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Last updated: Feb 11, 2025
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. GLMM models cover a wide
variety of models, from simple linear regression to complex multilevel models for non-normal
longitudinal data.
Properties |
Values | Property description |
---|---|---|
|
structured | The combination of values of the specified categorical fields that uniquely define subjects within the data set |
|
structured | Fields used to identify repeated observations. |
|
[field1 ... fieldN] | Fields that define independent sets of repeated effects covariance parameters. |
|
|
Specifies covariance structure for residuals. |
|
flag | Indicates whether to use target defined in upstream node ( ) or custom
target specified by ( ). |
|
field | Field to use as target if is . |
|
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
. |
|
|
Indicates whether field (default) or value is used to specify number of trials. |
|
field | Field to use to specify number of trials. |
|
integer | Value to use to specify number of trials. If specified, minimum value is 1. |
|
flag | Indicates whether custom reference category is to be used for a categorical target. Default
is . |
|
string | Reference category to use if is
. |
|
|
Common models for distribution of values for target. Choose to
specify a distribution from the list provided by . |
|
|
Distribution of values for target when is
. |
|
|
Link function to relate target
values to predictors. If is you can use anyof the listed link functions. If is you can use , , , , or .If is anything other than or you can use , , or . |
|
number | Link function parameter value to use. Only applicable if
or is
. |
|
flag | Indicates whether fixed effect fields are to be those defined upstream as input fields
( ) or those from ( ).
Default is . |
|
structured | If is , specifies the input
fields to use as fixed effect fields. |
|
flag | If (default), includes the intercept in the model. |
|
structured | List of fields to specify as random effects. |
|
field | Field to use as analysis weight field. |
|
|
Indicates how offset is specified. Value means no offset is
used. |
|
number | Value to use for offset if is set to
. |
|
field | Field to use for offset value if is set to
. |
|
|
Sorting order for categorical targets. Value specifies using the sort
order found in the data. Default is . |
|
|
Sorting order for categorical predictors. Value specifies using the
sort order found in the data. Default is . |
|
integer | Maximum number of iterations the algorithm will perform. A non-negative integer; default is 100. |
|
integer | Confidence level used to compute interval estimates of the model coefficients. A non-negative integer; maximum is 100, default is 95. |
|
|
Specifies how degrees of freedom are computed for significance test. |
|
|
Method for computing the parameter estimates covariance matrix. |
|
flag | Option for parameter convergence. |
|
number | Blank, or any positive value. |
|
|
|
|
flag | Option for log-likelihood convergence. |
|
number | Blank, or any positive value. |
|
|
|
|
flag | Option for Hessian convergence. |
|
number | Blank, or any positive value. |
|
|
|
|
integer | |
|
number | |
|
flag | Indicates whether to specify a custom name for the model ( ) or to use
the system-generated name ( ). Default is . |
|
string | If is , specifies the model name to
use. |
|
|
Basis for computing scoring confidence value: highest predicted probability, or difference between highest and second highest predicted probabilities. |
|
flag | If , produces predicted probabilities for categorical targets. Default
is . |
|
integer | If is , specifies maximum
number of categories to save. |
|
flag | If , produces propensity scores for flag target fields that indicate
likelihood of "true" outcome for field. |
|
structure | For each categorical field from the fixed effects list, specifies whether to produce estimated marginal means. |
|
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. |
|
|
Specifies whether to compute estimated marginal means based on the original scale of the target (default) or on the link function transformation. |
|
|
Adjustment method to use when performing hypothesis tests with multiple contrasts. |
|
|
|
|
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. |
|
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. |
|
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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