) stop("'t2' is missing")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
if (alp>1 || alp<0 || length(alp) >1) stop("'alpha' has to be between 0 and 1")
if ((class(a) != "integer") & (class(a) != "numeric") || length(a)>1 || a<0 ) stop("'a' has to be greater than or equal to 0")
if ((class(b) != "integer") & (class(b) != "numeric") || length(b)>1 || b<0 ) stop("'b' has to be greater than or equal to 0")
if (t1>t2) stop(" t1 has to be lesser than t2")
if ((class(t1) != "integer") & (class(t1) != "numeric") || length(t1)>1 || t1<0 || t1>1 ) stop("'t1' has to be between 0 and 1")
if ((class(t2) != "integer") & (class(t2) != "numeric") || length(t2)>1 || t2<0 || t2>1 ) stop("'t2' has to be between 0 and 1")
ID=method=Value=hp=cp=cpp=mcp=micp=NULL
####INPUT n
Scorecovp.df = covpSC(n,alp,a,b,t1,t2)
#ss1 = data.frame( MeanCP=Scorecovp.df$mcpS, MinCP= Scorecovp.df$micpS, RMSE_N=Scorecovp.df$RMSE_N,RMSE_M=Scorecovp.df$RMSE_M,RMSE_MI=Scorecovp.df$RMSE_MI,tol=Scorecovp.df$tol)
nndf=gcovpS(n,alp,a,b,t1,t2)
nndf$mcp=Scorecovp.df$mcpS
nndf$micp=Scorecovp.df$micpS
nndf$t1=t1
nndf$t2=t2
nndf$alp=alp
ggplot2::ggplot(nndf, ggplot2::aes(x=hp, y=cp))+
ggplot2::labs(title = "Coverage Probability for Score method") +
ggplot2::labs(y = "Coverage Probability") +
ggplot2::labs(x = "p") +
ggplot2::geom_hline(ggplot2::aes(yintercept=t1), color="red",linetype = 2) +
ggplot2::geom_hline(ggplot2::aes(yintercept=t2), color="blue",linetype = 2) +
ggplot2::geom_text(ggplot2::aes(y=t1, label="\nLower tolerance(t1)", x=.1), colour="red") +
ggplot2::geom_text(ggplot2::aes(y=t2, label="Higher tolerance(t2)", x=.1), colour="blue") +
ggplot2::geom_hline(ggplot2::aes(yintercept=micp,color="Minimum Coverage"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=mcp,color="Mean Coverage"))+
ggplot2::geom_line(ggplot2::aes(color=method)) +
ggplot2::guides(colour = ggplot2::guide_legend("Heading"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=1-(alp)),linetype = 2)
}
#############################################################################################################
#' Plots Coverage Probability for base Logit Wald method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param a - Beta parameters for hypo "p"
#' @param b - Beta parameters for hypo "p"
#' @param t1 - Lower tolerance limit to check the spread of coverage Probability
#' @param t2 - Upper tolerance limit to check the spread of coverage Probability
#' @details Plots Coverage Probability for base Logit Wald method
#' @family Basic coverage probability methods
#' @examples
#' \dontrun{
#' n= 10; alp=0.05; a=1; b=1; t1=0.93; t2=0.97
#' PlotcovpLT(n,alp,a,b,t1,t2)
#' }
#' @export
PlotcovpLT<-function(n,alp,a,b,t1,t2)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(a)) stop("'a' is missing")
if (missing(b)) stop("'b' is missing")
if (missing(t1)) stop("'t1' is missing")
if (missing(t2)) stop("'t2' is missing")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
if (alp>1 || alp<0 || length(alp) >1) stop("'alpha' has to be between 0 and 1")
if ((class(a) != "integer") & (class(a) != "numeric") || length(a)>1 || a<0 ) stop("'a' has to be greater than or equal to 0")
if ((class(b) != "integer") & (class(b) != "numeric") || length(b)>1 || b<0 ) stop("'b' has to be greater than or equal to 0")
if (t1>t2) stop(" t1 has to be lesser than t2")
if ((class(t1) != "integer") & (class(t1) != "numeric") || length(t1)>1 || t1<0 || t1>1 ) stop("'t1' has to be between 0 and 1")
if ((class(t2) != "integer") & (class(t2) != "numeric") || length(t2)>1 || t2<0 || t2>1 ) stop("'t2' has to be between 0 and 1")
ID=method=Value=hp=cp=cpp=mcp=micp=NULL
####INPUT n
WaldLcovp.df = covpLT(n,alp,a,b,t1,t2)
nndf=gcovpLT(n,alp,a,b,t1,t2)
