acf.summary: acf.summary

In genomic.autocorr: Models Dealing with Spatial Dependency in Genomic Data

Description

summarize the autocorrelation in

Usage

acf.summary(data, variables, order.by = NULL, lag.max = 100)

Arguments

data	data.table containing variables named in 'variables' and 'order.by'
variables	character vector listing columns of 'data' to be explored for autocorrelation
order.by	optionally, order 'data' by variables in character vector 'order.by'
lag.max	maximum block size to explore (default=100)

Examples

## simulate data with 10 repeated observations in a row - ie there
## should be autocorrelation only within windows <= 10
library(data.table)
data <- genomic.autocorr:::.sim.data() 
summ <- acf.summary(data,c("x","y0","y1"),lag.max=20)

## plot it
df <- melt(summ,c("lag","variable"),variable.name="acf")
par(mfrow=c(2,1))
matplot(matrix(df[acf=="full",]$value,ncol=3),
        main="full",
        pch=c("x","o","+"),
        type="b")
abline(h=0,lty=2)
legend("bottomright",
       c("x","y0","y1"),
       pch = "xo+", col = 1:3)
matplot(matrix(df[acf=="partial",]$value,ncol=3),
        main="partial",
        pch=c("x","o","+"),
        type="b")
abline(h=0,lty=2)
legend("bottomright",
       c("x","y0","y1"),
       pch = "xo+", col = 1:3)

genomic.autocorr documentation built on May 1, 2019, 8:48 p.m.