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R/122.ConfidenceIntervals_CC_n_Graph.R
In proportion: Inference on Single Binomial Proportion and Bayesian Computations
Defines functions
PlotciCAll
PlotciCAllg
PlotciCWD
PlotciCAS
PlotciCSC
PlotciCLT
PlotciCTW
Documented in
PlotciCAll
PlotciCAllg
PlotciCAS
PlotciCLT
PlotciCSC
PlotciCTW
PlotciCWD
#####################################################################################
#' Plots the CI estimation of 5 continuity corrected methods (Wald, Wald-T, Score, Logit-Wald, ArcSine) given n, alp and c
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param c - Continuity correction
#' @details Plots the Confidence Interval for 5 continuity corrected methods (Wald, Wald-T, Score, Logit-Wald, ArcSine) for \code{n} given \code{alp} along with Continuity correction \code{c}
#' @family Continuity correction methods of CI estimation
#' @examples
#' n=5; alp=0.05;c=1/(2*n)
#' PlotciCAll(n,alp,c)
#' @export
#7. Plot all CC methods
PlotciCAll<-function(n,alp,c)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(c)) stop("'c' 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") || n<=0 ) stop("'n' has to be greater than 0")
if (c<=0 || c>(1/(2*n))) stop("'c' has to be positive and less than or equal to 1/(2*n)")
Abberation=ID=Value=method=LowerLimit=UpperLimit=LowerAbb=UpperAbb=ZWI=NULL
ss1=ciCAll(n,alp,c)
id=1:nrow(ss1)
ss= data.frame(ID=id,ss1)
ll=subset(ss, LowerAbb=="YES")
ul=subset(ss, UpperAbb=="YES")
zl=subset(ss, ZWI=="YES")
if (nrow(ll)>0) {
ll=ll[,c(1,4)];
ll$Abberation="Lower";
colnames(ll)<-c("ID","Value","Abberation")}
if (nrow(ul)>0){
ul=ul[,c(1,5)]
ul$Abberation="Upper"
colnames(ul)<-c("ID","Value","Abberation")
}
if (nrow(zl)>0){
zl=zl[,c(1,4)]
zl$Abberation="ZWI"
colnames(zl)<-c("ID","Value","Abberation")
}
ldf= rbind(ll,ul,zl)
if(nrow(ldf)>0){
oo= ggplot2::ggplot()+
ggplot2::ggtitle("Confidence interval for continuity corrected methods") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit,
color= method),
size = 0.5)+
ggplot2::geom_point(data=ldf,
ggplot2::aes(x=Value, y=ID,
group = Abberation,shape=Abberation),
size = 4, fill = "red") +
ggplot2::scale_fill_manual(values=c("blue", "cyan4", "red", "black", "orange","brown")) +
ggplot2::scale_colour_manual(values=c("brown", "black", "blue", "cyan4", "red", "orange")) +
ggplot2::scale_shape_manual(values=c(21,22,23))
}
else {
oo= ggplot2::ggplot()+
ggplot2::ggtitle("Confidence interval for continuity corrected methods") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit, color= method),
size = 0.5)
}
oo
}
#####################################################################################
#' Plots the CI estimation of 5 continuity corrected methods (Wald, Wald-T, Score, Logit-Wald, ArcSine) grouped by x value
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param c - Continuity correction
#' @details Plots the Confidence Interval for 5 continuity corrected methods (Wald, Wald-T, Score, Logit-Wald, ArcSine) grouped by x for \code{n} given \code{alp} along with Continuity correction \code{c}
#' @family Continuity correction methods of CI estimation
#' @examples
#' n=5; alp=0.05; c=1/(2*n)
#' PlotciCAllg(n,alp,c)
#' @export
#8.All methods plots with grouping
PlotciCAllg<-function(n,alp,c)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(c)) stop("'c' 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") || n<=0 ) stop("'n' has to be greater than 0")
if (c<=0 || c>(1/(2*n))) stop("'c' has to be positive and less than or equal to 1/(2*n)")
Abberation=ID=Value=method=val1=val2=LowerLimit=UpperLimit=LowerAbb=UpperAbb=ZWI=NULL
ss1=ciCAll(n,alp,c)
nss= ss1[order(ss1$x, (ss1$UpperLimit-ss1$LowerLimit)),]
id=1:nrow(ss1)
ss= data.frame(ID=id,nss)
ll=subset(ss, LowerAbb=="YES")
ul=subset(ss, UpperAbb=="YES")
zl=subset(ss, ZWI=="YES")
if (nrow(ll)>0) {
ll=ll[,c(1,4)];
ll$Abberation="Lower";
colnames(ll)<-c("ID","Value","Abberation")}
if (nrow(ul)>0){
ul=ul[,c(1,5)]
ul$Abberation="Upper"
colnames(ul)<-c("ID","Value","Abberation")
}
if (nrow(zl)>0){
zl=zl[,c(1,4)]
zl$Abberation="ZWI"
colnames(zl)<-c("ID","Value","Abberation")
}
ldf= rbind(ll,ul,zl)
if((max(as.numeric(unique(ss$method)))-nrow(ss))==0){
if(nrow(ldf)>0){
oo= ggplot2::ggplot()+
