# mbfc summed badness (3216) - `mbfc = mean(mbfc_fact, mbfc_bias)`; see [[different measures of domain quality]] - threshold: 80 (for domains where original mbfc_min rating is > 80, badness = 0) - good domains (original ratings > 80 have new badness scores [y-axis] of 0) - see also [[220310_140604 mbfc_min threshold 80|mbfc_min threshold 80]] # descriptives 32888 obs 17 cols | | NUnique| PercentMissing| Mean| SD| Min| Median| Max| Histogram| |:----------|-------:|--------------:|-------:|-------:|--------:|-------:|--------:|----------:| |block | 5424| 0| 2897.31| 1577.02| 0| 2973.00| 5423| ▅▅▅▅▆▆▆▆▇▇| |weight | 21| 0| 2.00| 0.22| 1.67| 2.00| 2.50| ▂▁ ▇ ▁▁▁| |sum_t0 | 8707| 0| 1163.38| 1730.45| 0.00| 575.00| 58000.00| ▇| |sum_t1 | 7055| 0| 967.95| 1982.15| 0.00| 333.34| 65246.63| ▇| |count_t0 | 460| 0| 41.57| 61.35| 0| 20.00| 2167| ▇| |count_t1 | 569| 0| 36.08| 74.30| 0| 13.00| 2585| ▇| |conditionC | 2| 0| 0.00| 0.50| -0.50| -0.50| 0.50| ▇▇| |sum_t0C | 9435| 0| 0.00| 1730.45| -1163.38| -588.38| 56836.62| ▇| |count_t0L | 460| 0| 2.98| 1.35| 0.00| 3.04| 7.68| ▂▂▅▇▇▅▃▁| |count_t0LC | 460| 0| 0.00| 1.35| -2.98| 0.06| 4.70| ▂▂▅▇▇▅▃▁| |sum_t0L | 8707| 0| 5.91| 2.18| 0.00| 6.36| 10.97| ▂ ▁▃▇▇▃| |sum_t0LC | 8707| 0| 0.00| 2.18| -5.91| 0.44| 5.06| ▂ ▁▃▇▇▃| |count_t1L | 569| 0| 2.54| 1.53| 0.00| 2.64| 7.86| ▆▃▆▇▆▄▂▁| |count_t1LC | 569| 0| 0.00| 1.53| -2.54| 0.10| 5.31| ▆▃▆▇▆▄▂▁| winsorize (99th percentile) | | NUnique| PercentMissing| Mean| SD| Min| Median| Max| Histogram| |:----------|-------:|--------------:|-------:|-------:|--------:|-------:|--------:|----------:| |block | 5424| 0| 2897.31| 1577.02| 0| 2973.00| 5423| ▅▅▅▅▆▆▆▆▇▇| |weight | 21| 0| 2.00| 0.22| 1.67| 2.00| 2.50| ▂▁ ▇ ▁▁▁| |sum_t0 | 8380| 0| 1130.58| 1490.63| 0.00| 575.00| 8150.44| ▇▂▁▁| |sum_t1 | 6729| 0| 918.93| 1565.21| 0.00| 333.34| 9420.18| ▇▁▁| |count_t0 | 287| 0| 40.52| 53.56| 0.00| 20.00| 286.00| ▇▂▁▁| |count_t1 | 343| 0| 34.11| 57.58| 0.00| 13.00| 345.26| ▇▁▁| |conditionC | 2| 0| 0.00| 0.50| -0.50| -0.50| 0.50| ▇▇| |sum_t0C | 9435| 0| 0.00| 1730.45| -1163.38| -588.38| 56836.62| ▇| |count_t0L | 287| 0| 2.98| 1.34| 0.00| 3.04| 5.66| ▃▁▂▄▆▇▆▅▃▂| |count_t0LC | 287| 0| 0.00| 1.34| -2.98| 0.06| 2.68| ▃▁▂▄▆▇▆▅▃▂| |sum_t0L | 8380| 0| 5.91| 2.18| 0.00| 6.36| 9.01| ▃ ▁▃▇▇▅▂| |sum_t0LC | 8380| 0| 0.00| 2.18| -5.91| 0.45| 3.10| ▃ ▁▃▇▇▅▂| |count_t1L | 343| 0| 2.54| 1.52| 0.00| 2.64| 5.85| ▆▅▄▇▇▇▆▄▂▂| |count_t1LC | 343| 0| 0.00| 1.52| -2.54| 0.10| 3.31| ▆▅▄▇▇▇▆▄▂▂| # models ```r > m <- feglm(sum_t1 ~ conditionC * sum_t0LC | block, dt1[domain_type == "overall"], family = "quasipoisson", vcov = "HC1") NOTE: 22 fixed-effects (109 observations) removed because of only 0 outcomes. > m GLM estimation, family = quasipoisson, Dep. Var.: sum_t1 Observations: 32,779 Fixed-effects: block: 5,402 Standard-errors: Heteroskedasticity-robust Estimate Std. Error t value Pr(>|t|) conditionC -0.003249 0.021044 -0.154409 0.8772885 sum_t0LC 0.018340 0.006302 2.910115 0.0036159 ** conditionC:sum_t0LC 0.006472 0.011458 0.564867 0.5721685 # winsorize > m <- feglm(sum_t1 ~ conditionC * sum_t0LC | block, dt1[domain_type == "overall"], family = "quasipoisson", vcov = "HC1") NOTE: 22 fixed-effects (109 observations) removed because of only 0 outcomes. > m GLM estimation, family = quasipoisson, Dep. Var.: sum_t1 Observations: 32,779 Fixed-effects: block: 5,402 Standard-errors: Heteroskedasticity-robust Estimate Std. Error t value Pr(>|t|) conditionC -0.026127 0.016582 -1.57564 1.1512e-01 # sum_t0LC 0.041948 0.005155 8.13697 4.2234e-16 *** conditionC:sum_t0LC 0.015824 0.008680 1.82308 6.8301e-02 . # ``` # user CDFs Only showing time1 summed badness values up to 10000 (max is 65246). ![[dv_mbfc_cdf_summedbadness80.png|800]] winsorize - [[220311_105048 explore mbfc CDF results with regressions at each threshold]] ![[dv_mbfc_cdf_summedbadness80_winsorize.png|800]] # 3 bins ``` # winsorize # bin sum and size > dt1[, .(sum_t0 = mean(sum_t0), n = .N), keyby = .(bin = sum_t0_bin)] bin sum_t0 n 1: _1 128.3601 10986 2: _2 609.2274 10942 3: _3 2655.6818 10960 > m <- feols(sum_t1 ~ conditionC * sum_t0_bin | block, dt1[domain_type == "overall"], vcov = "HC1") > summary(m) OLS estimation, Dep. Var.: sum_t1 Observations: 32,888 Fixed-effects: block: 5,424 Standard-errors: Heteroskedasticity-robust Estimate Std. Error t value Pr(>|t|) conditionC -35.7561 20.2638 -1.76453 0.077654 . sum_t0_bin_2 16.8885 15.6227 1.08102 0.279698 sum_t0_bin_3 749.3882 27.6176 27.13449 < 2.2e-16 *** conditionC:sum_t0_bin_2 27.8610 24.8103 1.12296 0.261464 conditionC:sum_t0_bin_3 56.1588 37.0300 1.51657 0.129386 # model comparisons > m101 <- feols(sum_t1 ~ conditionC * sum_t0_bin | block, dt1[domain_type == "overall"]) > m102 <- feols(sum_t1 ~ sum_t0_bin | block, dt1[domain_type == "overall"]) > test_wald(m102, m101) Name | Model | df | df_diff | F | p ---------------------------------------------- m102 | fixest | 32886 | | | m101 | fixest | 32883 | 3.00 | 1.31 | 0.268 # treatment effect for each bin > m201 <- feols(sum_t1 ~ condition * sum_t0_bin | block, dt1[domain_type == "overall"], vcov = "HC1") > m201 OLS estimation, Dep. Var.: sum_t1 Observations: 32,888 Fixed-effects: block: 5,424 Standard-errors: Heteroskedasticity-robust Estimate Std. Error t value Pr(>|t|) conditiont -35.75610 20.2638 -1.764531 0.077654 . # bin 1 conditiont -7.89507 13.0714 -0.603994 0.54585 # bin 2 conditiont 20.4027 31.1134 0.655754 0.51199 # bin 3 ```