models_trilogue20220520
Aleksandra Khokhlova
5/20/2022
#data
load("~/Desktop/NORFACE/trilogues_number.RData")
load("~/Desktop/NORFACE/trilogues_duration.RData")
#subset data omitting outlier
trilogues_duration_noRegi <- trilogues_duration %>% filter(N_trilogues < 60)
trilogues_number_noRegi <- trilogues_number %>% filter(N_trilogues < 60)#correlation plots
correlation_data1 <- trilogues_duration %>% dplyr::select(N_trilogues,
proposal_t_eupolarizarion , proposal_t_wmean_eu_salience ,
Interinst_conflict , EP_polarization , council_dissent ,
N_eurovoc_terms , competence_length , ep_amendments_tabled , elections_days, duration_negotiations_days)
cor1 <- cor(correlation_data1, use = "complete.obs")
corrplot(cor1, method='color', tl.col = "black", tl.cex = 0.7, type = "full", tl.srt = 45, addCoef.col = "black", number.cex=0.7)
correlation_data2 <- trilogues_number %>% dplyr::select(N_trilogues,
proposal_t_eupolarizarion , proposal_t_wmean_eu_salience ,
Interinst_conflict , EP_polarization , council_dissent ,
N_eurovoc_terms , competence_length , ep_amendments_tabled , elections_days, duration_negotiations_days)
cor2 <- cor(correlation_data2, use = "complete.obs")
corrplot(cor2, method='color', tl.col = "black", tl.cex = 0.7, type = "full", tl.srt = 45,
addCoef.col = "black", number.cex=0.7)
rm(cor1, correlation_data1, cor2, correlation_data2)#Models INTENSITY OF TRILOGUES
Modelling:
1. no CAP FEs –> not enough observations
2. instead of year FE, EP term FE
3. ep_amenmdnets_tabled –> use it as a proxy for EP polarization (then we do not have issues with correlation between EP_polarization measure and inter-inst conflict)
4. Variables:
- main: EU polarization at the t of proposal and EU salience at the t of proposal (Average t between proposal and first trilogue is 13 months)
- institutional controls: Council dissent (sum of abstentions and against votes), EP polarization (n of tabled amendments), interinst conflict
- procedure controls: form, package_deal (1 if proposal is part of a package), complexity (N_eurovoc_terms), new vs amending (1 if new), days to next EP elections (from proposal) // election proximity dummy (1 if proposal submitted in the last year of EP term), competence_length, authority expansion
5. Method: negative binomial regression
6. Two sets of models: with and without outliers (regi package procedures with 85 trilogues)
#with outlier
#model with contrinous elections variable
m1_intensity <- glm.nb(N_trilogues ~
proposal_t_eupolarizarion + proposal_t_wmean_eu_salience +
Interinst_conflict + ep_amendments_tabled + council_dissent +
N_eurovoc_terms + factor(package_deal) + competence_length + factor(new) + factor(form) + elections_days + lr_proposal_probability +
factor(term),
data = trilogues_number)
summary(m1_intensity)##
## Call:
## glm.nb(formula = N_trilogues ~ proposal_t_eupolarizarion + proposal_t_wmean_eu_salience +
## Interinst_conflict + ep_amendments_tabled + council_dissent +
## N_eurovoc_terms + factor(package_deal) + competence_length +
## factor(new) + factor(form) + elections_days + lr_proposal_probability +
## factor(term), data = trilogues_number, init.theta = 2.149228153,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.1132 -0.7580 -0.2088 0.2670 4.7045
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -7.196e-01 4.658e-01 -1.545 0.12232
