Overall
ANOVA test result at national level
## [1] "Predicotr variable:major_event.conflict"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.663228e-18 3.663228e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.616e-32 0.426 0.514
## Residuals 51921 1.972e-27 3.798e-32
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:major_event.covid_19"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.151517e-18 2.151517e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.0e-32 7.722e-33 1.114 0.291
## Residuals 23082 1.6e-28 6.933e-33
## 28839 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:major_event.earthquake"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.082702e-17 1.082702e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 9.600e-34 0.025 0.874
## Residuals 51921 1.972e-27 3.798e-32
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:major_event.floods"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.137704e-18 4.137704e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-31 1.456e-31 3.833 0.0503 .
## Residuals 51921 1.972e-27 3.798e-32
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:major_event.avalanche"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.091073e-17 1.091073e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 9.400e-34 0.025 0.875
## Residuals 51921 1.972e-27 3.798e-32
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:major_event.drought"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.508417e-18 3.508417e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-31 6.973e-32 1.836 0.175
## Residuals 51921 1.972e-27 3.798e-32
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:major_event.other"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.972619e-18 6.972619e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.490e-33 0.066 0.798
## Residuals 51921 1.972e-27 3.798e-32
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:major_event.none"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.533261e-18 3.533261e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.00e+00 1.120e-35 0.019 0.89
## Residuals 9935 5.86e-30 5.899e-34
## 41986 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_covid19.income_src_lost"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -9.657053e-14 9.657053e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 5.160e-25 0.152 0.696
## Residuals 12161 4.119e-20 3.387e-24
## 39760 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_covid19.food_acc_limit"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.713738e-14 6.713738e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-23 5.354e-24 1.581 0.209
## Residuals 12161 4.118e-20 3.387e-24
## 39760 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_covid19.loss_shelter"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -9.101289e-13 9.101289e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-22 7.567e-23 0.323 0.57
## Residuals 5896 1.383e-18 2.345e-22
## 46025 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_covid19.loss_basic_serv"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.676965e-14 6.676965e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-23 5.084e-24 1.501 0.221
## Residuals 12161 4.118e-20 3.387e-24
## 39760 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_covid19.loss_edu"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -7.57458e-14 7.57458e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-23 1.028e-23 3.035 0.0815 .
## Residuals 12161 4.118e-20 3.386e-24
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 39760 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_covid19.loss_sanitation"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -7.359907e-14 7.359907e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.258e-24 0.372 0.542
## Residuals 12161 4.119e-20 3.387e-24
## 39760 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_covid19.hh_injury_death"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -7.614762e-14 7.614762e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-23 1.049e-23 3.099 0.0784 .
## Residuals 12161 4.118e-20 3.386e-24
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 39760 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_covid19.refused"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -8.335256e-12 8.335256e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 5.200e-25 0.002 0.963
## Residuals 5896 1.383e-18 2.345e-22
## 46025 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_covid19.other"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.973259e-13 3.973259e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.300e-26 0.007 0.934
## Residuals 12161 4.119e-20 3.387e-24
## 39760 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_natural_disaster.income_src_lost"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.079383e-13 2.079383e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.161e-23 0.184 0.668
## Residuals 42590 2.683e-18 6.299e-23
## 9331 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_natural_disaster.food_acc_limit"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.575838e-13 1.575838e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-22 1.148e-22 1.822 0.177
## Residuals 42590 2.683e-18 6.299e-23
## 9331 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_natural_disaster.loss_shelter"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.520592e-13 1.520592e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 4.841e-23 0.768 0.381
## Residuals 42590 2.683e-18 6.299e-23
## 9331 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_natural_disaster.loss_basic_serv"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.673885e-13 1.673885e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 2.000e-22 1.599e-22 2.538 0.111
## Residuals 42590 2.683e-18 6.299e-23
## 9331 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_natural_disaster.loss_edu"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -8.015625e-13 8.015625e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.145e-22 0.265 0.607
## Residuals 15639 6.767e-18 4.327e-22
## 36282 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_natural_disaster.loss_sanitation"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -8.825834e-13 8.825834e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 2.000e-21 2.222e-21 5.137 0.0234 *
## Residuals 15639 6.765e-18 4.326e-22
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 36282 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_natural_disaster.hh_injury_death"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.665829e-12 1.665829e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.800e-23 0.042 0.838
## Residuals 15639 6.767e-18 4.327e-22
## 36282 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_natural_disaster.refused"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.528782e-12 4.528782e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.000e-24 0.006 0.938
## Residuals 5106 8.396e-19 1.644e-22
## 46815 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_natural_disaster.other"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.513401e-13 3.513401e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 3.200e-24 0.051 0.822
## Residuals 42590 2.683e-18 6.299e-23
## 9331 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_conflict.income_src_lost"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -5.645897e-13 5.645897e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.203e-23 0.159 0.69
## Residuals 7699 5.824e-19 7.565e-23
## 44222 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_conflict.food_acc_limit"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.88572e-13 3.88572e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-22 7.737e-23 1.023 0.312
## Residuals 7699 5.823e-19 7.564e-23
## 44222 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_conflict.loss_shelter"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.192233e-13 4.192233e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 3.435e-23 0.454 0.5
## Residuals 7699 5.824e-19 7.564e-23
## 44222 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_conflict.loss_basic_serv"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.187028e-13 4.187028e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 2.000e-22 1.656e-22 2.189 0.139
## Residuals 7699 5.823e-19 7.563e-23
## 44222 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_conflict.loss_edu"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -8.028728e-13 8.028728e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 5.040e-24 0.067 0.796
## Residuals 7699 5.824e-19 7.565e-23
## 44222 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_conflict.loss_sanitation"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -9.119791e-13 9.119791e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 3.790e-24 0.05 0.823
## Residuals 7699 5.824e-19 7.565e-23
## 44222 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_conflict.hh_injury_death"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -5.850289e-13 5.850289e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.093e-23 0.144 0.704
## Residuals 7699 5.824e-19 7.565e-23
## 44222 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_conflict.new_mines"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -5.899997e-13 5.899997e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.068e-23 0.141 0.707
## Residuals 7699 5.824e-19 7.565e-23
## 44222 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_conflict.refused"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.669828e-12 2.669828e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 4.000e-25 0.005 0.942
## Residuals 7699 5.824e-19 7.565e-23
## 44222 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:event_conflict.other"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.076765e-12 2.076765e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 6.700e-25 0.009 0.925
## Residuals 7699 5.824e-19 7.565e-23
## 44222 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:main_income.agriculture"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -9.037593e-15 9.037593e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.986e-26 0.327 0.567
## Residuals 23082 2.105e-21 9.119e-26
## 28839 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:main_income.livestock"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.22232e-14 1.22232e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.182e-26 0.13 0.719
## Residuals 23082 2.105e-21 9.120e-26
## 28839 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:main_income.rent"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.110458e-14 3.110458e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.48e-27 0.016 0.899
## Residuals 23082 2.105e-21 9.12e-26
## 28839 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:main_income.small_business"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.144622e-14 1.144622e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.408e-26 0.154 0.694
## Residuals 23082 2.105e-21 9.120e-26
## 28839 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:main_income.daily_lab"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -7.941926e-15 7.941926e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-25 6.160e-26 0.676 0.411
## Residuals 23082 2.105e-21 9.119e-26
## 28839 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:main_income.formal_epml"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.491978e-14 1.491978e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 7.25e-27 0.079 0.778
## Residuals 23082 2.105e-21 9.12e-26
## 28839 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:main_income.gov_hum_assistance"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.073716e-14 3.073716e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.51e-27 0.017 0.897
## Residuals 23082 2.105e-21 9.12e-26
## 28839 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:main_income.gifts"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.440605e-14 2.440605e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.45e-27 0.027 0.87
## Residuals 23082 2.105e-21 9.12e-26
## 28839 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:main_income.loans"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.042376e-14 1.042376e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 5.000e-25 4.522e-25 4.96 0.026 *
## Residuals 23082 2.105e-21 9.120e-26
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 28839 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:main_income.selling_assets"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.486034e-14 3.486034e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.17e-27 0.013 0.91
## Residuals 23082 2.105e-21 9.12e-26
## 28839 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:main_income.other"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.030566e-14 3.030566e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.56e-27 0.017 0.896
## Residuals 23082 2.105e-21 9.12e-26
## 28839 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:what_humanitarian_information.none"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -9.585377e-18 9.585377e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.040e-34 0.03 0.862
## Residuals 9935 6.711e-29 6.755e-33
## 41986 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:what_humanitarian_information.food_livestock_prices"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.672759e-18 3.672759e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.405e-33 0.356 0.551
## Residuals 9935 6.711e-29 6.754e-33
## 41986 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:what_humanitarian_information.request_assistance"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.241528e-18 3.241528e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.00e-32 7.871e-33 1.165 0.28
## Residuals 9935 6.71e-29 6.754e-33
## 41986 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:what_humanitarian_information.food_assistance"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.70972e-18 3.70972e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.307e-33 0.342 0.559
## Residuals 9935 6.711e-29 6.754e-33
## 41986 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:what_humanitarian_information.education_services"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.557074e-18 3.557074e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.00e+00 2.775e-33 0.411 0.522
## Residuals 9935 6.71e-29 6.754e-33
## 41986 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:what_humanitarian_information.shelter"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.975316e-18 3.975316e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.784e-33 0.264 0.607
## Residuals 9935 6.711e-29 6.754e-33
## 41986 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:what_humanitarian_information.health_services"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.376354e-18 3.376354e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.00e-32 1.225e-32 1.814 0.178
## Residuals 9935 6.71e-29 6.753e-33
## 41986 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:what_humanitarian_information.nutrition_services"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.122088e-18 4.122088e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.581e-33 0.234 0.629
## Residuals 9935 6.711e-29 6.754e-33
## 41986 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:what_humanitarian_information.information_water_hygiene"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.157093e-18 4.157093e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.539e-33 0.228 0.633
## Residuals 9935 6.711e-29 6.754e-33
## 41986 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:what_humanitarian_information.protection"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -9.25293e-18 9.25293e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.200e-34 0.033 0.857
## Residuals 9935 6.711e-29 6.755e-33
## 41986 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:what_humanitarian_information.cfm"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -8.755015e-18 8.755015e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.470e-34 0.037 0.848
## Residuals 9935 6.711e-29 6.755e-33
## 41986 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:what_humanitarian_information.other"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.521205e-17 2.521205e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.800e-35 0.004 0.949
## Residuals 9935 6.711e-29 6.755e-33
## 41986 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Predicotr variable:lcsi_migrated"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -9.446927e-13 9.446927e-13 1
## not_applicable-exhausted 0 -1.038999e-12 1.038999e-12 1
## yes-exhausted 0 -1.002366e-12 1.002366e-12 1
## not_applicable-no 0 -6.044822e-13 6.044822e-13 1
## yes-no 0 -5.390882e-13 5.390882e-13 1
## yes-not_applicable 0 -6.911522e-13 6.911522e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.000e-21 2.949e-22 1.605 0.186
## Residuals 25522 4.691e-18 1.838e-22
## 26397 