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glmmnode properties

glmmnode properties

GLMM node iconA generalized linear mixed model (GLMM) extends the linear model so that the target can have a non-normal distribution, is linearly related to the factors and covariates via a specified link function, and so that the observations can be correlated. GLMM models cover a wide variety of models, from simple linear regression to complex multilevel models for non-normal longitudinal data.

Table 1. glmmnode properties
glmmnode Properties Values Property description
residual_subject_spec structured The combination of values of the specified categorical fields that uniquely define subjects within the data set
repeated_measures structured Fields used to identify repeated observations.
residual_group_spec [field1 ... fieldN] Fields that define independent sets of repeated effects covariance parameters.
residual_covariance_type
Diagonal
AR1
ARMA11
COMPOUND_SYMMETRY
IDENTITY
TOEPLITZ
UNSTRUCTURED
VARIANCE_COMPONENTS
Specifies covariance structure for residuals.
custom_target flag Indicates whether to use target defined in upstream node (false) or custom target specified by target_field (true).
target_field field Field to use as target if custom_target is true.
use_trials flag Indicates whether additional field or value specifying number of trials is to be used when target response is a number of events occurring in a set of trials. Default is false.
use_field_or_value
Field
Value
Indicates whether field (default) or value is used to specify number of trials.
trials_field field Field to use to specify number of trials.
trials_value integer Value to use to specify number of trials. If specified, minimum value is 1.
use_custom_target_reference flag Indicates whether custom reference category is to be used for a categorical target. Default is false.
target_reference_value string Reference category to use if use_custom_target_reference is true.
dist_link_combination
Nominal
Logit
GammaLog
BinomialLogit
PoissonLog
BinomialProbit
NegbinLog
BinomialLogC
Custom
Common models for distribution of values for target. Choose Custom to specify a distribution from the list provided bytarget_distribution.
target_distribution
Normal
Binomial
Multinomial
Gamma
Inverse
NegativeBinomial
Poisson
Distribution of values for target when dist_link_combination is Custom.
link_function_type
Identity
LogC
Log
CLOGLOGLogit
NLOGLOGPROBIT
POWER
CAUCHIT
Link function to relate target
values to predictors.
If target_distribution is
Binomial you can use any
of the listed link functions.
If target_distribution is
Multinomial you can use
CLOGLOG, CAUCHIT, LOGIT,
NLOGLOG, or PROBIT.
If target_distribution is
anything other than Binomial or
Multinomial you can use
IDENTITY, LOG, or POWER.
link_function_param number Link function parameter value to use. Only applicable if normal_link_function or link_function_type is POWER.
use_predefined_inputs flag Indicates whether fixed effect fields are to be those defined upstream as input fields (true) or those from fixed_effects_list (false). Default is false.
fixed_effects_list structured If use_predefined_inputs is false, specifies the input fields to use as fixed effect fields.
use_intercept flag If true (default), includes the intercept in the model.
random_effects_list structured List of fields to specify as random effects.
regression_weight_field field Field to use as analysis weight field.
use_offset
Noneoffset_valueoffset_field
Indicates how offset is specified. Value None means no offset is used.
offset_value number Value to use for offset if use_offset is set to offset_value.
offset_field field Field to use for offset value if use_offset is set to offset_field.
target_category_order
AscendingDescendingData
Sorting order for categorical targets. Value Data specifies using the sort order found in the data. Default is Ascending.
inputs_category_order
AscendingDescendingData
Sorting order for categorical predictors. Value Data specifies using the sort order found in the data. Default is Ascending.
max_iterations integer Maximum number of iterations the algorithm will perform. A non-negative integer; default is 100.
confidence_level integer Confidence level used to compute interval estimates of the model coefficients. A non-negative integer; maximum is 100, default is 95.
degrees_of_freedom_method
FixedVaried
Specifies how degrees of freedom are computed for significance test.
test_fixed_effects_coeffecients
ModelRobust
Method for computing the parameter estimates covariance matrix.
use_p_converge flag Option for parameter convergence.
p_converge number Blank, or any positive value.
p_converge_type
AbsoluteRelative
 
use_l_converge flag Option for log-likelihood convergence.
l_converge number Blank, or any positive value.
l_converge_type
AbsoluteRelative
 
use_h_converge flag Option for Hessian convergence.
h_converge number Blank, or any positive value.
h_converge_type
AbsoluteRelative
 
max_fisher_step integer  
sing_tolerance number  
use_model_name flag Indicates whether to specify a custom name for the model (true) or to use the system-generated name (false). Default is false.
model_name string If use_model_name is true, specifies the model name to use.
confidence
onProbabilityonIncrease
Basis for computing scoring confidence value: highest predicted probability, or difference between highest and second highest predicted probabilities.
score_category_probabilities flag If true, produces predicted probabilities for categorical targets. Default is false.
max_categories integer If score_category_probabilities is true, specifies maximum number of categories to save.
score_propensity flag If true, produces propensity scores for flag target fields that indicate likelihood of "true" outcome for field.
emeans structure For each categorical field from the fixed effects list, specifies whether to produce estimated marginal means.
covariance_list structure For each continuous field from the fixed effects list, specifies whether to use the mean or a custom value when computing estimated marginal means.
mean_scale
OriginalTransformed
Specifies whether to compute estimated marginal means based on the original scale of the target (default) or on the link function transformation.
comparison_adjustment_method
LSDSEQBONFERRONISEQSIDAK
Adjustment method to use when performing hypothesis tests with multiple contrasts.
use_trials_field_or_value
"field" "value"
residual_subject_ui_spec array Residual subject specification: The combination of values of the specified categorical fields should uniquely define subjects within the dataset. For example, a single Patient ID field should be sufficient to define subjects in a single hospital, but the combination of Hospital ID and Patient ID may be necessary if patient identification numbers are not unique across hospitals.
repeated_ui_measures array The fields specified here are used to identify repeated observations. For example, a single variable Week might identify the 10 weeks of observations in a medical study, or Month and Day might be used together to identify daily observations over the course of a year.
spatial_field array The variables in this list specify the coordinates of the repeated observations when one of the spatial covariance types is selected for the repeated covariance type.
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