## proposal_t_eupolarizarion 3.226e-01 2.022e-01 1.596 0.11058
## proposal_t_wmean_eu_salience 7.771e-01 3.117e-01 2.493 0.01266 *
## Interinst_conflict -1.624e-01 9.224e-02 -1.761 0.07826 .
## ep_amendments_tabled 2.537e-03 4.936e-04 5.141 2.73e-07 ***
## council_dissent 6.212e-02 3.000e-02 2.070 0.03841 *
## N_eurovoc_terms 3.424e-02 2.485e-02 1.378 0.16827
## factor(package_deal)1 1.374e+00 1.071e-01 12.824 < 2e-16 ***
## competence_length -6.568e-03 3.673e-03 -1.788 0.07376 .
## factor(new)1 1.782e-02 9.864e-02 0.181 0.85662
## factor(form)Directive 5.627e-01 1.906e-01 2.952 0.00316 **
## factor(form)Regulation 5.416e-01 1.796e-01 3.015 0.00257 **
## elections_days 9.848e-05 1.139e-04 0.865 0.38714
## lr_proposal_probability 4.750e-01 1.506e-01 3.154 0.00161 **
## factor(term)8 -9.458e-02 9.676e-02 -0.977 0.32834
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(2.1492) family taken to be 1)
##
## Null deviance: 730.43 on 427 degrees of freedom
## Residual deviance: 414.40 on 413 degrees of freedom
## (103 observations deleted due to missingness)
## AIC: 1981.3
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 2.149
## Std. Err.: 0.212
##
## 2 x log-likelihood: -1949.287#model with dummy elections variable
m2_intensity <- glm.nb(N_trilogues ~
proposal_t_eupolarizarion + proposal_t_wmean_eu_salience +
Interinst_conflict + ep_amendments_tabled + council_dissent +
N_eurovoc_terms + factor(package_deal) + competence_length + factor(new) + factor(form) + factor(elections_dummy) + lr_proposal_probability +
factor(term),
data = trilogues_number)
summary(m2_intensity)##
## Call:
## glm.nb(formula = N_trilogues ~ proposal_t_eupolarizarion + proposal_t_wmean_eu_salience +
## Interinst_conflict + ep_amendments_tabled + council_dissent +
## N_eurovoc_terms + factor(package_deal) + competence_length +
## factor(new) + factor(form) + factor(elections_dummy) + lr_proposal_probability +
## factor(term), data = trilogues_number, init.theta = 2.18031119,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.0952 -0.7730 -0.2093 0.2739 4.6455
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6447754 0.4485877 -1.437 0.15062
## proposal_t_eupolarizarion 0.3140623 0.1992997 1.576 0.11507
## proposal_t_wmean_eu_salience 0.8463453 0.3120576 2.712 0.00668 **
## Interinst_conflict -0.1801803 0.0919799 -1.959 0.05012 .
## ep_amendments_tabled 0.0024907 0.0004894 5.089 3.6e-07 ***
## council_dissent 0.0637549 0.0299215 2.131 0.03311 *
## N_eurovoc_terms 0.0379335 0.0246980 1.536 0.12456
## factor(package_deal)1 1.3377421 0.1072351 12.475 < 2e-16 ***
## competence_length -0.0059331 0.0036523 -1.624 0.10427
## factor(new)1 0.0290433 0.0984680 0.295 0.76803
## factor(form)Directive 0.5341906 0.1906333 2.802 0.00508 **
## factor(form)Regulation 0.5181724 0.1795574 2.886 0.00390 **
## factor(elections_dummy)1 -0.2863151 0.1398257 -2.048 0.04059 *
## lr_proposal_probability 0.4449176 0.1507092 2.952 0.00316 **
## factor(term)8 -0.1321846 0.0982512 -1.345 0.17850
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(2.1803) family taken to be 1)
##
## Null deviance: 736.94 on 427 degrees of freedom
## Residual deviance: 414.46 on 413 degrees of freedom
## (103 observations deleted due to missingness)
## AIC: 1977.9
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 2.180
## Std. Err.: 0.216
##
## 2 x log-likelihood: -1945.890#without outlier
#model with contrinous elections variable
m3_intensity <- glm.nb(N_trilogues ~
proposal_t_eupolarizarion + proposal_t_wmean_eu_salience +
Interinst_conflict + ep_amendments_tabled + council_dissent +
N_eurovoc_terms + factor(package_deal) + competence_length + factor(new) + factor(form) + elections_days + lr_proposal_probability +
factor(term),
data = trilogues_number_noRegi)
summary(m3_intensity)##
