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AnnaM
AnnaM
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Formula here:

Migration_it= β_o+α_i Indicator+Z_it Gini+I_it AbroadIncome+ ε_it

Here is my ANOVAthe output with the formula:   

 Regression Results
Dependent variable:

Migration

Rich_Poor_IndicatorMid.   206,974.300.
.(292,230.400)

Rich_Poor_IndicatorHigh.   188,436.900
(359,936.000)

AbroadIncome.    4.978
(59.609)

Constant.   1,203,225.000
(308,437.300)

Observations153
Log Likelihood.   -2,327.963
Akaike Inf. Crit..    4,677.926
Bayesian Inf. Crit..  4,711.261
Note:p<0.1; p<0.05; ***p<0.01
 Linear mixed model fit by REML ['lmerMod']
Formula: Migration ~ Rich_Poor_Indicator + (1 + Gini | Country.Name) +  
   AbroadIncome + (AbroadIncome | Country.Name)
  Data: final_data
Control: lmerControl(check.nobs.vs.nRE = "ignore")

REML criterion at convergence: 4655.9

Scaled residuals: 
   Min      1Q  Median      3Q     Max 
-5.7835 -0.1890 -0.0457  0.1119  6.5108 

Random effects:
Groups         Name         Variance  Std.Dev.  Corr 
Country.Name   (Intercept)  3.033e+11 550753.08      
               Gini         3.810e+09  61729.20 -0.42
Country.Name.1 (Intercept)  2.471e+11 497112.89      
               AbroadIncome 1.364e+03     36.93 1.00 
Residual                    1.780e+11 421957.35      
Number of obs: 153, groups:  Country.Name, 89

Fixed effects:
                        Estimate Std. Error t value
(Intercept)             1.203e+06  3.084e+05   3.901
Rich_Poor_IndicatorMid  2.070e+05  2.922e+05   0.708
Rich_Poor_IndicatorHigh 1.884e+05  3.599e+05   0.524
AbroadIncome            4.978e+00  5.961e+01   0.084

Correlation of Fixed Effects:
           (Intr) R_P_IM R_P_IH
Rch_Pr_IndM -0.546              
Rch_Pr_IndH -0.652  0.635       
AbroadIncom  0.037  0.004  0.077
fit warnings:
Some predictor variables are on very different scales: consider rescaling
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see ?isSingular

 

Formula here:

Migration_it= β_o+α_i Indicator+Z_it Gini+I_it AbroadIncome+ ε_it

Here is my ANOVA:  

 Regression Results
Dependent variable:

Migration

Rich_Poor_IndicatorMid.   206,974.300.
.(292,230.400)

Rich_Poor_IndicatorHigh.   188,436.900
(359,936.000)

AbroadIncome.    4.978
(59.609)

Constant.   1,203,225.000
(308,437.300)

Observations153
Log Likelihood.   -2,327.963
Akaike Inf. Crit..    4,677.926
Bayesian Inf. Crit..  4,711.261
Note:p<0.1; p<0.05; ***p<0.01

 

Here is the output with the formula: 

Linear mixed model fit by REML ['lmerMod']
Formula: Migration ~ Rich_Poor_Indicator + (1 + Gini | Country.Name) +  
   AbroadIncome + (AbroadIncome | Country.Name)
  Data: final_data
Control: lmerControl(check.nobs.vs.nRE = "ignore")

REML criterion at convergence: 4655.9

Scaled residuals: 
   Min      1Q  Median      3Q     Max 
-5.7835 -0.1890 -0.0457  0.1119  6.5108 

Random effects:
Groups         Name         Variance  Std.Dev.  Corr 
Country.Name   (Intercept)  3.033e+11 550753.08      
               Gini         3.810e+09  61729.20 -0.42
Country.Name.1 (Intercept)  2.471e+11 497112.89      
               AbroadIncome 1.364e+03     36.93 1.00 
Residual                    1.780e+11 421957.35      
Number of obs: 153, groups:  Country.Name, 89

