R/402.p-Confidence_p-Bias_BASE_All_Graph.R
In RajeswaranV/proportion: Inference on Single Binomial Proportion and Bayesian Computations
Defines functions
PlotpCOpBILR
PlotpCOpBITW
PlotpCOpBILT
PlotpCOpBIAS
PlotpCOpBISC
PlotpCOpBIWD
PlotpCOpBIAll
PlotpCOpBIBA
PlotpCOpBIEX
Documented in
PlotpCOpBIAll
PlotpCOpBIAS
PlotpCOpBIBA
PlotpCOpBIEX
PlotpCOpBILR
PlotpCOpBILT
PlotpCOpBISC
PlotpCOpBITW
PlotpCOpBIWD
#################################################################################################
#' Plots of p-confidence and p-bias of Exact method given n and alpha level
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param e - Exact method indicator in [0, 1] {1: Clopper Pearson, 0.5: Mid P}
#' The input can also be a range of values between 0 and 1.
#' @details Evaluation of Confidence interval for \code{p} based on inverting equal-tailed
#' binomial tests with null hypothesis \eqn{H0: p = p0} using p-confidence and p-bias for
#' the \eqn{n + 1} intervals
#' @return A dataframe with
#' \describe{
#' \item{x1}{ Number of successes (positive samples)}
#' \item{pconf }{ p-Confidence}
#' \item{pbias }{ p-Bias}
#' }
#' @family p-confidence and p-bias of base methods
#' @examples
#' n=5; alp=0.05;e=0.5; # Mid-p
#' PlotpCOpBIEX(n,alp,e)
#' n=5; alp=0.05;e=1; #Clopper-Pearson
#' PlotpCOpBIEX(n,alp,e)
#' n=5; alp=0.05;e=c(0.1,0.5,0.95,1); #Range including Mid-p and Clopper-Pearson
#' PlotpCOpBIEX(n,alp,e)
#' @references
#' [1] 2005 Vos PW and Hudson S.
#' Evaluation Criteria for Discrete Confidence Intervals: Beyond Coverage and Length.
#' The American Statistician: 59; 137 - 142.
#' @export
##### 1.EXACT METHODS - p-confidence and p-bias for a given n and alpha level
PlotpCOpBIEX<-function(n,alp,e)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(e)) stop("'e' 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")
x=Value=Heading=mark=NULL
CBEX = pCOpBIEX(n,alp,e)
W1 = data.frame(x=CBEX$x1, Value=CBEX$pconf, Heading="pconf")
W2 = data.frame(x=CBEX$x1, Value=CBEX$pbias, Heading="pbias")
b1=data.frame(e=CBEX$e)
b2=data.frame(e=CBEX$e)
bjoin=rbind(b1,b2)
fg=rbind(W1,W2)
gdf=cbind(fg,bjoin)
gdf$e=as.factor(gdf$e)
ggplot2::ggplot(gdf, ggplot2::aes(x=x, y=Value, colour = e)) +
ggplot2::facet_grid(Heading~.,scales="free_y") +
ggplot2::labs(title = "p-Confidence & p-Bias - Exact method") +
ggplot2::labs(y = "Confidence ") +
ggplot2::labs(x = "No of successes") +
ggplot2::scale_colour_manual(values=c("blue", "red", "black", "cyan4", "deeppink",
"orange","chartreuse4",
"blueviolet" , "grey", "darksalmon", "tan1")) +
ggplot2::geom_line(data=gdf,ggplot2::aes(color=e))
}
#######################################################################################################
#' Plots p-confidence and p-bias for Bayesian method given n and alpha level and priors a & b
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param a1 - Shape parameter 1 for prior Beta distribution in Bayesian model
#' @param a2 - Shape parameter 2 for prior Beta distribution in Bayesian model
#' @details Evaluation of Bayesian Highest Probability Density (HPD) and two tailed
#' intervals using p-confidence and p-bias for the \eqn{n + 1} intervals for the
#' Beta - Binomial conjugate prior model for the probability of success \code{p}
#' @family p-confidence and p-bias of base methods
#' @examples
#' n=5; alp=0.05;a1=1;a2=1
#' PlotpCOpBIBA(n,alp,a1,a2)
#' @references
#' [1] 2005 Vos PW and Hudson S.
#' Evaluation Criteria for Discrete Confidence Intervals: Beyond Coverage and Length.
#' The American Statistician: 59; 137 - 142.
