* DOGE firings vs Ideology Estimates on Tue Feb 25 19:16:30 2025 - input data directory: . - results directory: . - transcript to: ./2025-02-24-trump-polit-targets-agency-ideology-and-DOGE-mass-firings.txt Loading data: ------------- * Loading Bonica data from ./2025-02-24-trump-polit-targets-agency-ideology-and-DOGE-mass-firings.tsv - Found 123 rows x 7 columns: Agency, Perceived_Ideology_Estimate, Annual_Budget_USD, Total_Staff, doge_layoffs, targeted_for_dismantling, source Analyses: --------- * Scatterplots colored by layoffs/dismantling: - Scatterplot of Total_Staff vs ideology to ./2025-02-24-trump-polit-targets-Total_Staff-ideology-firings.png. - Scatterplot of Annual_Budget_USD vs ideology to ./2025-02-24-trump-polit-targets-Annual_Budget_USD-ideology-firings.png.* Intended actions on agencies, by ideology score: Left Right Totals Both 2 0 2 Dismantling Only 0 0 0 Layoffs Only 25 7 32 Neither 30 59 89 Totals 57 66 123 Fisher's Exact Test for Count Data data: freqs p-value = 1.076e-05 alternative hypothesis: two.sided * Biclustering correlations to ./2025-02-24-trump-polit-targets-bicluster.png. - Correlation matrix (pairwise complete observations): doge_layoffs Perceived_Ideology_Estimate doge_layoffs 1.00 -0.41 Perceived_Ideology_Estimate -0.41 1.00 Annual_Budget_USD 0.26 0.09 Total_Staff 0.07 0.33 Annual_Budget_USD Total_Staff doge_layoffs 0.26 0.07 Perceived_Ideology_Estimate 0.09 0.33 Annual_Budget_USD 1.00 0.65 Total_Staff 0.65 1.00 * Regressions: - Silly linear regression (uncrossvalidated): Call: lm(formula = doge_layoffs ~ Perceived_Ideology_Estimate + log(Annual_Budget_USD) + log(Total_Staff), data = bonicaData) Residuals: Min 1Q Median 3Q Max -0.6321 -0.2577 -0.1058 0.2919 0.9369 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.14007 0.41633 -2.738 0.00717 ** Perceived_Ideology_Estimate -0.21011 0.03938 -5.335 4.91e-07 *** log(Annual_Budget_USD) 0.05627 0.02396 2.349 0.02056 * log(Total_Staff) 0.01986 0.02865 0.693 0.48948 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3947 on 114 degrees of freedom (5 observations deleted due to missingness) Multiple R-squared: 0.2661, Adjusted R-squared: 0.2468 F-statistic: 13.78 on 3 and 114 DF, p-value: 9.938e-08 - Slightly less silly logistic regression (uncrossvalidated): Call: glm(formula = doge_layoffs ~ Perceived_Ideology_Estimate + log(Annual_Budget_USD) + log(Total_Staff), family = binomial(link = "logit"), data = bonicaData) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -9.4165 2.8449 -3.310 0.000933 *** Perceived_Ideology_Estimate -1.3391 0.3136 -4.270 1.95e-05 *** log(Annual_Budget_USD) 0.2873 0.1518 1.893 0.058415 . log(Total_Staff) 0.2001 0.1871 1.070 0.284681 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 141.71 on 117 degrees of freedom Residual deviance: 107.04 on 114 degrees of freedom (5 observations deleted due to missingness) AIC: 115.04 Number of Fisher Scoring iterations: 5 # R2 for Generalized Linear Regression R2: 0.262 adj. R2: 0.248 - Simplest model using just agency perceived ideology as a predictor: Call: glm(formula = doge_layoffs ~ Perceived_Ideology_Estimate, family = binomial(link = "logit"), data = bonicaData) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.1287 0.2398 -4.706 2.52e-06 *** Perceived_Ideology_Estimate -1.1480 0.2720 -4.220 2.44e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 145.03 on 122 degrees of freedom Residual deviance: 121.62 on 121 degrees of freedom AIC: 125.62 Number of Fisher Scoring iterations: 5 # R2 for Generalized Linear Regression R2: 0.161 adj. R2: 0.148 - Doing cross-validated, LASSO-regluated logistic regression with glmnet() o Plot of coeffs vs lambda to ./2025-02-24-trump-polit-targets-glmnet-coeffs-vs-lambda.png o Plot of crossvalidated error vs lambda to ./2025-02-24-trump-polit-targets-glmnet-cv-lse-vs-lambda.png o Coefficients at lambda min: 4 x 1 sparse Matrix of class "dgCMatrix" s1 (Intercept) -8.7953565 Perceived_Ideology_Estimate -1.2363961 Annual_Budget_USD 0.2782578 Total_Staff 0.1577868 o Coefficients at lambda 1se: 4 x 1 sparse Matrix of class "dgCMatrix" s1 (Intercept) -2.43285940 Perceived_Ideology_Estimate -0.46511838 Annual_Budget_USD 0.06741818 Total_Staff . * Computing confusion matrix for lambda.1se: lambda.1se doge_layoffs FALSE TRUE FALSE 84 0 TRUE 33 1 - Overall correct = 72.03% - Overall incorrect = 27.97% - PPV = Pr(DOGE layoffs | model positive) = 100.00% - NPV = Pr(No DOGE layoffs | model negative) = 71.79% - FDR = Pr(No DOGE layoffs | model positive) = 0.00% - NOV = Pr(DOGE layoffs | model negative) = 28.21% * Computing confusion matrix for lambda.min: lambda.min doge_layoffs FALSE TRUE FALSE 75 9 TRUE 18 16 - Overall correct = 77.12% - Overall incorrect = 22.88% - PPV = Pr(DOGE layoffs | model positive) = 64.00% - NPV = Pr(No DOGE layoffs | model negative) = 80.65% - FDR = Pr(No DOGE layoffs | model positive) = 36.00% - NOV = Pr(DOGE layoffs | model negative) = 19.35% * DOGE firings vs Ideology Estimates completed Tue Feb 25 19:16:31 2025 (1.0 sec elapsed).