ggplot2::ggtitle("Confidence interval for continuity corrected methods sorted by x") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit,
color= method),
size = 0.5)+
ggplot2::geom_point(data=ldf,
ggplot2::aes(x=Value, y=ID,
group = Abberation,shape=Abberation),
size = 4, fill = "red") +
ggplot2::scale_fill_manual(values=c("blue", "cyan4", "red", "black", "orange","brown")) +
ggplot2::scale_colour_manual(values=c("brown", "black", "blue", "cyan4", "red", "orange")) +
ggplot2::scale_shape_manual(values=c(21,22,23))
}
else {
oo= ggplot2::ggplot()+
ggplot2::ggtitle("Confidence interval for Continuity corrected methods sorted by x") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit, color= method),
size = 0.5)
}
oo
}
else {
ff= data.frame(val1=seq(0.5,max(ss$ID),by=(max(ss$ID)/(max(ss$x)+1))),val2=(0:max(ss$x)))
if(nrow(ldf)>0){
oo= ggplot2::ggplot()+
ggplot2::ggtitle("Confidence interval for Continuity corrected methods sorted by x") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit,
color= method),
size = 0.5)+
ggplot2::geom_point(data=ldf,
ggplot2::aes(x=Value, y=ID,
group = Abberation,shape=Abberation),
size = 4, fill = "red") +
ggplot2::scale_fill_manual(values=c("blue", "cyan4", "red", "black", "orange","brown")) +
ggplot2::scale_colour_manual(values=c("brown", "black", "blue", "cyan4", "red", "orange")) +
ggplot2::scale_shape_manual(values=c(21,22,23)) +
ggplot2::geom_hline(ggplot2::aes(yintercept=val1),data=ff) +
ggplot2::geom_text(ggplot2::aes(0,val1,label = paste("x=", sep="", val2),hjust=1.1, vjust = -1), data=ff)
}
else {
oo= ggplot2::ggplot()+
ggplot2::ggtitle("Confidence interval for Continuity corrected methods sorted by x") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit, color= method),
size = 0.5) +
ggplot2::geom_hline(ggplot2::aes(yintercept=val1),data=ff) +
ggplot2::geom_text(ggplot2::aes(0,val1,label = paste("x=", sep="", val2),hjust=1.1, vjust = -1), data=ff)
}
oo
}
}
#####################################################################################
#' Plots the CI estimation of continuity corrected Wald method given n, alp and c
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param c - Continuity correction
#' @details Plots the Confidence Interval for continuity corrected Wald method
#' for \code{n} given \code{alp} along with Continuity correction \code{c}
#' @family Continuity correction methods of CI estimation
#' @examples
#' n=5; alp=0.05;c=1/(2*n)
#' PlotciCWD(n,alp,c)
#' @export
#7. Plot all CC methods
PlotciCWD<-function(n,alp,c)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(c)) stop("'c' 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 (c<0 || length(c)>1) stop("'c' has to be positive")
Abberation=ID=Value=LowerLimit=UpperLimit=LowerAbb=UpperAbb=ZWI=NULL
WaldCI.df = ciCWD(n,alp,c)
ss1 = data.frame( x=WaldCI.df$x, LowerLimit = WaldCI.df$LCW, UpperLimit = WaldCI.df$UCW, LowerAbb = WaldCI.df$LABB, UpperAbb = WaldCI.df$UABB, ZWI = WaldCI.df$ZWI)
id=1:nrow(ss1)
ss= data.frame(ID=id,ss1)
ll=subset(ss, LowerAbb=="YES")
ul=subset(ss, UpperAbb=="YES")
zl=subset(ss, ZWI=="YES")
if (nrow(ll)>0) {
ll=ll[,c(1,3)];
ll$Abberation="Lower";
colnames(ll)<-c("ID","Value","Abberation")}
if (nrow(ul)>0){
ul=ul[,c(1,4)]
ul$Abberation="Upper"
colnames(ul)<-c("ID","Value","Abberation")
}
if (nrow(zl)>0){
zl=zl[,c(1,3)]
zl$Abberation="ZWI"
colnames(zl)<-c("ID","Value","Abberation")
}
ldf= rbind(ll,ul,zl)
if(nrow(ldf)>0){
oo= ggplot2::ggplot()+
ggplot2::labs(title = "Confidence Interval - Continuity corrected Wald method") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::labs(y = "ID") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit),
size = 0.5)+
ggplot2::geom_point(data=ldf,
ggplot2::aes(x=Value, y=ID,
group = Abberation,shape=Abberation),
size = 4, fill = "red") +
ggplot2::scale_shape_manual(values=c(21,22,23))
}
else {
oo= ggplot2::ggplot()+
ggplot2::labs(title = "Confidence Interval - Continuity corrected Wald method") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::labs(y = "ID") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit),
size = 0.5)
}
oo
}
#####################################################################################
#' Plots the CI estimation of continuity corrected ArcSine method given n, alp and c
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param c - Continuity correction
