Finds connected data sets, i.e. connected rows and columns of a numeric matrix M.
connectedSets(M)Numeric matrix with missing values assumed to be NA or 0.
Vector of set numbers of rows of M (>=1). A value of -1 indicates a row with missing data.
Vector of set numbers of columns of M(>=1). A value of -1 indicates a column with missing data.
In a two-way classification of linear models sometimes independent sets of normal equations are obtained due to missing data in the experiments design, i.e. the complete design matrix is not of full rank and thus no solution can be found. However, solutions of the independent sets of normal equations can still exist. This phenomenon is called 'connectedness' of the data. Especially in phenological analysis experimental designs are almost always unbalanced because of missing data. Thus, when combined time series are to be estimated, it is worth checking for and finding connected data sets for which combined time series can then be estimated. Example (also see example data(Simple) and example in 'maxConnectedSet'): In the following matrix dots represent missing values, X represent observations and the lines join the connected sets:
: X\_\_\_X . . : \(\mid\) : X\_\_\_X . . : : . . X\_\_\_X
Thus, in this matrix observations in rows 1 and 2 or colums 1 and 2 form one connected set. Likewise row 3 (or columns 3 and 4) form also one connected set.
Searle (1997) 'Linear Models'. Wiley. page 318.
# NOT RUN {
data(Simple)
connectedSets(Simple)
# }
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