Description Usage Arguments Details Value Examples
The WREG.WLS
function executes the multiple linear
regression analysis using weighted leastsquares regression.
1 
Y 
A numeric vector of the dependent variable of interest, with any transformations already applied. 
X 
A numeric matrix of the independent variables in the regression, with
any transformations already applied. Each row represents a site and each
column represents a particular independe variable. (If a leading constant
is used, it should be included here as a leading column of ones.) The rows
must be in the same order as the dependent variables in 
recordLengths 
A numeric vector whose rows are in the same order as

LP3 
A numeric matrix containing the fitted LogPearson Type III
standard deviate, standard deviation and skew for each site. The columns of
the matrix represent S, K, G, and an option regional skew value 
transY 
A required character string indicating if the the dependentvariable was transformed by the common logarithm ('log10'), transformed by the natural logarithm ('ln') or untransformed ('none'). 
x0 
A vector containing the independent variables (as above) for a particular target site. This variable is only used for ROI analysis. 
In this implementation, the weights for weighted leastsquares regression are defined by record lengths. See manual for details.
All outputs are returned as part of a list. The elements of the list depend on the type of regression performed. The elements of the list may include:
Coefs 
A data frame composed of four variables: (1)

ResLevInf 
A data frame composed of three variables for each site in
the regression. 
LevLim 
The critical value of
leverage. See 
InflLim 
The critical value of
influence. See 
LevInf.Sig 
A logical matrix indicating if the leverage (column 1) is significant and the influence (column 2) is significant for each site in the regression. 
PerformanceMetrics 
A list of not more than ten elements. All
regression types return the mean squared error of residuals ( 
X 
The input predictors. 
Y 
The input observations. 
fitted.values 
A vector of model estimates from the regression model. 
residuals 
A vector of model residuals. 
Weighting 
The weighting matrix used to develop regression estimates. 
Input 
A list of input parameters for error searching. Currently empty. 
1  # Import some example data

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