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:lcsi_savings"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -9.628689e-13 9.628689e-13 1
## not_applicable-exhausted 0 -1.005360e-12 1.005360e-12 1
## yes-exhausted 0 -9.889372e-13 9.889372e-13 1
## not_applicable-no 0 -5.593383e-13 5.593383e-13 1
## yes-no 0 -5.292512e-13 5.292512e-13 1
## yes-not_applicable 0 -6.031082e-13 6.031082e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 0.000e+00 1.521e-22 0.827 0.479
## Residuals 25522 4.691e-18 1.838e-22
## 26397 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:lcsi_household"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -9.464506e-13 9.464506e-13 1
## not_applicable-exhausted 0 -9.686722e-13 9.686722e-13 1
## yes-exhausted 0 -9.734126e-13 9.734126e-13 1
## not_applicable-no 0 -5.713383e-13 5.713383e-13 1
## yes-no 0 -5.793390e-13 5.793390e-13 1
## yes-not_applicable 0 -6.149722e-13 6.149722e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 3.000e-22 9.837e-23 0.832 0.476
## Residuals 14785 1.747e-18 1.182e-22
## 37134 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:lcsi_food"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -2.031144e-12 2.031144e-12 1
## not_applicable-exhausted 0 -2.184768e-12 2.184768e-12 1
## yes-exhausted 0 -1.874499e-12 1.874499e-12 1
## not_applicable-no 0 -1.411766e-12 1.411766e-12 1
## yes-no 0 -8.565133e-13 8.565133e-13 1
## yes-not_applicable 0 -1.175279e-12 1.175279e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 0.000e+00 6.080e-24 0.051 0.985
## Residuals 14785 1.748e-18 1.182e-22
## 37134 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:lcsi_income_equipment"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.076628e-12 1.076628e-12 1
## not_applicable-exhausted 0 -1.071188e-12 1.071188e-12 1
## yes-exhausted 0 -1.207222e-12 1.207222e-12 1
## not_applicable-no 0 -5.058680e-13 5.058680e-13 1
## yes-no 0 -7.522245e-13 7.522245e-13 1
## yes-not_applicable 0 -7.444181e-13 7.444181e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 2.000e-22 5.178e-23 0.438 0.726
## Residuals 14785 1.748e-18 1.182e-22
## 37134 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:lcsi_delayed_medical_care"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.454381e-12 1.454381e-12 1
## not_applicable-exhausted 0 -1.471529e-12 1.471529e-12 1
## yes-exhausted 0 -1.453504e-12 1.453504e-12 1
## not_applicable-no 0 -5.917226e-13 5.917226e-13 1
## yes-no 0 -5.453546e-13 5.453546e-13 1
## yes-not_applicable 0 -5.895633e-13 5.895633e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 3.000e-22 1.101e-22 0.931 0.425
## Residuals 14785 1.747e-18 1.182e-22
## 37134 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:lcsi_sold_land"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.093493e-12 1.093493e-12 1
## not_applicable-exhausted 0 -1.140414e-12 1.140414e-12 1
## yes-exhausted 0 -1.296056e-12 1.296056e-12 1
## not_applicable-no 0 -5.099426e-13 5.099426e-13 1
## yes-no 0 -7.995362e-13 7.995362e-13 1
## yes-not_applicable 0 -8.625980e-13 8.625980e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 0.000e+00 3.878e-23 0.211 0.889
## Residuals 25522 4.691e-18 1.838e-22
## 26397 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:lcsi_sold_fem_animal"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.011627e-12 1.011627e-12 1
## not_applicable-exhausted 0 -1.058476e-12 1.058476e-12 1
## yes-exhausted 0 -1.102531e-12 1.102531e-12 1
## not_applicable-no 0 -5.266761e-13 5.266761e-13 1
## yes-no 0 -6.104032e-13 6.104032e-13 1
## yes-not_applicable 0 -6.852542e-13 6.852542e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 0.000e+00 5.502e-23 0.299 0.826
## Residuals 25522 4.691e-18 1.838e-22
## 26397 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:lcsi_charity"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.392858e-12 1.392858e-12 1
## not_applicable-exhausted 0 -1.421128e-12 1.421128e-12 1
## yes-exhausted 0 -1.513898e-12 1.513898e-12 1
## not_applicable-no 0 -5.202323e-13 5.202323e-13 1
## yes-no 0 -7.368336e-13 7.368336e-13 1
## yes-not_applicable 0 -7.889701e-13 7.889701e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 3.000e-22 9.135e-23 0.773 0.509
## Residuals 14785 1.747e-18 1.182e-22
## 37134 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:lcsi_married_daughters"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.296942e-12 1.296942e-12 1
## not_applicable-exhausted 0 -1.316452e-12 1.316452e-12 1
## yes-exhausted 0 -1.571791e-12 1.571791e-12 1
## not_applicable-no 0 -4.932908e-13 4.932908e-13 1
## yes-no 0 -9.903617e-13 9.903617e-13 1
## yes-not_applicable 0 -1.015778e-12 1.015778e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 2.000e-22 7.049e-23 0.596 0.617
## Residuals 14785 1.748e-18 1.182e-22
## 37134 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:lcsi_engage_in_illegal_acts"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.312561e-12 1.312561e-12 1
## not_applicable-exhausted 0 -1.344646e-12 1.344646e-12 1
## yes-exhausted 0 -1.380556e-12 1.380556e-12 1
## not_applicable-no 0 -5.421251e-13 5.421251e-13 1
## yes-no 0 -6.259091e-13 6.259091e-13 1
## yes-not_applicable 0 -6.906658e-13 6.906658e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 5.000e-22 1.753e-22 1.483 0.217
## Residuals 14785 1.747e-18 1.182e-22
## 37134 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:lcsi_metal_sell"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted -7.275958e-12 -9.979947e-12 -4.571968e-12 0
## not_applicable-exhausted -7.275958e-12 -1.002345e-11 -4.528468e-12 0
## yes-exhausted -7.275958e-12 -1.010308e-11 -4.448831e-12 0
## not_applicable-no 0.000000e+00 -9.488593e-13 9.488593e-13 1
## yes-no 0.000000e+00 -1.159429e-12 1.159429e-12 1
## yes-not_applicable 0.000000e+00 -1.257545e-12 1.257545e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 5.200e-21 1.727e-21 13.51 8.95e-09
## Residuals 4983 6.372e-19 1.279e-22
##
## factor(df[[vars$predictor[i]]]) ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 46936 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:lcsi_children_work"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted -7.275958e-12 -1.001405e-11 -4.537869e-12 0
## not_applicable-exhausted -7.275958e-12 -1.005400e-11 -4.497916e-12 0
## yes-exhausted -7.275958e-12 -1.007006e-11 -4.481851e-12 0
## not_applicable-no 0.000000e+00 -8.777838e-13 8.777838e-13 1
## yes-no 0.000000e+00 -9.273749e-13 9.273749e-13 1
## yes-not_applicable 0.000000e+00 -1.039428e-12 1.039428e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 4.600e-21 1.53e-21 17.94 1.44e-11
## Residuals 4222 3.601e-19 8.53e-23
##
## factor(df[[vars$predictor[i]]]) ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 47697 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:shelter_defects"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no_damage-fully_destr 0 -5.071499e-13 5.071499e-13 1
## partia_damage-fully_destr 0 -4.940311e-13 4.940311e-13 1
## sign_damage-fully_destr 0 -5.197774e-13 5.197774e-13 1
## partia_damage-no_damage 0 -1.946673e-13 1.946673e-13 1
## sign_damage-no_damage 0 -2.529767e-13 2.529767e-13 1
## sign_damage-partia_damage 0 -2.255280e-13 2.255280e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.300e-21 4.233e-22 11.28 2.15e-07
## Residuals 37213 1.397e-18 3.750e-23
##
## factor(df[[vars$predictor[i]]]) ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 14706 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:income_cats"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## low-high 0 -1.294830e-13 1.294830e-13 1
## middle-high 0 -1.434714e-13 1.434714e-13 1
## very_low-high 0 -1.392457e-13 1.392457e-13 1
## middle-low 0 -8.614150e-14 8.614150e-14 1
## very_low-low 0 -7.890297e-14 7.890297e-14 1
## very_low-middle 0 -1.002191e-13 1.002191e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 0.000e+00 9.037e-24 1.18 0.316
## Residuals 51862 3.972e-19 7.659e-24
## 57 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:household_size_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -3.784287e-17 3.784287e-17 1
## 2021-2019 0 -4.183107e-17 4.183107e-17 1
## 2021-2020 0 -4.780241e-17 4.780241e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 9.422e-31 0.4 0.67
## Residuals 51920 1.222e-25 2.354e-30
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:boys_working_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -3.887371e-18 3.887371e-18 1
## 2021-2019 0 -4.297055e-18 4.297055e-18 1
## 2021-2020 0 -4.910455e-18 4.910455e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.00e+00 9.943e-33 0.4 0.67
## Residuals 51920 1.29e-27 2.484e-32
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:girls_working_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -2.644783e-19 2.644783e-19 1
## 2021-2019 0 -2.923512e-19 2.923512e-19 1
## 2021-2020 0 -3.340840e-19 3.340840e-19 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 4.602e-35 0.4 0.67
## Residuals 51920 5.971e-30 1.150e-34
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:total_cash_income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -1.932079e-14 1.932079e-14 1
## 2021-2019 0 -2.135697e-14 2.135697e-14 1
## 2021-2020 0 -2.440566e-14 2.440566e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 2.456e-25 0.4 0.67
## Residuals 51920 3.186e-20 6.137e-25
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -5.240708e-13 5.240708e-13 1
## 2021-2019 0 -5.793019e-13 5.793019e-13 1
## 2021-2020 0 -6.619965e-13 6.619965e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 1.807e-22 0.4 0.67
## Residuals 51920 2.344e-17 4.515e-22
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:food_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -4.242030e-14 4.242030e-14 1
## 2021-2019 0 -4.689091e-14 4.689091e-14 1
## 2021-2020 0 -5.358453e-14 5.358453e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 1.184e-24 0.4 0.67
## Residuals 51920 1.536e-19 2.958e-24
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:water_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -1.763063e-15 1.763063e-15 1
## 2021-2019 0 -1.948869e-15 1.948869e-15 1
## 2021-2020 0 -2.227068e-15 2.227068e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 2.045e-27 0.4 0.67
## Residuals 51920 2.653e-22 5.110e-27
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:rent_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -1.180167e-15 1.180167e-15 1
## 2021-2019 0 -1.304543e-15 1.304543e-15 1
## 2021-2020 0 -1.490765e-15 1.490765e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 9.164e-28 0.4 0.67
## Residuals 51920 1.189e-22 2.290e-27
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:health_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -2.541603e-14 2.541603e-14 1
## 2021-2019 0 -2.809459e-14 2.809459e-14 1
## 2021-2020 0 -3.210506e-14 3.210506e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 4.250e-25 0.4 0.67
## Residuals 51920 5.514e-20 1.062e-24
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:transportation_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -9.004592e-15 9.004592e-15 1
## 2021-2019 0 -9.953572e-15 9.953572e-15 1
## 2021-2020 0 -1.137443e-14 1.137443e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 5.335e-26 0.4 0.67
## Residuals 51920 6.921e-21 1.333e-25
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:education_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -2.779280e-16 2.779280e-16 1
## 2021-2019 0 -3.072184e-16 3.072184e-16 1
## 2021-2020 0 -3.510734e-16 3.510734e-16 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 5.082e-29 0.4 0.67
## Residuals 51920 6.593e-24 1.270e-28
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:communications_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -1.148678e-15 1.148678e-15 1
## 2021-2019 0 -1.269735e-15 1.269735e-15 1
## 2021-2020 0 -1.450988e-15 1.450988e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 8.681e-28 0.4 0.67
## Residuals 51920 1.126e-22 2.169e-27
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:fuel_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -7.256247e-17 7.256247e-17 1
## 2021-2019 0 -8.020972e-17 8.020972e-17 1
## 2021-2020 0 -9.165957e-17 9.165957e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 3.464e-30 0.4 0.67
## Residuals 51920 4.494e-25 8.656e-30
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:debt_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -5.917949e-15 5.917949e-15 1
## 2021-2019 0 -6.541632e-15 6.541632e-15 1
## 2021-2020 0 -7.475443e-15 7.475443e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 2.304e-26 0.4 0.67
## Residuals 51920 2.989e-21 5.758e-26
## [1] "TEST RESULT:Ho rejects"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:diarrhea_cases_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -3.863973e-18 3.863973e-18 1
## 2021-2019 0 -4.271191e-18 4.271191e-18 1
## 2021-2020 0 -4.880899e-18 4.880899e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 9.823e-33 0.4 0.67
## Residuals 51920 1.274e-27 2.454e-32
## [1] "TEST RESULT:Ho rejects"
By Population
ANOVA test result by population group
Population group: idps
## [1] "Population group:idps"
## [1] "Predicotr variable:major_event.conflict"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.117422e-18 1.117422e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 3.211e-34 0.234 0.629
## Residuals 27558 3.789e-29 1.375e-33
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:major_event.covid_19"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.87048e-18 1.87048e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 3.174e-33 0.936 0.333
## Residuals 14904 5.052e-29 3.390e-33
## 12654 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:major_event.earthquake"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.158583e-18 3.158583e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.750e-35 0.02 0.888
## Residuals 27558 3.789e-29 1.375e-33
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:major_event.floods"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.29618e-18 1.29618e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-32 9.098e-33 6.619 0.0101 *
## Residuals 27558 3.788e-29 1.375e-33
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:major_event.avalanche"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.391914e-18 3.391914e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.370e-35 0.017 0.896
## Residuals 27558 3.789e-29 1.375e-33
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:major_event.drought"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -9.641733e-19 9.641733e-19 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 3.357e-33 2.442 0.118
## Residuals 27558 3.789e-29 1.375e-33
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:major_event.other"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.817674e-18 1.817674e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 9.060e-35 0.066 0.797
## Residuals 27558 3.789e-29 1.375e-33
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:major_event.none"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.443136e-17 2.443136e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.00e+00 1.07e-34 0.01 0.919
## Residuals 6534 6.73e-29 1.03e-32
## 21024 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_covid19.income_src_lost"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.131099e-13 6.131099e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.314e-23 0.158 0.691
## Residuals 7206 5.989e-19 8.312e-23
## 20352 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_covid19.food_acc_limit"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.306476e-13 4.306476e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-22 1.275e-22 1.535 0.215
## Residuals 7206 5.988e-19 8.310e-23
## 20352 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_covid19.loss_shelter"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.725215e-13 4.725215e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-23 1.130e-23 0.298 0.585
## Residuals 3695 1.403e-19 3.796e-23
## 23863 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_covid19.loss_basic_serv"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.312096e-13 4.312096e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-22 1.291e-22 1.553 0.213
## Residuals 7206 5.988e-19 8.310e-23
## 20352 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_covid19.loss_edu"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.923833e-13 4.923833e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 3.000e-22 2.624e-22 3.158 0.0756 .
## Residuals 7206 5.987e-19 8.308e-23
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 20352 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_covid19.loss_sanitation"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.655808e-13 4.655808e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 3.337e-23 0.401 0.526
## Residuals 7206 5.989e-19 8.311e-23
## 20352 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_covid19.hh_injury_death"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.998647e-13 4.998647e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 3.000e-22 2.778e-22 3.343 0.0675 .