#' @export
####2.BAYESIAN p-confidence and p-bias
PlotpCOpBIBA<-function(n,alp,a1,a2)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' 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(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")
x=Value=Heading=mark=NULL
ndf=pCOpBIBA(n,alp,a1,a2)
W1 = data.frame(x=ndf$x1, Value=ndf$pconfQ, Heading="pconfQ")
W2 = data.frame(x=ndf$x1, Value=ndf$pbiasQ, Heading="pbiasQ")
W3 = data.frame(x=ndf$x1, Value=ndf$pconfH, Heading="pconfH")
W4 = data.frame(x=ndf$x1, Value=ndf$pbiasH, Heading="pbiasH")
nBA=rbind(W1,W2,W3,W4)
ggplot2::ggplot(nBA, ggplot2::aes(x=x, y=Value))+
ggplot2::labs(title = "p-Confidence & p-Bias - Bayesian methods") +
ggplot2::facet_grid(Heading ~ .,scales="free_y") +
ggplot2::labs(y = "Confidence ") +
ggplot2::labs(x = "No of successes") +
ggplot2::geom_line(data=W1,ggplot2::aes(color="pConf Quantile"))+
ggplot2::geom_point(data=W1, ggplot2::aes(color="pconfQ Values"),shape=22)+
ggplot2::geom_line(data=W2,ggplot2::aes(color="pbias Quantile"))+
ggplot2::geom_point(data=W2,ggplot2::aes(color="pbiasQ Values"),shape=23)+
ggplot2::geom_line(data=W3,ggplot2::aes(color="pConf HPD"))+
ggplot2::geom_point(data=W3, ggplot2::aes(color="pconfHPD Values"),shape=21)+
ggplot2::geom_line(data=W4,ggplot2::aes(color="pbias HPD"))+
ggplot2::geom_point(data=W4,ggplot2::aes(color="pbiasHPD Values"),shape=16)+
ggplot2::scale_colour_manual(name='Heading',
values=c('pConf Quantile'='red',
'pconfQ Values'='red',
'pbias Quantile'='blue',
'pbiasQ Values'='blue',
'pConf HPD'='orange',
'pconfHPD Values'='orange',
'pbias HPD'='black',
'pbiasHPD Values'='black'),
guide='Title') +
ggplot2::guides(colour = ggplot2::guide_legend(override.aes = list(linetype=c(1,1,1,1,1,1,1,1),
shape=c(NA, NA,16,23,NA,NA,21,22))))
}
###########################################################################################################3
#' Plots p-confidence and p-bias for a given n and alpha level for
#' 6 base methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine)
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @details p-confidence and p-bias plots for 6 base methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine)
#' @family p-confidence and p-bias of base methods
#' @examples
#' \dontrun{
#' n=5; alp=0.05
#' PlotpCOpBIAll(n,alp)
#' }
#' @references
#' [1] 2005 Vos PW and Hudson S.
#' Evaluation Criteria for Discrete Confidence Intervals: Beyond Coverage and Length.
#' The American Statistician: 59; 137 - 142.
#' @export
####3.All methods plots of p-confidence and p-bias
PlotpCOpBIAll<-function(n,alp)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (alp>1 || alp<0 || length(alp)>1) stop("'alpha' has to be between 0 and 1")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
x=Value=Heading=mark=val=NULL
nAll = pCOpBIAll(n,alp)
pc=data.frame(x=nAll$x1, val=nAll$pconf, Heading=nAll$method, mark="pconf",minmax=min(nAll$pbias, nAll$pconf))
pb=data.frame(x=nAll$x1, val=nAll$pbias, Heading=nAll$method, mark="pbias",minmax=max(nAll$pbias, nAll$pconf))
nndf=rbind(pc,pb)
cdfWDc = subset(nndf, Heading == "Wald" & mark== "pconf")
cdfWDb = subset(nndf, Heading == "Wald" & mark== "pbias")
cdfSCc = subset(nndf, Heading == "Score" & mark== "pconf")
cdfSCb = subset(nndf, Heading == "Score" & mark== "pbias")
cdfASc = subset(nndf, Heading == "ArcSine" & mark== "pconf")
cdfASb = subset(nndf, Heading == "ArcSine" & mark== "pbias")
cdfLTc = subset(nndf, Heading == "Logit-Wald" & mark== "pconf")
cdfLTb = subset(nndf, Heading == "Logit-Wald" & mark== "pbias")
cdfLRc = subset(nndf, Heading == "Likelihood" & mark== "pconf")
cdfLRb = subset(nndf, Heading == "Likelihood" & mark== "pbias")
cdfTWc = subset(nndf, Heading == "Wald-T" & mark== "pconf")
cdfTWb = subset(nndf, Heading == "Wald-T" & mark== "pbias")
ggplot2::ggplot(nndf, ggplot2::aes(x=x, y=val))+
ggplot2::labs(title = "p-Confidence & p-Bias - All methods") +
ggplot2::facet_wrap(Heading ~ mark,scales="free_y",ncol=2) +
ggplot2::labs(y = "Confidence ") +
ggplot2::labs(x = "No of successes") +
ggplot2::geom_line(data=cdfWDc,ggplot2::aes(color="pConf Wald"))+
ggplot2::geom_point(data=cdfWDc, ggplot2::aes(color="pConf Wald Values"),shape=22)+
ggplot2::geom_line(data=cdfWDb,ggplot2::aes(color="pbias Wald"))+
ggplot2::geom_point(data=cdfWDb,ggplot2::aes(color="pbias Wald Values"),shape=23)+
ggplot2::geom_line(data=cdfSCc,ggplot2::aes(color="pConf Score"))+
ggplot2::geom_point(data=cdfSCc, ggplot2::aes(color="pConf Score Values"),shape=21)+
ggplot2::geom_line(data=cdfSCb,ggplot2::aes(color="pbias Score"))+
ggplot2::geom_point(data=cdfSCb,ggplot2::aes(color="pbias Score Values"),shape=16)+
ggplot2::geom_line(data=cdfASc,ggplot2::aes(color="pConf ArcSine"))+
