glmmnode properties
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A 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. Generalized linear mixed models cover a wide variety of models, from simple linear regression to complex multilevel models for non-normal longitudinal data. |
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
CLOGLOG
Logit
NLOGLOG
PROBIT
POWER
CAUCHIT
|
Link function to relate target
values to predictors. If target_distribution isBinomial you can use anyof 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
|
None
offset_value
offset_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
|
Ascending
Descending
Data
|
Sorting order for categorical targets. Value Data specifies using the sort
order found in the data. Default is Ascending . |
inputs_category_order
|
Ascending
Descending
Data
|
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
|
Fixed
Varied
|
Specifies how degrees of freedom are computed for significance test. |
test_fixed_effects_coeffecients
|
Model
Robust
|
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 |
|
|
use_l_converge |
flag | Option for log-likelihood convergence. |
l_converge |
number | Blank, or any positive value. |
l_converge_type |
|
|
use_h_converge |
flag | Option for Hessian convergence. |
h_converge |
number | Blank, or any positive value. |
h_converge_type |
|
|
max_fisher_steps |
integer | |
singularity_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
|
onProbability
onIncrease
|
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
|
Original
Transformed
|
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
|
LSD
SEQBONFERRONI
SEQSIDAK
|
Adjustment method to use when performing hypothesis tests with multiple contrasts. |