## Residuals 7206 5.987e-19 8.308e-23
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 20352 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_covid19.refused"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.031497e-12 4.031497e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 9.000e-26 0.002 0.961
## Residuals 3695 1.403e-19 3.796e-23
## 23863 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_covid19.other"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.419077e-12 2.419077e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 6.400e-25 0.008 0.93
## Residuals 7206 5.989e-19 8.312e-23
## 20352 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_natural_disaster.income_src_lost"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.084544e-12 1.084544e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.584e-22 0.18 0.672
## Residuals 22288 1.964e-17 8.813e-22
## 5270 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_natural_disaster.food_acc_limit"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -8.176473e-13 8.176473e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 2.000e-21 1.644e-21 1.865 0.172
## Residuals 22288 1.964e-17 8.813e-22
## 5270 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_natural_disaster.loss_shelter"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -7.881378e-13 7.881378e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-21 6.544e-22 0.742 0.389
## Residuals 22288 1.964e-17 8.813e-22
## 5270 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_natural_disaster.loss_basic_serv"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -8.637817e-13 8.637817e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 2.000e-21 2.216e-21 2.515 0.113
## Residuals 22288 1.964e-17 8.812e-22
## 5270 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_natural_disaster.loss_edu"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -7.202631e-13 7.202631e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-22 6.231e-23 0.266 0.606
## Residuals 10437 2.443e-18 2.340e-22
## 17121 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_natural_disaster.loss_sanitation"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -7.701101e-13 7.701101e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.100e-21 1.094e-21 4.675 0.0306 *
## Residuals 10437 2.442e-18 2.339e-22
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 17121 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_natural_disaster.hh_injury_death"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.385011e-12 1.385011e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.158e-23 0.049 0.824
## Residuals 10437 2.443e-18 2.341e-22
## 17121 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_natural_disaster.refused"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.1763e-12 6.1763e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.00e+00 1.260e-24 0.006 0.936
## Residuals 3149 6.21e-19 1.972e-22
## 24409 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_natural_disaster.other"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.903815e-12 1.903815e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 4.040e-23 0.046 0.83
## Residuals 22288 1.964e-17 8.813e-22
## 5270 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_conflict.income_src_lost"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -9.24529e-13 9.24529e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.057e-23 0.15 0.699
## Residuals 5462 7.513e-19 1.376e-22
## 22096 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_conflict.food_acc_limit"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.225046e-13 6.225046e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-22 1.485e-22 1.08 0.299
## Residuals 5462 7.512e-19 1.375e-22
## 22096 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_conflict.loss_shelter"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.762073e-13 6.762073e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-22 6.007e-23 0.437 0.509
## Residuals 5462 7.513e-19 1.376e-22
## 22096 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_conflict.loss_basic_serv"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.762072e-13 6.762072e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 3.00e-22 3.152e-22 2.292 0.13
## Residuals 5462 7.51e-19 1.375e-22
## 22096 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_conflict.loss_edu"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.298406e-12 1.298406e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 8.950e-24 0.065 0.799
## Residuals 5462 7.513e-19 1.376e-22
## 22096 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_conflict.loss_sanitation"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.485807e-12 1.485807e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 6.620e-24 0.048 0.826
## Residuals 5462 7.513e-19 1.376e-22
## 22096 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_conflict.hh_injury_death"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -9.223273e-13 9.223273e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.071e-23 0.151 0.698
## Residuals 5462 7.513e-19 1.376e-22
## 22096 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_conflict.new_mines"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -9.653078e-13 9.653078e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.834e-23 0.133 0.715
## Residuals 5462 7.513e-19 1.376e-22
## 22096 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_conflict.refused"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.609093e-12 4.609093e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 6.300e-25 0.005 0.946
## Residuals 5462 7.514e-19 1.376e-22
## 22096 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:event_conflict.other"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.203791e-12 3.203791e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.320e-24 0.01 0.922
## Residuals 5462 7.513e-19 1.376e-22
## 22096 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:main_income.agriculture"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -8.119608e-14 8.119608e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 8.690e-25 0.226 0.635
## Residuals 14904 5.732e-20 3.846e-24
## 12654 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:main_income.livestock"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.117103e-13 1.117103e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 3.670e-25 0.095 0.758
## Residuals 14904 5.732e-20 3.846e-24
## 12654 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:main_income.rent"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.538284e-13 2.538284e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 6.100e-26 0.016 0.9
## Residuals 14904 5.732e-20 3.846e-24
## 12654 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:main_income.small_business"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -9.088362e-14 9.088362e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 6.230e-25 0.162 0.687
## Residuals 14904 5.732e-20 3.846e-24
## 12654 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:main_income.daily_lab"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.590181e-14 6.590181e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.094e-24 0.544 0.461
## Residuals 14904 5.732e-20 3.846e-24
## 12654 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:main_income.formal_epml"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.265044e-13 1.265044e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.730e-25 0.071 0.79
## Residuals 14904 5.732e-20 3.846e-24
## 12654 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:main_income.gov_hum_assistance"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.271964e-13 2.271964e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 7.700e-26 0.02 0.888
## Residuals 14904 5.732e-20 3.846e-24
## 12654 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:main_income.gifts"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.026408e-13 2.026408e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 9.800e-26 0.025 0.873
## Residuals 14904 5.732e-20 3.846e-24
## 12654 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:main_income.loans"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -8.248705e-14 8.248705e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 2.000e-23 1.788e-23 4.652 0.031 *
## Residuals 14904 5.731e-20 3.845e-24
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 12654 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:main_income.selling_assets"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.729902e-13 2.729902e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 5.300e-26 0.014 0.907
## Residuals 14904 5.732e-20 3.846e-24
## 12654 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:main_income.other"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.527632e-13 2.527632e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 6.200e-26 0.016 0.899
## Residuals 14904 5.732e-20 3.846e-24
## 12654 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:what_humanitarian_information.none"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.565004e-18 3.565004e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.66e-35 0.028 0.866
## Residuals 6534 3.803e-30 5.82e-34
## 21024 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:what_humanitarian_information.food_livestock_prices"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.350659e-18 1.350659e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 2.000e-33 1.746e-33 3.001 0.0833 .
## Residuals 6534 3.801e-30 5.818e-34
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 21024 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:what_humanitarian_information.request_assistance"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.173335e-18 1.173335e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 4.992e-34 0.858 0.354
## Residuals 6534 3.802e-30 5.820e-34
## 21024 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:what_humanitarian_information.food_assistance"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.34874e-18 1.34874e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.952e-34 0.335 0.562
## Residuals 6534 3.803e-30 5.820e-34
## 21024 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:what_humanitarian_information.education_services"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.292288e-18 1.292288e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.35e-34 0.404 0.525
## Residuals 6534 3.803e-30 5.82e-34
## 21024 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:what_humanitarian_information.shelter"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.336531e-18 1.336531e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.026e-34 0.348 0.555
## Residuals 6534 3.803e-30 5.820e-34
## 21024 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:what_humanitarian_information.health_services"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.234354e-18 1.234354e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 3.006e-34 0.516 0.472
## Residuals 6534 3.803e-30 5.820e-34
## 21024 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:what_humanitarian_information.nutrition_services"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.484699e-18 1.484699e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.384e-34 0.238 0.626
## Residuals 6534 3.803e-30 5.820e-34
## 21024 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:what_humanitarian_information.information_water_hygiene"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.526944e-18 1.526944e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.266e-34 0.218 0.641
## Residuals 6534 3.803e-30 5.820e-34
## 21024 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:what_humanitarian_information.protection"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.429923e-18 3.429923e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.80e-35 0.031 0.86
## Residuals 6534 3.803e-30 5.82e-34
## 21024 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:what_humanitarian_information.cfm"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.438436e-18 3.438436e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.79e-35 0.031 0.861
## Residuals 6534 3.803e-30 5.82e-34
## 21024 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:what_humanitarian_information.other"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -9.293532e-18 9.293532e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.30e-36 0.004 0.95
## Residuals 6534 3.803e-30 5.82e-34
## 21024 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:idps"
## [1] "Predicotr variable:lcsi_migrated"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -8.937723e-13 8.937723e-13 1
## not_applicable-exhausted 0 -9.630990e-13 9.630990e-13 1
## yes-exhausted 0 -9.570194e-13 9.570194e-13 1
## not_applicable-no 0 -5.296167e-13 5.296167e-13 1
## yes-no 0 -5.184788e-13 5.184788e-13 1
## yes-not_applicable 0 -6.305165e-13 6.305165e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 4.000e-22 1.242e-22 1.347 0.257
## Residuals 14990 1.382e-18 9.221e-23
## 12566 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:lcsi_savings"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -8.466713e-13 8.466713e-13 1
## not_applicable-exhausted 0 -8.751881e-13 8.751881e-13 1
## yes-exhausted 0 -8.715843e-13 8.715843e-13 1
## not_applicable-no 0 -5.066202e-13 5.066202e-13 1
## yes-no 0 -5.003689e-13 5.003689e-13 1
## yes-not_applicable 0 -5.472393e-13 5.472393e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 2.000e-22 8.024e-23 0.87 0.456
## Residuals 14990 1.382e-18 9.222e-23
## 12566 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:lcsi_household"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.958760e-12 1.958760e-12 1
## not_applicable-exhausted 0 -1.998173e-12 1.998173e-12 1
## yes-exhausted 0 -1.998539e-12 1.998539e-12 1
## not_applicable-no 0 -1.210921e-12 1.210921e-12 1
## yes-no 0 -1.211525e-12 1.211525e-12 1
## yes-not_applicable 0 -1.274263e-12 1.274263e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.000e-21 2.833e-22 0.83 0.477
## Residuals 9691 3.306e-18 3.412e-22
## 17865 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:lcsi_food"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -4.389936e-12 4.389936e-12 1
## not_applicable-exhausted 0 -4.677674e-12 4.677674e-12 1
## yes-exhausted 0 -4.103432e-12 4.103432e-12 1
## not_applicable-no 0 -2.831721e-12 2.831721e-12 1
## yes-no 0 -1.725157e-12 1.725157e-12 1
## yes-not_applicable 0 -2.363315e-12 2.363315e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 0.000e+00 1.910e-23 0.056 0.983
## Residuals 9691 3.307e-18 3.413e-22
## 17865 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:lcsi_income_equipment"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -2.261379e-12 2.261379e-12 1
## not_applicable-exhausted 0 -2.246042e-12 2.246042e-12 1
## yes-exhausted 0 -2.494535e-12 2.494535e-12 1
## not_applicable-no 0 -1.071116e-12 1.071116e-12 1
## yes-no 0 -1.524891e-12 1.524891e-12 1
## yes-not_applicable 0 -1.502052e-12 1.502052e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 0.000e+00 1.501e-22 0.44 0.724
## Residuals 9691 3.307e-18 3.412e-22
## 17865 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:lcsi_delayed_medical_care"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -3.252313e-12 3.252313e-12 1
## not_applicable-exhausted 0 -3.278209e-12 3.278209e-12 1
## yes-exhausted 0 -3.239977e-12 3.239977e-12 1
## not_applicable-no 0 -1.251227e-12 1.251227e-12 1
## yes-no 0 -1.147329e-12 1.147329e-12 1
## yes-not_applicable 0 -1.218801e-12 1.218801e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.000e-21 3.108e-22 0.911 0.435
## Residuals 9691 3.306e-18 3.412e-22
## 17865 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:lcsi_sold_land"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -9.604385e-13 9.604385e-13 1
## not_applicable-exhausted 0 -9.956023e-13 9.956023e-13 1
## yes-exhausted 0 -1.143960e-12 1.143960e-12 1
## not_applicable-no 0 -4.588833e-13 4.588833e-13 1
## yes-no 0 -7.266325e-13 7.266325e-13 1
## yes-not_applicable 0 -7.725131e-13 7.725131e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.000e-22 2.293e-23 0.249 0.862
## Residuals 14990 1.382e-18 9.223e-23
## 12566 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:lcsi_sold_fem_animal"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -8.843874e-13 8.843874e-13 1
## not_applicable-exhausted 0 -9.200366e-13 9.200366e-13 1
## yes-exhausted 0 -9.781735e-13 9.781735e-13 1
## not_applicable-no 0 -4.751192e-13 4.751192e-13 1
## yes-no 0 -5.797363e-13 5.797363e-13 1
## yes-not_applicable 0 -6.327879e-13 6.327879e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.000e-22 3.036e-23 0.329 0.804
## Residuals 14990 1.382e-18 9.223e-23
## 12566 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:lcsi_charity"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -2.921524e-12 2.921524e-12 1
## not_applicable-exhausted 0 -2.972818e-12 2.972818e-12 1
## yes-exhausted 0 -3.174458e-12 3.174458e-12 1
## not_applicable-no 0 -1.080311e-12 1.080311e-12 1
## yes-no 0 -1.551326e-12 1.551326e-12 1
## yes-not_applicable 0 -1.645891e-12 1.645891e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.000e-21 2.459e-22 0.721 0.54
## Residuals 9691 3.306e-18 3.412e-22
## 17865 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:lcsi_married_daughters"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -2.648966e-12 2.648966e-12 1
## not_applicable-exhausted 0 -2.688652e-12 2.688652e-12 1
## yes-exhausted 0 -3.209063e-12 3.209063e-12 1
## not_applicable-no 0 -1.035439e-12 1.035439e-12 1
## yes-no 0 -2.035036e-12 2.035036e-12 1
## yes-not_applicable 0 -2.086432e-12 2.086432e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.000e-21 2.005e-22 0.588 0.623
## Residuals 9691 3.307e-18 3.412e-22
## 17865 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:lcsi_engage_in_illegal_acts"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -2.653189e-12 2.653189e-12 1
## not_applicable-exhausted 0 -2.723419e-12 2.723419e-12 1
## yes-exhausted 0 -2.779120e-12 2.779120e-12 1
## not_applicable-no 0 -1.150694e-12 1.150694e-12 1
## yes-no 0 -1.276947e-12 1.276947e-12 1
## yes-not_applicable 0 -1.417107e-12 1.417107e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.000e-21 4.528e-22 1.327 0.263
## Residuals 9691 3.306e-18 3.411e-22
## 17865 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:lcsi_metal_sell"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -4.443401e-14 4.443401e-14 1
## not_applicable-exhausted 0 -4.506071e-14 4.506071e-14 1
## yes-exhausted 0 -4.608144e-14 4.608144e-14 1
## not_applicable-no 0 -1.520158e-14 1.520158e-14 1
## yes-no 0 -1.800332e-14 1.800332e-14 1
## yes-not_applicable 0 -1.949886e-14 1.949886e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 2.000e-26 7.611e-27 0.342 0.795
## Residuals 3403 7.581e-23 2.228e-26
## 24153 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:lcsi_children_work"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.069340e-12 1.069340e-12 1
## not_applicable-exhausted 0 -1.081532e-12 1.081532e-12 1
## yes-exhausted 0 -1.086460e-12 1.086460e-12 1
## not_applicable-no 0 -3.187208e-13 3.187208e-13 1
## yes-no 0 -3.350593e-13 3.350593e-13 1
## yes-not_applicable 0 -3.721409e-13 3.721409e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 2.500e-23 8.248e-24 1.071 0.36
## Residuals 2870 2.209e-20 7.698e-24
## 24686 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:shelter_defects"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no_damage-fully_destr 0 -6.196315e-13 6.196315e-13 1
## partia_damage-fully_destr 0 -5.984564e-13 5.984564e-13 1
## sign_damage-fully_destr 0 -6.321862e-13 6.321862e-13 1
## partia_damage-no_damage 0 -2.643370e-13 2.643370e-13 1
## sign_damage-no_damage 0 -3.337414e-13 3.337414e-13 1
## sign_damage-partia_damage 0 -2.925581e-13 2.925581e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.000e-21 3.34e-22 9.2 4.45e-06
## Residuals 20363 7.393e-19 3.63e-23
##
## factor(df[[vars$predictor[i]]]) ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 7193 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:income_cats"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## low-high 0 -1.754783e-12 1.754783e-12 1
## middle-high 0 -1.915822e-12 1.915822e-12 1
## very_low-high 0 -1.833917e-12 1.833917e-12 1
## middle-low 0 -1.013430e-12 1.013430e-12 1
## very_low-low 0 -8.484795e-13 8.484795e-13 1
## very_low-middle 0 -1.145002e-12 1.145002e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 2.000e-21 5.275e-22 1.033 0.377
## Residuals 27519 1.405e-17 5.107e-22
## 37 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:household_size_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -3.766306e-18 3.766306e-18 1
## 2021-2019 0 -4.071947e-18 4.071947e-18 1
## 2021-2020 0 -4.412619e-18 4.412619e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 7.662e-33 0.589 0.555
## Residuals 27557 3.585e-28 1.301e-32
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:boys_working_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -3.027008e-18 3.027008e-18 1
