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Table 6 Mean estimates from 20000 replicate simulations of bias (MC error of bias), variance (MC Error Variance), and Power (MC Error Power), from the fitted linear, beta, variable-dispersion beta and fractional logit regression models estimated on the multinomial distributed response data (Power experiments)

From: A Monte Carlo simulation study comparing linear regression, beta regression, variable-dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design

   

Linear regression model

Beta regression model

Variable dispersion beta regression model

Fractional logit regression model

N0 = N1

E(Y0)

E(Y1)

Bias

MC error bias

Variance

MC error variance

Power

MC error power

Bias

MC error bias

Variance

MC error variance

Power

MC error power

Bias

MC error bias

Variance

MC error variance

Power

MC error power

Bias

MC error bias

Variance

MC error variance

Power

MC error power

25

0.5

0.6

-1.36E-04

2.63E-04

1.40E-03

3.04E-05

0.745

0.003

2.32E-03

2.76E-04

1.44E-03

3.22E-05

0.769

0.003

1.77E-03

2.73E-04

1.43E-03

3.20E-05

0.767

0.003

-1.36E-04

2.63E-04

1.35E-03

2.98E-05

0.774

0.003

100

0.5

0.6

-2.17E-04

1.33E-04

3.50E-04

7.54E-06

1.000

0.000

2.37E-03

1.39E-04

3.72E-04

8.18E-06

1.000

0.000

1.86E-03

1.38E-04

3.72E-04

8.16E-06

1.000

0.000

-2.17E-04

1.33E-04

3.46E-04

7.50E-06

1.000

0.000

250

0.5

0.6

-5.06E-05

8.32E-05

1.40E-04

3.02E-06

1.000

0.000

2.55E-03

8.73E-05

1.50E-04

3.29E-06

1.000

0.000

2.05E-03

8.64E-05

1.50E-04

3.28E-06

1.000

0.000

-5.06E-05

8.32E-05

1.39E-04

3.01E-06

1.000

0.000

750

0.5

0.6

-8.70E-05

4.85E-05

4.66E-05

1.01E-06

1.000

0.000

2.55E-03

5.08E-05

5.02E-05

1.10E-06

1.000

0.000

2.05E-03

5.04E-05

5.02E-05

1.10E-06

1.000

0.000

-8.70E-05

4.85E-05

4.66E-05

1.01E-06

1.000

0.000

25

0.215

0.315

2.82E-04

3.70E-04

2.75E-03

3.42E-05

0.466

0.004

1.35E-02

2.96E-04

2.02E-03

3.04E-05

0.725

0.003

3.33E-03

3.55E-04

2.21E-03

3.07E-05

0.589

0.003

2.82E-04

3.70E-04

2.64E-03

3.35E-05

0.501

0.004

100

0.215

0.315

6.30E-06

1.84E-04

6.85E-04

8.36E-06

0.966

0.001

1.36E-02

1.46E-04

5.17E-04

7.65E-06

1.000

0.000

3.10E-03

1.77E-04

5.68E-04

7.71E-06

0.987

0.001

6.30E-06

1.84E-04

6.78E-04

8.32E-06

0.967

0.001

250

0.215

0.315

1.87E-05

1.17E-04

2.74E-04

3.31E-06

1.000

0.000

1.37E-02

9.28E-05

2.08E-04

3.04E-06

1.000

0.000

3.14E-03

1.12E-04

2.28E-04

3.07E-06

1.000

0.000

1.87E-05

1.17E-04

2.73E-04

3.30E-06

1.000

0.000

750

0.215

0.315

1.02E-04

6.74E-05

9.14E-05

1.10E-06

1.000

0.000

1.38E-02

5.33E-05

6.94E-05

1.02E-06

1.000

0.000

3.22E-03

6.45E-05

7.64E-05

1.03E-06

1.000

0.000

1.02E-04

6.74E-05

9.13E-05

1.11E-06

1.000

0.000

  1. Response variables were generated from a discrete multinomial distribution with probability mass observed only on points in (0,1). Multinomial response probabilities for this experiment are given in Table 2 above.
  2. ∆ = 0.10 (power experiments).
  3. Power refers to the proportion of null hypothesis rejected.