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genlinnode properties

genlinnode properties

Last updated: Feb 11, 2025
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.

Example

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  
distribution BINOMIAL GAMMA IGAUSS NEGBIN NORMAL POISSON TWEEDIE MULTINOMIAL IGAUSS: Inverse Gaussian. NEGBIN: Negative binomial.
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