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#adapted (taken) from dotchart with some minor addition of confidence intervals and to interface with statsBy, describeBy and cohen.d
#July 17, 2016
#input is the mean + standard errors, and (optionally, alpha)
#August 12, added the ability to find (and save) the stats using describe or describeBy
#Modified Oct, 4, 2019 to include cohen.d values
#corrected April 27, 2022 to correctly call Item in dictionary
"error.dots" <-
function (x=NULL,var=NULL, se=NULL, group=NULL,sd=FALSE, effect=NULL,
stats=NULL, head = 12, tail = 12, sort=TRUE,decreasing=TRUE,main=NULL,
alpha=.05,eyes=FALSE, items=FALSE, min.n = NULL,max.labels =40, labels = NULL,
label.width=NULL, select=NULL,
groups = NULL, gdata = NULL, cex = par("cex"),
pt.cex = cex, pch = 21, gpch = 21,
bg = par("bg"), fg=par("fg"), color = par("fg"),
gcolor = par("fg"), lcolor = "gray",
xlab = NULL, ylab = NULL,xlim=NULL,add=FALSE,order=NULL, ...)
{
opar <- par("mai", "mar", "cex", "yaxs")
on.exit(par(opar))
par(cex = cex, yaxs = "i")
#first, see if the data come from a psych object with sd and n or se
if(length(class(x)) > 1) {
cohen.d <- fa.ci <- NULL #strange fix to R compiler
names <- cs(statsBy,describe,describeBy, fa.ci,bestScales,cohen.d, cor.ci,corr.test)
value <- inherits(x,names,which=TRUE) # value <- class(x)[2]
if(any(value > 1) ) { obj <- names[which(value > 0)]} else {obj <- "other"}
} else {obj <- "other"}
# if(length(class(x)) > 1 ) {if (class(x)[1] == "psych") {obj <- class(x)[2]
des <- NULL
switch(obj,
statsBy = {if(is.null(min.n)) { if(!is.null(effect)) { #convert means to effect sizes compared to a particular group
x$mean[,var] <- ( x$mean[,var] -x$mean[effect,var])/x$sd[effect,var]
}
se <- x$sd[,var]/sqrt(x$n[,var])
x <- x$mean[,var]
} else {se <- x$sd[,var]
n.obs <- x$n[,var]
x <- x$mean[,var]
if(!is.null(effect)) { #convert means to effect sizes compared to a particular group
x <- ( x$mean[,var] -x$mean[effect,var])/x$sd[effect,var]
}
if(sd) {se <- x$sd[,var] } else {se <- se/sqrt(n.obs)}
x <- subset(x,n.obs > min.n)
se <- subset(se,n.obs > min.n)
n.obs <- subset(n.obs, n.obs > min.n)
}},
describe = {if(sd) {se <- x$sd} else {se <- x$se}
if(is.null(labels)) labels <- rownames(x)
x <- x$mean
names(x) <- labels
},
describeBy = {des <- x
if(is.null(xlab)) xlab <- var
var <- which(rownames(des[[1]]) == var)
x <- se <- rep(NA,length(des))
for(grp in 1:length(x)) {
x[grp] <- des[[grp]][["mean"]][var]
if(sd) {se[grp] <- des[[grp]][["sd"]][var]} else {se[grp] <- des[[grp]][["se"]][var]}
}
names(x) <- names(des)
if(is.null(xlab)) xlab <- var
},
fa.ci ={se = x$cis$sds
if(is.null(labels)) labels <-rownames(x$cis$means)
x <-x$cis$means },
bestScales = {if(!missing(items)) {
se=x$items[[items]][,3,drop=FALSE]
browser()
rn=rownames(x$items[[items]])
x <- x$items[[items]][,2,drop=FALSE]
names(x) <- rn
des <- NULL
} else {se <- x$stats$se
rn <- rownames(x$stats)
x <- x$stats$mean
names(x) <-rn
des <- NULL}
},
cohen.d = {des <- x$cohen.d[,"effect"]
se <- x$se
if(!is.null(x$dict)) {
