plot {nptest}

Plots Permutation Distribution for Nonparametric Tests

Description

plot methods for object classes "np.cor.test", "np.loc.test", and "np.reg.test"

Usage

## S3 method for class 'np.cor.test'
plot(x, alpha = 0.05, col = "grey", col.rr = "red",
     col.stat = "black", lty.stat = 2, lwd.stat = 2, 
     xlab = "Test Statistic", main = "Permutation Distribution", 
     breaks = "scott", border = NA, box = TRUE, ...)
           
## S3 method for class 'np.loc.test'
plot(x, alpha = 0.05, col = "grey", col.rr = "red",
     col.stat = "black", lty.stat = 2, lwd.stat = 2, 
     xlab = "Test Statistic", main = "Permutation Distribution", 
     breaks = "scott", border = NA, box = TRUE, ...)
           
## S3 method for class 'np.reg.test'
plot(x, alpha = 0.05, col = "grey", col.rr = "red",
     col.stat = "black", lty.stat = 2, lwd.stat = 2, 
     xlab = "Test Statistic", main = "Permutation Distribution", 
     breaks = "scott", border = NA, box = TRUE, SQRT = TRUE, ...)

Arguments

x

an object of class "np.cor.test" output by the np.cor.test function, "np.loc.test" output by the np.loc.test function, or "np.reg.test" output by the np.reg.test function

alpha

significance level of the nonparametric test

col

color for plotting the non-rejection region

col.rr

color for plotting the rejection region

col.stat

color for plotting the observed test statistic

lty.stat

line type for plotting the observed test statistic

lwd.stat

line width for plotting the observed test statistic

xlab

x-axis label for the plot

main

title for the plot

breaks

defines the breaks of the histogram (see hist)

border

color of the border around the bars

box

should a box be drawn around the plot?

SQRT

for regression tests, should the permutation distribution (and test statistic) be plotted on the square-root scale?

...

additional arguments to be passed to hist

Details

Plots a histogram of the permutation distribution and the observed test statistic. The argument 'alpha' controls the rejection region of the nonparametric test, which is plotted using a separate color (default is red).

Author(s)

Nathaniel E. Helwig <[e-mail]>

References

Helwig, N. E. (2019). Statistical nonparametric mapping: Multivariate permutation tests for location, correlation, and regression problems in neuroimaging. WIREs Computational Statistics, 11(2), e1457. doi: 10.1002/wics.1457

See Also

np.cor.test for information on nonparametric correlation tests

np.loc.test for information on nonparametric location tests

np.reg.test for information on nonparametric regression tests

Examples

######******######   np.cor.test   ######******######

# generate data
rho <- 0.5
val <- c(sqrt(1 + rho), sqrt(1 - rho))
corsqrt <- matrix(c(val[1], -val[2], val), 2, 2) / sqrt(2)
set.seed(1)
n <- 50
z <- cbind(rnorm(n), rnorm(n)) %*% corsqrt
x <- z[,1]
y <- z[,2]

# test H0: rho = 0
set.seed(0)
test <- np.cor.test(x, y)

# plot results
plot(test)


######******######   np.loc.test   ######******######

# generate data
set.seed(1)
n <- 50
x <- rnorm(n, mean = 0.5)

# one sample t-test
set.seed(0)
test <- np.loc.test(x)

# plot results
plot(test)


######******######   np.reg.test   ######******######

# generate data
set.seed(1)
n <- 50
x <- cbind(rnorm(n), rnorm(n))
beta <- c(0.25, 0.5)
y <- x %*% beta + rnorm(n)

# Wald test (method = "perm")
set.seed(0)
test <- np.reg.test(x, y)

# plot results
plot(test)