prism: Title: PRISM
In beanb2/snowload: Ground snow load prediction functions.

A function that implements the PRISM algorithm as developed by Daly et.al. The PRISM model is weighted least squares regression primarily based on similarities in location and elevation to the prediction location. Note that a unique linear model is fit at each and every predictio location.

prism(formula = log(RESPONSE) ~ ELEVATION, locations, newdata,
  distImp = 0.8, minRad = 10, wdistance = 2, welevRange = list(lwr
  = 200, upr = 2500), welevation = c("ELEVATION", 1), wbasin = c(NA,
  3), weco = c(NA, 3), bound = FALSE, weights = FALSE, ...)

`locations`	The measurement locations, of class SpatialPointsDataFrame, containing all the necessary colunmns to run the PRISM algorithm.
`newdata`	The prediction locations, typically of class SpatialPointsDataFrame, SpatialGridDataFrame, or SpatialPixelsDataFrame. This object will be appended and returned by the prism function. Note that the projection of this object must match the projection of the locations variable.
`distImp`	A weight controlling the distance importance factor relative to the elevation importance factor. Must be a number between 0 and 1.
`minRad`	The minimum radius of influence, defined in the units of the dataset projection.
`wdistance`	An exponent controlling the severity of the distance weight.
`welevRange`	The range of elevations considered in the elevation weight. Differences less than the minimum receive weights of 1 while differences greater than the maximum receive weights of 0.
`welevation`	A vector of length 2. The first argument specifies the name of the column where elevation information can be obtained. The second argument specifies the exponent that controls the severity of the elevation weight.
`wbasin`	A vector of length 2. The first argument specifies the name of column where water basin information can be obtained. The second argument specifies the exponent that controls the severity of the water basin weight. If the first argument is NA, then this weight is ignored.
`weco`	Same structure as wbasin, but specifies weights based on the EPA's ecoregions.
`bound`	If true, predictions are not allowed to extrapolate beyond the observed range of values in the input data. Thus predictions are capped on both ends by the most extreme observed values. If false, predictions are allowed to extrapolate.
`weights`	If true, return the slope, intercept, and weights for each of the predictions. This argument is only relevant if one wishes to plot the PRISM predictions.
`variables`	The variables used in the PRISM regression. The first variable is the reponse and all remaining variables are treated as explanatory. This is a replacement for a formula call as the use of lsfit greatly speeds the computations.
`score`	Additional arguments to

If weights = FALSE, a numeric vector of predictions with length equal to the number of rows in newdata. Else, a list with the first component containing the prediction, intercept, and slope, and the second component containing a matrix of weights.

beanb2/snowload documentation built on Jan. 7, 2020, 9:48 p.m.