## 2021-2019 0 -3.272654e-18 3.272654e-18 1
## 2021-2020 0 -3.546454e-18 3.546454e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 1.000e-32 4.949e-33 0.589 0.555
## Residuals 27557 2.316e-28 8.404e-33
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:girls_working_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -1.476807e-19 1.476807e-19 1
## 2021-2019 0 -1.596652e-19 1.596652e-19 1
## 2021-2020 0 -1.730233e-19 1.730233e-19 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 1.178e-35 0.589 0.555
## Residuals 27557 5.512e-31 2.000e-35
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:total_cash_income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -1.196896e-13 1.196896e-13 1
## 2021-2019 0 -1.294026e-13 1.294026e-13 1
## 2021-2020 0 -1.402288e-13 1.402288e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 7.738e-24 0.589 0.555
## Residuals 27557 3.621e-19 1.314e-23
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -2.193772e-13 2.193772e-13 1
## 2021-2019 0 -2.371799e-13 2.371799e-13 1
## 2021-2020 0 -2.570231e-13 2.570231e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 1.000e-22 2.600e-23 0.589 0.555
## Residuals 27557 1.216e-18 4.414e-23
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:food_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -3.953482e-14 3.953482e-14 1
## 2021-2019 0 -4.274312e-14 4.274312e-14 1
## 2021-2020 0 -4.631914e-14 4.631914e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.00e+00 8.443e-25 0.589 0.555
## Residuals 27557 3.95e-20 1.434e-24
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:water_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -1.755127e-16 1.755127e-16 1
## 2021-2019 0 -1.897559e-16 1.897559e-16 1
## 2021-2020 0 -2.056314e-16 2.056314e-16 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 1.664e-29 0.589 0.555
## Residuals 27557 7.785e-25 2.825e-29
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:rent_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -2.060327e-15 2.060327e-15 1
## 2021-2019 0 -2.227526e-15 2.227526e-15 1
## 2021-2020 0 -2.413887e-15 2.413887e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 2.293e-27 0.589 0.555
## Residuals 27557 1.073e-22 3.893e-27
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:health_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -1.637997e-14 1.637997e-14 1
## 2021-2019 0 -1.770922e-14 1.770922e-14 1
## 2021-2020 0 -1.919083e-14 1.919083e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 1.449e-25 0.589 0.555
## Residuals 27557 6.781e-21 2.461e-25
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:transportation_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -7.130492e-15 7.130492e-15 1
## 2021-2019 0 -7.709141e-15 7.709141e-15 1
## 2021-2020 0 -8.354111e-15 8.354111e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 1.000e-25 2.746e-26 0.589 0.555
## Residuals 27557 1.285e-21 4.663e-26
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:education_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -3.071244e-15 3.071244e-15 1
## 2021-2019 0 -3.320480e-15 3.320480e-15 1
## 2021-2020 0 -3.598281e-15 3.598281e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 1.000e-26 5.095e-27 0.589 0.555
## Residuals 27557 2.384e-22 8.651e-27
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:communications_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -8.120328e-16 8.120328e-16 1
## 2021-2019 0 -8.779305e-16 8.779305e-16 1
## 2021-2020 0 -9.513808e-16 9.513808e-16 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 1.000e-27 3.562e-28 0.589 0.555
## Residuals 27557 1.666e-23 6.048e-28
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:fuel_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -5.132488e-15 5.132488e-15 1
## 2021-2019 0 -5.548996e-15 5.548996e-15 1
## 2021-2020 0 -6.013242e-15 6.013242e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 1.423e-26 0.589 0.555
## Residuals 27557 6.658e-22 2.416e-26
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:debt_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -4.490927e-17 4.490927e-17 1
## 2021-2019 0 -4.855372e-17 4.855372e-17 1
## 2021-2020 0 -5.261587e-17 5.261587e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 1.089e-30 0.589 0.555
## Residuals 27557 5.097e-26 1.850e-30
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:idps"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:diarrhea_cases_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -1.032691e-17 1.032691e-17 1
## 2021-2019 0 -1.116495e-17 1.116495e-17 1
## 2021-2020 0 -1.209905e-17 1.209905e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 1.000e-31 5.761e-32 0.589 0.555
## Residuals 27557 2.695e-27 9.781e-32
## [1] "TEST RESULT:Ho rejects"
Population group: returnees
## [1] "Population group:returnees"
## [1] "Predicotr variable:major_event.conflict"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.591862e-18 2.591862e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 5.000e-33 4.516e-33 1.381 0.24
## Residuals 7678 2.511e-29 3.271e-33
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:major_event.covid_19"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -5.303297e-18 5.303297e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 4.000e-33 3.748e-33 1.056 0.304
## Residuals 1941 6.889e-30 3.549e-33
## 5737 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:major_event.earthquake"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.656881e-18 6.656881e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.310e-34 0.04 0.842
## Residuals 7678 2.512e-29 3.271e-33
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:major_event.floods"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.238592e-18 3.238592e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.40e-32 1.365e-32 4.173 0.0411 *
## Residuals 7678 2.51e-29 3.270e-33
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:major_event.avalanche"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.734105e-18 6.734105e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.270e-34 0.039 0.844
## Residuals 7678 2.512e-29 3.271e-33
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:major_event.drought"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.650471e-18 2.650471e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 6.000e-33 5.584e-33 1.707 0.191
## Residuals 7678 2.511e-29 3.271e-33
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:major_event.other"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.599649e-18 4.599649e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 3.020e-34 0.092 0.761
## Residuals 7678 2.512e-29 3.271e-33
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:event_covid19.income_src_lost"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.205944e-12 3.205944e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-23 5.900e-24 0.049 0.824
## Residuals 996 1.191e-19 1.195e-22
## 6682 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:event_covid19.food_acc_limit"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.554923e-12 1.554923e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 4.00e-23 4.120e-23 0.345 0.557
## Residuals 996 1.19e-19 1.195e-22
## 6682 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:event_covid19.loss_basic_serv"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.357948e-12 1.357948e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.200e-22 1.248e-22 1.045 0.307
## Residuals 996 1.189e-19 1.194e-22
## 6682 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:event_covid19.loss_edu"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.36109e-12 1.36109e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.400e-22 1.380e-22 1.156 0.283
## Residuals 996 1.189e-19 1.194e-22
## 6682 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:event_covid19.loss_sanitation"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.261744e-12 2.261744e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-23 1.330e-23 0.111 0.739
## Residuals 996 1.191e-19 1.195e-22
## 6682 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:event_covid19.hh_injury_death"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.381015e-12 1.381015e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.700e-22 1.734e-22 1.453 0.228
## Residuals 996 1.189e-19 1.194e-22
## 6682 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:event_covid19.other"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -8.785634e-12 8.785634e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 7.200e-25 0.006 0.938
## Residuals 996 1.191e-19 1.195e-22
## 6682 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:event_natural_disaster.income_src_lost"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.367442e-13 4.367442e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-23 9.290e-24 0.208 0.649
## Residuals 6321 2.826e-19 4.471e-23
## 1357 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:event_natural_disaster.food_acc_limit"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.501694e-13 3.501694e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 2.000e-23 2.217e-23 0.496 0.481
## Residuals 6321 2.826e-19 4.471e-23
## 1357 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:event_natural_disaster.loss_shelter"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.330296e-13 3.330296e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 6.000e-23 5.949e-23 1.331 0.249
## Residuals 6321 2.826e-19 4.470e-23
## 1357 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:event_natural_disaster.loss_basic_serv"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.569821e-13 3.569821e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 2.000e-23 1.992e-23 0.446 0.504
## Residuals 6321 2.826e-19 4.471e-23
## 1357 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:event_natural_disaster.loss_edu"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.728779e-13 2.728779e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-24 5.530e-25 0.166 0.684
## Residuals 1412 4.711e-21 3.337e-24
## 6266 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:event_natural_disaster.loss_sanitation"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.165901e-13 2.165901e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-24 1.186e-24 0.356 0.551
## Residuals 1412 4.711e-21 3.336e-24
## 6266 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:event_natural_disaster.hh_injury_death"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.618673e-13 4.618673e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.560e-25 0.047 0.829
## Residuals 1412 4.712e-21 3.337e-24
## 6266 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:event_natural_disaster.other"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.235884e-13 6.235884e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 3.660e-24 0.082 0.775
## Residuals 6321 2.826e-19 4.471e-23
## 1357 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:main_income.agriculture"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.299118e-13 1.299118e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-24 1.174e-24 0.59 0.443
## Residuals 1941 3.862e-21 1.990e-24
## 5737 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:main_income.livestock"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.665558e-13 1.665558e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 4.115e-25 0.207 0.649
## Residuals 1941 3.863e-21 1.990e-24
## 5737 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:main_income.rent"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -5.682945e-13 5.682945e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.49e-26 0.013 0.911
## Residuals 1941 3.863e-21 1.99e-24
## 5737 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:main_income.small_business"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.792544e-13 1.792544e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 3.321e-25 0.167 0.683
## Residuals 1941 3.863e-21 1.990e-24
## 5737 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:main_income.daily_lab"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.279212e-13 1.279212e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-24 1.346e-24 0.676 0.411
## Residuals 1941 3.862e-21 1.990e-24
## 5737 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:main_income.formal_epml"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.659953e-13 2.659953e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.251e-25 0.063 0.802
## Residuals 1941 3.863e-21 1.990e-24
## 5737 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:main_income.gov_hum_assistance"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -7.42142e-13 7.42142e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.44e-26 0.007 0.932
## Residuals 1941 3.863e-21 1.99e-24
## 5737 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:main_income.gifts"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.266821e-13 4.266821e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 4.50e-26 0.023 0.88
## Residuals 1941 3.863e-21 1.99e-24
## 5737 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:main_income.loans"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.861942e-13 1.861942e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.30e-23 1.329e-23 6.699 0.00972 **
## Residuals 1941 3.85e-21 1.983e-24
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 5737 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:main_income.selling_assets"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.945702e-13 6.945702e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.65e-26 0.008 0.927