#' @details Plots the Confidence Interval for continuity corrected ArcSine method
#' for \code{n} given \code{alp} along with Continuity correction \code{c}
#' @family Continuity correction methods of CI estimation
#' @examples
#' n=5; alp=0.05;c=1/(2*n)
#' PlotciCAS(n,alp,c)
#' @export
PlotciCAS<-function(n,alp,c)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(c)) stop("'c' 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 (c<0 || length(c)>1) stop("'c' has to be positive")
Abberation=ID=Value=LowerLimit=UpperLimit=LowerAbb=UpperAbb=ZWI=NULL
ArcSineCI.df = ciCAS(n,alp,c)
ss1 = data.frame(x=ArcSineCI.df$x, LowerLimit = ArcSineCI.df$LCA, UpperLimit = ArcSineCI.df$UCA, LowerAbb = ArcSineCI.df$LABB, UpperAbb = ArcSineCI.df$UABB, ZWI = ArcSineCI.df$ZWI)
id=1:nrow(ss1)
ss= data.frame(ID=id,ss1)
ll=subset(ss, LowerAbb=="YES")
ul=subset(ss, UpperAbb=="YES")
zl=subset(ss, ZWI=="YES")
if (nrow(ll)>0) {
ll=ll[,c(1,3)];
ll$Abberation="Lower";
colnames(ll)<-c("ID","Value","Abberation")}
if (nrow(ul)>0){
ul=ul[,c(1,4)]
ul$Abberation="Upper"
colnames(ul)<-c("ID","Value","Abberation")
}
if (nrow(zl)>0){
zl=zl[,c(1,3)]
zl$Abberation="ZWI"
colnames(zl)<-c("ID","Value","Abberation")
}
ldf= rbind(ll,ul,zl)
if(nrow(ldf)>0){
oo= ggplot2::ggplot()+
ggplot2::labs(title = "Confidence Interval - Continuity corrected ArcSine method") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::labs(y = "ID") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit),
size = 0.5)+
ggplot2::geom_point(data=ldf,
ggplot2::aes(x=Value, y=ID,
group = Abberation,shape=Abberation),
size = 4, fill = "red") +
ggplot2::scale_shape_manual(values=c(21,22,23))
}
else {
oo= ggplot2::ggplot()+
ggplot2::labs(title = "Confidence Interval - Continuity corrected ArcSine method") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::labs(y = "ID") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit),
size = 0.5)
}
oo
}
#####################################################################################
#' Plots the CI estimation of continuity corrected Score method given n, alp and c
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param c - Continuity correction
#' @details Plots the Confidence Interval for continuity corrected Score method
#' for \code{n} given \code{alp} along with Continuity correction \code{c}
#' @family Continuity correction methods of CI estimation
#' @examples
#' n=5; alp=0.05;c=1/(2*n)
#' PlotciCSC(n,alp,c)
#' @export
#7. Plot all CC methods
PlotciCSC<-function(n,alp,c)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(c)) stop("'c' 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 (c<0 || length(c)>1) stop("'c' has to be positive")
Abberation=ID=Value=LowerLimit=UpperLimit=LowerAbb=UpperAbb=ZWI=NULL
ScoreCI.df = ciCSC(n,alp,c)
ss1 = data.frame(x=ScoreCI.df$x, LowerLimit = ScoreCI.df$LCS, UpperLimit = ScoreCI.df$UCS, LowerAbb = ScoreCI.df$LABB, UpperAbb = ScoreCI.df$UABB, ZWI = ScoreCI.df$ZWI)
id=1:nrow(ss1)
ss= data.frame(ID=id,ss1)
ll=subset(ss, LowerAbb=="YES")
ul=subset(ss, UpperAbb=="YES")
zl=subset(ss, ZWI=="YES")
if (nrow(ll)>0) {
ll=ll[,c(1,3)];
ll$Abberation="Lower";
colnames(ll)<-c("ID","Value","Abberation")}
if (nrow(ul)>0){
ul=ul[,c(1,4)]
ul$Abberation="Upper"
colnames(ul)<-c("ID","Value","Abberation")
}
if (nrow(zl)>0){
zl=zl[,c(1,3)]
zl$Abberation="ZWI"
colnames(zl)<-c("ID","Value","Abberation")
}
ldf= rbind(ll,ul,zl)
if(nrow(ldf)>0){
oo= ggplot2::ggplot()+
ggplot2::labs(title = "Confidence Interval - Continuity corrected Score method") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::labs(y = "ID") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit),
size = 0.5)+
ggplot2::geom_point(data=ldf,
ggplot2::aes(x=Value, y=ID,
group = Abberation,shape=Abberation),
size = 4, fill = "red") +
ggplot2::scale_shape_manual(values=c(21,22,23))
}
else {
oo= ggplot2::ggplot()+
ggplot2::labs(title = "Confidence Interval - Continuity corrected Score method") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::labs(y = "ID") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit),
size = 0.5)
}
oo
}
#####################################################################################
#' Plots the CI estimation of continuity corrected Logit Wald method given n, alp and c