cn <- colnames(x$dict)[which(colnames(x$dict) %in% c("Content","Items","content","item","Item"))]
# names <- x$dict %in% c("Content","Items","content","item","Items")
names <- x$dict[,cn]
# names <- x$dict[,"Content"]
} else {names <- rownames(x$cohen.d)}
x <- des
names(x) <- names
sd <- TRUE #use these values for the confidence intervals
},
reliability ={ x <- x$splits
if (sort) { if(is.null(order)) {ord <- order(x,decreasing=!decreasing) } else {ord<- order}
} else {ord <- n.var:1}
x <- x[ord]
names <- rownames(x)
se <- NULL
},
cor.ci = {des=x$means
se = x$sds
names= rownames(x$ci)
x <- x$means
names(x) <- names
if(missing(main)) {main="Confidence intervals of correlations"}
if(missing(xlab) ) {xlab="Correlation"}
},
corr.test = {des= x$ci$r
if(x$sym) {se= x$se[lower.tri(x$se)]} else {
se = as.vector(x$se)}
names= rownames(x$ci)
x <- x$ci$r
names(x) <- names
if(missing(main)) {main="Confidence intervals of correlations"}
if(missing(xlab) ) {xlab="Correlation"}
},
other = {} #an empty operator
)#end switch
if (obj=="other"){
if(is.null(group)) { #the case of just one observation per condition
if(is.null(stats)) {
if(is.null(dim(x))) {se <- rep(0,length(x))
des <- x
labels=NULL } else {
des <- describe(x)
x <-des$mean
if(sd) { se <- des$sd} else {se <- des$se}
names(x) <- rownames(des)}
} else { #the normal case is to find the means and se
x <- stats$mean
se <- stats$se
names(x) <- rownames(stats)
des <- NULL
}
} else {
if(is.null(xlab)) xlab <- var
des <- describeBy(x,group=group)
x <- se <- rep(NA,length(des))
names(x) <- names(des)
var <- which(rownames(des[[1]]) == var)
for(grp in 1:length(des)) {
x[grp] <- des[[grp]][["mean"]][var]
if(sd) { se[grp] <- des[[grp]][["sd"]][var]} else {se[grp] <- des[[grp]][["se"]][var]}
}}
}
n.var <- length(x)
# if(!is.null(se) && !sd) {ci <- qnorm((1-alpha/2))*se} else {ci <- NULL}
if (sort) { if(is.null(order)) {ord <- order(x,decreasing=!decreasing) } else {ord<- order}
} else {ord <- n.var:1}
x <- x[ord]
se <- se[ord]
temp <- temp.se <- rep(NA,min(head+tail,n.var))
if((head+tail) < n.var) {
if (head > 0 ){ temp[1:head] <- x[1:head]
temp.se[1:head] <- se[1:head]
names(temp) <- names(x)[1:head]
}
if(tail > 0 ) {temp[(head + 1):(head + tail)] <- x[(length(x)-tail+1):length(x)]
temp.se[(head + 1):(head + tail)] <- se[(length(x)-tail+1):length(x)]
names(temp)[(head + 1):(head + tail)] <- names(x)[(length(x)-tail+1):length(x)]
}
x <- temp
se <- temp.se
}
if(missing(main)) {if(sd) {main <- "means + standard deviation"} else {main="Confidence Intervals around the mean"}}
if(is.null(labels)) labels <- names(x)
if(sd) {ci <- se} else {ci <- qnorm((1-alpha/2))*se}
# if(!is.null(se)) {ci <- qnorm((1-alpha/2))*se} else {ci <- NULL}
if(!is.null(ci) && is.null(xlim)) xlim <- c(min(x - ci,na.rm=TRUE),max(x + ci,na.rm=TRUE))
labels <- substr(labels,1,max.labels)
if(eyes) { #get ready to draw catseyes
ln <- seq(-3,3,.1)
rev <- (length(ln):1)
}
if (!is.numeric(x))
stop("'x' must be a numeric vector or matrix")
n <- length(x)
if (is.matrix(x)) {
if (is.null(labels))
labels <- rownames(x)
if (is.null(labels))
