genlinnode properties

GenLin node iconThe Generalized Linear (GenLin) model expands the general linear model so that the dependent variable is linearly related to the factors and covariates through a specified link function. Moreover, the model allows for the dependent variable to have a non-normal distribution. It covers the functionality of a wide number of statistical models, including linear regression, logistic regression, loglinear models for count data, and interval-censored survival models.


node = stream.create("genlin", "My node")
node.setPropertyValue("model_type", "MainAndAllTwoWayEffects")
node.setPropertyValue("offset_type", "Variable")
node.setPropertyValue("offset_field", "Claimant")
Table 1. genlinnode properties
genlinnode Properties Values Property description
target field GenLin models require a single target field which must be a nominal or flag field, and one or more input fields. A weight field can also be specified. See Common modeling node properties for more information.
use_weight flag  
weight_field field Field type is only continuous.
target_represents_trials flag  
trials_type Variable FixedValue  
trials_field field Field type is continuous, flag, or ordinal.
trials_number number Default value is 10.
model_type MainEffects MainAndAllTwoWayEffects  
offset_type Variable FixedValue  
offset_field field Field type is only continuous.
offset_value number Must be a real number.
base_category Last First  
include_intercept flag  
mode Simple Expert  
negbin_para_type Specify Estimate  
negbin_parameter number Default value is 1. Must contain a non-negative real number.
tweedie_parameter number  
link_function IDENTITY CLOGLOG LOG LOGC LOGIT NEGBIN NLOGLOG ODDSPOWER PROBIT POWER CUMCAUCHIT CUMCLOGLOG CUMLOGIT CUMNLOGLOG CUMPROBIT CLOGLOG: Complementary log-log. LOGC: log complement. NEGBIN: Negative binomial. NLOGLOG: Negative log-log. CUMCAUCHIT: Cumulative cauchit. CUMCLOGLOG: Cumulative complementary log-log. CUMLOGIT: Cumulative logit. CUMNLOGLOG: Cumulative negative log-log. CUMPROBIT: Cumulative probit.
power number Value must be real, nonzero number.
method Hybrid Fisher NewtonRaphson  
max_fisher_iterations number Default value is 1; only positive integers allowed.
scale_method MaxLikelihoodEstimate Deviance PearsonChiSquare FixedValue  
scale_value number Default value is 1; must be greater than 0.
covariance_matrix ModelEstimator RobustEstimator  
max_iterations number Default value is 100; non-negative integers only.
max_step_halving number Default value is 5; positive integers only.
check_separation flag  
start_iteration number Default value is 20; only positive integers allowed.
estimates_change flag  
estimates_change_min number Default value is 1E-006; only positive numbers allowed.
estimates_change_type Absolute Relative  
loglikelihood_change flag  
loglikelihood_change_min number Only positive numbers allowed.
loglikelihood_change_type Absolute Relative  
hessian_convergence flag  
hessian_convergence_min number Only positive numbers allowed.
hessian_convergence_type Absolute Relative  
case_summary flag  
contrast_matrices flag  
descriptive_statistics flag  
estimable_functions flag  
model_info flag  
iteration_history flag  
goodness_of_fit flag  
print_interval number Default value is 1; must be positive integer.
model_summary flag  
lagrange_multiplier flag  
parameter_estimates flag  
include_exponential flag  
covariance_estimates flag  
correlation_estimates flag  
analysis_type TypeI TypeIII TypeIAndTypeIII  
statistics Wald LR  
citype Wald Profile  
tolerancelevel number Default value is 0.0001.
confidence_interval number Default value is 95.
loglikelihood_function Full Kernel  
singularity_tolerance 1E-007 1E-008 1E-009 1E-010 1E-011 1E-012  
value_order Ascending Descending DataOrder  
calculate_variable_importance flag  
calculate_raw_propensities flag  
calculate_adjusted_propensities flag  
adjusted_propensity_partition Test Validation