lamap: lamap
In wccarleton/Rlamap: The Locally Adaptive Model of Archaeological Potential

Description Usage Arguments Value

The primary lamap function. It estimates a lamap value given a single observation and a training dataset based on known archaeological sites. The observation is a single raster cell that might have multiple variable dimensions—i.e., a single raster cell from a raster stack. Most analyses will need to be parallelized. This is the function that should be called in in parallel. It can be thought of as a computational kernel that processes a single raster cell from the desired output raster map (i.e., the lamap surface). The lamap package contains a wrapper function for parallel processing using the R package 'snow'—see 'parLamap'.

1 2	lamap(observed, knownsite_pcdfs, knownsite_coords, steps, maxsites = NULL, weightfun = NULL, weightparams = NULL, combinations = NULL, ...)

`observed`	Single row dataframe of observed data (will be coerced). Each column should be one variable that corresponds to one of the cdfs in pcdfobj.
`knownsite_pcdfs`	List of lists containing cdfs for each known site. See 'pcdf()'. This parameter should be the output of the function 'knownsitePcdfs'.
`knownsite_coords`	Dataframe containing xy coordinates for each known site. The columns need to be named "id","x","y". The ids should be ordered to correspond to the order of sites in the knownsite_pcdfs list. For now, only UTM coordinates should be used. See 'knownsiteCoords', a convenience function for creating this list from a dataframe.
`steps`	A vector of window sizes for each variable of interest. The steps will be used to estimate integrals from the knownsite_pcdfs. They will be added/subtracted from the observed values to create integration intervals. The order of steps in this vector must correspond to the order of column variables in the 'observed' dataframe—which must also match the order of columns in training dataframe—i.e., 'traindf'—passed to the 'pcdf' function.
`maxsites`	The maximum number of sites from the known-site training data to include in the calculations. This is primarily a convenience parameter that can be used to speed up calculations when the training set contains a large number of sites.
`weightfun`	The function used to weight the lamap values by distance. It can be one of 'uniform' or 'exponential'—see 'weight' function. Default is Null, meaning no weighting is applied.
`weightparams`	A vector of parameters passed to the 'weight' function. Default is Null.
`combinations`	A conveience paramter used to speed up calculations. The lamap calculation involves the use of hte inclusion-exclusion principle for estimating the total probability of a union of independent events. Consequently, the R 'combn' function is used and can slow the calculations considerably. However, the user can instead use a utility function provided with this package—'prepCombinations'. It computes the combinations which can then be saved and passed to the lamap function with this parameter, saving the algorithm from having to regenerate the combinations for every cell in in the desired output map. Default is Null.
`...`	Additional paramters passed to the 'jdensity' function: the 'nosupport', 'partial', and 'interpolate' options.