ggplot2::geom_point(data=cdfASc, ggplot2::aes(color="pConf ArcSine Values"),shape=22)+
ggplot2::geom_line(data=cdfASb,ggplot2::aes(color="pbias ArcSine"))+
ggplot2::geom_point(data=cdfASb,ggplot2::aes(color="pbias ArcSine Values"),shape=23)+
ggplot2::geom_line(data=cdfTWc,ggplot2::aes(color="pConf Wald-T"))+
ggplot2::geom_point(data=cdfTWc, ggplot2::aes(color="pConf Wald-T Values"),shape=21)+
ggplot2::geom_line(data=cdfTWb,ggplot2::aes(color="pbias Wald-T"))+
ggplot2::geom_point(data=cdfTWb,ggplot2::aes(color="pbias Wald-T Values"),shape=16)+
ggplot2::geom_line(data=cdfLRc,ggplot2::aes(color="pConf Likelihood"))+
ggplot2::geom_point(data=cdfLRc, ggplot2::aes(color="pConf Likelihood Values"),shape=22)+
ggplot2::geom_line(data=cdfLRb,ggplot2::aes(color="pbias Likelihood"))+
ggplot2::geom_point(data=cdfLRb,ggplot2::aes(color="pbias Likelihood Values"),shape=23)+
ggplot2::geom_line(data=cdfLTc,ggplot2::aes(color="pConf Logit-Wald"))+
ggplot2::geom_point(data=cdfLTc, ggplot2::aes(color="pConf Logit-Wald Values"),shape=21)+
ggplot2::geom_line(data=cdfLTb,ggplot2::aes(color="pbias Logit-Wald"))+
ggplot2::geom_point(data=cdfLTb,ggplot2::aes(color="pbias Logit-Wald Values"),shape=16)+
ggplot2::scale_colour_manual(name='Heading',
values=c(
'pConf Wald'='red',
'pConf Wald Values'='red',
'pbias Wald'='black',
'pbias Wald Values'='black',
'pConf Score'='red',
'pConf Score Values'='red',
'pbias Score'='black',
'pbias Score Values'='black',
'pConf ArcSine'='red',
'pConf ArcSine Values'='red',
'pbias ArcSine'='black',
'pbias ArcSine Values'='black',
'pConf Logit-Wald'='red',
'pConf Logit-Wald Values'='red',
'pbias Logit-Wald'='black',
'pbias Logit-Wald Values'='black',
'pConf Wald-T'='red',
'pConf Wald-T Values'='red',
'pbias Wald-T'='black',
'pbias Wald-T Values'='black',
'pConf Likelihood'='red',
'pConf Likelihood Values'='red',
'pbias Likelihood'='black',
'pbias Likelihood Values'='black'))
}
######################################################################
#' Plots p-confidence and p-bias for base Wald method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @details p-confidence and p-bias plots for base Wald method
#' @family p-confidence and p-bias of base methods
#' @examples
#' n=5; alp=0.05
#' PlotpCOpBIWD(n,alp)
#' @references
#' [1] 2005 Vos PW and Hudson S.
#' Evaluation Criteria for Discrete Confidence Intervals: Beyond Coverage and Length.
#' The American Statistician: 59; 137 - 142.
#' @export
PlotpCOpBIWD<-function(n,alp)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (alp>1 || alp<0 || length(alp)>1) stop("'alpha' has to be between 0 and 1")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
x=Value=Heading=mark=NULL
CBEX = pCOpBIWD(n,alp)
W1 = data.frame(x=CBEX$x1, Value=CBEX$pconf, Heading="pconf")
W2 = data.frame(x=CBEX$x1, Value=CBEX$pbias, Heading="pbias")
gdf=rbind(W1,W2)
ggplot2::ggplot(gdf, ggplot2::aes(x=x, y=Value)) +
ggplot2::facet_grid(Heading~.,scales="free_y") +
ggplot2::labs(title = "p-Confidence & p-Bias - Wald method") +
ggplot2::labs(y = "Confidence ") +
ggplot2::labs(x = "No of successes") +
ggplot2::geom_line(data=gdf,ggplot2::aes(x=x, y=Value))
}
######################################################################
#' Plots p-confidence and p-bias for base Score method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @details p-confidence and p-bias plots for base Score method
#' @family p-confidence and p-bias of base methods
#' @examples
#' n=5; alp=0.05
#' PlotpCOpBISC(n,alp)
#' @references
#' [1] 2005 Vos PW and Hudson S.
#' Evaluation Criteria for Discrete Confidence Intervals: Beyond Coverage and Length.
#' The American Statistician: 59; 137 - 142.
#' @export
PlotpCOpBISC<-function(n,alp)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (alp>1 || alp<0 || length(alp)>1) stop("'alpha' has to be between 0 and 1")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
x=Value=Heading=mark=NULL
CBEX = pCOpBISC(n,alp)
W1 = data.frame(x=CBEX$x1, Value=CBEX$pconf, Heading="pconf")
W2 = data.frame(x=CBEX$x1, Value=CBEX$pbias, Heading="pbias")
gdf=rbind(W1,W2)
ggplot2::ggplot(gdf, ggplot2::aes(x=x, y=Value)) +
ggplot2::facet_grid(Heading~.,scales="free_y") +
ggplot2::labs(title = "p-Confidence & p-Bias - Score method") +
ggplot2::labs(y = "Confidence ") +
ggplot2::labs(x = "No of successes") +
ggplot2::geom_line(data=gdf,ggplot2::aes(x=x, y=Value))
}
######################################################################
#' Plots p-confidence and p-bias for base ArcSine method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @details p-confidence and p-bias plots for base ArcSine method
#' @family p-confidence and p-bias of base methods
#' @examples
#' n=5; alp=0.05
#' PlotpCOpBIAS(n,alp)
#' @references
#' [1] 2005 Vos PW and Hudson S.