nndf$mcp=WaldLcovp.df$mcpLT
nndf$micp=WaldLcovp.df$micpLT
nndf$t1=t1
nndf$t2=t2
nndf$alp=alp
ggplot2::ggplot(nndf, ggplot2::aes(x=hp, y=cp))+
ggplot2::labs(title = "Coverage Probability for Logit Wald method") +
ggplot2::labs(y = "Coverage Probability") +
ggplot2::labs(x = "p") +
ggplot2::geom_hline(ggplot2::aes(yintercept=t1), color="red",linetype = 2) +
ggplot2::geom_hline(ggplot2::aes(yintercept=t2), color="blue",linetype = 2) +
ggplot2::geom_text(ggplot2::aes(y=t1, label="\nLower tolerance(t1)", x=.1), colour="red") +
ggplot2::geom_text(ggplot2::aes(y=t2, label="Higher tolerance(t2)", x=.1), colour="blue") +
ggplot2::geom_hline(ggplot2::aes(yintercept=micp,color="Minimum Coverage"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=mcp,color="Mean Coverage"))+
ggplot2::geom_line(ggplot2::aes(color=method)) +
ggplot2::guides(colour = ggplot2::guide_legend("Heading"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=1-(alp)),linetype = 2)
}
#############################################################################################################
#' Plots Coverage Probability for base Wald-T method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param a - Beta parameters for hypo "p"
#' @param b - Beta parameters for hypo "p"
#' @param t1 - Lower tolerance limit to check the spread of coverage Probability
#' @param t2 - Upper tolerance limit to check the spread of coverage Probability
#' @details Plots Coverage Probability for base Wald-T method
#' @family Basic coverage probability methods
#' @examples
#' \dontrun{
#' n= 10; alp=0.05; a=1; b=1; t1=0.93; t2=0.97
#' PlotcovpTW(n,alp,a,b,t1,t2)
#' }
#' @export
PlotcovpTW<-function(n,alp,a,b,t1,t2)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(a)) stop("'a' is missing")
if (missing(b)) stop("'b' is missing")
if (missing(t1)) stop("'t1' is missing")
if (missing(t2)) stop("'t2' is missing")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
if (alp>1 || alp<0 || length(alp) >1) stop("'alpha' has to be between 0 and 1")
if ((class(a) != "integer") & (class(a) != "numeric") || length(a)>1 || a<0 ) stop("'a' has to be greater than or equal to 0")
if ((class(b) != "integer") & (class(b) != "numeric") || length(b)>1 || b<0 ) stop("'b' has to be greater than or equal to 0")
if (t1>t2) stop(" t1 has to be lesser than t2")
if ((class(t1) != "integer") & (class(t1) != "numeric") || length(t1)>1 || t1<0 || t1>1 ) stop("'t1' has to be between 0 and 1")
if ((class(t2) != "integer") & (class(t2) != "numeric") || length(t2)>1 || t2<0 || t2>1 ) stop("'t2' has to be between 0 and 1")
ID=method=Value=hp=cp=cpp=mcp=micp=NULL
####INPUT n
AdWaldcovp.df = covpTW(n,alp,a,b,t1,t2)
nndf=gcovpTW(n,alp,a,b,t1,t2)
nndf$mcp=AdWaldcovp.df$mcpTW
nndf$micp=AdWaldcovp.df$micpTW
nndf$t1=t1
nndf$t2=t2
nndf$alp=alp
ggplot2::ggplot(nndf, ggplot2::aes(x=hp, y=cp))+
ggplot2::labs(title = "Coverage Probability for Wald-T method") +
ggplot2::labs(y = "Coverage Probability") +
ggplot2::labs(x = "p") +
ggplot2::geom_hline(ggplot2::aes(yintercept=t1), color="red",linetype = 2) +
ggplot2::geom_hline(ggplot2::aes(yintercept=t2), color="blue",linetype = 2) +
ggplot2::geom_text(ggplot2::aes(y=t1, label="\nLower tolerance(t1)", x=.1), colour="red") +
ggplot2::geom_text(ggplot2::aes(y=t2, label="Higher tolerance(t2)", x=.1), colour="blue") +
ggplot2::geom_hline(ggplot2::aes(yintercept=micp,color="Minimum Coverage"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=mcp,color="Mean Coverage"))+
ggplot2::geom_line(ggplot2::aes(color=method)) +
ggplot2::guides(colour = ggplot2::guide_legend("Heading"))+
ggplot2::geom_hline(ggplot2::aes(yintercept=1-(alp)),linetype = 2)
}
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