chrishanretty

library(vdemdata)
library(tidyverse)
library(hrbrthemes)
library(ggtext)

data("vdem")
vdem <- vdem |>
    filter(year > 1945)

### Plot trends in v2lgfemleg

chrishanretty

### Load libraries
library(tidyverse)
library(haven)
library(fixest)
library(modelsummary)

### Download data from https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GTEBJA#
gwz <- read_rds("data_main.rds") |>
  mutate(inc_share_diff = inc_share_diff * 100) |>
  filter(main_sample == 1)

chrishanretty

chrishanretty

### Purpose of this code: estimate a conditional logit model, and 
### calculate the average effect of changing one non-induced 
### observation in each stratum to an induced observation.

library(survival)
library(marginaleffects)
data("infert")

## Create a subset, and identify a non-induced observation as a
## "focal" observation for which we will later change the value

chrishanretty

### Purpose of this code:
###  - To generate pairwise average travel times between Westminster constituencies
###  - To sample contiguous pairings of Westminster constituencies
###  - To evaluate the travel times between pairings
###  - To evaluate the compactness of pairings

### Inputs
###  - Travel time data from https://data.ubdc.ac.uk/datasets/public-transport-travel-time-matrices-for-great-britain-ttm-2023
###  - Shapefiles from https://geoportal.statistics.gov.uk/datasets/0d698c9712de4afcac9377367d831c1a_0/explore
###  - lookups from https://geoportal.statistics.gov.uk/

chrishanretty

library(tidyverse)

dat <- tribble(~party, ~seat_share, ~vote_share,
               "Lab", 411/632, .338,
               "Con", 119/632, .237,
               "LDem", 71/632, .122,
               "SNP", 9/632, .024,
               "Ind", 6/632, 0.02,
               "Ref", 4/632, .143,
               "Green", 4/632, .068,

chrishanretty

library(here)
library(tidyverse)
library(colorspace)
library(RColorBrewer)
library(hrbrthemes)

set.seed(2686)

here::i_am("R/rr_cog.R")

chrishanretty

### Load libraries
library(tidyverse)
library(haven) ## for Stata import
library(readxl)
library(marginaleffects) ## for easy predictions and contrasts
library(nnet) ## for nnet::multinom
library(furrr) ## to perform computations in parallel

### Set fewer workers if you have fewer cores
plan(multisession, workers = 12)

chrishanretty

### PURPOSE OF THIS CODE: estimate a large model in bam and speed-test
### predictions using mgcv::predict.bam and
### marginaleffects::predictions with and without discretization and
### parallelization

### ##################################################################
### load libraries
### ##################################################################
library(mgcv)
library(marginaleffects)

chrishanretty

library(tidyverse)
library(rstan)
library(cmdstanr)
dat <- read.delim("data_attractiveness_juge_results.csv")

### Set subject IDs using the same factor levels
subject_levels <- unique(c(dat$ID_left,
                           dat$ID_right))

dat <- dat |>