#' Evaluation Criteria for Discrete Confidence Intervals: Beyond Coverage and Length.
#' The American Statistician: 59; 137 - 142.
#' @export
PlotpCOpBIAS<-function(n,alp)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (alp>1 || alp<0 || length(alp)>1) stop("'alpha' has to be between 0 and 1")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
x=Value=Heading=mark=NULL
CBEX = pCOpBIAS(n,alp)
W1 = data.frame(x=CBEX$x1, Value=CBEX$pconf, Heading="pconf")
W2 = data.frame(x=CBEX$x1, Value=CBEX$pbias, Heading="pbias")
gdf=rbind(W1,W2)
ggplot2::ggplot(gdf, ggplot2::aes(x=x, y=Value)) +
ggplot2::facet_grid(Heading~.,scales="free_y") +
ggplot2::labs(title = "p-Confidence & p-Bias - ArcSine method") +
ggplot2::labs(y = "Confidence ") +
ggplot2::labs(x = "No of successes") +
ggplot2::geom_line(data=gdf,ggplot2::aes(x=x, y=Value))
}
######################################################################
#' Plots p-confidence and p-bias for base Logit Wald method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @details p-confidence and p-bias plots for base Logit Wald method
#' @family p-confidence and p-bias of base methods
#' @examples
#' n=5; alp=0.05
#' PlotpCOpBILT(n,alp)
#' @references
#' [1] 2005 Vos PW and Hudson S.
#' Evaluation Criteria for Discrete Confidence Intervals: Beyond Coverage and Length.
#' The American Statistician: 59; 137 - 142.
#' @export
PlotpCOpBILT<-function(n,alp)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (alp>1 || alp<0 || length(alp)>1) stop("'alpha' has to be between 0 and 1")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
x=Value=Heading=mark=NULL
CBEX = pCOpBILT(n,alp)
W1 = data.frame(x=CBEX$x1, Value=CBEX$pconf, Heading="pconf")
W2 = data.frame(x=CBEX$x1, Value=CBEX$pbias, Heading="pbias")
gdf=rbind(W1,W2)
ggplot2::ggplot(gdf, ggplot2::aes(x=x, y=Value)) +
ggplot2::facet_grid(Heading~.,scales="free_y") +
ggplot2::labs(title = "p-Confidence & p-Bias - Logit Wald method") +
ggplot2::labs(y = "Confidence ") +
ggplot2::labs(x = "No of successes") +
ggplot2::geom_line(data=gdf,ggplot2::aes(x=x, y=Value))
}
######################################################################
#' Plots p-confidence and p-bias for base Wald-T method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @details p-confidence and p-bias plots for base Wald-T method
#' @family p-confidence and p-bias of base methods
#' @examples
#' n=5; alp=0.05
#' PlotpCOpBITW(n,alp)
#' @references
#' [1] 2005 Vos PW and Hudson S.
#' Evaluation Criteria for Discrete Confidence Intervals: Beyond Coverage and Length.
#' The American Statistician: 59; 137 - 142.
#' @export
PlotpCOpBITW<-function(n,alp)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (alp>1 || alp<0 || length(alp)>1) stop("'alpha' has to be between 0 and 1")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
x=Value=Heading=mark=NULL
CBEX = pCOpBITW(n,alp)
W1 = data.frame(x=CBEX$x1, Value=CBEX$pconf, Heading="pconf")
W2 = data.frame(x=CBEX$x1, Value=CBEX$pbias, Heading="pbias")
gdf=rbind(W1,W2)
ggplot2::ggplot(gdf, ggplot2::aes(x=x, y=Value)) +
ggplot2::facet_grid(Heading~.,scales="free_y") +
ggplot2::labs(title = "p-Confidence & p-Bias - Wald-T method") +
ggplot2::labs(y = "Confidence ") +
ggplot2::labs(x = "No of successes") +
ggplot2::geom_line(data=gdf,ggplot2::aes(x=x, y=Value))
}
######################################################################
#' Plots p-confidence and p-bias for base Likelihood Ratio method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @details p-confidence and p-bias plots for base Likelihood Ratio method
#' @family p-confidence and p-bias of base methods
#' @examples
#' n=5; alp=0.05
#' PlotpCOpBILR(n,alp)
#' @references
#' [1] 2005 Vos PW and Hudson S.
#' Evaluation Criteria for Discrete Confidence Intervals: Beyond Coverage and Length.
#' The American Statistician: 59; 137 - 142.
#' @export
PlotpCOpBILR<-function(n,alp)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (alp>1 || alp<0 || length(alp)>1) stop("'alpha' has to be between 0 and 1")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
x=Value=Heading=mark=NULL
CBEX = pCOpBILR(n,alp)
W1 = data.frame(x=CBEX$x1, Value=CBEX$pconf, Heading="pconf")
W2 = data.frame(x=CBEX$x1, Value=CBEX$pbias, Heading="pbias")
gdf=rbind(W1,W2)
ggplot2::ggplot(gdf, ggplot2::aes(x=x, y=Value)) +
ggplot2::facet_grid(Heading~.,scales="free_y") +
ggplot2::labs(title = "p-Confidence & p-Bias - Likelihood Ratio method") +
ggplot2::labs(y = "Confidence ") +
ggplot2::labs(x = "No of successes") +
ggplot2::geom_line(data=gdf,ggplot2::aes(x=x, y=Value))
}
RajeswaranV/proportion documentation built on June 17, 2022, 9:11 a.m.
