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linearasnode properties
Last updated: Jan 17, 2024
linearasnode properties

Linear-AS node iconLinear 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 ascending descending The sort order for categorical predictors. The default value is ascending.
var_select_method ForwardStepwise BestSubsets none The model selection method to use. The default value is ForwardStepwise.
criteria_for_forward_stepwise AICC Fstatistics AdjustedRSquare ASE 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 AICC AdjustedRSquare ASE The mode of criteria to use. The default value is AdjustedRSquare.
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