## Residuals 1941 3.863e-21 1.99e-24
## 5737 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:main_income.other"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.945702e-13 6.945702e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.65e-26 0.008 0.927
## Residuals 1941 3.863e-21 1.99e-24
## 5737 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:lcsi_migrated"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.512662e-12 1.512662e-12 1
## not_applicable-exhausted 0 -2.182284e-12 2.182284e-12 1
## yes-exhausted 0 -1.554984e-12 1.554984e-12 1
## not_applicable-no 0 -1.760727e-12 1.760727e-12 1
## yes-no 0 -8.693523e-13 8.693523e-13 1
## yes-not_applicable 0 -1.797217e-12 1.797217e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 7.000e-23 2.473e-23 0.326 0.807
## Residuals 3166 2.404e-19 7.593e-23
## 4510 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:returnees"
## [1] "Predicotr variable:lcsi_savings"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.754223e-12 1.754223e-12 1
## not_applicable-exhausted 0 -1.957440e-12 1.957440e-12 1
## yes-exhausted 0 -1.809871e-12 1.809871e-12 1
## not_applicable-no 0 -1.182383e-12 1.182383e-12 1
## yes-no 0 -9.176560e-13 9.176560e-13 1
## yes-not_applicable 0 -1.263474e-12 1.263474e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.700e-22 5.789e-23 0.763 0.515
## Residuals 3166 2.403e-19 7.590e-23
## 4510 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:returnees"
## [1] "Predicotr variable:lcsi_household"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -8.579805e-14 8.579805e-14 1
## not_applicable-exhausted 0 -9.183491e-14 9.183491e-14 1
## yes-exhausted 0 -9.280973e-14 9.280973e-14 1
## not_applicable-no 0 -6.084621e-14 6.084621e-14 1
## yes-no 0 -6.230775e-14 6.230775e-14 1
## yes-not_applicable 0 -7.038893e-14 7.038893e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 3.100e-25 1.023e-25 1.047 0.371
## Residuals 1118 1.093e-22 9.773e-26
## 6558 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:returnees"
## [1] "Predicotr variable:lcsi_food"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -2.049114e-13 2.049114e-13 1
## not_applicable-exhausted 0 -2.285922e-13 2.285922e-13 1
## yes-exhausted 0 -1.916162e-13 1.916162e-13 1
## not_applicable-no 0 -1.488893e-13 1.488893e-13 1
## yes-no 0 -8.142721e-14 8.142721e-14 1
## yes-not_applicable 0 -1.299851e-13 1.299851e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 2.000e-26 5.700e-27 0.058 0.982
## Residuals 1118 1.095e-22 9.799e-26
## 6558 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:returnees"
## [1] "Predicotr variable:lcsi_income_equipment"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.086639e-13 1.086639e-13 1
## not_applicable-exhausted 0 -1.092035e-13 1.092035e-13 1
## yes-exhausted 0 -1.222651e-13 1.222651e-13 1
## not_applicable-no 0 -5.323561e-14 5.323561e-14 1
## yes-no 0 -7.653346e-14 7.653346e-14 1
## yes-not_applicable 0 -7.729773e-14 7.729773e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.500e-25 5.069e-26 0.518 0.67
## Residuals 1118 1.094e-22 9.787e-26
## 6558 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:returnees"
## [1] "Predicotr variable:lcsi_delayed_medical_care"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.431003e-13 1.431003e-13 1
## not_applicable-exhausted 0 -1.465725e-13 1.465725e-13 1
## yes-exhausted 0 -1.443328e-13 1.443328e-13 1
## not_applicable-no 0 -6.202911e-14 6.202911e-14 1
## yes-no 0 -5.653367e-14 5.653367e-14 1
## yes-not_applicable 0 -6.482178e-14 6.482178e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.400e-25 4.759e-26 0.486 0.692
## Residuals 1118 1.094e-22 9.788e-26
## 6558 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:returnees"
## [1] "Predicotr variable:lcsi_sold_land"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -2.195611e-12 2.195611e-12 1
## not_applicable-exhausted 0 -2.372706e-12 2.372706e-12 1
## yes-exhausted 0 -2.554632e-12 2.554632e-12 1
## not_applicable-no 0 -1.115028e-12 1.115028e-12 1
## yes-no 0 -1.462771e-12 1.462771e-12 1
## yes-not_applicable 0 -1.717184e-12 1.717184e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 3.000e-23 9.400e-24 0.124 0.946
## Residuals 3166 2.404e-19 7.595e-23
## 4510 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:returnees"
## [1] "Predicotr variable:lcsi_sold_fem_animal"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.872747e-12 1.872747e-12 1
## not_applicable-exhausted 0 -2.014870e-12 2.014870e-12 1
## yes-exhausted 0 -2.143873e-12 2.143873e-12 1
## not_applicable-no 0 -1.024959e-12 1.024959e-12 1
## yes-no 0 -1.259775e-12 1.259775e-12 1
## yes-not_applicable 0 -1.462721e-12 1.462721e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 4.000e-23 1.450e-23 0.191 0.903
## Residuals 3166 2.404e-19 7.594e-23
## 4510 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:returnees"
## [1] "Predicotr variable:lcsi_charity"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.636883e-13 1.636883e-13 1
## not_applicable-exhausted 0 -1.673061e-13 1.673061e-13 1
## yes-exhausted 0 -1.864304e-13 1.864304e-13 1
## not_applicable-no 0 -5.482178e-14 5.482178e-14 1
## yes-no 0 -9.884520e-14 9.884520e-14 1
## yes-not_applicable 0 -1.047275e-13 1.047275e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 2.600e-25 8.611e-26 0.881 0.451
## Residuals 1118 1.093e-22 9.777e-26
## 6558 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:returnees"
## [1] "Predicotr variable:lcsi_married_daughters"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.256247e-13 1.256247e-13 1
## not_applicable-exhausted 0 -1.283810e-13 1.283810e-13 1
## yes-exhausted 0 -1.486652e-13 1.486652e-13 1
## not_applicable-no 0 -5.286587e-14 5.286587e-14 1
## yes-no 0 -9.173037e-14 9.173037e-14 1
## yes-not_applicable 0 -9.547027e-14 9.547027e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 8.000e-26 2.648e-26 0.27 0.847
## Residuals 1118 1.095e-22 9.793e-26
## 6558 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:returnees"
## [1] "Predicotr variable:lcsi_engage_in_illegal_acts"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.502073e-13 1.502073e-13 1
## not_applicable-exhausted 0 -1.557221e-13 1.557221e-13 1
## yes-exhausted 0 -1.569531e-13 1.569531e-13 1
## not_applicable-no 0 -6.084091e-14 6.084091e-14 1
## yes-no 0 -6.392594e-14 6.392594e-14 1
## yes-not_applicable 0 -7.598459e-14 7.598459e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 3.600e-25 1.197e-25 1.226 0.299
## Residuals 1118 1.092e-22 9.768e-26
## 6558 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:returnees"
## [1] "Predicotr variable:shelter_defects"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no_damage-fully_destr -1.455192e-11 -1.958596e-11 -9.517872e-12 0
## partia_damage-fully_destr -1.455192e-11 -1.950300e-11 -9.600835e-12 0
## sign_damage-fully_destr -1.455192e-11 -1.969099e-11 -9.412845e-12 0
## partia_damage-no_damage 0.000000e+00 -1.775020e-12 1.775020e-12 1
## sign_damage-no_damage 0.000000e+00 -2.246676e-12 2.246676e-12 1
## sign_damage-partia_damage 0.000000e+00 -2.054059e-12 2.054059e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.390e-20 4.649e-21 13.28 1.26e-08
## Residuals 4004 1.401e-18 3.500e-22
##
## factor(df[[vars$predictor[i]]]) ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 3672 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:returnees"
## [1] "Predicotr variable:income_cats"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## low-high 0 -1.185966e-12 1.185966e-12 1
## middle-high 0 -1.292513e-12 1.292513e-12 1
## very_low-high 0 -1.425416e-12 1.425416e-12 1
## middle-low 0 -8.392862e-13 8.392862e-13 1
## very_low-low 0 -1.032290e-12 1.032290e-12 1
## very_low-middle 0 -1.153125e-12 1.153125e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 8.000e-22 2.827e-22 2.1 0.0979 .
## Residuals 7667 1.032e-18 1.346e-22
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 9 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:returnees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:household_size_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -2.820684e-17 2.820684e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-31 1.018e-31 0.339 0.561
## Residuals 7678 2.307e-27 3.005e-31
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:boys_working_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -2.343131e-19 2.343131e-19 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-35 7.023e-36 0.339 0.561
## Residuals 7678 1.592e-31 2.074e-35
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:girls_working_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -2.45035e-19 2.45035e-19 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-35 7.680e-36 0.339 0.561
## Residuals 7678 1.741e-31 2.268e-35
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:total_cash_income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -4.967831e-14 4.967831e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 3.157e-25 0.339 0.561
## Residuals 7678 7.157e-21 9.322e-25
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -4.369132e-13 4.369132e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.442e-23 0.339 0.561
## Residuals 7678 5.536e-19 7.210e-23
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:food_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -4.266302e-14 4.266302e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.328e-25 0.339 0.561
## Residuals 7678 5.279e-21 6.875e-25
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:water_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -1.621e-15 1.621e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.00e+00 3.361e-28 0.339 0.561
## Residuals 7678 7.62e-24 9.925e-28
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:rent_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -2.645016e-15 2.645016e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-27 8.949e-28 0.339 0.561
## Residuals 7678 2.029e-23 2.642e-27
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:health_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -7.32378e-15 7.32378e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-26 6.861e-27 0.339 0.561
## Residuals 7678 1.555e-22 2.026e-26
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:fuel_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -6.781066e-15 6.781066e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-26 5.882e-27 0.339 0.561
## Residuals 7678 1.333e-22 1.737e-26
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:debt_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -5.649937e-15 5.649937e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 4.083e-27 0.339 0.561
## Residuals 7678 9.258e-23 1.206e-26
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:returnees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:diarrhea_cases_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -7.810437e-20 7.810437e-20 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-36 7.803e-37 0.339 0.561
## Residuals 7678 1.769e-32 2.304e-36
## [1] "TEST RESULT:Ho can't be rejected"
Population group: refugees
## [1] "Population group:refugees"
## [1] "Predicotr variable:major_event.conflict"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.86274e-18 3.86274e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 3.000e-34 2.867e-34 0.359 0.549
## Residuals 1057 8.437e-31 7.982e-34
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:major_event.covid_19"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.22081e-18 1.22081e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 8.000e-36 7.698e-36 0.259 0.611
## Residuals 469 1.394e-32 2.973e-35
## 588 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:major_event.earthquake"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.856091e-17 1.856091e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.00e+00 6.800e-36 0.009 0.926
## Residuals 1057 8.44e-31 7.984e-34
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:major_event.floods"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.563886e-18 3.563886e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 4.000e-34 4.355e-34 0.546 0.46
## Residuals 1057 8.435e-31 7.980e-34
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:major_event.avalanche"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -5.725011e-18 5.725011e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-34 8.690e-35 0.109 0.742
## Residuals 1057 8.439e-31 7.984e-34
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:major_event.drought"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.44752e-18 3.44752e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 6.000e-34 5.849e-34 0.733 0.392
## Residuals 1057 8.434e-31 7.979e-34
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:major_event.other"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -7.549775e-18 7.549775e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 4.540e-35 0.057 0.812
## Residuals 1057 8.439e-31 7.984e-34
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:major_event.none"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.788593e-17 1.788593e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 8.800e-35 8.810e-35 0.161 0.69
## Residuals 55 3.013e-32 5.478e-34
## 1002 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_covid19.income_src_lost"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.457712e-11 1.457712e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 8.000e-23 7.930e-23 0.14 0.709
## Residuals 95 5.386e-20 5.669e-22
## 962 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_covid19.food_acc_limit"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 -1.455192e-11 -2.859127e-11 -5.125576e-13 0.0423556
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 3.980e-21 3.98e-21 7.568 0.00712 **
## Residuals 95 4.996e-20 5.26e-22
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 962 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_covid19.loss_shelter"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -8.468183e-12 8.468183e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.188e-22 1.188e-22 1 0.327
## Residuals 26 3.089e-21 1.188e-22
## 1031 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_covid19.loss_basic_serv"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -9.653698e-12 9.653698e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 7.700e-22 7.674e-22 1.371 0.245
## Residuals 95 5.317e-20 5.597e-22
## 962 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_covid19.loss_edu"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.327164e-11 1.327164e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-22 1.028e-22 0.181 0.671
## Residuals 95 5.384e-20 5.667e-22
## 962 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_covid19.loss_sanitation"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.513757e-11 1.513757e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 7.000e-23 7.19e-23 0.127 0.723
## Residuals 95 5.387e-20 5.67e-22
## 962 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_covid19.hh_injury_death"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -9.653698e-12 9.653698e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 7.700e-22 7.674e-22 1.371 0.245
## Residuals 95 5.317e-20 5.597e-22
## 962 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_covid19.other"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.754799e-11 4.754799e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-23 5.900e-24 0.01 0.919
## Residuals 95 5.393e-20 5.677e-22
## 962 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_natural_disaster.income_src_lost"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -5.69828e-12 5.69828e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-22 7.380e-23 0.118 0.731
## Residuals 784 4.904e-19 6.255e-22
## 273 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_natural_disaster.food_acc_limit"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.957049e-12 3.957049e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 2.000e-22 2.278e-22 0.364 0.546
## Residuals 784 4.903e-19 6.254e-22
## 273 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_natural_disaster.loss_shelter"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.253065e-12 4.253065e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 2.000e-22 1.724e-22 0.276 0.6
## Residuals 784 4.903e-19 6.254e-22
## 273 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_natural_disaster.loss_basic_serv"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.318998e-12 4.318998e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 2.000e-22 1.635e-22 0.261 0.609
## Residuals 784 4.903e-19 6.254e-22
## 273 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_natural_disaster.loss_edu"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -5.97932e-12 5.97932e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 8.000e-23 7.623e-23 0.254 0.615
## Residuals 200 6.009e-20 3.005e-22
## 857 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_natural_disaster.loss_sanitation"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.417409e-11 1.417409e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-23 9.160e-24 0.03 0.862
## Residuals 200 6.016e-20 3.008e-22
## 857 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_natural_disaster.hh_injury_death"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 -4.365575e-11 -5.865182e-11 -2.865967e-11 0
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.482e-20 1.482e-20 65.35 5.9e-14
## Residuals 200 4.535e-20 2.270e-22
##
## factor(df[[vars$predictor[i]]]) ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 857 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_natural_disaster.other"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -7.538085e-12 7.538085e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 3.790e-23 0.061 0.806
## Residuals 784 4.905e-19 6.256e-22
## 273 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_conflict.income_src_lost"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.291322e-11 3.291322e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 7.00e-24 7.08e-24 0.028 0.869
## Residuals 34 8.67e-21 2.55e-22
## 1023 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_conflict.food_acc_limit"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.117405e-11 1.117405e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.400e-22 1.401e-22 0.558 0.46
## Residuals 34 8.537e-21 2.511e-22
## 1023 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_conflict.loss_shelter"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.066632e-11 1.066632e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 2.480e-22 2.479e-22 1 0.324
## Residuals 34 8.429e-21 2.479e-22
## 1023 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_conflict.loss_basic_serv"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.07538e-11 1.07538e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 3.47e-22 3.471e-22 1.417 0.242
## Residuals 34 8.33e-21 2.450e-22
## 1023 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_conflict.loss_edu"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.291322e-11 3.291322e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 7.00e-24 7.08e-24 0.028 0.869
## Residuals 34 8.67e-21 2.55e-22
## 1023 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_conflict.hh_injury_death"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.447775e-11 1.447775e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 5.000e-23 4.958e-23 0.195 0.661
## Residuals 34 8.628e-21 2.538e-22
## 1023 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:event_conflict.new_mines"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.162341e-11 1.162341e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 6.450e-22 6.446e-22 2.728 0.108
## Residuals 34 8.033e-21 2.363e-22
## 1023 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:main_income.agriculture"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -5.01819e-13 5.01819e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.210e-27 0.009 0.926
## Residuals 469 1.213e-22 2.587e-25
## 588 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:main_income.livestock"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.541895e-13 2.541895e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-26 9.080e-27 0.035 0.851
## Residuals 469 1.213e-22 2.586e-25
## 588 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:main_income.rent"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -7.081677e-13 7.081677e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.100e-27 0.004 0.948
## Residuals 469 1.213e-22 2.587e-25
## 588 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:main_income.small_business"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.034701e-13 1.034701e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-25 9.632e-26 0.373 0.542
## Residuals 469 1.212e-22 2.585e-25
## 588 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:main_income.daily_lab"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.071522e-13 1.071522e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 8.100e-25 8.083e-25 3.146 0.0768 .
