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linear-mixed-effects: Linear mixed effects modeling

Citations
  • Skipper Seabold and Josef Perktold. Statsmodels: econometric and statistical modeling with python. In Proceedings of the 9th Python in Science Conference, volume 57, 61. SciPy society Austin, 2010.

Docstring:

Usage: qiime longitudinal linear-mixed-effects [OPTIONS]

  Linear mixed effects models evaluate the contribution of exogenous
  covariates "group_columns" and "random_effects" to a single dependent
  variable, "metric". Perform LME and plot line plots of each group column.
  A feature table artifact is required input, though whether "metric" is
  derived from the feature table or metadata is optional.

Inputs:
  --i-table ARTIFACT FeatureTable[RelativeFrequency]
                          Feature table containing metric.          [optional]
Parameters:
  --m-metadata-file METADATA...
    (multiple arguments   Sample metadata file containing
     will be merged)      individual-id-column.                     [required]
  --p-state-column TEXT   Metadata column containing state (time) variable
                          information.                              [required]
  --p-individual-id-column TEXT
                          Metadata column containing IDs for individual
                          subjects.                                 [required]
  --p-metric TEXT         Dependent variable column name. Must be a column
                          name located in the metadata or feature table files.
                                                                    [optional]
  --p-group-columns TEXT  Comma-separated list (without spaces) of metadata
                          columns to use as independent covariates used to
                          determine mean structure of "metric".     [optional]
  --p-random-effects TEXT Comma-separated list (without spaces) of metadata
                          columns to use as independent covariates used to
                          determine the variance and covariance structure
                          (random effects) of "metric". To add a random slope,
                          the same value passed to "state-column" should be
                          passed here. A random intercept for each individual
                          is set by default and does not need to be passed
                          here.                                     [optional]
  --p-palette TEXT Choices('Set1', 'Set2', 'Set3', 'Pastel1', 'Pastel2',
    'Paired', 'Accent', 'Dark2', 'tab10', 'tab20', 'tab20b', 'tab20c',
    'viridis', 'plasma', 'inferno', 'magma', 'terrain', 'rainbow')
                          Color palette to use for generating boxplots.
                                                             [default: 'Set1']
  --p-lowess / --p-no-lowess
                          Estimate locally weighted scatterplot smoothing.
                          Note that this will eliminate confidence interval
                          plotting.                           [default: False]
  --p-ci NUMBER           Size of the confidence interval for the regression
    Range(0, 100)         estimate.                              [default: 95]
  --p-formula TEXT        R-style formula to use for model specification. A
                          formula must be used if the "metric" parameter is
                          None. Note that the metric and group columns
                          specified in the formula will override metric and
                          group columns that are passed separately as
                          parameters to this method. Formulae will be in the
                          format "a ~ b + c", where "a" is the metric
                          (dependent variable) and "b" and "c" are independent
                          covariates. Use "+" to add a variable; "+ a:b" to
                          add an interaction between variables a and b; "*" to
                          include a variable and all interactions; and "-" to
                          subtract a particular term (e.g., an interaction
                          term). See
                          https://patsy.readthedocs.io/en/latest/formulas.html
                          for full documentation of valid formula operators.
                          Always enclose formulae in quotes to avoid
                          unpleasant surprises.                     [optional]
Outputs:
  --o-visualization VISUALIZATION
                                                                    [required]
Miscellaneous:
  --output-dir PATH       Output unspecified results to a directory
  --verbose / --quiet     Display verbose output to stdout and/or stderr
                          during execution of this action. Or silence output
                          if execution is successful (silence is golden).
  --citations             Show citations and exit.
  --help                  Show this message and exit.

Import:

from qiime2.plugins.longitudinal.visualizers import linear_mixed_effects

Docstring:

Linear mixed effects modeling

Linear mixed effects models evaluate the contribution of exogenous
covariates "group_columns" and "random_effects" to a single dependent
variable, "metric". Perform LME and plot line plots of each group column. A
feature table artifact is required input, though whether "metric" is
derived from the feature table or metadata is optional.

Parameters
----------
metadata : Metadata
    Sample metadata file containing individual_id_column.
state_column : Str
    Metadata column containing state (time) variable information.
individual_id_column : Str
    Metadata column containing IDs for individual subjects.
metric : Str, optional
    Dependent variable column name. Must be a column name located in the
    metadata or feature table files.
group_columns : Str, optional
    Comma-separated list (without spaces) of metadata columns to use as
    independent covariates used to determine mean structure of "metric".
random_effects : Str, optional
    Comma-separated list (without spaces) of metadata columns to use as
    independent covariates used to determine the variance and covariance
    structure (random effects) of "metric". To add a random slope, the same
    value passed to "state_column" should be passed here. A random
    intercept for each individual is set by default and does not need to be
    passed here.
table : FeatureTable[RelativeFrequency], optional
    Feature table containing metric.
palette : Str % Choices('Set1', 'Set2', 'Set3', 'Pastel1', 'Pastel2', 'Paired', 'Accent', 'Dark2', 'tab10', 'tab20', 'tab20b', 'tab20c', 'viridis', 'plasma', 'inferno', 'magma', 'terrain', 'rainbow'), optional
    Color palette to use for generating boxplots.
lowess : Bool, optional
    Estimate locally weighted scatterplot smoothing. Note that this will
    eliminate confidence interval plotting.
ci : Float % Range(0, 100), optional
    Size of the confidence interval for the regression estimate.
formula : Str, optional
    R-style formula to use for model specification. A formula must be used
    if the "metric" parameter is None. Note that the metric and group
    columns specified in the formula will override metric and group columns
    that are passed separately as parameters to this method. Formulae will
    be in the format "a ~ b + c", where "a" is the metric (dependent
    variable) and "b" and "c" are independent covariates. Use "+" to add a
    variable; "+ a:b" to add an interaction between variables a and b; "*"
    to include a variable and all interactions; and "-" to subtract a
    particular term (e.g., an interaction term). See
    https://patsy.readthedocs.io/en/latest/formulas.html for full
    documentation of valid formula operators. Always enclose formulae in
    quotes to avoid unpleasant surprises.

Returns
-------
visualization : Visualization