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Defines functions PlotpCOpBILR PlotpCOpBITW PlotpCOpBILT PlotpCOpBIAS PlotpCOpBISC PlotpCOpBIWD PlotpCOpBIAll PlotpCOpBIBA PlotpCOpBIEX
Documented in PlotpCOpBIAll PlotpCOpBIAS PlotpCOpBIBA PlotpCOpBIEX PlotpCOpBILR PlotpCOpBILT PlotpCOpBISC PlotpCOpBITW PlotpCOpBIWD
#################################################################################################
#' Plots of p-confidence and p-bias of Exact method given n and alpha level
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param e - Exact method indicator in [0, 1] {1: Clopper Pearson, 0.5: Mid P}
#' The input can also be a range of values between 0 and 1.
#' @details Evaluation of Confidence interval for \code{p} based on inverting equal-tailed
#' binomial tests with null hypothesis \eqn{H0: p = p0} using p-confidence and p-bias for
#' the \eqn{n + 1} intervals
#' @return A dataframe with
#' \describe{
#' \item{x1}{ Number of successes (positive samples)}
#' \item{pconf }{ p-Confidence}
#' \item{pbias }{ p-Bias}
#' }
#' @family p-confidence and p-bias of base methods
#' @examples
#' n=5; alp=0.05;e=0.5; # Mid-p
#' PlotpCOpBIEX(n,alp,e)
#' n=5; alp=0.05;e=1; #Clopper-Pearson
#' PlotpCOpBIEX(n,alp,e)
#' n=5; alp=0.05;e=c(0.1,0.5,0.95,1); #Range including Mid-p and Clopper-Pearson
#' PlotpCOpBIEX(n,alp,e)
#' @references
#' [1] 2005 Vos PW and Hudson S.
#' Evaluation Criteria for Discrete Confidence Intervals: Beyond Coverage and Length.
#' The American Statistician: 59; 137 - 142.
#' @export
##### 1.EXACT METHODS - p-confidence and p-bias for a given n and alpha level
PlotpCOpBIEX<-function(n,alp,e)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(e)) stop("'e' 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")
x=Value=Heading=mark=NULL
CBEX = pCOpBIEX(n,alp,e)
W1 = data.frame(x=CBEX$x1, Value=CBEX$pconf, Heading="pconf")
W2 = data.frame(x=CBEX$x1, Value=CBEX$pbias, Heading="pbias")
b1=data.frame(e=CBEX$e)
b2=data.frame(e=CBEX$e)
bjoin=rbind(b1,b2)
fg=rbind(W1,W2)
gdf=cbind(fg,bjoin)
gdf$e=as.factor(gdf$e)
ggplot2::ggplot(gdf, ggplot2::aes(x=x, y=Value, colour = e)) +
ggplot2::facet_grid(Heading~.,scales="free_y") +
ggplot2::labs(title = "p-Confidence & p-Bias - Exact method") +
ggplot2::labs(y = "Confidence ") +
ggplot2::labs(x = "No of successes") +
ggplot2::scale_colour_manual(values=c("blue", "red", "black", "cyan4", "deeppink",
"orange","chartreuse4",
"blueviolet" , "grey", "darksalmon", "tan1")) +
ggplot2::geom_line(data=gdf,ggplot2::aes(color=e))
}
#######################################################################################################
#' Plots p-confidence and p-bias for Bayesian method given n and alpha level and priors a & b
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param a1 - Shape parameter 1 for prior Beta distribution in Bayesian model
#' @param a2 - Shape parameter 2 for prior Beta distribution in Bayesian model
#' @details Evaluation of Bayesian Highest Probability Density (HPD) and two tailed
#' intervals using p-confidence and p-bias for the \eqn{n + 1} intervals for the
#' Beta - Binomial conjugate prior model for the probability of success \code{p}
#' @family p-confidence and p-bias of base methods
#' @examples
#' n=5; alp=0.05;a1=1;a2=1
#' PlotpCOpBIBA(n,alp,a1,a2)
#' @references
#' [1] 2005 Vos PW and Hudson S.
#' Evaluation Criteria for Discrete Confidence Intervals: Beyond Coverage and Length.
#' The American Statistician: 59; 137 - 142.
