specifies the angle of the major axis for anisotropic models, measured in degrees clockwise from the N-S axis. In the case of a nested semivariogram model, you can specify an angle for each nesting. The default is ANGLE=0.

specifies the functional form (type) of the semivariogram model. All the supported models are two-parameter models (SCALE= and RANGE=). A FORM= value is required; in the case of a nested semivariogram model, you must specify a form for each nesting.

See the section Theoretical Semivariogram Models for details on how the FORM= forms are determined.

specifies the input data set that contains parameter values for the covariance or semivariogram model. The MDATA= data set must contain variables named SCALE, RANGE, and FORM=, and it can optionally contain variables NUGGET, ANGLE, and RATIO.

The FORM variable must be a character variable, assuming only the values allowed in the explicit FORM= syntax described previously. The RANGE and SCALE variables must be numeric. The optional variables ANGLE, RATIO, and NUGGET must also be numeric if present.

The number of observations present in the MDATA= data set corresponds to the level of nesting of the semivariogram model.

For example, to specify a non-nested model that uses a spherical covariance, an MDATA= data set might be given by the following statement:

   data md1;
      input scale range form $;
      datalines;
      25 10 SPH
   run;

The PROC KRIGE2D statement to use the MDATA= specification is of the form shown in the following:

   proc krige2d data=...;
      predict var=....;
      model mdata=md1;
   run;

This is equivalent to the following explicit specification of the covariance model parameters:

   proc krige2d data=...;
      predict var=....;
      model scale=25 range=10 form=sph;
   run;

The following MDATA= data set is an example of an anisotropic nested model:

   data md1;
      input scale range form $ nugget angle ratio;
      datalines;
      20 8 S  5 35 0.7
      12 3 G  5 0  0.8
      4  1 G  5 45 0.5
      ;

This is equivalent to the following explicit specification of the covariance model parameters:

   proc krige2d data=...;
      predict var=....;
      model scale=(20,12,4) range=(8,3,1) form=(S,G,G)
            angle=(35,0,45) ratio=(0.7,0.8,0.5) nugget=5;
   run;

This example is somewhat artificial in that it is usually hard to detect different anisotropy directions and ratios for different nestings using an experimental semivariogram. Note that the NUGGET value is the same for all nestings. This is always the case; the nugget effect is a single additive term for all models. For further details, see the section Details: VARIOGRAM Procedure in the VARIOGRAM procedure.

specifies the nugget effect for the model. The nugget effect is due to a discontinuity in the semivariogram as determined by plotting the sample semivariogram (see the chapter on the VARIOGRAM procedure for details). For models without any nugget effect, this option is left out; the default is NUGGET=0.

specifies the range parameter in semivariogram models. If you have anisotropy, you must specify the range of the major anisotropy axis. In the case of a nested semivariogram model, you must specify a range for each nested structure.

The range parameter is the divisor in the exponent in all supported models except the power model. It has the units of distance, and it is related to the correlation scale for the underlying spatial process.

See the section Theoretical Semivariogram Models for details on how the RANGE= values are determined.

specifies the ratio of the length of the minor axis to the length of the major axis for anisotropic models. The value of the RATIO= option must be between 0 and 1. In the case of a nested semivariogram model, you can specify a ratio for each nesting. The default is RATIO=1.

specifies the scale parameter in semivariogram models. In the case of a nested semivariogram model, you must specify a scale for each nesting.

The scale parameter is the multiplicative factor in all supported models; it has the same units as the variance of the VAR= variable in the preceding PREDICT statement.

See the section Theoretical Semivariogram Models for details on how the SCALE= values are determined.

gives the singularity criteria for solving kriging systems. The larger the value of the SINGULAR= option, the easier it is for a kriging system to be declared singular. The default is SINGULAR=1E-7. See the section Ordinary Kriging for more detailed information.