## Call:
## glm.nb(formula = N_trilogues ~ proposal_t_eupolarizarion + proposal_t_wmean_eu_salience +
## Interinst_conflict + ep_amendments_tabled + council_dissent +
## N_eurovoc_terms + factor(package_deal) + competence_length +
## factor(new) + factor(form) + elections_days + lr_proposal_probability +
## factor(term), data = trilogues_number_noRegi, init.theta = 6.312253359,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.2412 -0.7548 -0.2243 0.3951 3.8231
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.637e-01 3.644e-01 0.449 0.653299
## proposal_t_eupolarizarion 7.182e-02 1.597e-01 0.450 0.652915
## proposal_t_wmean_eu_salience -1.469e-01 2.374e-01 -0.619 0.536070
## Interinst_conflict -1.470e-01 7.429e-02 -1.979 0.047797 *
## ep_amendments_tabled 1.592e-03 3.920e-04 4.061 4.89e-05 ***
## council_dissent 7.381e-02 2.250e-02 3.280 0.001038 **
## N_eurovoc_terms 2.220e-02 1.997e-02 1.111 0.266479
## factor(package_deal)1 6.824e-01 8.575e-02 7.958 1.74e-15 ***
## competence_length -6.017e-03 2.888e-03 -2.083 0.037247 *
## factor(new)1 4.085e-02 7.688e-02 0.531 0.595210
## factor(form)Directive 5.397e-01 1.507e-01 3.581 0.000343 ***
## factor(form)Regulation 4.124e-01 1.434e-01 2.875 0.004038 **
## elections_days 4.816e-05 9.178e-05 0.525 0.599788
## lr_proposal_probability 8.845e-01 1.204e-01 7.344 2.08e-13 ***
## factor(term)8 6.018e-02 7.634e-02 0.788 0.430471
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(6.3123) family taken to be 1)
##
## Null deviance: 659.21 on 422 degrees of freedom
## Residual deviance: 437.06 on 408 degrees of freedom
## (103 observations deleted due to missingness)
## AIC: 1758.7
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 6.31
## Std. Err.: 1.22
##
## 2 x log-likelihood: -1726.707#model with dummy elections variable
m4_intensity <- glm.nb(N_trilogues ~
proposal_t_eupolarizarion + proposal_t_wmean_eu_salience +
Interinst_conflict + ep_amendments_tabled + council_dissent +
N_eurovoc_terms + factor(package_deal) + competence_length + factor(new) + factor(form) + factor(elections_dummy) + lr_proposal_probability +
factor(term),
data = trilogues_number_noRegi)
summary(m4_intensity)##
## Call:
## glm.nb(formula = N_trilogues ~ proposal_t_eupolarizarion + proposal_t_wmean_eu_salience +
## Interinst_conflict + ep_amendments_tabled + council_dissent +
## N_eurovoc_terms + factor(package_deal) + competence_length +
## factor(new) + factor(form) + factor(elections_dummy) + lr_proposal_probability +
## factor(term), data = trilogues_number_noRegi, init.theta = 6.417957895,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.2274 -0.7422 -0.2154 0.3802 3.7963
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2081344 0.3502335 0.594 0.552329
## proposal_t_eupolarizarion 0.0652934 0.1575082 0.415 0.678479
## proposal_t_wmean_eu_salience -0.1106138 0.2380491 -0.465 0.642169
## Interinst_conflict -0.1576615 0.0741189 -2.127 0.033408 *
## ep_amendments_tabled 0.0015696 0.0003894 4.031 5.55e-05 ***
## council_dissent 0.0748465 0.0224607 3.332 0.000861 ***
## N_eurovoc_terms 0.0243153 0.0198834 1.223 0.221370
## factor(package_deal)1 0.6695117 0.0857775 7.805 5.94e-15 ***
## competence_length -0.0056985 0.0028700 -1.986 0.047089 *
## factor(new)1 0.0469095 0.0768044 0.611 0.541354
## factor(form)Directive 0.5188231 0.1512799 3.430 0.000605 ***
## factor(form)Regulation 0.3940440 0.1438357 2.740 0.006152 **
## factor(elections_dummy)1 -0.1559720 0.1128788 -1.382 0.167044
## lr_proposal_probability 0.8665095 0.1207665 7.175 7.23e-13 ***
## factor(term)8 0.0396510 0.0776250 0.511 0.609490
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(6.418) family taken to be 1)
##
## Null deviance: 662.56 on 422 degrees of freedom
## Residual deviance: 437.57 on 408 degrees of freedom
## (103 observations deleted due to missingness)
## AIC: 1757.1
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 6.42
## Std. Err.: 1.26
##
## 2 x log-likelihood: -1725.067Thoughts:
1. no effect of polarization, significant positive effect of salience
2. effect of salience dissappears if using a restricted sample –> have it in appendix only?
