find_difference: Find the regions in which the smooth is significantly different from zero.

Description

Find the regions in which the smooth is significantly different from zero.

Usage

find_difference(mean, se, xVals = NULL, f = 1, as.vector = FALSE)

Arguments

mean

A vector with smooth predictions.

A vector with the standard error on the smooth predictions.

xVals

Optional vector with x values for the smooth. When xVals is provided, the regions are returned in terms of x- values, otherwise as indices.

A number to multiply the se with, to convert the se into confidence intervals. Use 1.96 for 95% CI and 2.58 for 99%CI.

as.vector

Logical: whether or not to return the data points as vector, or not. Default is FALSE, and a list with start and end points will be returned.

Value

The function returns a list with start points of each region (start) and end points of each region (end). The logical xVals indicates whether the returned values are on the x-scale (TRUE) or indices (FALSE).

Examples

Run this code

data(simdat)

# Use aggregate to calculate mean and standard deviation per timestamp:
avg <- aggregate(simdat$Y, by=list(Time=simdat$Time),
    function(x){c(mean=mean(x), sd=sd(x))})
head(avg)
# Note that column x has two values in each row:
head(avg$x)
head(avg$x[,1])

# Plot line and standard deviation:
emptyPlot(range(avg$Time), c(-20,20), h0=0)
plot_error(avg$Time, avg$x[,'mean'], avg$x[,'sd'],
   shade=TRUE, lty=3, lwd=3)

# Show difference with 0:
x <- find_difference(avg$x[,'mean'], avg$x[,'sd'], xVals=avg$Time)
# Add arrows:
abline(v=c(x$start, x$end), lty=3, col='red')
arrows(x0=x$start, x1=x$end, y0=-5, y1=-5, code=3, length=.1, col='red')

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