|
|
Discriminant analysis makes more stringent assumptions than logistic regression but can be a
valuable alternative or supplement to a logistic regression analysis when those assumptions are met.
|
Example
node = stream.create("discriminant", "My node")
node.setPropertyValue("target", "custcat")
node.setPropertyValue("use_partitioned_data", False)
node.setPropertyValue("method", "Stepwise")
Table 1. discriminantnode properties
discriminantnode Properties |
Values |
Property description |
target
|
field
|
Discriminant models require a single target field and one or more input fields. Weight and
frequency fields are not used. See the topic Common modeling node properties for more information.
|
method
|
Enter
Stepwise
|
|
mode
|
Simple
Expert
|
|
prior_probabilities
|
AllEqual
ComputeFromSizes
|
|
covariance_matrix
|
WithinGroups
SeparateGroups
|
|
means
|
flag
|
Statistics options in the Advanced Output dialog box. |
univariate_anovas
|
flag
|
|
box_m
|
flag
|
|
within_group_covariance
|
flag
|
|
within_groups_correlation
|
flag
|
|
separate_groups_covariance
|
flag
|
|
total_covariance
|
flag
|
|
fishers
|
flag
|
|
unstandardized
|
flag
|
|
casewise_results
|
flag
|
Classification options in the Advanced Output dialog box. |
limit_to_first
|
number
|
Default value is 10. |
summary_table
|
flag
|
|
leave_one_classification
|
flag
|
|
combined_groups
|
flag
|
|
separate_groups_covariance
|
flag
|
Matrices option Separate-groups covariance. |
territorial_map
|
flag
|
|
combined_groups
|
flag
|
Plot option Combined-groups. |
separate_groups
|
flag
|
Plot option Separate-groups. |
summary_of_steps
|
flag
|
|
F_pairwise
|
flag
|
|
stepwise_method
|
WilksLambda
UnexplainedVariance
MahalanobisDistance
SmallestF
RaosV
|
|
V_to_enter
|
number
|
|
criteria
|
UseValue
UseProbability
|
|
F_value_entry
|
number
|
Default value is 3.84. |
F_value_removal
|
number
|
Default value is 2.71. |
probability_entry
|
number
|
Default value is 0.05. |
probability_removal
|
number
|
Default value is 0.10. |
calculate_variable_importance
|
flag
|
|
calculate_raw_propensities
|
flag
|
|
calculate_adjusted_propensities
|
flag
|
|
adjusted_propensity_partition
|
Test
Validation
|
|