## Residuals 469 1.205e-22 2.569e-25
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 588 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:main_income.formal_epml"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -5.01819e-13 5.01819e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.210e-27 0.009 0.926
## Residuals 469 1.213e-22 2.587e-25
## 588 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:main_income.gov_hum_assistance"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.810748e-13 2.810748e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-26 7.330e-27 0.028 0.866
## Residuals 469 1.213e-22 2.586e-25
## 588 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:main_income.gifts"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.733027e-13 1.733027e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 2.000e-26 2.136e-26 0.083 0.774
## Residuals 469 1.213e-22 2.586e-25
## 588 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:main_income.loans"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -9.321537e-14 9.321537e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 3.60e-25 3.590e-25 1.392 0.239
## Residuals 469 1.21e-22 2.579e-25
## 588 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:main_income.selling_assets"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.283577e-13 2.283577e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-26 1.145e-26 0.044 0.833
## Residuals 469 1.213e-22 2.586e-25
## 588 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:main_income.other"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.541895e-13 2.541895e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-26 9.080e-27 0.035 0.851
## Residuals 469 1.213e-22 2.586e-25
## 588 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:what_humanitarian_information.none"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.019246e-17 4.019246e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 3.000e-35 2.770e-35 0.036 0.851
## Residuals 55 4.269e-32 7.762e-34
## 1002 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:what_humanitarian_information.food_livestock_prices"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.941362e-17 1.941362e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.600e-34 1.623e-34 0.21 0.649
## Residuals 55 4.256e-32 7.738e-34
## 1002 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:what_humanitarian_information.request_assistance"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.758471e-17 1.758471e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 2.30e-34 2.254e-34 0.292 0.591
## Residuals 55 4.25e-32 7.726e-34
## 1002 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:what_humanitarian_information.food_assistance"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.640633e-17 1.640633e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 3.000e-34 2.977e-34 0.386 0.537
## Residuals 55 4.242e-32 7.713e-34
## 1002 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:what_humanitarian_information.education_services"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.562305e-17 1.562305e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 3.800e-34 3.814e-34 0.495 0.484
## Residuals 55 4.234e-32 7.698e-34
## 1002 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:what_humanitarian_information.shelter"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.48861e-17 1.48861e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 5.500e-34 5.548e-34 0.724 0.399
## Residuals 55 4.217e-32 7.666e-34
## 1002 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:what_humanitarian_information.health_services"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.466055e-17 1.466055e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 7.900e-34 7.901e-34 1.036 0.313
## Residuals 55 4.193e-32 7.624e-34
## 1002 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:what_humanitarian_information.nutrition_services"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.126694e-17 2.126694e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.20e-34 1.245e-34 0.161 0.69
## Residuals 55 4.26e-32 7.745e-34
## 1002 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:what_humanitarian_information.information_water_hygiene"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.608271e-17 1.608271e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.950e-33 1.955e-33 2.637 0.11
## Residuals 55 4.077e-32 7.412e-34
## 1002 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:what_humanitarian_information.protection"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.408045e-17 2.408045e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 9.000e-35 8.970e-35 0.116 0.735
## Residuals 55 4.263e-32 7.751e-34
## 1002 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:what_humanitarian_information.cfm"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -5.634024e-17 5.634024e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-35 1.360e-35 0.018 0.895
## Residuals 55 4.271e-32 7.765e-34
## 1002 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:what_humanitarian_information.other"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.311379e-17 3.311379e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 4.000e-35 4.24e-35 0.055 0.816
## Residuals 55 4.268e-32 7.76e-34
## 1002 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:lcsi_migrated"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -2.592367e-11 2.592367e-11 1
## not_applicable-exhausted 0 -2.616285e-11 2.616285e-11 1
## yes-exhausted 0 -2.941735e-11 2.941735e-11 1
## not_applicable-no 0 -9.889192e-12 9.889192e-12 1
## yes-no 0 -1.669377e-11 1.669377e-11 1
## yes-not_applicable 0 -1.706283e-11 1.706283e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.800e-21 6.071e-22 0.536 0.658
## Residuals 354 4.013e-19 1.134e-21
## 701 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:lcsi_savings"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -2.195755e-11 2.195755e-11 1
## not_applicable-exhausted 0 -2.144067e-11 2.144067e-11 1
## yes-exhausted 0 -2.135025e-11 2.135025e-11 1
## not_applicable-no 0 -1.213293e-11 1.213293e-11 1
## yes-no 0 -1.197243e-11 1.197243e-11 1
## yes-not_applicable 0 -1.099579e-11 1.099579e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 2.000e-21 6.681e-22 0.59 0.622
## Residuals 354 4.011e-19 1.133e-21
## 701 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:lcsi_household"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.243763e-11 1.243763e-11 1
## not_applicable-exhausted 0 -1.224673e-11 1.224673e-11 1
## yes-exhausted 0 -1.332430e-11 1.332430e-11 1
## not_applicable-no 0 -8.773054e-12 8.773054e-12 1
## yes-no 0 -1.022355e-11 1.022355e-11 1
## yes-not_applicable 0 -9.990437e-12 9.990437e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.190e-21 3.983e-22 0.708 0.548
## Residuals 284 1.598e-19 5.626e-22
## 771 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:lcsi_food"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -2.815658e-11 2.815658e-11 1
## not_applicable-exhausted 0 -3.183574e-11 3.183574e-11 1
## yes-exhausted 0 -2.356282e-11 2.356282e-11 1
## not_applicable-no 0 -2.693001e-11 2.693001e-11 1
## yes-no 0 -1.633763e-11 1.633763e-11 1
## yes-not_applicable 0 -2.208255e-11 2.208255e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 7.000e-23 2.170e-23 0.038 0.99
## Residuals 284 1.609e-19 5.666e-22
## 771 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:lcsi_income_equipment"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -2.909536e-11 2.909536e-11 1
## not_applicable-exhausted 0 -2.779406e-11 2.779406e-11 1
## yes-exhausted 0 -3.889685e-11 3.889685e-11 1
## not_applicable-no 0 -1.029974e-11 1.029974e-11 1
## yes-no 0 -2.909536e-11 2.909536e-11 1
## yes-not_applicable 0 -2.779406e-11 2.779406e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.200e-22 4.120e-23 0.073 0.975
## Residuals 284 1.608e-19 5.663e-22
## 771 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:lcsi_delayed_medical_care"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -4.391510e-11 4.391510e-11 1
## not_applicable-exhausted 0 -4.394063e-11 4.394063e-11 1
## yes-exhausted 0 -4.375576e-11 4.375576e-11 1
## not_applicable-no 0 -9.588631e-12 9.588631e-12 1
## yes-no 0 -8.702287e-12 8.702287e-12 1
## yes-not_applicable 0 -8.830191e-12 8.830191e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 7.600e-22 2.544e-22 0.451 0.717
## Residuals 284 1.602e-19 5.641e-22
## 771 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:lcsi_sold_land"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -5.064143e-11 5.064143e-11 1
## not_applicable-exhausted 0 -5.063903e-11 5.063903e-11 1
## yes-exhausted 0 -7.939731e-11 7.939731e-11 1
## not_applicable-no 0 -9.258504e-12 9.258504e-12 1
## yes-no 0 -6.184934e-11 6.184934e-11 1
## yes-not_applicable 0 -6.184738e-11 6.184738e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.200e-21 3.892e-22 0.343 0.794
## Residuals 354 4.019e-19 1.135e-21
## 701 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:lcsi_sold_fem_animal"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -3.378708e-11 3.378708e-11 1
## not_applicable-exhausted 0 -3.339979e-11 3.339979e-11 1
## yes-exhausted 0 -3.604866e-11 3.604866e-11 1
## not_applicable-no 0 -1.009131e-11 1.009131e-11 1
## yes-no 0 -1.690546e-11 1.690546e-11 1
## yes-not_applicable 0 -1.611750e-11 1.611750e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 2.300e-21 7.655e-22 0.676 0.567
## Residuals 354 4.008e-19 1.132e-21
## 701 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:lcsi_charity"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.375522e-11 1.375522e-11 1
## not_applicable-exhausted 0 -1.420505e-11 1.420505e-11 1
## yes-exhausted 0 -2.038620e-11 2.038620e-11 1
## not_applicable-no 0 -7.930096e-12 7.930096e-12 1
## yes-no 0 -1.663431e-11 1.663431e-11 1
## yes-not_applicable 0 -1.700816e-11 1.700816e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 5.200e-22 1.72e-22 0.304 0.822
## Residuals 284 1.605e-19 5.65e-22
## 771 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:lcsi_married_daughters"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -3.581891e-11 3.581891e-11 1
## not_applicable-exhausted 0 -3.588671e-11 3.588671e-11 1
## yes-exhausted 0 -4.000841e-11 4.000841e-11 1
## not_applicable-no 0 -7.450260e-12 7.450260e-12 1
## yes-no 0 -1.919176e-11 1.919176e-11 1
## yes-not_applicable 0 -1.931801e-11 1.931801e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 5.200e-22 1.744e-22 0.309 0.819
## Residuals 284 1.604e-19 5.649e-22
## 771 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:lcsi_engage_in_illegal_acts"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -4.372739e-11 4.372739e-11 1
## not_applicable-exhausted 0 -4.377700e-11 4.377700e-11 1
## yes-exhausted 0 -4.623576e-11 4.623576e-11 1
## not_applicable-no 0 -7.484578e-12 7.484578e-12 1
## yes-no 0 -1.665348e-11 1.665348e-11 1
## yes-not_applicable 0 -1.678331e-11 1.678331e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 5.500e-22 1.818e-22 0.322 0.81
## Residuals 284 1.604e-19 5.649e-22
## 771 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:lcsi_metal_sell"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## yes-not_applicable 0 -2.182986e-11 2.182986e-11 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 3.510e-22 3.513e-22 1 0.337
## Residuals 12 4.216e-21 3.513e-22
## 1045 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:refugees"
## [1] "Predicotr variable:lcsi_children_work"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0.000000e+00 -5.182238e-11 5.182238e-11 1.0000000
## not_applicable-exhausted 0.000000e+00 -5.255749e-11 5.255749e-11 1.0000000
## yes-exhausted 1.455192e-11 -4.420914e-11 7.331297e-11 0.8714307
## not_applicable-no 0.000000e+00 -2.905224e-11 2.905224e-11 1.0000000
## yes-no 1.455192e-11 -2.462212e-11 5.372595e-11 0.6768529
## yes-not_applicable 1.455192e-11 -2.558953e-11 5.469336e-11 0.6924062
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 2.108e-21 7.027e-22 2.857 0.0908 .
