Updated documentation

Author: emcghee73

2025/README_model_no_2024_pvote.md

@@ -18,7 +18,19 @@
 
 ## Methodology: Models without 2024 Presidential Vote
 
-### The Big Picture
+This is the model we use when we do not have the latest geographic
+election data for a particular state. When we do have such data, we use
+a slightly different model described in README_model_with_2024_pvote.md.
+
+To run this model for Congress, use the code in
+planscore_models_congress.R in the section headed “CONGRESS: 2024
+PRESIDENTIAL VOTE NOT AVAILABLE.” To run the model for state
+legislatures, use the code in planscore_models_state_leg.R. In both
+cases, the code asks the user at the top of the file to designate a
+pathname for saving output. The code otherwise automatically draws the
+necessary data from this github page.
+
+### The Models: The Big Picture
 
 We use the correlation between the presidential vote on the one hand and
 state legislative or congressional votes on the other to predict how new
@@ -33,10 +45,6 @@ which partisan tides have changed party performance from one election to
 the next. This enables us to generate the most accurate, data-driven,
 and transparent prediction we can.
 
-This is the model we use when we do not have the latest geographic
-election data for a particular state. When we do have such data, we use
-a slightly different model described here.
-
 ### The Details
 
 We use a Bayesian hierarchical model of district-level election returns,
@@ -159,7 +167,7 @@ partisan tides that occurred over the last decade.
 
 Full results for our four separate models can be found below.
 
-    ## ### Congress prediction model with incumbency ($k=2$)
+### Congress prediction model with incumbency ($k=2$)
 
 | Term | Estimate | 95% Credible Interval |
 |:---|---:|---:|
@@ -180,7 +188,7 @@ Full results for our four separate models can be found below.
 | Intercept – Incumbency ($\rho\,\sigma_{\beta_{0c}}\sigma_{\beta_{2c}}$) | -0.15 | \[-0.73, 0.53\] |
 | Pres. vote – Incumbency ($\rho\,\sigma_{\beta_{1c}}\sigma_{\beta_{2c}}$) | -0.55 | \[-0.94, 0.25\] |
 
-    ## ### Congress prediction model without incumbency ($k=1$)
+### Congress prediction model without incumbency ($k=1$)
 
 | Term | Estimate | 95% Credible Interval |
 |:---|---:|---:|
@@ -196,10 +204,11 @@ Full results for our four separate models can be found below.
 | **CYCLE-LEVEL** *Correlations* |  |  |
 | Intercept – Pres. vote ($\rho\,\sigma_{\beta_{0c}}\sigma_{\beta_{1c}}$) | -0.50 | \[-0.93, 0.35\] |
 
-    ## 
-    ## > Note: Model estimated in brms for R. Model based on 4 MCMC chains run for 6000 iterations each with a 2000 iteration warm-up. All model parameters converged well with $\hat{R}\le 1.01$.
+> Note: Model estimated in brms for R. Model based on 4 MCMC chains run
+> for 6000 iterations each with a 2000 iteration warm-up. All model
+> parameters converged well with $\hat{R}\le 1.01$.
 
-    ## ### State legislature prediction model with incumbency ($k=2$)
+### State legislature prediction model with incumbency ($k=2$)
 
 | Term | Estimate | 95% Credible Interval |
 |:---|---:|---:|
@@ -220,10 +229,11 @@ Full results for our four separate models can be found below.
 | Intercept – Incumbency ($\rho\,\sigma_{\beta_{0c}}\sigma_{\beta_{2c}}$) | 0.20 | \[-0.50, 0.77\] |
 | Pres. vote – Incumbency ($\rho\,\sigma_{\beta_{1c}}\sigma_{\beta_{2c}}$) | -0.75 | \[-0.97, -0.21\] |
 
-    ## 
-    ## > Note: Model estimated in brms for R. Model based on 4 MCMC chains run for 6000 iterations each with a 2000 iteration warm-up. All model parameters converged well with $\hat{R}\le 1.01$.
+> Note: Model estimated in brms for R. Model based on 4 MCMC chains run
+> for 6000 iterations each with a 2000 iteration warm-up. All model
+> parameters converged well with $\hat{R}\le 1.01$.
 
