Using raster data in Netlogo

I am trying to replicate the procedure described on page 111-114 in AGIS. The generated clipped population data file cannot be read into NetLogo. After some digging, here is the problem and the solution is really simple: just add '-co force_cellsize=true' to the text box for additional command-line parameters.

Unix is turning 50

Wow!

NetLogo

I enjoyed reading the book Agent-Based Modeling & Geographical Information Systems. I first got interested in agent-based modeling around 1999. At that time, the only software platform for this kind of modeling is Mathematica. Now with free and open source software like NetLogo, it is much easier to translate ideas into executable code and see the results.

A large number of agent-based models have been developed. OpenABM is an online repository for such models, the majority of which were developed in NetLogo.

The red-blue divide in the US

1. Diverging economics
2. Diverging life expectancy
• Collapse of traditional values

Interactive computing using C++ within the Atom editor

With the xeus-cling project, it is possible to do interactive computing using C++ inside a Jupyter notebook. With the Hydrogen project, I would think that the same can be done inside the Atom editor. I gave it a try with no luck. The problem was that while xeus-cling created a number of C++ kernels such as "C++ 11", "C++14", and "C++17", it did not create a kernel named "C++", which is what Hydrogen looks for. Follow the suggestion mentioned in this post, I was able to get rid of the error message and get the system to work. Wow!

Spatial Data Analysis with INLA

A post on the use of INLA. Worth reading.

‘Metrics Monday: Using the Front-Door Criterion to Estimate Causal Effects in a Regression Context (Updated)

Useful information here.

How to use math symbols with ggdag

The wonderful package ggdag can easily make DAG like this:

However, what we really want to include in publications is something like this:

The second one can include subscript and superscript, among many others. After some tweaking, I found a solution, not perfect but usable for now.

-----------------------------------------------------------------
library(dagitty)
library(ggdag)
library(ggraph)
library(cowplot)
library(dplyr)

```{r, echo=FALSE}
dag <- dagify(Y1 ~ X + Z1 + Z0 + U + P,
              Y0 ~ Z0 + U,
              X ~ Y0 + Z1 + Z0 + P,
              Z1 ~ Z0,
              P ~ Y0 + Z1 + Z0,
              exposure = "X",
              outcome = "Y1")


dag %>% 
  tidy_dagitty(layout = "auto", seed = 12345) %>%
  arrange(name) %>% 
  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) +
  geom_dag_point() +
  geom_dag_edges() +
  geom_dag_text(parse = TRUE, label = c("P", "U", "X", expression(Y[0]), expression(Y[1]), expression(Z[0]), expression(Z[1]))) +
  theme_dag()
-----------------------------------------------------------------

Here the trick is to sort the tidy version of the DAG data by "name", then we can assign labels by the order of the name of the nodes. I hope a more automated approach can be developed in the future.

By the way, with the package latex2exp, it is straightforward to use LaTeX instead of plotmath commands.

A useful disk.frame tutorial

Useful package and useful tutorial here.