labels <- as.character(1L:nrow(x))
labels <- rep_len(labels, n)
if (is.null(groups))
groups <- col(x, as.factor = TRUE)
glabels <- levels(groups)
}
else {
if (is.null(labels))
labels <- names(x)
glabels <- if (!is.null(groups))
levels(groups)
if (!is.vector(x)) {
warning("'x' is neither a vector nor a matrix: using as.numeric(x)")
x <- as.numeric(x)
}
}
if(!add) plot.new()
linch <- if (!is.null(labels))
max(strwidth(labels, "inch"),label.width, na.rm = TRUE)
else 0
if (is.null(glabels)) {
ginch <- 0
goffset <- 0
}
else {
ginch <- max(strwidth(glabels, "inch"),label.width, na.rm = TRUE)
goffset <- 0.4
}
if (!(is.null(labels) && is.null(glabels))) {
nmai <- par("mai")
nmai[2L] <- nmai[4L] + max(linch + goffset, ginch) +
0.1
par(mai = nmai)
}
if (is.null(groups)) {
o <- 1L:n
if(!is.null(select)) o <- o[select]
y <- o
x <- x[o]
if(!is.null(ci)) ci <- ci[o]
ylim <- c(0, n + 1)
}
else {
o <- sort.list(as.numeric(groups), decreasing = TRUE)
x <- x[o]
groups <- groups[o]
color <- rep_len(color, length(groups))[o]
lcolor <- rep_len(lcolor, length(groups))[o]
offset <- cumsum(c(0, diff(as.numeric(groups)) != 0))
y <- 1L:n + 2 * offset
ylim <- range(0, y + 2)
}
plot.window(xlim = xlim, ylim = ylim, log = "")
lheight <- par("csi")
if (!is.null(labels)) {
linch <- max(strwidth(labels, "inch"), na.rm = TRUE)
loffset <- (linch + 0.1)/lheight
labs <- labels[o]
mtext(labs, side = 2, line = loffset, at = y, adj = 0,
col = color, las = 2, cex = cex, ...)
}
abline(h = y, lty = "dotted", col = lcolor)
points(x, y, pch = pch, col = color, bg = bg, cex = pt.cex/cex)
if(!is.null(ci)) {if(!eyes) {
segments(x - ci, y, x+ci, y,bg=bg,col=fg,...)
# col = par("fg"), lty = par("lty"), lwd = par("lwd"))
} }
if (!is.null(groups)) {
gpos <- rev(cumsum(rev(tapply(groups, groups, length)) +
2) - 1)
ginch <- max(strwidth(glabels, "inch"), na.rm = TRUE)
goffset <- (max(linch + 0.2, ginch, na.rm = TRUE) + 0.1)/lheight
mtext(glabels, side = 2, line = goffset, at = gpos, adj = 0,
col = gcolor, las = 2, cex = cex, ...)
if (!is.null(gdata)) {
abline(h = gpos, lty = "dotted")
points(gdata, gpos, pch = gpch, col = gcolor, bg = bg,
cex = pt.cex/cex, ...)
}
}
if(eyes) {
for (e in 1:(min(head+tail,n.var))) {catseye(x[e],y[e],ci[e]/qnorm(1-alpha/2),alpha=alpha,density=density) }}
if(!add) axis(1)
if(!add) box()
title(main = main, xlab = xlab, ylab = ylab, ...)
result <- list(des =des,order=ord)
invisible(result)
#report the order if sort
}
#modified from catseyes in error.bars
"catseye" <- function(x,y,ci,alpha,density=density,col=col) {
SCALE=.7
ln <- seq(-3,3,.1)
rev <- (length(ln):1)
norm <- dnorm(ln)
# clim <- qnorm(alpha/2)
#norm <- dt(ln,n-1)
clim <- qnorm(alpha/2)
#clim <- ci
norm <- c(norm,-norm[rev])
ln <- seq(-3,3,.1)
cln <- seq(clim,-clim,.1)
cnorm <- dnorm(cln)
cnorm <- c(0,cnorm,0,-cnorm,0) #this closes the probability interval
# polygon(norm*SCALE*ci+x,c(ln,ln[rev])+y)
polygon(c(ln,ln[rev])*ci+x, norm*SCALE+y)
# polygon(cnorm*SCALE+x,c(clim,cln,-clim,-cln,clim)*ci+y,density=density,col=col)
}
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