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param c - Continuity correction
#' @details Plots the Confidence Interval for continuity corrected Logit Wald method
#' for \code{n} given \code{alp} along with Continuity correction \code{c}
#' @family Continuity correction methods of CI estimation
#' @examples
#' n=5; alp=0.05;c=1/(2*n)
#' PlotciCLT(n,alp,c)
#' @export
#7. Plot all CC methods
PlotciCLT<-function(n,alp,c)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(c)) stop("'c' 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(c) != "integer") & (class(c) != "numeric") || length(c) >1 || c<0 ) stop("'c' has to be positive")
Abberation=ID=Value=LowerLimit=UpperLimit=LowerAbb=UpperAbb=ZWI=NULL
WaldLCI.df = ciCLT(n,alp,c)
ss1= data.frame(x=WaldLCI.df$x, LowerLimit = WaldLCI.df$LCLT, UpperLimit = WaldLCI.df$UCLT, LowerAbb = WaldLCI.df$LABB, UpperAbb = WaldLCI.df$UABB, ZWI = WaldLCI.df$ZWI)
id=1:nrow(ss1)
ss= data.frame(ID=id,ss1)
ll=subset(ss, LowerAbb=="YES")
ul=subset(ss, UpperAbb=="YES")
zl=subset(ss, ZWI=="YES")
if (nrow(ll)>0) {
ll=ll[,c(1,3)];
ll$Abberation="Lower";
colnames(ll)<-c("ID","Value","Abberation")}
if (nrow(ul)>0){
ul=ul[,c(1,4)]
ul$Abberation="Upper"
colnames(ul)<-c("ID","Value","Abberation")
}
if (nrow(zl)>0){
zl=zl[,c(1,3)]
zl$Abberation="ZWI"
colnames(zl)<-c("ID","Value","Abberation")
}
ldf= rbind(ll,ul,zl)
if(nrow(ldf)>0){
oo= ggplot2::ggplot()+
ggplot2::labs(title = "Confidence Interval - Continuity corrected Logit Wald method") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::labs(y = "ID") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit),
size = 0.5)+
ggplot2::geom_point(data=ldf,
ggplot2::aes(x=Value, y=ID,
group = Abberation,shape=Abberation),
size = 4, fill = "red") +
ggplot2::scale_shape_manual(values=c(21,22,23))
}
else {
oo= ggplot2::ggplot()+
ggplot2::labs(title = "Confidence Interval - Continuity corrected Logit Wald method") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::labs(y = "ID") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit),
size = 0.5)
}
oo
}
#####################################################################################
#' Plots the CI estimation of continuity corrected Wald-T method given n, alp and c
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param c - Continuity correction
#' @details Plots the Confidence Interval for continuity corrected Wald-T method
#' for \code{n} given \code{alp} along with Continuity correction \code{c}
#' @family Continuity correction methods of CI estimation
#' @examples
#' n=5; alp=0.05;c=1/(2*n)
#' PlotciCTW(n,alp,c)
#' @export
#7. Plot all CC methods
PlotciCTW<-function(n,alp,c)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(c)) stop("'c' 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(c) != "integer") & (class(c) != "numeric") || length(c) >1 || c<0 ) stop("'c' has to be positive")
Abberation=ID=Value=LowerLimit=UpperLimit=LowerAbb=UpperAbb=ZWI=NULL
WaldTCI.df = ciCTW(n,alp,c)
ss1 = data.frame( x=WaldTCI.df$x, LowerLimit = WaldTCI.df$LCTW, UpperLimit = WaldTCI.df$UCTW, LowerAbb = WaldTCI.df$LABB, UpperAbb = WaldTCI.df$UABB, ZWI = WaldTCI.df$ZWI)
id=1:nrow(ss1)
ss= data.frame(ID=id,ss1)
ll=subset(ss, LowerAbb=="YES")
ul=subset(ss, UpperAbb=="YES")
zl=subset(ss, ZWI=="YES")
if (nrow(ll)>0) {
ll=ll[,c(1,3)];
ll$Abberation="Lower";
colnames(ll)<-c("ID","Value","Abberation")}
if (nrow(ul)>0){
ul=ul[,c(1,4)]
ul$Abberation="Upper"
colnames(ul)<-c("ID","Value","Abberation")
}
if (nrow(zl)>0){
zl=zl[,c(1,3)]
zl$Abberation="ZWI"
colnames(zl)<-c("ID","Value","Abberation")
}
ldf= rbind(ll,ul,zl)
if(nrow(ldf)>0){
oo= ggplot2::ggplot()+
ggplot2::labs(title = "Confidence Interval - Continuity corrected Wald-T method") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::labs(y = "ID") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit),
size = 0.5)+
ggplot2::geom_point(data=ldf,
ggplot2::aes(x=Value, y=ID,
group = Abberation,shape=Abberation),
size = 4, fill = "red") +
ggplot2::scale_shape_manual(values=c(21,22,23))
}
else {
oo= ggplot2::ggplot()+
ggplot2::labs(title = "Confidence Interval - Continuity corrected Wald-T method") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::labs(y = "ID") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit),
size = 0.5)
}
oo
}