-    ## ### State legislature prediction model without incumbency ($k=1$)
+### State legislature prediction model without incumbency ($k=1$)
 
 | Term | Estimate | 95% Credible Interval |
 |:---|---:|---:|
@@ -239,5 +249,6 @@ Full results for our four separate models can be found below.
 | **CYCLE-LEVEL** *Correlations* |  |  |
 | Intercept – Pres. vote ($\rho\,\sigma_{\beta_{0c}}\sigma_{\beta_{1c}}$) | -0.40 | \[-0.87, 0.33\] |
 
-    ## 
-    ## > Note: Model estimated in brms for R. Model based on 4 MCMC chains run for 6000 iterations each with a 2000 iteration warm-up. All model parameters converged well with $\hat{R}\le 1.01$.
+> Note: Model estimated in brms for R. Model based on 4 MCMC chains run
+> for 6000 iterations each with a 2000 iteration warm-up. All model
+> parameters converged well with $\hat{R}\le 1.01$.

2025/README_model_with_2024_pvote.md

@@ -18,6 +18,21 @@
 
 ## Methodology: Models with 2024 Presidential Vote
 
+This is the model we use when we have the latest geographic election
+data for a particular state. When we do not have such data, we use a
+slightly different model described in README_model_no_2024_pvote.md. At
+present we only have fully updated data for Congress, so only
+congressional results are reported on this methods page. See the
+companion page for state legislative results, and for the results of a
+modified version of the congressional model for states that do not have
+fully updated data.
+
+To run this model for Congress, use the code in
+planscore_models_congress.R in the section headed “CONGRESS: 2024
+PRESIDENTIAL VOTE AVAILABLE.” The code asks the user at the top of the
+file to designate a pathname for saving output. The code otherwise
+automatically draws the necessary data from this github page.
+
 ### The Big Picture
 
 We use the correlation between the presidential vote on the one hand and
@@ -33,14 +48,6 @@ which partisan tides have changed party performance from one election to
 the next. This enables us to generate the most accurate, data-driven,
 and transparent prediction we can.
 
-This is the model we use when we have the latest geographic election
-data for a particular state. When we do not have such data, we use a
-slightly different model described here. At present we only have fully
-updated data for Congress, so only congressional results are reported on
-this methods page. See the companion page for state legislative results,
-and for the results of a modified version of the congressional model for
-states that do not have fully updated data.
-
 ### The Details
 
 We use a Bayesian hierarchical model of district-level election returns,
@@ -155,7 +162,7 @@ partisan tides that occurred over the last decade.
 
 Full results for our four separate models can be found below.
 
-    ## ### Congress prediction model with incumbency ($k=2$)
+### Congress prediction model with incumbency ($k=2$)
 
 | Term | Estimate | 95% Credible Interval |
 |:---|---:|---:|
@@ -176,7 +183,7 @@ Full results for our four separate models can be found below.
 | Intercept – Incumbency ($\rho\,\sigma_{\beta_{0c}}\sigma_{\beta_{2c}}$) | -0.37 | \[-0.87, 0.34\] |
 | Pres. vote – Incumbency ($\rho\,\sigma_{\beta_{1c}}\sigma_{\beta_{2c}}$) | -0.59 | \[-0.94, 0.16\] |
 
-    ## ### Congress prediction model without incumbency ($k=1$)
+### Congress prediction model without incumbency ($k=1$)
 
 | Term | Estimate | 95% Credible Interval |
 |:---|---:|---:|
@@ -192,5 +199,6 @@ Full results for our four separate models can be found below.
 | **CYCLE-LEVEL** *Correlations* |  |  |
 | Intercept – Pres. vote ($\rho\,\sigma_{\beta_{0c}}\sigma_{\beta_{1c}}$) | -0.55 | \[-0.95, 0.29\] |
 
-    ## 
-    ## > Note: Model estimated in brms for R. Model based on 4 MCMC chains run for 6000 iterations each with a 2000 iteration warm-up. All model parameters converged well with $\hat{R}\le 1.01$.
+> Note: Model estimated in brms for R. Model based on 4 MCMC chains run
+> for 6000 iterations each with a 2000 iteration warm-up. All model
+> parameters converged well with $\hat{R}\le 1.01$.