#' @export
####2.BAYESIAN p-confidence and p-bias
PlotpCOpBIBA<-function(n,alp,a1,a2)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' 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(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")
x=Value=Heading=mark=NULL
ndf=pCOpBIBA(n,alp,a1,a2)
W1 = data.frame(x=ndf$x1, Value=ndf$pconfQ, Heading="pconfQ")
W2 = data.frame(x=ndf$x1, Value=ndf$pbiasQ, Heading="pbiasQ")
W3 = data.frame(x=ndf$x1, Value=ndf$pconfH, Heading="pconfH")
W4 = data.frame(x=ndf$x1, Value=ndf$pbiasH, Heading="pbiasH")
nBA=rbind(W1,W2,W3,W4)
ggplot2::ggplot(nBA, ggplot2::aes(x=x, y=Value))+
ggplot2::labs(title = "p-Confidence & p-Bias - Bayesian methods") +
ggplot2::facet_grid(Heading ~ .,scales="free_y") +
ggplot2::labs(y = "Confidence ") +
ggplot2::labs(x = "No of successes") +
ggplot2::geom_line(data=W1,ggplot2::aes(color="pConf Quantile"))+
ggplot2::geom_point(data=W1, ggplot2::aes(color="pconfQ Values"),shape=22)+
ggplot2::geom_line(data=W2,ggplot2::aes(color="pbias Quantile"))+
ggplot2::geom_point(data=W2,ggplot2::aes(color="pbiasQ Values"),shape=23)+
ggplot2::geom_line(data=W3,ggplot2::aes(color="pConf HPD"))+
ggplot2::geom_point(data=W3, ggplot2::aes(color="pconfHPD Values"),shape=21)+
ggplot2::geom_line(data=W4,ggplot2::aes(color="pbias HPD"))+
ggplot2::geom_point(data=W4,ggplot2::aes(color="pbiasHPD Values"),shape=16)+
ggplot2::scale_colour_manual(name='Heading',
values=c('pConf Quantile'='red',
'pconfQ Values'='red',
'pbias Quantile'='blue',
'pbiasQ Values'='blue',
'pConf HPD'='orange',
'pconfHPD Values'='orange',
'pbias HPD'='black',
'pbiasHPD Values'='black'),
guide='Title') +
ggplot2::guides(colour = ggplot2::guide_legend(override.aes = list(linetype=c(1,1,1,1,1,1,1,1),
shape=c(NA, NA,16,23,NA,NA,21,22))))
}
###########################################################################################################3
#' Plots p-confidence and p-bias for a given n and alpha level for
#' 6 base methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine)
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @details p-confidence and p-bias plots for 6 base methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine)
#' @family p-confidence and p-bias of base methods
#' @examples
#' \dontrun{
#' n=5; alp=0.05
#' PlotpCOpBIAll(n,alp)
#' }
#' @references
#' [1] 2005 Vos PW and Hudson S.
#' Evaluation Criteria for Discrete Confidence Intervals: Beyond Coverage and Length.
#' The American Statistician: 59; 137 - 142.
#' @export
####3.All methods plots of p-confidence and p-bias
PlotpCOpBIAll<-function(n,alp)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (alp>1 || alp<0 || length(alp)>1) stop("'alpha' has to be between 0 and 1")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
x=Value=Heading=mark=val=NULL
nAll = pCOpBIAll(n,alp)
pc=data.frame(x=nAll$x1, val=nAll$pconf, Heading=nAll$method, mark="pconf",minmax=min(nAll$pbias, nAll$pconf))
pb=data.frame(x=nAll$x1, val=nAll$pbias, Heading=nAll$method, mark="pbias",minmax=max(nAll$pbias, nAll$pconf))
nndf=rbind(pc,pb)
cdfWDc = subset(nndf, Heading == "Wald" & mark== "pconf")
cdfWDb = subset(nndf, Heading == "Wald" & mark== "pbias")
cdfSCc = subset(nndf, Heading == "Score" & mark== "pconf")
cdfSCb = subset(nndf, Heading == "Score" & mark== "pbias")
cdfASc = subset(nndf, Heading == "ArcSine" & mark== "pconf")
cdfASb = subset(nndf, Heading == "ArcSine" & mark== "pbias")
cdfLTc = subset(nndf, Heading == "Logit-Wald" & mark== "pconf")
cdfLTb = subset(nndf, Heading == "Logit-Wald" & mark== "pbias")
cdfLRc = subset(nndf, Heading == "Likelihood" & mark== "pconf")
cdfLRb = subset(nndf, Heading == "Likelihood" & mark== "pbias")
cdfTWc = subset(nndf, Heading == "Wald-T" & mark== "pconf")
cdfTWb = subset(nndf, Heading == "Wald-T" & mark== "pbias")
ggplot2::ggplot(nndf, ggplot2::aes(x=x, y=val))+
ggplot2::labs(title = "p-Confidence & p-Bias - All methods") +
ggplot2::facet_wrap(Heading ~ mark,scales="free_y",ncol=2) +
ggplot2::labs(y = "Confidence ") +
ggplot2::labs(x = "No of successes") +
ggplot2::geom_line(data=cdfWDc,ggplot2::aes(color="pConf Wald"))+
ggplot2::geom_point(data=cdfWDc, ggplot2::aes(color="pConf Wald Values"),shape=22)+
ggplot2::geom_line(data=cdfWDb,ggplot2::aes(color="pbias Wald"))+
ggplot2::geom_point(data=cdfWDb,ggplot2::aes(color="pbias Wald Values"),shape=23)+
ggplot2::geom_line(data=cdfSCc,ggplot2::aes(color="pConf Score"))+
ggplot2::geom_point(data=cdfSCc, ggplot2::aes(color="pConf Score Values"),shape=21)+
ggplot2::geom_line(data=cdfSCb,ggplot2::aes(color="pbias Score"))+
ggplot2::geom_point(data=cdfSCb,ggplot2::aes(color="pbias Score Values"),shape=16)+
ggplot2::geom_line(data=cdfASc,ggplot2::aes(color="pConf ArcSine"))+
ggplot2::geom_point(data=cdfASc, ggplot2::aes(color="pConf ArcSine Values"),shape=22)+