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Defines functions PlotciCAll PlotciCAllg PlotciCWD PlotciCAS PlotciCSC PlotciCLT PlotciCTW
Documented in PlotciCAll PlotciCAllg PlotciCAS PlotciCLT PlotciCSC PlotciCTW PlotciCWD
#####################################################################################
#' Plots the CI estimation of 5 continuity corrected methods (Wald, Wald-T, Score, Logit-Wald, ArcSine) given n, alp and c
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param c - Continuity correction
#' @details Plots the Confidence Interval for 5 continuity corrected methods (Wald, Wald-T, Score, Logit-Wald, ArcSine) for \code{n} given \code{alp} along with Continuity correction \code{c}
#' @family Continuity correction methods of CI estimation
#' @examples
#' n=5; alp=0.05;c=1/(2*n)
#' PlotciCAll(n,alp,c)
#' @export
#7. Plot all CC methods
PlotciCAll<-function(n,alp,c)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(c)) stop("'c' 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") || n<=0 ) stop("'n' has to be greater than 0")
if (c<=0 || c>(1/(2*n))) stop("'c' has to be positive and less than or equal to 1/(2*n)")
Abberation=ID=Value=method=LowerLimit=UpperLimit=LowerAbb=UpperAbb=ZWI=NULL
ss1=ciCAll(n,alp,c)
id=1:nrow(ss1)
ss= data.frame(ID=id,ss1)
ll=subset(ss, LowerAbb=="YES")
ul=subset(ss, UpperAbb=="YES")
zl=subset(ss, ZWI=="YES")
if (nrow(ll)>0) {
ll=ll[,c(1,4)];
ll$Abberation="Lower";
colnames(ll)<-c("ID","Value","Abberation")}
if (nrow(ul)>0){
ul=ul[,c(1,5)]
ul$Abberation="Upper"
colnames(ul)<-c("ID","Value","Abberation")
}
if (nrow(zl)>0){
zl=zl[,c(1,4)]
zl$Abberation="ZWI"
colnames(zl)<-c("ID","Value","Abberation")
}
ldf= rbind(ll,ul,zl)
if(nrow(ldf)>0){
oo= ggplot2::ggplot()+
ggplot2::ggtitle("Confidence interval for continuity corrected methods") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit,
color= method),
size = 0.5)+
ggplot2::geom_point(data=ldf,
ggplot2::aes(x=Value, y=ID,
group = Abberation,shape=Abberation),
size = 4, fill = "red") +
ggplot2::scale_fill_manual(values=c("blue", "cyan4", "red", "black", "orange","brown")) +
ggplot2::scale_colour_manual(values=c("brown", "black", "blue", "cyan4", "red", "orange")) +
ggplot2::scale_shape_manual(values=c(21,22,23))
}
else {
oo= ggplot2::ggplot()+
ggplot2::ggtitle("Confidence interval for continuity corrected methods") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit, color= method),
size = 0.5)
}
oo
}
#####################################################################################
#' Plots the CI estimation of 5 continuity corrected methods (Wald, Wald-T, Score, Logit-Wald, ArcSine) grouped by x value
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param c - Continuity correction
#' @details Plots the Confidence Interval for 5 continuity corrected methods (Wald, Wald-T, Score, Logit-Wald, ArcSine) grouped by x for \code{n} given \code{alp} along with Continuity correction \code{c}
#' @family Continuity correction methods of CI estimation
#' @examples
#' n=5; alp=0.05; c=1/(2*n)
#' PlotciCAllg(n,alp,c)
#' @export
#8.All methods plots with grouping
PlotciCAllg<-function(n,alp,c)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(c)) stop("'c' 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") || n<=0 ) stop("'n' has to be greater than 0")
if (c<=0 || c>(1/(2*n))) stop("'c' has to be positive and less than or equal to 1/(2*n)")
Abberation=ID=Value=method=val1=val2=LowerLimit=UpperLimit=LowerAbb=UpperAbb=ZWI=NULL
ss1=ciCAll(n,alp,c)
nss= ss1[order(ss1$x, (ss1$UpperLimit-ss1$LowerLimit)),]
id=1:nrow(ss1)
ss= data.frame(ID=id,nss)
ll=subset(ss, LowerAbb=="YES")
ul=subset(ss, UpperAbb=="YES")
zl=subset(ss, ZWI=="YES")
if (nrow(ll)>0) {
ll=ll[,c(1,4)];
ll$Abberation="Lower";
colnames(ll)<-c("ID","Value","Abberation")}
if (nrow(ul)>0){
ul=ul[,c(1,5)]
ul$Abberation="Upper"
colnames(ul)<-c("ID","Value","Abberation")
}
if (nrow(zl)>0){
zl=zl[,c(1,4)]
zl$Abberation="ZWI"
colnames(zl)<-c("ID","Value","Abberation")
}
ldf= rbind(ll,ul,zl)
if((max(as.numeric(unique(ss$method)))-nrow(ss))==0){
if(nrow(ldf)>0){
oo= ggplot2::ggplot()+
ggplot2::ggtitle("Confidence interval for continuity corrected methods sorted by x") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit,
color= method),
size = 0.5)+
ggplot2::geom_point(data=ldf,
