The data for this example are the same as for Example 79.1, except that a continuous variable T
, which indicates the time of death of the animal, has been added.
data a; input S1 S2 T Dose @@; datalines; 0 1 104 1 1 0 80 1 0 1 104 1 0 1 104 1 0 1 100 1 1 0 104 1 1 0 85 2 1 0 60 2 0 1 89 2 1 0 96 2 0 1 96 2 1 0 99 2 1 0 60 3 1 0 50 3 1 0 80 3 0 1 98 3 0 1 99 3 1 0 50 3 ;
proc multtest data=a bootstrap nsample=10000 seed=37081 outsamp=res; test ft(S1 S2 / lowertailed) mean(T / lowertailed); class Dose; contrast 'Linear Trend' 0 1 2; run;
proc print data=res(obs=36); run;
The BOOTSTRAP
option in the PROC MULTTEST statement requests bootstrap resampling, and NSAMPLE=
10000 requests 10,000 bootstrap samples. The SEED=
37081 option provides a starting value for the random number generator. The OUTSAMP=
res option creates an output SAS data set res
containing the 10,000 bootstrap samples.
The TEST
statement specifies the Freeman-Tukey test for S1
and S2
and specifies the t test for T
. Both tests are lower-tailed. The grouping variable in the CLASS
statement is Dose
, and the coefficients across the levels of Dose
are 0, 1, and 2, as specified in the CONTRAST
statement. The PROC PRINT statement displays the first 36 observations of the res
data set containing the bootstrap samples.
The results from this analysis are listed in Output 79.2.1 through Output 79.2.5.
The "Model Information" table in Output 79.2.1 corresponds to the specifications in the invocation of PROC MULTTEST.
Output 79.2.1: FT and t tests with Bootstrap Resampling
Model Information | |
---|---|
Test for discrete variables | Freeman-Tukey |
Test for continuous variables | Mean t-test |
Degrees of Freedom Method | Pooled |
Tails for discrete tests | Lower-tailed |
Tails for continuous tests | Lower-tailed |
Strata weights | None |
P-value adjustment | Bootstrap |
Center continuous variables | Yes |
Number of resamples | 10000 |
Seed | 37081 |
The "Contrast Coefficients" table in Output 79.2.2 shows the coefficients from the CONTRAST statement after centering, and they model a linear trend.
Output 79.2.2: Contrast Coefficients
The summary statistics are displayed in Output 79.2.3. The values for the discrete variables S1
and S2
are the same as those from Example 79.1. The mean, standard deviation, and sample size for the continuous variable T
at each level of Dose
are displayed in the "Continuous Variable Tabulations" table.
Output 79.2.3: Summary Statistics
The p-values, displayed in Output 79.2.4, are from the Freeman-Tukey test for S1
and S2
, and are from the t test for T
.
Output 79.2.4: p-Values
The Raw column in Output 79.2.4 contains the results from the tests on the original data, while the Bootstrap column contains the bootstrap resampled adjustment
to raw_p
. Note that the adjusted p-values are larger than the raw p-values for all three variables. The adjusted p-values more accurately reflect the correlation of the raw p-values, the small size of the data, and the multiple testing.
Output 79.2.5 displays the first 36 observations of the SAS data set resulting from the OUTSAMP=RES option in the PROC MULTTEST statement. The entire data set has 180,000 observations, which is 10,000 times the number of observations in the data set.
Output 79.2.5: Resampling Data Set
Obs | _sample_ | _class_ | _obs_ | S1 | S2 | T |
---|---|---|---|---|---|---|
1 | 1 | 1 | 17 | 0 | 1 | 26.1667 |
2 | 1 | 1 | 8 | 1 | 0 | -27.5000 |
3 | 1 | 1 | 5 | 0 | 1 | 0.6667 |
4 | 1 | 1 | 9 | 0 | 1 | 1.5000 |
5 | 1 | 1 | 7 | 1 | 0 | -2.5000 |
6 | 1 | 1 | 3 | 0 | 1 | 4.6667 |
7 | 1 | 2 | 12 | 1 | 0 | 11.5000 |
8 | 1 | 2 | 12 | 1 | 0 | 11.5000 |
9 | 1 | 2 | 14 | 1 | 0 | -22.8333 |
10 | 1 | 2 | 17 | 0 | 1 | 26.1667 |
11 | 1 | 2 | 1 | 0 | 1 | 4.6667 |
12 | 1 | 2 | 15 | 1 | 0 | 7.1667 |
13 | 1 | 3 | 4 | 0 | 1 | 4.6667 |
14 | 1 | 3 | 17 | 0 | 1 | 26.1667 |
15 | 1 | 3 | 14 | 1 | 0 | -22.8333 |
16 | 1 | 3 | 15 | 1 | 0 | 7.1667 |
17 | 1 | 3 | 15 | 1 | 0 | 7.1667 |
18 | 1 | 3 | 6 | 1 | 0 | 4.6667 |
19 | 2 | 1 | 6 | 1 | 0 | 4.6667 |
20 | 2 | 1 | 17 | 0 | 1 | 26.1667 |
21 | 2 | 1 | 8 | 1 | 0 | -27.5000 |
22 | 2 | 1 | 13 | 1 | 0 | -12.8333 |
23 | 2 | 1 | 9 | 0 | 1 | 1.5000 |
24 | 2 | 1 | 8 | 1 | 0 | -27.5000 |
25 | 2 | 2 | 9 | 0 | 1 | 1.5000 |
26 | 2 | 2 | 18 | 1 | 0 | -22.8333 |
27 | 2 | 2 | 15 | 1 | 0 | 7.1667 |
28 | 2 | 2 | 14 | 1 | 0 | -22.8333 |
29 | 2 | 2 | 9 | 0 | 1 | 1.5000 |
30 | 2 | 2 | 17 | 0 | 1 | 26.1667 |
31 | 2 | 3 | 16 | 0 | 1 | 25.1667 |
32 | 2 | 3 | 11 | 0 | 1 | 8.5000 |
33 | 2 | 3 | 14 | 1 | 0 | -22.8333 |
34 | 2 | 3 | 18 | 1 | 0 | -22.8333 |
35 | 2 | 3 | 18 | 1 | 0 | -22.8333 |
36 | 2 | 3 | 10 | 1 | 0 | 8.5000 |
The _sample_
variable is the sample indicator and _class_
indicates the resampling group—that is, the level of the CLASS
variable Dose
assigned to the new observation. The number of the observation in the original data set is represented by _obs_
. Also listed are the values of the original test variables, S1
and S2
, and the mean-centered values of T
.