## Residuals 10 2.459e-21 2.459e-22
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1045 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:shelter_defects"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no_damage-fully_destr 0 -1.364485e-11 1.364485e-11 1
## partia_damage-fully_destr 0 -1.282238e-11 1.282238e-11 1
## sign_damage-fully_destr 0 -1.312973e-11 1.312973e-11 1
## partia_damage-no_damage 0 -5.245470e-12 5.245470e-12 1
## sign_damage-no_damage 0 -5.957468e-12 5.957468e-12 1
## sign_damage-partia_damage 0 -3.704450e-12 3.704450e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 9.000e-23 2.907e-23 0.133 0.941
## Residuals 704 1.543e-19 2.192e-22
## 351 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:income_cats"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## low-high 0 -3.373277e-12 3.373277e-12 1
## middle-high 0 -3.497542e-12 3.497542e-12 1
## very_low-high 0 -3.457278e-12 3.457278e-12 1
## middle-low 0 -1.288933e-12 1.288933e-12 1
## very_low-low 0 -1.175296e-12 1.175296e-12 1
## very_low-middle 0 -1.495034e-12 1.495034e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 3.000e-23 9.350e-24 0.256 0.857
## Residuals 1055 3.847e-20 3.647e-23
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:household_size_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -5.318659e-17 5.318659e-17 1
## 2021-2019 0 -1.150035e-16 1.150035e-16 1
## 2021-2020 0 -1.171197e-16 1.171197e-16 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 1.000e-31 4.991e-32 0.4 0.67
## Residuals 1056 1.317e-28 1.248e-31
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:boys_working_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -2.725895e-18 2.725895e-18 1
## 2021-2019 0 -5.894104e-18 5.894104e-18 1
## 2021-2020 0 -6.002565e-18 6.002565e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 3.000e-34 1.311e-34 0.4 0.67
## Residuals 1056 3.461e-31 3.277e-34
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:girls_working_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -1.824920e-20 1.824920e-20 1
## 2021-2019 0 -3.945959e-20 3.945959e-20 1
## 2021-2020 0 -4.018571e-20 4.018571e-20 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 1.200e-38 5.876e-39 0.4 0.67
## Residuals 1056 1.551e-35 1.469e-38
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:total_cash_income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -4.402543e-14 4.402543e-14 1
## 2021-2019 0 -9.519461e-14 9.519461e-14 1
## 2021-2020 0 -9.694635e-14 9.694635e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 7.000e-26 3.420e-26 0.4 0.67
## Residuals 1056 9.027e-23 8.548e-26
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -9.088776e-13 9.088776e-13 1
## 2021-2019 0 -1.965233e-12 1.965233e-12 1
## 2021-2020 0 -2.001397e-12 2.001397e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 3.000e-23 1.458e-23 0.4 0.67
## Residuals 1056 3.847e-20 3.643e-23
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:food_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -8.303277e-14 8.303277e-14 1
## 2021-2019 0 -1.795388e-13 1.795388e-13 1
## 2021-2020 0 -1.828426e-13 1.828426e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 2.000e-25 1.216e-25 0.4 0.67
## Residuals 1056 3.211e-22 3.041e-25
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:water_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -5.323364e-15 5.323364e-15 1
## 2021-2019 0 -1.151052e-14 1.151052e-14 1
## 2021-2020 0 -1.172233e-14 1.172233e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 1.00e-27 5.00e-28 0.4 0.67
## Residuals 1056 1.32e-24 1.25e-27
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:rent_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -5.118458e-15 5.118458e-15 1
## 2021-2019 0 -1.106746e-14 1.106746e-14 1
## 2021-2020 0 -1.127112e-14 1.127112e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 9.00e-28 4.622e-28 0.4 0.67
## Residuals 1056 1.22e-24 1.156e-27
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:health_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -1.836623e-14 1.836623e-14 1
## 2021-2019 0 -3.971264e-14 3.971264e-14 1
## 2021-2020 0 -4.044342e-14 4.044342e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 1.200e-26 5.952e-27 0.4 0.67
## Residuals 1056 1.571e-23 1.488e-26
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:transportation_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -1.481175e-14 1.481175e-14 1
## 2021-2019 0 -3.202691e-14 3.202691e-14 1
## 2021-2020 0 -3.261626e-14 3.261626e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 8.000e-27 3.871e-27 0.4 0.67
## Residuals 1056 1.022e-23 9.676e-27
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:education_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -6.847611e-17 6.847611e-17 1
## 2021-2019 0 -1.480634e-16 1.480634e-16 1
## 2021-2020 0 -1.507880e-16 1.507880e-16 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 1.700e-31 8.273e-32 0.4 0.67
## Residuals 1056 2.184e-28 2.068e-31
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:communications_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -2.090873e-17 2.090873e-17 1
## 2021-2019 0 -4.521021e-17 4.521021e-17 1
## 2021-2020 0 -4.604215e-17 4.604215e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 1.500e-32 7.714e-33 0.4 0.67
## Residuals 1056 2.036e-29 1.928e-32
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:fuel_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -1.187616e-14 1.187616e-14 1
## 2021-2019 0 -2.567940e-14 2.567940e-14 1
## 2021-2020 0 -2.615194e-14 2.615194e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 5.000e-27 2.489e-27 0.4 0.67
## Residuals 1056 6.569e-24 6.221e-27
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:debt_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -8.112589e-16 8.112589e-16 1
## 2021-2019 0 -1.754156e-15 1.754156e-15 1
## 2021-2020 0 -1.786435e-15 1.786435e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 2.300e-29 1.161e-29 0.4 0.67
## Residuals 1056 3.065e-26 2.903e-29
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:refugees"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:diarrhea_cases_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -3.683531e-18 3.683531e-18 1
## 2021-2019 0 -7.964767e-18 7.964767e-18 1
## 2021-2020 0 -8.111332e-18 8.111332e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 5.000e-34 2.394e-34 0.4 0.67
## Residuals 1056 6.319e-31 5.984e-34
## [1] "TEST RESULT:Ho rejects"
Population group: non_displaced
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:major_event.conflict"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 0 0 NaN
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0 0
## Residuals 15622 0 0
## [1] "TEST RESULT:NA"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:major_event.covid_19"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 0 0 NaN
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0 0
## Residuals 5762 0 0
## 9860 observations deleted due to missingness
## [1] "TEST RESULT:NA"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:major_event.earthquake"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 0 0 NaN
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0 0
## Residuals 15622 0 0
## [1] "TEST RESULT:NA"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:major_event.floods"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 0 0 NaN
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0 0
## Residuals 15622 0 0
## [1] "TEST RESULT:NA"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:major_event.avalanche"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 0 0 NaN
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0 0
## Residuals 15622 0 0
## [1] "TEST RESULT:NA"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:major_event.drought"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 0 0 NaN
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0 0
## Residuals 15622 0 0
## [1] "TEST RESULT:NA"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:major_event.other"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 0 0 NaN
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0 0
## Residuals 15622 0 0
## [1] "TEST RESULT:NA"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:major_event.none"
## [1] "Outcome variable:idp_disp_times_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 0 0 NaN
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0 0
## Residuals 3342 0 0
## 12280 observations deleted due to missingness
## [1] "TEST RESULT:NA"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_covid19.income_src_lost"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -7.803116e-14 7.803116e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-25 1.31e-25 0.171 0.679
## Residuals 3858 2.947e-21 7.64e-25
## 11764 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_covid19.food_acc_limit"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -5.59753e-14 5.59753e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.100e-24 1.078e-24 1.411 0.235
## Residuals 3858 2.946e-21 7.637e-25
## 11764 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_covid19.loss_shelter"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.488764e-13 6.488764e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 2.000e-23 1.677e-23 0.361 0.548
## Residuals 2171 1.007e-19 4.640e-23
## 13451 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_covid19.loss_basic_serv"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -5.676159e-14 5.676159e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.200e-24 1.236e-24 1.619 0.203
## Residuals 3858 2.946e-21 7.637e-25
## 11764 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_covid19.loss_edu"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -7.036022e-14 7.036022e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 3.300e-24 3.269e-24 4.284 0.0385 *
## Residuals 3858 2.944e-21 7.630e-25
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 11764 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_covid19.loss_sanitation"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.079069e-14 6.079069e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 3.000e-25 3.118e-25 0.408 0.523
## Residuals 3858 2.947e-21 7.639e-25
## 11764 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_covid19.hh_injury_death"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.625572e-14 6.625572e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 2.700e-24 2.664e-24 3.491 0.0618 .
## Residuals 3858 2.945e-21 7.633e-25
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 11764 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_covid19.refused"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.685633e-12 6.685633e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 9.00e-26 0.002 0.966
## Residuals 2171 1.007e-19 4.64e-23
## 13451 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_covid19.other"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.749822e-13 3.749822e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 4.20e-27 0.005 0.941
## Residuals 3858 2.948e-21 7.64e-25
## 11764 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_natural_disaster.income_src_lost"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.4919e-13 3.4919e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.00e+00 1.022e-23 0.185 0.667
## Residuals 13191 7.28e-19 5.519e-23
## 2431 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_natural_disaster.food_acc_limit"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.612496e-13 2.612496e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.00e+00 3.375e-23 0.612 0.434
## Residuals 13191 7.28e-19 5.519e-23
## 2431 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_natural_disaster.loss_shelter"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.560282e-13 2.560282e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.00e+00 4.173e-23 0.756 0.385
## Residuals 13191 7.28e-19 5.519e-23
## 2431 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_natural_disaster.loss_basic_serv"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.849858e-13 2.849858e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.00e+00 2.059e-23 0.373 0.541
## Residuals 13191 7.28e-19 5.519e-23
## 2431 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_natural_disaster.loss_edu"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -5.598411e-13 5.598411e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 2.000e-23 1.585e-23 0.303 0.582
## Residuals 3584 1.872e-19 5.223e-23
## 12038 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_natural_disaster.loss_sanitation"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -8.152661e-13 8.152661e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 5.100e-22 5.118e-22 9.825 0.00174
## Residuals 3584 1.867e-19 5.210e-23
##
## factor(df[[vars$predictor[i]]]) **
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 12038 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_natural_disaster.hh_injury_death"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.773703e-12 1.773703e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 9.600e-25 0.018 0.892
## Residuals 3584 1.872e-19 5.223e-23
## 12038 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_natural_disaster.refused"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.910309e-12 2.910309e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.390e-25 0.006 0.94
## Residuals 1919 4.622e-20 2.408e-23
## 13703 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_natural_disaster.other"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.288433e-13 6.288433e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.00e+00 2.450e-24 0.044 0.833
## Residuals 13191 7.28e-19 5.519e-23
## 2431 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_conflict.income_src_lost"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.099568e-13 6.099568e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-23 5.230e-24 0.186 0.666
## Residuals 2199 6.189e-20 2.815e-23
## 13423 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_conflict.food_acc_limit"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.440276e-13 4.440276e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 3.000e-23 2.539e-23 0.902 0.342
## Residuals 2199 6.187e-20 2.814e-23
## 13423 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_conflict.loss_shelter"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.716318e-13 4.716318e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-23 1.385e-23 0.492 0.483
## Residuals 2199 6.188e-20 2.814e-23
## 13423 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_conflict.loss_basic_serv"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.693804e-13 4.693804e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-23 1.425e-23 0.506 0.477
## Residuals 2199 6.188e-20 2.814e-23
## 13423 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_conflict.loss_edu"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -8.911003e-13 8.911003e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.999e-24 0.071 0.79
## Residuals 2199 6.189e-20 2.815e-23
## 13423 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_conflict.loss_sanitation"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -9.924953e-13 9.924953e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.565e-24 0.056 0.814
## Residuals 2199 6.189e-20 2.815e-23
## 13423 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_conflict.hh_injury_death"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.976685e-13 6.976685e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 3.621e-24 0.129 0.72
## Residuals 2199 6.189e-20 2.815e-23
## 13423 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_conflict.new_mines"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.463216e-13 6.463216e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 4.440e-24 0.158 0.691
## Residuals 2199 6.189e-20 2.815e-23
## 13423 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_conflict.refused"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.610535e-12 2.610535e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.00e+00 2.060e-25 0.007 0.932
## Residuals 2199 6.19e-20 2.815e-23
## 13423 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:event_conflict.other"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.610535e-12 2.610535e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.00e+00 2.060e-25 0.007 0.932
## Residuals 2199 6.19e-20 2.815e-23
## 13423 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:main_income.agriculture"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -6.013211e-14 6.013211e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-24 8.052e-25 0.627 0.429
## Residuals 5762 7.401e-21 1.284e-24
## 9860 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:main_income.livestock"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -7.71938e-14 7.71938e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.706e-25 0.211 0.646
## Residuals 5762 7.402e-21 1.285e-24
## 9860 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:main_income.rent"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.14862e-13 2.14862e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.480e-26 0.019 0.89
## Residuals 5762 7.402e-21 1.285e-24
## 9860 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:main_income.small_business"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -9.562233e-14 9.562233e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.100e-23 1.101e-23 8.586 0.0034 **
## Residuals 5762 7.391e-21 1.283e-24
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 9860 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:main_income.daily_lab"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -5.879056e-14 5.879056e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-24 1.063e-24 0.827 0.363
## Residuals 5762 7.401e-21 1.284e-24
## 9860 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:main_income.formal_epml"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -9.647629e-14 9.647629e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.467e-25 0.114 0.735
## Residuals 5762 7.402e-21 1.285e-24
## 9860 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:main_income.gov_hum_assistance"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.932308e-13 2.932308e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.310e-26 0.01 0.92
## Residuals 5762 7.402e-21 1.285e-24
## 9860 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:main_income.gifts"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.803527e-13 1.803527e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 3.570e-26 0.028 0.868
## Residuals 5762 7.402e-21 1.285e-24
## 9860 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:main_income.loans"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -8.523391e-14 8.523391e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 8.000e-24 8.135e-24 6.339 0.0118 *
## Residuals 5762 7.394e-21 1.283e-24
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 9860 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:main_income.selling_assets"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.010427e-13 3.010427e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.240e-26 0.01 0.922
