Linear regression models predict a continuous target based on linear
relationships between the target and one or more predictors.
Table 1. linearasnode properties
linearasnode Properties
Values
Property description
target
field
Specifies a single target field.
inputs
[field1 ... fieldN]
Predictor fields used by the model.
weight_field
field
Analysis field used by the model.
custom_fields
flag
The default value is TRUE.
intercept
flag
The default value is TRUE.
detect_2way_interaction
flag
Whether or not to consider two way interaction. The default value is TRUE.
cin
number
The interval of confidence used to compute estimates of the model coefficients. Specify a
value greater than 0 and less than 100. The default value is 95.
factor_order
ascendingdescending
The sort order for categorical predictors. The default value is
ascending.
var_select_method
ForwardStepwiseBestSubsetsnone
The model selection method to use. The default value is
ForwardStepwise.
criteria_for_forward_stepwise
AICCFstatisticsAdjustedRSquareASE
The statistic used to determine whether an effect should be added to or removed from the
model. The default value is AdjustedRSquare.
pin
number
The effect that has the smallest p-value less than this specified pin
threshold is added to the model. The default value is 0.05.
pout
number
Any effects in the model with a p-value greater than this specified pout
threshold are removed. The default value is 0.10.
use_custom_max_effects
flag
Whether to use max number of effects in the final model. The default value is
FALSE.
max_effects
number
Maximum number of effects to use in the final model. The default value is
1.
use_custom_max_steps
flag
Whether to use the maximum number of steps. The default value is
FALSE.
max_steps
number
The maximum number of steps before the stepwise algorithm stops. The default value is
1.
criteria_for_best_subsets
AICCAdjustedRSquareASE
The mode of criteria to use. The default value is AdjustedRSquare.
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