3. effect of salience changes the direction in models with a restricted sample if we move from continious elections variable to a dummy
4. models with elections dummy have a slightly better performance
5. we lose ~ a hundred observations due to missings in public opinion data –> should we think of data imputations?
#visualizations (based on model 2)
#polarization (whole range)
i_predict_polarization <- ggpredict(m2_intensity, terms = "proposal_t_eupolarizarion[all]")
ggplot(i_predict_polarization, aes(x=x, y=predicted)) +
geom_line(stat = "identity", linetype="solid") +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high) , linetype=1, alpha = .3, fill = "deepskyblue2") +
theme_minimal() +
labs(title = NULL, x = "Polarization (time proposal)", y = "Predicted number of trilogues") 
#salience
i_predict_salience <- ggpredict(m2_intensity, terms = "proposal_t_wmean_eu_salience[all]")
ggplot(i_predict_salience, aes(x=x, y=predicted)) +
geom_line(stat = "identity", linetype="solid") +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high) , linetype=1, alpha = .3, fill = "deepskyblue2") +
theme_minimal() +
labs(title = NULL, x = "Salience (time proposal)", y = "Predicted number of trilogues") 
#authority expansion
i_predict_eu <- ggpredict(m2_intensity, terms = "lr_proposal_probability[all]")
ggplot(i_predict_eu, aes(x=x, y=predicted)) +
geom_line(stat = "identity", linetype="solid") +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high) , linetype=1, alpha = .3, fill = "deepskyblue2") +
theme_minimal() +
labs(title = NULL, x = "Pr. of EU authority expansion", y = "Predicted number of trilogues") 
#ep amendments
i_predict_ep <- ggpredict(m2_intensity, terms = "ep_amendments_tabled[all]")
ggplot(i_predict_ep, aes(x=x, y=predicted)) +
geom_line(stat = "identity", linetype="solid") +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high) , linetype=1, alpha = .3, fill = "deepskyblue2") +
theme_minimal() +
labs(title = NULL, x = "N of EP amendments", y = "Predicted number of trilogues") 
#council dissent
i_predict_council <- ggpredict(m2_intensity, terms = "council_dissent[all]")
ggplot(i_predict_council, aes(x=x, y=predicted)) +
geom_line(stat = "identity", linetype="solid") +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high) , linetype=1, alpha = .3, fill = "deepskyblue2") +
theme_minimal() +
labs(title = NULL, x = "Council dissent", y = "Predicted number of trilogues") 
#interinst conflict
i_predict_insitutions <- ggpredict(m2_intensity, terms = "Interinst_conflict[all]")
ggplot(i_predict_insitutions, aes(x=x, y=predicted)) +
geom_line(stat = "identity", linetype="solid") +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high) , linetype=1, alpha = .3, fill = "deepskyblue2") +
theme_minimal() +
labs(title = NULL, x = "Interinstitutional conflict", y = "Predicted number of trilogues") 
#Models DURATION OF TRILOGUES
Modelling:
1. no CAP FEs –> not enough observations
2. instead of year FE, EP term FE
3. ep_amenmdnets_tabled –> use it as a proxy for EP polarization (then we do not have issues with correlation between EP_polarization measure and inter-inst conflict)
4. Variables:
- main: EU polarization at the t of proposal and EU salience at the t of proposal (Average t between proposal and first trilogue is 13 months)
- institutional controls: Council dissent (sum of abstentions and against votes), EP polarization (n of tabled amendments), interinst conflict
