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|
The Generalized Linear 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.
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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
|
Generalized Linear 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 the topic 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
|
|
