find_difference | R Documentation |
Return the regions in which the smooth is significantly different from zero.
find_difference(mean, se, xVals = NULL, f = 1, as.vector = FALSE)
mean |
A vector with smooth predictions. |
se |
A vector with the standard error on the smooth predictions. |
xVals |
Optional vector with x values for the smooth.
When |
f |
A number to multiply the |
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. |
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).
Jacolien van Rij
Other Utility functions:
convertNonAlphanumeric()
,
corfit()
,
diff_terms()
,
missing_est()
,
modeledf()
,
observations()
,
print_summary()
,
refLevels()
,
res_df()
,
summary_data()
,
timeBins()
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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