- procedure controls: form, package_deal (1 if proposal is part of a package), complexity (N_eurovoc_terms), new vs amending (1 if new), days to next EP elections (from proposal) // election proximity dummy (1 if proposal submitted in the last year of EP term), competence_length, authority expansion; N_trilogues should not be used (correlation 0.65)
5. Method: Negative binomial model (below) and Cox survival model (TBD)
6. One set of models: only without outliers (we don’t have info on duration of negotiations for the outlier with 85 trilogues)
#model with continious elections variable
m1_duration <- glm.nb(duration_negotiations_days ~
proposal_t_eupolarizarion + proposal_t_wmean_eu_salience +
Interinst_conflict + ep_amendments_tabled + council_dissent +
N_eurovoc_terms + factor(package_deal) + competence_length + factor(new) + factor(form) + elections_days + lr_proposal_probability +
factor(term),
data = trilogues_duration)
summary(m1_duration)##
## Call:
## glm.nb(formula = duration_negotiations_days ~ proposal_t_eupolarizarion +
## proposal_t_wmean_eu_salience + Interinst_conflict + ep_amendments_tabled +
## council_dissent + N_eurovoc_terms + factor(package_deal) +
## competence_length + factor(new) + factor(form) + elections_days +
## lr_proposal_probability + factor(term), data = trilogues_duration,
## init.theta = 0.6875067857, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4789 -1.1205 -0.3413 0.3116 2.5418
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.6214362 0.6250626 2.594 0.009486 **
## proposal_t_eupolarizarion 0.1151913 0.2845405 0.405 0.685601
## proposal_t_wmean_eu_salience 0.0296648 0.4204935 0.071 0.943758
## Interinst_conflict -0.1928231 0.1295318 -1.489 0.136588
## ep_amendments_tabled 0.0027440 0.0007711 3.558 0.000373 ***
## council_dissent 0.0583364 0.0411993 1.416 0.156788
## N_eurovoc_terms 0.0861234 0.0347488 2.478 0.013195 *
## factor(package_deal)1 0.7411478 0.1703862 4.350 1.36e-05 ***
## competence_length -0.0064616 0.0051835 -1.247 0.212556
## factor(new)1 0.2019334 0.1410996 1.431 0.152390
## factor(form)Directive 0.8688806 0.2617015 3.320 0.000900 ***
## factor(form)Regulation 0.8287800 0.2445761 3.389 0.000702 ***
## elections_days 0.0006551 0.0001652 3.966 7.29e-05 ***
## lr_proposal_probability 1.1307861 0.2201100 5.137 2.79e-07 ***
## factor(term)8 -0.1462926 0.1364094 -1.072 0.283517
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(0.6875) family taken to be 1)
##
## Null deviance: 572.81 on 390 degrees of freedom
## Residual deviance: 459.49 on 376 degrees of freedom
## (165 observations deleted due to missingness)
## AIC: 4109.9
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 0.6875
## Std. Err.: 0.0444
##
## 2 x log-likelihood: -4077.9330#model with dummy elections variable
m2_duration <- glm.nb(duration_negotiations_days ~
proposal_t_eupolarizarion + proposal_t_wmean_eu_salience +
Interinst_conflict + ep_amendments_tabled + council_dissent +
N_eurovoc_terms + factor(package_deal) + competence_length + factor(new) + factor(form) + factor(elections_dummy) + lr_proposal_probability +
factor(term),
data = trilogues_duration)
summary(m2_duration)##
## Call:
## glm.nb(formula = duration_negotiations_days ~ proposal_t_eupolarizarion +
## proposal_t_wmean_eu_salience + Interinst_conflict + ep_amendments_tabled +
## council_dissent + N_eurovoc_terms + factor(package_deal) +
## competence_length + factor(new) + factor(form) + factor(elections_dummy) +
## lr_proposal_probability + factor(term), data = trilogues_duration,
## init.theta = 0.668353591, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4248 -1.1396 -0.3846 0.2822 2.3099
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.356255 0.617769 3.814 0.000137 ***
