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random vectors for multivariate explanatory variables #36

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akgandhi opened this issue Nov 25, 2020 · 8 comments
Open

random vectors for multivariate explanatory variables #36

akgandhi opened this issue Nov 25, 2020 · 8 comments
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enhancement New feature or request

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@akgandhi
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There does not seem to be an obvious mechanism to define random vectors in a DAG, for example, to use in a multivariate regression. Is there an option/ability to apply dag_plate to index over the X_i's in a multivariate random vector (where the underlying data object would then be a design matrix).
@flyaflya
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flyaflya commented Dec 1, 2020

Thanks Amit for the note. There is some support for this already, but it is non-obvious and under-supported with documentation at this moment. Maybe you can help guide my development with some use cases you would like to see?

In regards to current functionality, you can see a demonstration of design matrices in a DAG using the dev version of causact:

remotes::install_github("flyaflya/causact")

and looking at examples 3, 4, and 5 of this R-script: https://raw.githubusercontent.com/flyaflya/causact/master/gretaExamples.R

The DAG for example #3 is shown below.
Example DAG is shown below:
image

Let me know what you think. I can make a release from the DEV version so this capability is available to your students via CRAN.

@akgandhi
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akgandhi commented Dec 1, 2020 via email

@akgandhi
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akgandhi commented Dec 3, 2020

Hi
I am trying the code from "greta example #3" in the script provided above with the developer version of causact you recommended. In particular:

packageVersion("causact")
[1] ‘0.3.3.9000’

The graph is processed and generates a visual DAG (the same one you show above). However when trying to run the actual simulation the following error arises:

drawsDF = graph %>% dag_greta()
Error: objects of class function cannot be coerced to greta arrays

Does this error arise for you?

@flyaflya
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flyaflya commented Dec 3, 2020

My bad. The "int" node needs to be defined in the DAG. Without the "int" node, the code thinks the int in int + design %*% coefs is the function int. If you add the node, it works:

library(greta)
library(tidyverse)
library(causact)
### greta example #3
graph = dag_create() %>%
  dag_node("Rating","y",
           data = attitude$rating,
           rhs = normal(mu,sd)) %>%
  dag_node("Exp. Rating","mu",
           rhs = int + design %*% coefs,
           child = "y") %>%
  dag_node("Intercept","int",
           rhs = normal(0,10),
           child = "mu") %>%
  dag_node("Std Dev of Ratings","sd",
           rhs = cauchy(0,3, truncation = c(0,Inf)),
           child = "y") %>%
  dag_node("Predictor Coefficients","coefs",
           rhs = normal(0,10),
           child = "mu",
           extract = FALSE) %>%
  dag_node("Predictors","design",
           data = attitude[, 2:7],
           keepAsDF = TRUE,
           child = "mu") %>%
  dag_plate(descr = "Predictors",label = "j",
            nodeLabels = c("coefs"),
            data = names(attitude[, 2:7])) %>%
  dag_plate(descr = "Observation", label = "i",
            nodeLabels = c("mu","y"))

graph %>% dag_render()
graph %>% dag_render(shortLabel = TRUE)
drawsDF = graph %>% dag_greta()
drawsDF %>% dagp_plot()

I updated the gretaExamples file on github to reflect the change. Thanks for finding this bug.

@akgandhi
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akgandhi commented Dec 4, 2020

This works! Thank you. Two quick follow up questions (happy to start a new issue if you think it more appropriate).

  1. are there any examples where the extract = TRUE option would arise (vs passing the index variable as a separate parent node to the common child).

  2. I see here you use the plate iterator to define a vector greta array for "coeffs". Could we equivalently have used an array valued greta distribution (e.g., multivariate normal) - will test out myself.

@flyaflya
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flyaflya commented Dec 5, 2020 •
edited

  1. Most of my multi-level examples use extract=TRUE which is the default. This is not readily clear as I often add the index variable as a parent in these cases using dag_plate(..., addDataNode = TRUE). The dotted line in this DAG from the top of Chapter 25 (causact.com) indicates that extract=TRUE:
    image

other examples with extract = TRUE are shown in the readme file from CRAN: https://cran.r-project.org/web/packages/causact/readme/README.html

  1. not sure about this off the top of my head. let me know your findings :-)

@akgandhi
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akgandhi commented Apr 1, 2021 via email

@flyaflya
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flyaflya commented Apr 1, 2021 via email

@flyaflya flyaflya added the enhancement New feature or request label Jun 2, 2022
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