HelpersMG (version 5.1)

series.compare: Data series comparison using Akaike weight

Description

This function is used as a replacement of t.test() to not use p-value.

Usage

series.compare(..., criterion = c("BIC", "AIC", "AICc"), var.equal = TRUE)

Arguments

...

Series of data (at least two or data are in a table with series in different rows)

criterion

Which criterion is used for model selection. can be AIC, AICc or BIC

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

Details

series.compare compares series of data using Akaike weight.

References

Girondot, M., Guillon, J.-M., 2018. The w-value: An alternative to t- and X2 tests. Journal of Biostatistics & Biometrics 1, 1-4.

See Also

Other w-value functions: compare(), contingencyTable.compare()

Examples

# NOT RUN {
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)
# }