sp.correlogram {spdep} | R Documentation

## Spatial correlogram

### Description

Spatial correlograms for Moran's I and the autocorrelation coefficient, with print and plot helper functions.

### Usage

    sp.correlogram(neighbours, var, order = 1, method = "corr", style = "W",
      randomisation = TRUE, zero.policy = FALSE, spChk=NULL)
    plot.spcor(x, main, ylab, ylim, ...)
    print.spcor(x, p.adj.method="none", ...)

### Arguments

`neighbours` | an object of class `nb`
`var` | a numeric vector
`order` | maximum lag order
`method` | "corr" for correlation, "I" for Moran's I
`style` | `style` can take values W, B, C, and S
`randomisation` | variance of I calculated under the assumption of randomisation, if FALSE normality
`zero.policy` | If FALSE stop with error for any empty neighbour sets, if TRUE permit the weights list to be formed with zero-length weights vectors
`spChk` | should the data vector names be checked against the spatial objects for identity integrity, TRUE, or FALSE, default NULL to use `get.spChkOption()`
`x` | an object from `sp.correlogram()` of class `spcor`
`p.adj.method` | correction method as in `p.adjust`
`main` | an overall title for the plot
`ylab` | a title for the y axis
`ylim` | the y limits of the plot
`...` | further arguments passed through

### Details

The print function also calculates the standard deviates of Moran's I and a two-sided probability value, optionally using `p.adjust` to correct by the nymber of lags. The plot function plots a bar from the estimated Moran's I value to +/- twice the square root of its variance (in previous releases only once, not twice).

### Value

returns a list of class `spcor`:

`res` | for "corr" a vector of values; for "I", a matrix of estimates of "I", expectations, and variances
`method` | "I" or "corr"
`cardnos` | list of tables of neighbour cardinalities for the lag orders used
`var` | variable name

### Author(s)

Roger Bivand, Roger.Bivand@nhh.no

### References

Cliff, A. D., Ord, J. K. 1981 Spatial processes, Pion, pp. 118–122, Martin, R. L., Oeppen, J. E. 1975 The identification of regional forecasting models using space-time correlation functions, Transactions of the Institute of British Geographers, 66, 95–118.

### See Also

`nblag`, `moran`, `p.adjust`

### Examples

    data(nc.sids)
    ft.SID74 <- sqrt(1000)*(sqrt(nc.sids$SID74/nc.sids$BIR74) +
      sqrt((nc.sids$SID74+1)/nc.sids$BIR74))
    tr.SIDS74 <- ft.SID74*sqrt(nc.sids$BIR74)
    names(tr.SIDS74) <- rownames(nc.sids)
    cspc <- sp.correlogram(ncCC89.nb, tr.SIDS74, order=8, method="corr",
     zero.policy=TRUE)
    print(cspc)
    plot(cspc)
    Ispc <- sp.correlogram(ncCC89.nb, tr.SIDS74, order=8, method="I",
     zero.policy=TRUE)
    print(Ispc)
    print(Ispc, "bonferroni")
    plot(Ispc)
    drop.no.neighs <- !(1:length(ncCC89.nb) %in% which(card(ncCC89.nb) == 0))
    sub.ncCC89.nb <- subset(ncCC89.nb, drop.no.neighs)
    plot(sp.correlogram(sub.ncCC89.nb, subset(tr.SIDS74,  drop.no.neighs),
     order=8, method="corr"))

[Package spdep version 0.3-25 Index]