ggplot2::geom_line(data=cdfASb,ggplot2::aes(color="pbias ArcSine"))+
ggplot2::geom_point(data=cdfASb,ggplot2::aes(color="pbias ArcSine Values"),shape=23)+
ggplot2::geom_line(data=cdfTWc,ggplot2::aes(color="pConf Wald-T"))+
ggplot2::geom_point(data=cdfTWc, ggplot2::aes(color="pConf Wald-T Values"),shape=21)+
ggplot2::geom_line(data=cdfTWb,ggplot2::aes(color="pbias Wald-T"))+
ggplot2::geom_point(data=cdfTWb,ggplot2::aes(color="pbias Wald-T Values"),shape=16)+
ggplot2::geom_line(data=cdfLRc,ggplot2::aes(color="pConf Likelihood"))+
ggplot2::geom_point(data=cdfLRc, ggplot2::aes(color="pConf Likelihood Values"),shape=22)+
ggplot2::geom_line(data=cdfLRb,ggplot2::aes(color="pbias Likelihood"))+
ggplot2::geom_point(data=cdfLRb,ggplot2::aes(color="pbias Likelihood Values"),shape=23)+
ggplot2::geom_line(data=cdfLTc,ggplot2::aes(color="pConf Logit-Wald"))+
ggplot2::geom_point(data=cdfLTc, ggplot2::aes(color="pConf Logit-Wald Values"),shape=21)+
ggplot2::geom_line(data=cdfLTb,ggplot2::aes(color="pbias Logit-Wald"))+
ggplot2::geom_point(data=cdfLTb,ggplot2::aes(color="pbias Logit-Wald Values"),shape=16)+
ggplot2::scale_colour_manual(name='Heading',
values=c(
'pConf Wald'='red',
'pConf Wald Values'='red',
'pbias Wald'='black',
'pbias Wald Values'='black',
'pConf Score'='red',
'pConf Score Values'='red',
'pbias Score'='black',
'pbias Score Values'='black',
'pConf ArcSine'='red',
'pConf ArcSine Values'='red',
'pbias ArcSine'='black',
'pbias ArcSine Values'='black',
'pConf Logit-Wald'='red',
'pConf Logit-Wald Values'='red',
'pbias Logit-Wald'='black',
'pbias Logit-Wald Values'='black',
'pConf Wald-T'='red',
'pConf Wald-T Values'='red',
'pbias Wald-T'='black',
'pbias Wald-T Values'='black',
'pConf Likelihood'='red',
'pConf Likelihood Values'='red',
'pbias Likelihood'='black',
'pbias Likelihood Values'='black'))
}
######################################################################
#' Plots p-confidence and p-bias for base Wald method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @details p-confidence and p-bias plots for base Wald method
#' @family p-confidence and p-bias of base methods
#' @examples
#' n=5; alp=0.05
#' PlotpCOpBIWD(n,alp)
#' @references
#' [1] 2005 Vos PW and Hudson S.
#' Evaluation Criteria for Discrete Confidence Intervals: Beyond Coverage and Length.
#' The American Statistician: 59; 137 - 142.
#' @export
PlotpCOpBIWD<-function(n,alp)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (alp>1 || alp<0 || length(alp)>1) stop("'alpha' has to be between 0 and 1")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
x=Value=Heading=mark=NULL
CBEX = pCOpBIWD(n,alp)
W1 = data.frame(x=CBEX$x1, Value=CBEX$pconf, Heading="pconf")
W2 = data.frame(x=CBEX$x1, Value=CBEX$pbias, Heading="pbias")
gdf=rbind(W1,W2)
ggplot2::ggplot(gdf, ggplot2::aes(x=x, y=Value)) +
ggplot2::facet_grid(Heading~.,scales="free_y") +
ggplot2::labs(title = "p-Confidence & p-Bias - Wald method") +
ggplot2::labs(y = "Confidence ") +
ggplot2::labs(x = "No of successes") +
ggplot2::geom_line(data=gdf,ggplot2::aes(x=x, y=Value))
}
######################################################################
#' Plots p-confidence and p-bias for base Score method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @details p-confidence and p-bias plots for base Score method
#' @family p-confidence and p-bias of base methods
#' @examples
#' n=5; alp=0.05
#' PlotpCOpBISC(n,alp)
#' @references
#' [1] 2005 Vos PW and Hudson S.
#' Evaluation Criteria for Discrete Confidence Intervals: Beyond Coverage and Length.
#' The American Statistician: 59; 137 - 142.
#' @export
PlotpCOpBISC<-function(n,alp)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (alp>1 || alp<0 || length(alp)>1) stop("'alpha' has to be between 0 and 1")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
x=Value=Heading=mark=NULL
CBEX = pCOpBISC(n,alp)
W1 = data.frame(x=CBEX$x1, Value=CBEX$pconf, Heading="pconf")
W2 = data.frame(x=CBEX$x1, Value=CBEX$pbias, Heading="pbias")
gdf=rbind(W1,W2)
ggplot2::ggplot(gdf, ggplot2::aes(x=x, y=Value)) +
ggplot2::facet_grid(Heading~.,scales="free_y") +
ggplot2::labs(title = "p-Confidence & p-Bias - Score method") +
ggplot2::labs(y = "Confidence ") +
ggplot2::labs(x = "No of successes") +
ggplot2::geom_line(data=gdf,ggplot2::aes(x=x, y=Value))
}
######################################################################
#' Plots p-confidence and p-bias for base ArcSine method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @details p-confidence and p-bias plots for base ArcSine method
#' @family p-confidence and p-bias of base methods
#' @examples
#' n=5; alp=0.05
#' PlotpCOpBIAS(n,alp)
#' @references
#' [1] 2005 Vos PW and Hudson S.
#' Evaluation Criteria for Discrete Confidence Intervals: Beyond Coverage and Length.
