corrMatrix {spaMM}

Using a corrMatrix argument

Description

corrMatrix is an argument of HLCor, of class dist or matrix, with can be used if the model formula contains a term of the form corrMatrix(1|<...>). It describes a correlation matrix, possibly as a dist object. A covariance matrix can actually be passed through this argument, but then it must be a full matrix, not a dist object. The way the rows and columns of the matrix are matched to the rows of the data depends on the nature of the grouping term <...>.

The covStruct argument can be used for the same purpose and is much more general, in particular allowing to specify several correlation matrices.

Details

The simplest case is illustrated in the first two examples below: the grouping term is identical to a single variable which is present in the data, whose levels match the rownames of the corrMatrix. As illustrated by the second example, the order of the data does not matter in that case, because the factor levels are used to match the data rows to the appropriate row and columns of the corrMatrix. The corrMatrix may even contain rows (and columns) in excess of the levels of the grouping term, in which case these rows are ignored.

These convenient properties no longer hold when the grouping term is not a single variable from the data (third example below), or when its levels do not correspond to row names of the matrix. In these cases, (1) no attempt is made to match the data rows to the row and column names of the corrMatrix. Such attempt could succeed only if the user had given names to the matrix matching those that the called function could create from the information in the data, in which case the user should find easier to specify a single variable that can be matched; (2) the order of data and corrMatrix matter; Internally, a single factor variable is constructed from all levels of the variables in the grouping term (i.e., from all levels of latitude and longitude, in the third example), with levels 1,2,3... that are matched to rows 1,2,3... of the corrMatrix. Thus the first row of the data is always associated to the first row of the matrix; (3) further, the dimension of the matrix must match the number of levels implied by the grouping term. For example, one might consider the case of 14 response values but of correlations between only 7 levels of a random effect, with two responses for each level. Then the matrix must be of dimension 7x7.

Examples

data("blackcap") 
## Here we manually reconstruct the correlation matrix 
##  of the ML fit produced by corrHLfit:
MLcorMat <- MaternCorr(proxy::dist(blackcap[,c("longitude","latitude")]),
                        nu=0.6285603,rho=0.0544659)
blackcap$name <- as.factor(rownames(blackcap))                
#

## (1) Single variable present in the data 
#
HLCor(migStatus ~ means+ corrMatrix(1|name),data=blackcap,
      corrMatrix=MLcorMat,method="ML")

## (2) Same, permuted: still gives correct result
#
perm <- sample(14)
# Permuted matrix (with permuted names) as 'dist' object
pmat <- as.matrix(MLcorMat)[perm,perm] 
HLCor(migStatus ~ means+ corrMatrix(1|name),data=blackcap,
      corrMatrix=as.dist(pmat),method="ML")
#
# Permuted matrix (with permuted names) as correlation matrix
pcorr <- proxy::as.matrix(MLcorMat, diag=1)[perm,perm] 
HLCor(migStatus ~ means+ corrMatrix(1|name),data=blackcap,
      corrMatrix=pcorr,method="ML")
#

## (3) Other grouping terms (note the messages):
#
HLCor(migStatus ~ means+ corrMatrix(1|longitude+latitude),data=blackcap,
      corrMatrix=MLcorMat,method="ML")