Fixed effects:
                        Estimate Std. Error t value
(Intercept)             1.203e+06  3.084e+05   3.901
Rich_Poor_IndicatorMid  2.070e+05  2.922e+05   0.708
Rich_Poor_IndicatorHigh 1.884e+05  3.599e+05   0.524
AbroadIncome            4.978e+00  5.961e+01   0.084

Correlation of Fixed Effects:
           (Intr) R_P_IM R_P_IH
Rch_Pr_IndM -0.546              
Rch_Pr_IndH -0.652  0.635       
AbroadIncom  0.037  0.004  0.077
fit warnings:
Some predictor variables are on very different scales: consider rescaling
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see ?isSingular
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Source Link
AnnaM
AnnaM
  • 21
  • 3

Formula here:

Migration_it= β_o+α_i Indicator+Z_it Gini+I_it AbroadIncome+ ε_it

Here is my ANOVA:

Random Effects [1]: https://i.sstatic.net/hYsnd.pngCIs

Computing bootstrap confidence intervals ...

383 message(s): boundary (singular) fit: see ?isSingular
252 warning(s): Model failed to converge with max|grad| = 0.264562 (tol = 0.002, component 1) (and others)

                               2.5 %       97.5 %
.sig01                   1.256697e+01 2.401887e+06
.sig02                  -9.928984e-01 6.604565e-01
.sig03                   4.208632e+04 9.651804e+04
.sig04                   7.887378e+01 1.627314e+06
.sig05                  -1.000000e+00 1.000000e+00
.sig06                   1.215940e+00 4.086320e+02
.sigma                   3.244136e+05 4.811134e+05
(Intercept)              6.679768e+05 1.873740e+06
Rich_Poor_IndicatorMid  -3.883818e+05 7.826973e+05
Rich_Poor_IndicatorHigh -5.643505e+05 7.930619e+05
AbroadIncome            -1.591774e+02 1.744238e+02

CIs for some countries [2]: https://i.sstatic.net/flEgG.pngRandom Effects

ranef(mix_model)$Country.Name
                    (Intercept)        Gini (Intercept) AbroadIncome
Algeria              -7446.2412   4451.2294    8980.157    0.6670574
Argentina            43191.8751 -18340.8448  -25998.904   -1.9312313
Armenia              38682.2221 -18581.0017  -29973.432   -2.2264643
Australia            39926.3675 -29014.5905  -65750.935   -4.8840623
Austria              34477.4962 -26956.5430  -65238.233   -4.8459781
Bangladesh         -125521.7158 111794.9842  291254.949   21.6347845
Belarus             -95005.4210  26977.6087    6192.779    0.4600074
Belgium              34126.4423 -33087.1346  -94934.288   -7.0518385
Belize               60286.3676 -23623.0023  -27433.462   -2.0377921
Benin                39787.0007 -21281.5020  -39571.309   -2.9394067
Bolivia              46788.4767 -18622.8722  -22439.365   -1.6668243
Botswana             58262.5126 -22310.0691  -24148.621   -1.7937900
Brazil               51117.2733 -13322.0661    2669.639    0.1983042
Bulgaria             27963.1360 -17092.4417  -35819.130   -2.6606901
Burkina Faso         55388.0338  -2956.0938   43594.547    3.2382579
Burundi              31971.0802 -26061.9075  -65023.664   -4.8300397
Cameroon             50944.1843 -24683.1290  -40962.994   -3.0427828
Canada                 887.1362  -5137.1674  -15053.915   -1.1182238
Chad                 50077.7077 -25918.0053  -45854.910   -3.4061605

Here is my ANOVA:

Random Effects [1]: https://i.sstatic.net/hYsnd.png

CIs for some countries [2]: https://i.sstatic.net/flEgG.png

Formula here:

Migration_it= β_o+α_i Indicator+Z_it Gini+I_it AbroadIncome+ ε_it

Here is my ANOVA:

CIs

Computing bootstrap confidence intervals ...

