Table 1.
The ratio of the observed type I error rates of the set-valued (SV), logistic regression (LG and oLG), and the usual ordered Probit (oPRB) methods over the given significance levels α using SVsimu data generation method and random sampling scheme.
n | p A | 0.05 | 0.01 | 1×10−5 | 1×10−6 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
LG | SV | oLG | oPRB | LG | SV | oLG | oPRB | LG | SV | oLG | oPRB | LG | SV | oLG | oPRB | ||
150 | 0.05 | 0.8 | 1.02 | 0.96 | 1 | 0.37 | 0.9 | 0.77 | 0.86 | 0 | 0.4 | 57 | 1.3 | 0 | 0.26 | 560 | 11 |
150 | 0.2 | 0.98 | 1.06 | 1.02 | 1.04 | 0.84 | 1.1 | 0.98 | 1 | 0.07 | 1.10 | 0.4 | 0.7 | 0.1 | 1.1 | 0.2 | 0.48 |
2500 | 0.0025 | 0.66 | 0.9 | 0.88 | 0.9 | 0.2 | 0.69 | 0.79 | 0.69 | 0 | 0.16 | 190 | 0.16 | 0 | 0.07 | 1900 | 0.068 |
5000 | 0.0025 | 0.9 | 0.96 | 0.96 | 0.96 | 0.64 | 0.88 | 0.83 | 0.88 | 0 | 0.4 | 0.8 | 0.35 | 0 | 0.3 | 4.3 | 0.12 |
1000 | 0.0075 | 0.76 | 0.98 | 0.92 | 0.96 | 0.31 | 0.81 | 0.73 | 0.8 | 0 | 0.26 | 55 | 0.22 | 0 | 0.1 | 550 | 0.19 |
2000 | 0.0075 | 0.9 | 1 | 0.98 | 1 | 0.70 | 0.95 | 0.88 | 0.94 | 0.01 | 0.42 | 0.4 | 0.4 | 0 | 0.3 | 0.5 | 0.34 |
1000 | 0.01 | 0.84 | 0.98 | 0.94 | 0.98 | 0.50 | 0.89 | 0.79 | 0.88 | 0 | 0.34 | 4.50 | 0.29 | 0 | 0.2 | 43 | 0.18 |
2000 | 0.01 | 0.92 | 1 | 0.98 | 1 | 0.77 | 0.96 | 0.92 | 0.95 | 0.04 | 0.58 | 0.54 | 0.57 | 0 | 0.4 | 0.5 | 0.44 |
1000 | 0.05 | 0.98 | 1 | 1 | 1 | 0.92 | 1 | 0.99 | 0.99 | 0.39 | 0.86 | 0.71 | 0.81 | 0.2 | 0.8 | 0.6 | 0.74 |
2000 | 0.05 | 1 | 1 | 1.02 | 1 | 0.97 | 1 | 1 | 1 | 0.69 | 0.94 | 0.86 | 0.92 | 0.5 | 0.8 | 0.8 | 0.8 |
1000 | 0.2 | 1 | 1 | 1.02 | 1 | 0.97 | 1 | 1 | 1 | 0.77 | 1 | 0.93 | 0.96 | 0.7 | 0.9 | 0.7 | 0.78 |
2000 | 0.2 | 1 | 1 | 1.02 | 1 | 0.99 | 1 | 1 | 1 | 0.85 | 1.10 | 1 | 1.1 | 0.7 | 1 | 0.9 | 0.98 |
n is the number of individuals sampled from the population; pA is minor allele frequency of SNP; LG stands for logistic regression model on the regrouped binary outcome (recoding as 0 or greater than 0); SV stands for the set-valued method; oLG stands for ordered logistic regression method; oPRB stands for the usual ordered probit model with the traditional IRWLS algorithm. Values in bold means inflated type I error rates.