Table 1 Comparison of the MSE for the two methods in the 100 replicated simulation experiments

From: Generalized linear mixed models for mapping multiple quantitative trait loci

Data type

Model

Bias a

Error b

MSE c

Binomial

Expectation

6.00

3.52

9.52

 

Overdispersion

6.01

3.14

9.15

Binary

Expected

5.97

8.06

14.03

 

Overdispersion

6.63

5.04

11.67

  1. Abbreviation: MSE, mean-squared errors.
  2. aBias is defined as the sum of squared differences between the true QTL effects and the average estimated QTL effects.
  3. bError is defined as the sum of the variances of the estimated effects obtained from all replicates.
  4. cMSE is the sum of Bias and the Error. Please see equation (31) in the text for details.
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