## proposal_t_eupolarizarion 0.373769 0.284377 1.314 0.188730
## proposal_t_wmean_eu_salience 0.094994 0.426147 0.223 0.823602
## Interinst_conflict -0.225020 0.132031 -1.704 0.088326 .
## ep_amendments_tabled 0.002353 0.000780 3.017 0.002552 **
## council_dissent 0.052179 0.041876 1.246 0.212752
## N_eurovoc_terms 0.057998 0.035154 1.650 0.098976 .
## factor(package_deal)1 0.762435 0.173266 4.400 1.08e-05 ***
## competence_length -0.009272 0.005241 -1.769 0.076861 .
## factor(new)1 0.172679 0.143031 1.207 0.227323
## factor(form)Directive 0.916399 0.266377 3.440 0.000581 ***
## factor(form)Regulation 0.845952 0.248671 3.402 0.000669 ***
## factor(elections_dummy)1 -0.216851 0.191488 -1.132 0.257445
## lr_proposal_probability 1.159324 0.223845 5.179 2.23e-07 ***
## factor(term)8 -0.072504 0.141342 -0.513 0.607970
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(0.6684) family taken to be 1)
##
## Null deviance: 557.60 on 390 degrees of freedom
## Residual deviance: 461.02 on 376 degrees of freedom
## (165 observations deleted due to missingness)
## AIC: 4123.7
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 0.6684
## Std. Err.: 0.0429
##
## 2 x log-likelihood: -4091.7400Thoughts:
1. Salience and polarization are not significant
2. we lose ~ a hundred observations due to missings in public opinion data –> should we think of data imputations?
#visualizations (based on model 2)
#polarization (whole range)
d_predict_polarization <- ggpredict(m2_duration, terms = "proposal_t_eupolarizarion[all]")
ggplot(d_predict_polarization, aes(x=x, y=predicted)) +
geom_line(stat = "identity", linetype="solid") +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high) , linetype=1, alpha = .3, fill = "deepskyblue2") +
theme_minimal() +
labs(title = NULL, x = "Polarization (time proposal)", y = "Predicted number of days") 
#salience
d_predict_salience <- ggpredict(m2_duration, terms = "proposal_t_wmean_eu_salience[all]")
ggplot(d_predict_salience, aes(x=x, y=predicted)) +
geom_line(stat = "identity", linetype="solid") +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high) , linetype=1, alpha = .3, fill = "deepskyblue2") +
theme_minimal() +
labs(title = NULL, x = "Salience (time proposal)", y = "Predicted number of days") 
#authority expansion
d_predict_eu <- ggpredict(m2_duration, terms = "lr_proposal_probability[all]")
ggplot(d_predict_eu, aes(x=x, y=predicted)) +
geom_line(stat = "identity", linetype="solid") +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high) , linetype=1, alpha = .3, fill = "deepskyblue2") +
theme_minimal() +
labs(title = NULL, x = "Pr. of EU authority expansion", y = "Predicted number of days") 
#ep amendments
d_predict_ep <- ggpredict(m2_duration, terms = "ep_amendments_tabled[all]")
ggplot(d_predict_ep, aes(x=x, y=predicted)) +
geom_line(stat = "identity", linetype="solid") +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high) , linetype=1, alpha = .3, fill = "deepskyblue2") +
theme_minimal() +
labs(title = NULL, x = "N of EP amendments", y = "Predicted number of days") 
#council dissent
d_predict_council <- ggpredict(m2_duration, terms = "council_dissent[all]")
ggplot(d_predict_council, aes(x=x, y=predicted)) +
geom_line(stat = "identity", linetype="solid") +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high) , linetype=1, alpha = .3, fill = "deepskyblue2") +
theme_minimal() +
labs(title = NULL, x = "Council dissent", y = "Predicted number of days") 
#interinst conflict
d_predict_insitutions <- ggpredict(m2_duration, terms = "Interinst_conflict[all]")
ggplot(d_predict_insitutions, aes(x=x, y=predicted)) +
geom_line(stat = "identity", linetype="solid") +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high) , linetype=1, alpha = .3, fill = "deepskyblue2") +
theme_minimal() +
labs(title = NULL, x = "Interinstitutional conflict", y = "Predicted number of days")