383 message(s): boundary (singular) fit: see ?isSingular
252 warning(s): Model failed to converge with max|grad| = 0.264562 (tol = 0.002, component 1) (and others)

                               2.5 %       97.5 %
.sig01                   1.256697e+01 2.401887e+06
.sig02                  -9.928984e-01 6.604565e-01
.sig03                   4.208632e+04 9.651804e+04
.sig04                   7.887378e+01 1.627314e+06
.sig05                  -1.000000e+00 1.000000e+00
.sig06                   1.215940e+00 4.086320e+02
.sigma                   3.244136e+05 4.811134e+05
(Intercept)              6.679768e+05 1.873740e+06
Rich_Poor_IndicatorMid  -3.883818e+05 7.826973e+05
Rich_Poor_IndicatorHigh -5.643505e+05 7.930619e+05
AbroadIncome            -1.591774e+02 1.744238e+02

Random Effects

ranef(mix_model)$Country.Name
                    (Intercept)        Gini (Intercept) AbroadIncome
Algeria              -7446.2412   4451.2294    8980.157    0.6670574
Argentina            43191.8751 -18340.8448  -25998.904   -1.9312313
Armenia              38682.2221 -18581.0017  -29973.432   -2.2264643
Australia            39926.3675 -29014.5905  -65750.935   -4.8840623
Austria              34477.4962 -26956.5430  -65238.233   -4.8459781
Bangladesh         -125521.7158 111794.9842  291254.949   21.6347845
Belarus             -95005.4210  26977.6087    6192.779    0.4600074
Belgium              34126.4423 -33087.1346  -94934.288   -7.0518385
Belize               60286.3676 -23623.0023  -27433.462   -2.0377921
Benin                39787.0007 -21281.5020  -39571.309   -2.9394067
Bolivia              46788.4767 -18622.8722  -22439.365   -1.6668243
Botswana             58262.5126 -22310.0691  -24148.621   -1.7937900
Brazil               51117.2733 -13322.0661    2669.639    0.1983042
Bulgaria             27963.1360 -17092.4417  -35819.130   -2.6606901
Burkina Faso         55388.0338  -2956.0938   43594.547    3.2382579
Burundi              31971.0802 -26061.9075  -65023.664   -4.8300397
Cameroon             50944.1843 -24683.1290  -40962.994   -3.0427828
Canada                 887.1362  -5137.1674  -15053.915   -1.1182238
Chad                 50077.7077 -25918.0053  -45854.910   -3.4061605
Source Link
AnnaM
AnnaM
  • 21
  • 3

Interpreting mixed effects model results. Why are my coefficients for mixed effects model are so large?

I am an economics grad student and I am in the process of writing a paper disproving using the Gini coefficient as a solitary measure of income inequality in migration determinants analysis.

I have taken Gini and migration rates from different countries for two time periods and ran a mixed model where random effects was Gini and AbroadIncome, and fixed effect was whether the country was 'Rich_Poor_Indicator'.

The idea was that Gini coefficient is not a sufficient measure of income inequality as a determinant for migration (I have done lit. review prior), so I expected some garbage results, but I feel like my model is too bizarre.

Here is my ANOVA: 

 Regression Results
Dependent variable:

Migration

Rich_Poor_IndicatorMid.   206,974.300.
.(292,230.400)

Rich_Poor_IndicatorHigh.   188,436.900
(359,936.000)

AbroadIncome.    4.978
(59.609)

Constant.   1,203,225.000
(308,437.300)

Observations153
Log Likelihood.   -2,327.963
Akaike Inf. Crit..    4,677.926
Bayesian Inf. Crit..  4,711.261
Note:p<0.1; p<0.05; ***p<0.01

But, my CIs and Random Effects per each country are more comprehensible! But Gini does not show up in Random Effects...

Random Effects [1]: https://i.sstatic.net/hYsnd.png

CIs for some countries [2]: https://i.sstatic.net/flEgG.png

Sorry for the messy formatting, I am still learning how to use this platform! Any help is appreciated! Thanks in advance!