#' The American Statistician: 59; 137 - 142.
#' @export
PlotpCOpBIAS<-function(n,alp)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (alp>1 || alp<0 || length(alp)>1) stop("'alpha' has to be between 0 and 1")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
x=Value=Heading=mark=NULL
CBEX = pCOpBIAS(n,alp)
W1 = data.frame(x=CBEX$x1, Value=CBEX$pconf, Heading="pconf")
W2 = data.frame(x=CBEX$x1, Value=CBEX$pbias, Heading="pbias")
gdf=rbind(W1,W2)
ggplot2::ggplot(gdf, ggplot2::aes(x=x, y=Value)) +
ggplot2::facet_grid(Heading~.,scales="free_y") +
ggplot2::labs(title = "p-Confidence & p-Bias - ArcSine method") +
ggplot2::labs(y = "Confidence ") +
ggplot2::labs(x = "No of successes") +
ggplot2::geom_line(data=gdf,ggplot2::aes(x=x, y=Value))
}
######################################################################
#' Plots p-confidence and p-bias for base Logit Wald method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @details p-confidence and p-bias plots for base Logit Wald method
#' @family p-confidence and p-bias of base methods
#' @examples
#' n=5; alp=0.05
#' PlotpCOpBILT(n,alp)
#' @references
#' [1] 2005 Vos PW and Hudson S.
#' Evaluation Criteria for Discrete Confidence Intervals: Beyond Coverage and Length.
#' The American Statistician: 59; 137 - 142.
#' @export
PlotpCOpBILT<-function(n,alp)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (alp>1 || alp<0 || length(alp)>1) stop("'alpha' has to be between 0 and 1")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
x=Value=Heading=mark=NULL
CBEX = pCOpBILT(n,alp)
W1 = data.frame(x=CBEX$x1, Value=CBEX$pconf, Heading="pconf")
W2 = data.frame(x=CBEX$x1, Value=CBEX$pbias, Heading="pbias")
gdf=rbind(W1,W2)
ggplot2::ggplot(gdf, ggplot2::aes(x=x, y=Value)) +
ggplot2::facet_grid(Heading~.,scales="free_y") +
ggplot2::labs(title = "p-Confidence & p-Bias - Logit Wald method") +
ggplot2::labs(y = "Confidence ") +
ggplot2::labs(x = "No of successes") +
ggplot2::geom_line(data=gdf,ggplot2::aes(x=x, y=Value))
}
######################################################################
#' Plots p-confidence and p-bias for base Wald-T method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @details p-confidence and p-bias plots for base Wald-T method
#' @family p-confidence and p-bias of base methods
#' @examples
#' n=5; alp=0.05
#' PlotpCOpBITW(n,alp)
#' @references
#' [1] 2005 Vos PW and Hudson S.
#' Evaluation Criteria for Discrete Confidence Intervals: Beyond Coverage and Length.
#' The American Statistician: 59; 137 - 142.
#' @export
PlotpCOpBITW<-function(n,alp)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (alp>1 || alp<0 || length(alp)>1) stop("'alpha' has to be between 0 and 1")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
x=Value=Heading=mark=NULL
CBEX = pCOpBITW(n,alp)
W1 = data.frame(x=CBEX$x1, Value=CBEX$pconf, Heading="pconf")
W2 = data.frame(x=CBEX$x1, Value=CBEX$pbias, Heading="pbias")
gdf=rbind(W1,W2)
ggplot2::ggplot(gdf, ggplot2::aes(x=x, y=Value)) +
ggplot2::facet_grid(Heading~.,scales="free_y") +
ggplot2::labs(title = "p-Confidence & p-Bias - Wald-T method") +
ggplot2::labs(y = "Confidence ") +
ggplot2::labs(x = "No of successes") +
ggplot2::geom_line(data=gdf,ggplot2::aes(x=x, y=Value))
}
######################################################################
#' Plots p-confidence and p-bias for base Likelihood Ratio method
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @details p-confidence and p-bias plots for base Likelihood Ratio method
#' @family p-confidence and p-bias of base methods
#' @examples
#' n=5; alp=0.05
#' PlotpCOpBILR(n,alp)
#' @references
#' [1] 2005 Vos PW and Hudson S.
#' Evaluation Criteria for Discrete Confidence Intervals: Beyond Coverage and Length.
#' The American Statistician: 59; 137 - 142.
#' @export
PlotpCOpBILR<-function(n,alp)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (alp>1 || alp<0 || length(alp)>1) stop("'alpha' has to be between 0 and 1")
if ((class(n) != "integer") & (class(n) != "numeric") || length(n) >1|| n<=0 ) stop("'n' has to be greater than 0")
x=Value=Heading=mark=NULL
CBEX = pCOpBILR(n,alp)
W1 = data.frame(x=CBEX$x1, Value=CBEX$pconf, Heading="pconf")
W2 = data.frame(x=CBEX$x1, Value=CBEX$pbias, Heading="pbias")
gdf=rbind(W1,W2)
ggplot2::ggplot(gdf, ggplot2::aes(x=x, y=Value)) +
ggplot2::facet_grid(Heading~.,scales="free_y") +
ggplot2::labs(title = "p-Confidence & p-Bias - Likelihood Ratio method") +
ggplot2::labs(y = "Confidence ") +
ggplot2::labs(x = "No of successes") +
ggplot2::geom_line(data=gdf,ggplot2::aes(x=x, y=Value))
}
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