Estimates a regression where documents are the units, the outcome is the proportion of each document about a topic in an STM model and the covariates are document-meta data. This procedure incorporates measurement uncertainty from the STM model using the method of composition.

1 2 |

`formula` |
A formula for the regression. It should have an integer or vector of numbers on the left-hand side and an equation with covariates on the right hand side. See Details for more information. |

`stmobj` |
Model output from STM |

`metadata` |
A dataframe where all predictor variables in the formula can
be found. If |

`uncertainty` |
Which procedure should be used to approximate the measurement uncertainty in the topic proportions. See details for more information. Defaults to the Global approximation. |

`documents` |
If uncertainty is set to |

`nsims` |
The number of simulated draws from the variational posterior. Defaults to 25. This can often go even lower without affecting the results too dramatically. |

`prior` |
This argument allows the user to specify a ridge penalty to be
added to the least squares solution for the purposes of numerical stability.
If its a scalar it is added to all coefficients. If its a matrix it should
be the prior precision matrix (a diagonal matrix of the same dimension as
the |

This function performs a regression where topic-proportions are the outcome variable. This allows us to conditional expectation of topic prevalence given document characteristics. Use of the method of composition allows us to incorporate our estimation uncertainty in the dependent variable. Mechanically this means we draw a set of topic proportions from the variational posterior, compute our coefficients, then repeat. To compute quantities of interest we simulate within each batch of coefficients and then average over all our results.

The formula specifies the nature of the linear model. On the left hand-side
we use a vector of integers to indicate the topics to be included as outcome
variables. If left blank then the default of all topics is used. On the
right hand-side we can specify a linear model of covariates including
standard transformations. Thus the model `2:4 ~ var1 + s(var2)`

would
indicate that we want to run three regressions on Topics 2, 3 and 4 with
predictor variables `var1`

and a b-spline transformed `var2`

. We
encourage the use of spline functions for non-linear transformations of
variables.

The function allows the user to specify any variables in the model.
However, we caution that for the assumptions of the method of composition to
be the most plausible the topic model should contain at least all the
covariates contained in the `estimateEffect`

regression. However the
inverse need not be true. The function will automatically check whether the
covariate matrix is singular which generally results from linearly dependent
columns. Some common causes include a factor variable with an unobserved
level, a spline with degrees of freedom that are too high, or a spline with
a continuous variable where a gap in the support of the variable results in
several empty basis functions. In these cases the function will still
estimate by adding a small ridge penalty to the likelihood. However, we
emphasize that while this will produce an estimate it is only identified by
the penalty. In many cases this will be an indication that the user should
specify a different model.

The function can handle factors and numeric variables. Dates should be converted to numeric variables before analysis.

We offer several different methods of incorporating uncertainty. Ideally we
would want to use the covariance matrix that governs the variational
posterior for each document (*ν*). The updates for the global
parameters rely only on the sum of these matrices and so we do not store
copies for each individual document. The default uncertainty method
`Global`

uses an approximation to the average covariance matrix formed
using the global parameters. The uncertainty method `Local`

steps
through each document and updates the parameters calculating and then saving
the local covariance matrix. The option `None`

simply uses the map
estimates for *θ* and does not incorporate any uncertainty. We
strongly recommend the `Global`

approximation as it provides the best
tradeoff of accuracy and computational tractability.

Effects are plotted based on the results of `estimateEffect`

which contains information on how the estimates are constructed. Note that
in some circumstances the expected value of a topic proportion given a
covariate level can be above 1 or below 0. This is because we use a Normal
distribution rather than something constrained to the range between 0 and 1.
If a continuous variable goes above 0 or 1 within the range of the data it
may indicate that a more flexible non-linear specification is needed (such
as using a spline or a spline with greater degrees of freedom).

`parameters` |
A list of K elements each corresponding to a topic. Each element is itself a list of n elements one per simulation. Each simulation contains the MLE of the parameter vector and the variance covariance matrix |

`topics` |
The topic vector |

`call` |
The original call |

`uncertainty` |
The user choice of uncertainty measure |

`formula` |
The formula object |

`data` |
The original user provided meta data. |

`modelframe` |
The model frame created from the formula and data |

`varlist` |
A variable list useful for mapping terms with columns in the design matrix |

`plot.estimateEffect`

`summary.estimateEffect`

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
#Just one topic (note we need c() to indicate it is a vector)
prep <- estimateEffect(c(1) ~ treatment, gadarianFit, gadarian)
summary(prep)
plot(prep, "treatment", model=gadarianFit, method="pointestimate")
#three topics at once
prep <- estimateEffect(1:3 ~ treatment, gadarianFit, gadarian)
summary(prep)
plot(prep, "treatment", model=gadarianFit, method="pointestimate")
#with interactions
prep <- estimateEffect(1 ~ treatment*s(pid_rep), gadarianFit, gadarian)
summary(prep)
``` |

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