ggplot2::aes(x=Value, y=ID,
group = Abberation,shape=Abberation),
size = 4, fill = "red") +
ggplot2::scale_fill_manual(values=c("blue", "cyan4", "red", "black", "orange","brown")) +
ggplot2::scale_colour_manual(values=c("brown", "black", "blue", "cyan4", "red", "orange")) +
ggplot2::scale_shape_manual(values=c(21,22,23))
}
else {
oo= ggplot2::ggplot()+
ggplot2::ggtitle("Confidence interval for Continuity corrected methods sorted by x") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit, color= method),
size = 0.5)
}
oo
}
else {
ff= data.frame(val1=seq(0.5,max(ss$ID),by=(max(ss$ID)/(max(ss$x)+1))),val2=(0:max(ss$x)))
if(nrow(ldf)>0){
oo= ggplot2::ggplot()+
ggplot2::ggtitle("Confidence interval for Continuity corrected methods sorted by x") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit,
color= method),
size = 0.5)+
ggplot2::geom_point(data=ldf,
ggplot2::aes(x=Value, y=ID,
group = Abberation,shape=Abberation),
size = 4, fill = "red") +
ggplot2::scale_fill_manual(values=c("blue", "cyan4", "red", "black", "orange","brown")) +
ggplot2::scale_colour_manual(values=c("brown", "black", "blue", "cyan4", "red", "orange")) +
ggplot2::scale_shape_manual(values=c(21,22,23)) +
ggplot2::geom_hline(ggplot2::aes(yintercept=val1),data=ff) +
ggplot2::geom_text(ggplot2::aes(0,val1,label = paste("x=", sep="", val2),hjust=1.1, vjust = -1), data=ff)
}
else {
oo= ggplot2::ggplot()+
ggplot2::ggtitle("Confidence interval for Continuity corrected methods sorted by x") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit, color= method),
size = 0.5) +
ggplot2::geom_hline(ggplot2::aes(yintercept=val1),data=ff) +
ggplot2::geom_text(ggplot2::aes(0,val1,label = paste("x=", sep="", val2),hjust=1.1, vjust = -1), data=ff)
}
oo
}
}
#####################################################################################
#' Plots the CI estimation of continuity corrected Wald method given n, alp and c
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param c - Continuity correction
#' @details Plots the Confidence Interval for continuity corrected Wald method
#' for \code{n} given \code{alp} along with Continuity correction \code{c}
#' @family Continuity correction methods of CI estimation
#' @examples
#' n=5; alp=0.05;c=1/(2*n)
#' PlotciCWD(n,alp,c)
#' @export
#7. Plot all CC methods
PlotciCWD<-function(n,alp,c)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(c)) stop("'c' 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 (c<0 || length(c)>1) stop("'c' has to be positive")
Abberation=ID=Value=LowerLimit=UpperLimit=LowerAbb=UpperAbb=ZWI=NULL
WaldCI.df = ciCWD(n,alp,c)
ss1 = data.frame( x=WaldCI.df$x, LowerLimit = WaldCI.df$LCW, UpperLimit = WaldCI.df$UCW, LowerAbb = WaldCI.df$LABB, UpperAbb = WaldCI.df$UABB, ZWI = WaldCI.df$ZWI)
id=1:nrow(ss1)
ss= data.frame(ID=id,ss1)
ll=subset(ss, LowerAbb=="YES")
ul=subset(ss, UpperAbb=="YES")
zl=subset(ss, ZWI=="YES")
if (nrow(ll)>0) {
ll=ll[,c(1,3)];
ll$Abberation="Lower";
colnames(ll)<-c("ID","Value","Abberation")}
if (nrow(ul)>0){
ul=ul[,c(1,4)]
ul$Abberation="Upper"
colnames(ul)<-c("ID","Value","Abberation")
}
if (nrow(zl)>0){
zl=zl[,c(1,3)]
zl$Abberation="ZWI"
colnames(zl)<-c("ID","Value","Abberation")
}
ldf= rbind(ll,ul,zl)
if(nrow(ldf)>0){
oo= ggplot2::ggplot()+
ggplot2::labs(title = "Confidence Interval - Continuity corrected Wald method") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::labs(y = "ID") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit),
size = 0.5)+
ggplot2::geom_point(data=ldf,
ggplot2::aes(x=Value, y=ID,
group = Abberation,shape=Abberation),
size = 4, fill = "red") +
ggplot2::scale_shape_manual(values=c(21,22,23))
}
else {
oo= ggplot2::ggplot()+
ggplot2::labs(title = "Confidence Interval - Continuity corrected Wald method") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::labs(y = "ID") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit),
size = 0.5)
}
oo
}
#####################################################################################
#' Plots the CI estimation of continuity corrected ArcSine method given n, alp and c
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param c - Continuity correction
#' @details Plots the Confidence Interval for continuity corrected ArcSine method
#' for \code{n} given \code{alp} along with Continuity correction \code{c}
