Model
Interpolation
Create surfaces from sample data using these interpolation methods:
- Inverse distance weighted
- Radial-based functions, which include the following kernels
- Thin plate spline
- Spline with tension
- Multiquadratic
- Inverse multiquadratic
- Completely regularized spline kernels
- Global and local polynomials
- Kriging for exact data and for error-contaminated data
- Ordinary, for data with unknown constant mean value
- Simple, for data with known mean value
- Universal, for data with mean value as a function on coordinates
- Indicator, for discrete data or data transformed to discrete
- Probability, for discrete data as primary variable and continuous data as
secondary variables
- Disjunctive, for nonlinear predictions
- Cokriging (multivariate version of the above-mentioned kriging models)
- Isotropical or anisotropical models
Kriging Output Surface Types
- Prediction
- Prediction standard error (measure of the prediction quality)
- Probability map (probability that specified threshold value is exceeded)
- Error of indicators (measure of the probability map uncertainty)
- Quantile map (over- and underpredicted values)
Modeling Tools for Kriging
- Data transformations
- Box–Cox
- Logarithmic
- Arcsine
- Normal score
- Data detrending
- Global polynomial
- Local polynomial
- Variography
- Models (four can be used simultaneously)
- Nugget
- Circular
- Spherical
- Tetraspherical
- Pentaspherical
- Exponential
- Gaussian
- Rational quadratic
- Hole effect
- K-Bessel
- J-Bessel
- Stable
- Semivariogram/Covariance surface
- Anisotropy
- Specifying or estimating the proportion of measurement error in the nugget
- Cross-covariance option for shift between variables
- Estimation of all or part of the model parameters by a modified weighted least squares algorithm
- Declustering
- Checking for data bivariate distribution
Searching Neighborhood
To select neighboring data to predict the value for the target point
- Ellipse with one, four, or eight angular sectors
- Minimum and maximum number of points in each sector