## Residuals 5762 7.402e-21 1.285e-24
## 9860 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:main_income.other"
## [1] "Outcome variable:income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -2.041462e-13 2.041462e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 2.750e-26 0.021 0.884
## Residuals 5762 7.402e-21 1.285e-24
## 9860 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:what_humanitarian_information.none"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.038798e-17 1.038798e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 9.790e-35 0.033 0.855
## Residuals 3342 9.804e-30 2.934e-33
## 12280 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:what_humanitarian_information.food_livestock_prices"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.052864e-18 4.052864e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-33 1.190e-33 0.406 0.524
## Residuals 3342 9.803e-30 2.933e-33
## 12280 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:what_humanitarian_information.request_assistance"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.680869e-18 3.680869e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 3.000e-33 3.362e-33 1.146 0.284
## Residuals 3342 9.801e-30 2.933e-33
## 12280 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:what_humanitarian_information.food_assistance"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.182576e-18 4.182576e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-33 1.035e-33 0.353 0.553
## Residuals 3342 9.804e-30 2.933e-33
## 12280 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:what_humanitarian_information.education_services"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.016694e-18 4.016694e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-33 1.242e-33 0.424 0.515
## Residuals 3342 9.803e-30 2.933e-33
## 12280 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:what_humanitarian_information.shelter"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -5.89634e-18 5.89634e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 3.582e-34 0.122 0.727
## Residuals 3342 9.804e-30 2.934e-33
## 12280 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:what_humanitarian_information.health_services"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.779013e-18 3.779013e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 5.0e-33 4.747e-33 1.619 0.203
## Residuals 3342 9.8e-30 2.932e-33
## 12280 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:what_humanitarian_information.nutrition_services"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.722422e-18 4.722422e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-33 6.689e-34 0.228 0.633
## Residuals 3342 9.804e-30 2.933e-33
## 12280 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:what_humanitarian_information.information_water_hygiene"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -4.61392e-18 4.61392e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 1.000e-33 7.212e-34 0.246 0.62
## Residuals 3342 9.804e-30 2.933e-33
## 12280 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:what_humanitarian_information.protection"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -1.025139e-17 1.025139e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.007e-34 0.034 0.853
## Residuals 3342 9.804e-30 2.934e-33
## 12280 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:what_humanitarian_information.cfm"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -8.734801e-18 8.734801e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.426e-34 0.049 0.826
## Residuals 3342 9.804e-30 2.934e-33
## 12280 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:what_humanitarian_information.other"
## [1] "Outcome variable:awd_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 1-0 0 -3.071185e-17 3.071185e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 1 0.000e+00 1.060e-35 0.004 0.952
## Residuals 3342 9.805e-30 2.934e-33
## 12280 observations deleted due to missingness
## [1] "TEST RESULT:Ho can't be rejected"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:lcsi_migrated"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -8.155073e-13 8.155073e-13 1
## not_applicable-exhausted 0 -9.063460e-13 9.063460e-13 1
## yes-exhausted 0 -8.623231e-13 8.623231e-13 1
## not_applicable-no 0 -5.325410e-13 5.325410e-13 1
## yes-no 0 -4.535834e-13 4.535834e-13 1
## yes-not_applicable 0 -6.017881e-13 6.017881e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 3.000e-23 1.028e-23 0.281 0.839
## Residuals 7000 2.563e-19 3.662e-23
## 8620 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:lcsi_savings"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -9.419794e-13 9.419794e-13 1
## not_applicable-exhausted 0 -9.821300e-13 9.821300e-13 1
## yes-exhausted 0 -9.615670e-13 9.615670e-13 1
## not_applicable-no 0 -4.844670e-13 4.844670e-13 1
## yes-no 0 -4.412935e-13 4.412935e-13 1
## yes-not_applicable 0 -5.215305e-13 5.215305e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 5.000e-23 1.563e-23 0.427 0.734
## Residuals 7000 2.563e-19 3.661e-23
## 8620 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:lcsi_household"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.711190e-12 1.711190e-12 1
## not_applicable-exhausted 0 -1.746403e-12 1.746403e-12 1
## yes-exhausted 0 -1.764355e-12 1.764355e-12 1
## not_applicable-no 0 -8.835600e-13 8.835600e-13 1
## yes-no 0 -9.185342e-13 9.185342e-13 1
## yes-not_applicable 0 -9.825766e-13 9.825766e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.800e-22 5.935e-23 0.807 0.49
## Residuals 3680 2.707e-19 7.356e-23
## 11940 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:lcsi_food"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -3.142665e-12 3.142665e-12 1
## not_applicable-exhausted 0 -3.401059e-12 3.401059e-12 1
## yes-exhausted 0 -2.761965e-12 2.761965e-12 1
## not_applicable-no 0 -2.545903e-12 2.545903e-12 1
## yes-no 0 -1.594637e-12 1.594637e-12 1
## yes-not_applicable 0 -2.057602e-12 2.057602e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.000e-23 2.880e-24 0.039 0.99
## Residuals 3680 2.709e-19 7.361e-23
## 11940 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:lcsi_income_equipment"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.675633e-12 1.675633e-12 1
## not_applicable-exhausted 0 -1.679912e-12 1.679912e-12 1
## yes-exhausted 0 -1.978535e-12 1.978535e-12 1
## not_applicable-no 0 -7.864961e-13 7.864961e-13 1
## yes-no 0 -1.308081e-12 1.308081e-12 1
## yes-not_applicable 0 -1.313558e-12 1.313558e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.000e-22 3.429e-23 0.466 0.706
## Residuals 3680 2.708e-19 7.358e-23
## 11940 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:lcsi_delayed_medical_care"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -1.998125e-12 1.998125e-12 1
## not_applicable-exhausted 0 -2.040464e-12 2.040464e-12 1
## yes-exhausted 0 -2.018085e-12 2.018085e-12 1
## not_applicable-no 0 -9.207924e-13 9.207924e-13 1
## yes-no 0 -8.700753e-13 8.700753e-13 1
## yes-not_applicable 0 -9.633388e-13 9.633388e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.600e-22 5.328e-23 0.724 0.537
## Residuals 3680 2.707e-19 7.357e-23
## 11940 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:lcsi_sold_land"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -9.951221e-13 9.951221e-13 1
## not_applicable-exhausted 0 -1.042110e-12 1.042110e-12 1
## yes-exhausted 0 -1.168398e-12 1.168398e-12 1
## not_applicable-no 0 -4.492994e-13 4.492994e-13 1
## yes-no 0 -6.935637e-13 6.935637e-13 1
## yes-not_applicable 0 -7.594447e-13 7.594447e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 2.000e-23 6.560e-24 0.179 0.911
## Residuals 7000 2.563e-19 3.662e-23
## 8620 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:lcsi_sold_fem_animal"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -9.779046e-13 9.779046e-13 1
## not_applicable-exhausted 0 -1.028606e-12 1.028606e-12 1
## yes-exhausted 0 -1.026190e-12 1.026190e-12 1
## not_applicable-no 0 -4.795141e-13 4.795141e-13 1
## yes-no 0 -4.743086e-13 4.743086e-13 1
## yes-not_applicable 0 -5.715787e-13 5.715787e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 3.000e-23 1.046e-23 0.286 0.836
## Residuals 7000 2.563e-19 3.662e-23
## 8620 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:lcsi_charity"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -2.289525e-12 2.289525e-12 1
## not_applicable-exhausted 0 -2.346114e-12 2.346114e-12 1
## yes-exhausted 0 -2.439868e-12 2.439868e-12 1
## not_applicable-no 0 -8.528286e-13 8.528286e-13 1
## yes-no 0 -1.084444e-12 1.084444e-12 1
## yes-not_applicable 0 -1.199310e-12 1.199310e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 2.000e-22 6.746e-23 0.917 0.432
## Residuals 3680 2.707e-19 7.356e-23
## 11940 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:lcsi_married_daughters"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -2.224714e-12 2.224714e-12 1
## not_applicable-exhausted 0 -2.256884e-12 2.256884e-12 1
## yes-exhausted 0 -2.735023e-12 2.735023e-12 1
## not_applicable-no 0 -7.780134e-13 7.780134e-13 1
## yes-no 0 -1.729777e-12 1.729777e-12 1
## yes-not_applicable 0 -1.770960e-12 1.770960e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.400e-22 4.512e-23 0.613 0.606
## Residuals 3680 2.708e-19 7.358e-23
## 11940 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:lcsi_engage_in_illegal_acts"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -2.189391e-12 2.189391e-12 1
## not_applicable-exhausted 0 -2.232119e-12 2.232119e-12 1
## yes-exhausted 0 -2.331681e-12 2.331681e-12 1
## not_applicable-no 0 -8.320190e-13 8.320190e-13 1
## yes-no 0 -1.070812e-12 1.070812e-12 1
## yes-not_applicable 0 -1.155664e-12 1.155664e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.700e-22 5.737e-23 0.78 0.505
## Residuals 3680 2.707e-19 7.357e-23
## 11940 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:lcsi_metal_sell"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 0 -8.687979e-13 8.687979e-13 1
## not_applicable-exhausted 0 -8.873106e-13 8.873106e-13 1
## yes-exhausted 0 -9.326803e-13 9.326803e-13 1
## not_applicable-no 0 -3.224918e-13 3.224918e-13 1
## yes-no 0 -4.319416e-13 4.319416e-13 1
## yes-not_applicable 0 -4.680638e-13 4.680638e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.690e-22 5.636e-23 12.06 8.38e-08
## Residuals 1562 7.302e-21 4.670e-24
##
## factor(df[[vars$predictor[i]]]) ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 14058 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:lcsi_children_work"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no-exhausted 7.275958e-12 4.320601e-12 1.023131e-11 0
## not_applicable-exhausted 7.275958e-12 4.254217e-12 1.029770e-11 0
## yes-exhausted 7.275958e-12 4.228256e-12 1.032366e-11 0
## not_applicable-no 0.000000e+00 -1.070406e-12 1.070406e-12 1
## yes-no 0.000000e+00 -1.141640e-12 1.141640e-12 1
## yes-not_applicable 0.000000e+00 -1.303888e-12 1.303888e-12 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 1.700e-21 5.671e-22 14.48 2.77e-09
## Residuals 1334 5.225e-20 3.920e-23
##
## factor(df[[vars$predictor[i]]]) ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 14286 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:shelter_defects"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## no_damage-fully_destr 0 -1.886343e-12 1.886343e-12 1
## partia_damage-fully_destr 0 -1.856197e-12 1.856197e-12 1
## sign_damage-fully_destr 0 -1.945604e-12 1.945604e-12 1
## partia_damage-no_damage 0 -6.009222e-13 6.009222e-13 1
## sign_damage-no_damage 0 -8.372660e-13 8.372660e-13 1
## sign_damage-partia_damage 0 -7.669373e-13 7.669373e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 3.000e-22 1.036e-22 0.83 0.477
## Residuals 12130 1.513e-18 1.248e-22
## 3490 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:income_cats"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## low-high 0 -7.153172e-13 7.153172e-13 1
## middle-high 0 -8.093645e-13 8.093645e-13 1
## very_low-high 0 -7.845957e-13 7.845957e-13 1
## middle-low 0 -5.468066e-13 5.468066e-13 1
## very_low-low 0 -5.094280e-13 5.094280e-13 1
## very_low-middle 0 -6.347512e-13 6.347512e-13 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 3 4.000e-22 1.431e-22 1.524 0.206
## Residuals 15609 1.466e-18 9.392e-23
## 11 observations deleted due to missingness
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:household_size_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -8.205264e-17 8.205264e-17 1
## 2021-2019 0 -7.238035e-17 7.238035e-17 1
## 2021-2020 0 -9.652964e-17 9.652964e-17 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.00e+00 6.959e-31 0.292 0.747
## Residuals 15621 3.72e-26 2.381e-30
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:boys_working_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -1.964547e-18 1.964547e-18 1
## 2021-2019 0 -1.732968e-18 1.732968e-18 1
## 2021-2020 0 -2.311163e-18 2.311163e-18 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 1.000e-33 3.989e-34 0.292 0.747
## Residuals 15621 2.132e-29 1.365e-33
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:girls_working_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -6.129301e-19 6.129301e-19 1
## 2021-2019 0 -5.406785e-19 5.406785e-19 1
## 2021-2020 0 -7.210728e-19 7.210728e-19 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 1.000e-34 3.883e-35 0.292 0.747
## Residuals 15621 2.076e-30 1.329e-34
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:total_cash_income_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -1.161854e-14 1.161854e-14 1
## 2021-2019 0 -1.024896e-14 1.024896e-14 1
## 2021-2020 0 -1.366847e-14 1.366847e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 1.395e-26 0.292 0.747
## Residuals 15621 7.458e-22 4.774e-26
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:debt_amount_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -3.589469e-14 3.589469e-14 1
## 2021-2019 0 -3.166345e-14 3.166345e-14 1
## 2021-2020 0 -4.222778e-14 4.222778e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 1.332e-25 0.292 0.747
## Residuals 15621 7.118e-21 4.557e-25
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:food_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -7.485931e-15 7.485931e-15 1
## 2021-2019 0 -6.603497e-15 6.603497e-15 1
## 2021-2020 0 -8.806715e-15 8.806715e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 1.000e-26 5.793e-27 0.292 0.747
## Residuals 15621 3.096e-22 1.982e-26
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:water_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -1.582569e-15 1.582569e-15 1
## 2021-2019 0 -1.396017e-15 1.396017e-15 1
## 2021-2020 0 -1.861790e-15 1.861790e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 1.000e-27 2.589e-28 0.292 0.747
## Residuals 15621 1.384e-23 8.858e-28
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:rent_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -6.613655e-15 6.613655e-15 1
## 2021-2019 0 -5.834043e-15 5.834043e-15 1
## 2021-2020 0 -7.780539e-15 7.780539e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 1.000e-26 4.521e-27 0.292 0.747
## Residuals 15621 2.417e-22 1.547e-26
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:health_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -3.447779e-14 3.447779e-14 1
## 2021-2019 0 -3.041358e-14 3.041358e-14 1
## 2021-2020 0 -4.056090e-14 4.056090e-14 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 1.229e-25 0.292 0.747
## Residuals 15621 6.567e-21 4.204e-25
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:transportation_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -4.944375e-15 4.944375e-15 1
## 2021-2019 0 -4.361537e-15 4.361537e-15 1
## 2021-2020 0 -5.816738e-15 5.816738e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 1.000e-26 2.527e-27 0.292 0.747
## Residuals 15621 1.351e-22 8.646e-27
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:education_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -3.852185e-15 3.852185e-15 1
## 2021-2019 0 -3.398093e-15 3.398093e-15 1
## 2021-2020 0 -4.531847e-15 4.531847e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 1.534e-27 0.292 0.747
## Residuals 15621 8.198e-23 5.248e-27
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:communications_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -2.720144e-15 2.720144e-15 1
## 2021-2019 0 -2.399496e-15 2.399496e-15 1
## 2021-2020 0 -3.200074e-15 3.200074e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 7.648e-28 0.292 0.747
## Residuals 15621 4.088e-23 2.617e-27
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:fuel_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -2.388059e-15 2.388059e-15 1
## 2021-2019 0 -2.106557e-15 2.106557e-15 1
## 2021-2020 0 -2.809398e-15 2.809398e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 1.000e-27 5.895e-28 0.292 0.747
## Residuals 15621 3.151e-23 2.017e-27
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:debt_exp_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -4.176890e-15 4.176890e-15 1
## 2021-2019 0 -3.684522e-15 3.684522e-15 1
## 2021-2020 0 -4.913842e-15 4.913842e-15 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 0.000e+00 1.803e-27 0.292 0.747
## Residuals 15621 9.639e-23 6.170e-27
## [1] "TEST RESULT:Ho rejects"
## [1] "Population group:non_displaced"
## [1] "Predicotr variable:year"
## [1] "Outcome variable:diarrhea_cases_avg"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df[[vars$outcome[i]]] ~ factor(df[[vars$predictor[i]]]))
##
## $`factor(df[[vars$predictor[i]]])`
## diff lwr upr p adj
## 2020-2019 0 -7.206703e-20 7.206703e-20 1
## 2021-2019 0 -6.357184e-20 6.357184e-20 1
## 2021-2020 0 -8.478222e-20 8.478222e-20 1
##
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(df[[vars$predictor[i]]]) 2 1.000e-36 5.369e-37 0.292 0.747
## Residuals 15621 2.869e-32 1.837e-36
## [1] "TEST RESULT:Ho rejects"
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