#' @family Continuity correction methods of CI estimation
#' @examples
#' n=5; alp=0.05;c=1/(2*n)
#' PlotciCAS(n,alp,c)
#' @export
PlotciCAS<-function(n,alp,c)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(c)) stop("'c' 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 (c<0 || length(c)>1) stop("'c' has to be positive")
Abberation=ID=Value=LowerLimit=UpperLimit=LowerAbb=UpperAbb=ZWI=NULL
ArcSineCI.df = ciCAS(n,alp,c)
ss1 = data.frame(x=ArcSineCI.df$x, LowerLimit = ArcSineCI.df$LCA, UpperLimit = ArcSineCI.df$UCA, LowerAbb = ArcSineCI.df$LABB, UpperAbb = ArcSineCI.df$UABB, ZWI = ArcSineCI.df$ZWI)
id=1:nrow(ss1)
ss= data.frame(ID=id,ss1)
ll=subset(ss, LowerAbb=="YES")
ul=subset(ss, UpperAbb=="YES")
zl=subset(ss, ZWI=="YES")
if (nrow(ll)>0) {
ll=ll[,c(1,3)];
ll$Abberation="Lower";
colnames(ll)<-c("ID","Value","Abberation")}
if (nrow(ul)>0){
ul=ul[,c(1,4)]
ul$Abberation="Upper"
colnames(ul)<-c("ID","Value","Abberation")
}
if (nrow(zl)>0){
zl=zl[,c(1,3)]
zl$Abberation="ZWI"
colnames(zl)<-c("ID","Value","Abberation")
}
ldf= rbind(ll,ul,zl)
if(nrow(ldf)>0){
oo= ggplot2::ggplot()+
ggplot2::labs(title = "Confidence Interval - Continuity corrected ArcSine method") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::labs(y = "ID") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit),
size = 0.5)+
ggplot2::geom_point(data=ldf,
ggplot2::aes(x=Value, y=ID,
group = Abberation,shape=Abberation),
size = 4, fill = "red") +
ggplot2::scale_shape_manual(values=c(21,22,23))
}
else {
oo= ggplot2::ggplot()+
ggplot2::labs(title = "Confidence Interval - Continuity corrected ArcSine method") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::labs(y = "ID") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit),
size = 0.5)
}
oo
}
#####################################################################################
#' Plots the CI estimation of continuity corrected Score method given n, alp and c
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param c - Continuity correction
#' @details Plots the Confidence Interval for continuity corrected Score method
#' for \code{n} given \code{alp} along with Continuity correction \code{c}
#' @family Continuity correction methods of CI estimation
#' @examples
#' n=5; alp=0.05;c=1/(2*n)
#' PlotciCSC(n,alp,c)
#' @export
#7. Plot all CC methods
PlotciCSC<-function(n,alp,c)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(c)) stop("'c' 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 (c<0 || length(c)>1) stop("'c' has to be positive")
Abberation=ID=Value=LowerLimit=UpperLimit=LowerAbb=UpperAbb=ZWI=NULL
ScoreCI.df = ciCSC(n,alp,c)
ss1 = data.frame(x=ScoreCI.df$x, LowerLimit = ScoreCI.df$LCS, UpperLimit = ScoreCI.df$UCS, LowerAbb = ScoreCI.df$LABB, UpperAbb = ScoreCI.df$UABB, ZWI = ScoreCI.df$ZWI)
id=1:nrow(ss1)
ss= data.frame(ID=id,ss1)
ll=subset(ss, LowerAbb=="YES")
ul=subset(ss, UpperAbb=="YES")
zl=subset(ss, ZWI=="YES")
if (nrow(ll)>0) {
ll=ll[,c(1,3)];
ll$Abberation="Lower";
colnames(ll)<-c("ID","Value","Abberation")}
if (nrow(ul)>0){
ul=ul[,c(1,4)]
ul$Abberation="Upper"
colnames(ul)<-c("ID","Value","Abberation")
}
if (nrow(zl)>0){
zl=zl[,c(1,3)]
zl$Abberation="ZWI"
colnames(zl)<-c("ID","Value","Abberation")
}
ldf= rbind(ll,ul,zl)
if(nrow(ldf)>0){
oo= ggplot2::ggplot()+
ggplot2::labs(title = "Confidence Interval - Continuity corrected Score method") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::labs(y = "ID") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit),
size = 0.5)+
ggplot2::geom_point(data=ldf,
ggplot2::aes(x=Value, y=ID,
group = Abberation,shape=Abberation),
size = 4, fill = "red") +
ggplot2::scale_shape_manual(values=c(21,22,23))
}
else {
oo= ggplot2::ggplot()+
ggplot2::labs(title = "Confidence Interval - Continuity corrected Score method") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::labs(y = "ID") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit),
size = 0.5)
}
oo
}
#####################################################################################
#' Plots the CI estimation of continuity corrected Logit Wald method given n, alp and c
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param c - Continuity correction
#' @details Plots the Confidence Interval for continuity corrected Logit Wald method
