% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/series.compare.R
\name{series.compare}
\alias{series.compare}
\title{Data series comparison using Akaike weight}
\usage{
series.compare(..., criterion = c("BIC", "AIC", "AICc"), var.equal = TRUE)
}
\arguments{
\item{...}{Series of data (at least two or data are in a table with series in different rows)}

\item{criterion}{Which criterion is used for model selection. can be AIC, AICc or BIC}

\item{var.equal}{Should the variances of all series being equal? Default TRUE}
}
\value{
The probability that a single proportion model is sufficient to explain the data
}
\description{
This function is used as a replacement of t.test() to not use p-value.
}
\details{
series.compare compares series of data using Akaike weight.
}
\examples{
\dontrun{
library("HelpersMG")
A <- rnorm(100, 10, 2)
B <- rnorm(100, 11.1, 2)
series.compare(A, B, criterion = "BIC", var.equal=TRUE)
B <- B[1:10]
series.compare(A, B, criterion = "BIC", var.equal=TRUE)
A <- rnorm(100, 10, 2)
B <- rnorm(100, 10.1, 2)
C <- rnorm(100, 10.5, 2)
series.compare(A, B, C, criterion = "BIC", var.equal=TRUE)
B <- B[1:10]
series.compare(A, B, criterion = "BIC", var.equal=TRUE)
t.test(A, B, var.equal=TRUE)
# Example with a data.frame
series.compare(t(data.frame(A=c(10, 27, 19, 20, NA), B=c(10, 20, NA, NA, NA))))
# Test in the context of big data
A <- rnorm(10000, 10, 2)
B <- rnorm(10000, 10.1, 2)
series.compare(A, B, criterion = "BIC", var.equal=TRUE)
t.test(A, B, var.equal=TRUE)
###########################
w <- NULL
p <- NULL

for (i in 1:1000) {
  
  A <- rnorm(50000, 10, 2)
  B <- rnorm(50000, 10.01, 2)
  w <- c(w, unname(series.compare(A, B, criterion = "BIC", var.equal=TRUE)[1]))
  p <- c(p, t.test(A, B, var.equal=TRUE)$p.value)

}

layout(mat = 1:2)
par(mar=c(4, 4, 1, 1)+0.4)
hist(p, main="", xlim=c(0, 1), las=1, breaks = (0:20)/20, 
     freq=FALSE, xlab = expression(italic("p")*"-value"))
hist(w, main="", xlim=c(0, 1), las=1, breaks = (0:20)/20, 
    freq=FALSE, xlab = expression(italic("w")*"-value"))
###########################

x <- seq(from=8, to=13, by=0.1)

pv <- NULL
aw <- NULL
A <- rnorm(100, mean=10, sd=2)
B <- A-2

for (meanB in x) {
  pv <- c(pv, t.test(A, B, var.equal = FALSE)$p.value)
  aw <- c(aw, series.compare(A, B, criterion="BIC", var.equal = FALSE)[1])
  B <- B + 0.1
}

par(mar=c(4, 4, 2, 1)+0.4)
y <- pv
plot(x=x, y=y, type="l", lwd=2,
     bty="n", las=1, xlab="Mean B value (SD = 4)", ylab="Probability", ylim=c(0,1), 
     main="")
y2 <- aw
lines(x=x, y=y2, type="l", col="red", lwd=2)

l1 <- which(aw>0.05)[1]
l2 <- max(which(aw>0.05))

aw[l1]
pv[l1]

aw[l2]
pv[l2]

l1 <- which(pv>0.05)[1]
l2 <- max(which(pv>0.05))

aw[l1]
pv[l1]

aw[l2]
pv[l2]

par(xpd=TRUE)
segments(x0=10-1.96*2/10, x1=10+1.96*2/10, y0=1.1, y1=1.1, lwd=2)
segments(x0=10, x1=10, y0=1.15, y1=1.05, lwd=2)
par(xpd=TRUE)
text(x=10.5, y=1.1, labels = "Mean A = 10, SD = 2", pos=4)

v1 <- c(expression(italic("p")*"-value"), expression("based on "*italic("t")*"-test"))
v2 <- c(expression(italic("w")*"-value for A"), expression("and B identical models"))
legend("topright", legend=c(v1, v2), 
       y.intersp = 1, 
       col=c("black", "black", "red", "red"), bty="n", lty=c(1, 0, 1, 0))

segments(x0=min(x), x1=max(x), y0=0.05, y1=0.05, lty=2)
par(xpd = TRUE)
text(x=13.05, y=0.05, labels = "0.05", pos=4)
}
}
\references{
Girondot, M., Guillon, J.-M., 2018. The w-value: An alternative to t- and X2 tests. Journal of Biostatistics & Biometrics 1, 1-4.
}
\seealso{
Other w-value functions: 
\code{\link{compare}()},
\code{\link{contingencyTable.compare}()}
}
\author{
Marc Girondot \email{marc.girondot@gmail.com}
}
\concept{w-value functions}