#' for \code{n} given \code{alp} along with Continuity correction \code{c}
#' @family Continuity correction methods of CI estimation
#' @examples
#' n=5; alp=0.05;c=1/(2*n)
#' PlotciCLT(n,alp,c)
#' @export
#7. Plot all CC methods
PlotciCLT<-function(n,alp,c)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(c)) stop("'c' 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(c) != "integer") & (class(c) != "numeric") || length(c) >1 || c<0 ) stop("'c' has to be positive")
Abberation=ID=Value=LowerLimit=UpperLimit=LowerAbb=UpperAbb=ZWI=NULL
WaldLCI.df = ciCLT(n,alp,c)
ss1= data.frame(x=WaldLCI.df$x, LowerLimit = WaldLCI.df$LCLT, UpperLimit = WaldLCI.df$UCLT, LowerAbb = WaldLCI.df$LABB, UpperAbb = WaldLCI.df$UABB, ZWI = WaldLCI.df$ZWI)
id=1:nrow(ss1)
ss= data.frame(ID=id,ss1)
ll=subset(ss, LowerAbb=="YES")
ul=subset(ss, UpperAbb=="YES")
zl=subset(ss, ZWI=="YES")
if (nrow(ll)>0) {
ll=ll[,c(1,3)];
ll$Abberation="Lower";
colnames(ll)<-c("ID","Value","Abberation")}
if (nrow(ul)>0){
ul=ul[,c(1,4)]
ul$Abberation="Upper"
colnames(ul)<-c("ID","Value","Abberation")
}
if (nrow(zl)>0){
zl=zl[,c(1,3)]
zl$Abberation="ZWI"
colnames(zl)<-c("ID","Value","Abberation")
}
ldf= rbind(ll,ul,zl)
if(nrow(ldf)>0){
oo= ggplot2::ggplot()+
ggplot2::labs(title = "Confidence Interval - Continuity corrected Logit Wald method") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::labs(y = "ID") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit),
size = 0.5)+
ggplot2::geom_point(data=ldf,
ggplot2::aes(x=Value, y=ID,
group = Abberation,shape=Abberation),
size = 4, fill = "red") +
ggplot2::scale_shape_manual(values=c(21,22,23))
}
else {
oo= ggplot2::ggplot()+
ggplot2::labs(title = "Confidence Interval - Continuity corrected Logit Wald method") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::labs(y = "ID") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit),
size = 0.5)
}
oo
}
#####################################################################################
#' Plots the CI estimation of continuity corrected Wald-T method given n, alp and c
#' @param n - Number of trials
#' @param alp - Alpha value (significance level required)
#' @param c - Continuity correction
#' @details Plots the Confidence Interval for continuity corrected Wald-T method
#' for \code{n} given \code{alp} along with Continuity correction \code{c}
#' @family Continuity correction methods of CI estimation
#' @examples
#' n=5; alp=0.05;c=1/(2*n)
#' PlotciCTW(n,alp,c)
#' @export
#7. Plot all CC methods
PlotciCTW<-function(n,alp,c)
{
if (missing(n)) stop("'n' is missing")
if (missing(alp)) stop("'alpha' is missing")
if (missing(c)) stop("'c' 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(c) != "integer") & (class(c) != "numeric") || length(c) >1 || c<0 ) stop("'c' has to be positive")
Abberation=ID=Value=LowerLimit=UpperLimit=LowerAbb=UpperAbb=ZWI=NULL
WaldTCI.df = ciCTW(n,alp,c)
ss1 = data.frame( x=WaldTCI.df$x, LowerLimit = WaldTCI.df$LCTW, UpperLimit = WaldTCI.df$UCTW, LowerAbb = WaldTCI.df$LABB, UpperAbb = WaldTCI.df$UABB, ZWI = WaldTCI.df$ZWI)
id=1:nrow(ss1)
ss= data.frame(ID=id,ss1)
ll=subset(ss, LowerAbb=="YES")
ul=subset(ss, UpperAbb=="YES")
zl=subset(ss, ZWI=="YES")
if (nrow(ll)>0) {
ll=ll[,c(1,3)];
ll$Abberation="Lower";
colnames(ll)<-c("ID","Value","Abberation")}
if (nrow(ul)>0){
ul=ul[,c(1,4)]
ul$Abberation="Upper"
colnames(ul)<-c("ID","Value","Abberation")
}
if (nrow(zl)>0){
zl=zl[,c(1,3)]
zl$Abberation="ZWI"
colnames(zl)<-c("ID","Value","Abberation")
}
ldf= rbind(ll,ul,zl)
if(nrow(ldf)>0){
oo= ggplot2::ggplot()+
ggplot2::labs(title = "Confidence Interval - Continuity corrected Wald-T method") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::labs(y = "ID") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit),
size = 0.5)+
ggplot2::geom_point(data=ldf,
ggplot2::aes(x=Value, y=ID,
group = Abberation,shape=Abberation),
size = 4, fill = "red") +
ggplot2::scale_shape_manual(values=c(21,22,23))
}
else {
oo= ggplot2::ggplot()+
ggplot2::labs(title = "Confidence Interval - Continuity corrected Wald-T method") +
ggplot2::labs(x = "Lower and Upper limits") +
ggplot2::labs(y = "ID") +
ggplot2::geom_errorbarh(data= ss,
ggplot2::aes(x = UpperLimit,y = ID,
xmin = LowerLimit,
xmax = UpperLimit),
size = 0.5)
}
oo
}
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