pEstimate-methods: Methods for class 'mmctestres' and 'mmctest', Package...
In simctest: Safe Implementation of Monte Carlo Tests

Description Usage Arguments Methods Examples

Function which shows current estimates of p-values.

1	pEstimate(obj)

obj

object of type ‘mmctestres’ or ‘mmctest’.

pEstimate(obj): works with object of type mmctestres or mmctest.

  fun <- function(ind,n,data) sapply(1:length(ind), function(i) sum(runif(n[i])<=data[ind[i]]));
  i <- mmctSampler(fun,num=500,data=runif(500));
  a <- mmctest(h=hBH);
  a <- run(a, i, maxsteps=list(maxnum=1000000,undecided=10));
  pEstimate(a);

  [1] 0.5714285714 0.6071428571 1.0000000000 0.3043478261 0.1764705882
  [6] 0.4642857143 0.4285714286 0.4285714286 0.7857142857 0.2826086957
 [11] 0.3043478261 0.3260869565 0.5357142857 1.0000000000 0.7500000000
 [16] 0.5357142857 0.7857142857 0.9230769231 0.8461538462 0.7142857143
 [21] 0.1368421053 0.8461538462 0.3260869565 0.0636363636 0.0110650069
 [26] 0.2500000000 0.5714285714 0.6785714286 0.3913043478 0.5357142857
 [31] 0.0089903181 0.9230769231 0.5000000000 0.0219178082 0.5714285714
 [36] 0.6785714286 0.3913043478 0.0530035336 0.4642857143 1.0000000000
 [41] 0.5357142857 0.0769230769 0.8928571429 0.5357142857 1.0000000000
 [46] 0.8928571429 0.3695652174 0.4285714286 0.3043478261 0.9230769231
 [51] 0.3913043478 0.4642857143 0.8461538462 1.0000000000 0.3529411765
 [56] 1.0000000000 0.5714285714 1.0000000000 0.5714285714 0.5357142857
 [61] 0.6071428571 0.3478260870 0.4642857143 1.0000000000 0.6428571429
 [66] 0.8461538462 0.7142857143 0.2352941176 0.0159059474 0.5357142857
 [71] 0.8461538462 0.5357142857 0.5714285714 0.8461538462 0.3478260870
 [76] 0.0769230769 0.8461538462 0.1368421053 0.0530035336 0.6785714286
 [81] 0.9230769231 0.4285714286 0.5000000000 0.4347826087 0.0828402367
 [86] 0.4642857143 0.1368421053 0.7857142857 0.5000000000 0.2826086957
 [91] 0.4642857143 0.6428571429 0.2794117647 0.8461538462 1.0000000000
 [96] 0.1911764706 0.4642857143 0.5714285714 0.6071428571 0.6785714286
[101] 0.1473684211 0.5000000000 0.8461538462 0.2941176471 0.3478260870
[106] 0.7857142857 0.3043478261 0.2352941176 0.3260869565 0.5357142857
[111] 0.5714285714 1.0000000000 0.6071428571 0.4285714286 0.3260869565
[116] 0.4642857143 0.9230769231 0.5000000000 0.7857142857 0.7142857143
[121] 0.2058823529 0.4285714286 0.0937500000 0.1015625000 0.5714285714
[126] 0.2058823529 0.6071428571 0.1328125000 0.2205882353 0.2826086957
[131] 0.0360110803 0.3695652174 0.8461538462 0.4285714286 0.8214285714
[136] 0.7857142857 0.7500000000 0.3260869565 0.2500000000 0.5000000000
[141] 0.5000000000 0.9230769231 0.0937500000 0.0145228216 0.6071428571
[146] 0.3913043478 0.2352941176 0.2205882353 0.3260869565 0.0110650069
[151] 0.7142857143 0.7500000000 0.6071428571 0.9230769231 0.2205882353
[156] 0.6785714286 0.3695652174 0.2205882353 0.2352941176 0.6785714286
[161] 0.9230769231 0.1406250000 0.6428571429 0.6785714286 1.0000000000
[166] 0.4285714286 0.4130434783 0.3043478261 0.6428571429 0.6785714286
[171] 0.8461538462 0.6785714286 0.6785714286 1.0000000000 0.6785714286
[176] 0.3695652174 0.1764705882 1.0000000000 0.2352941176 0.8214285714
[181] 0.3478260870 0.7142857143 0.3043478261 0.2205882353 0.1911764706
[186] 0.6071428571 0.0710059172 0.1368421053 0.4642857143 0.2205882353
[191] 1.0000000000 0.0232876712 0.9230769231 0.0769230769 0.4642857143
[196] 0.6071428571 0.5714285714 0.1911764706 0.0006915629 0.2352941176
[201] 0.8461538462 0.5000000000 0.5357142857 0.0205479452 0.6428571429
[206] 0.5000000000 0.7857142857 0.1263157895 0.1764705882 0.6428571429
[211] 0.2205882353 0.8571428571 0.9230769231 0.2500000000 0.0138312586
[216] 1.0000000000 0.5714285714 0.8214285714 0.6785714286 0.1911764706
[221] 1.0000000000 0.1328125000 0.1764705882 0.6071428571 1.0000000000
[226] 0.8461538462 0.9230769231 0.6071428571 1.0000000000 0.1764705882
[231] 0.8461538462 0.1764705882 0.4285714286 0.4285714286 0.9230769231
[236] 1.0000000000 0.1911764706 0.3043478261 0.5000000000 0.4642857143
[241] 0.6071428571 0.8461538462 0.5357142857 0.6785714286 0.3695652174
[246] 0.9230769231 0.5357142857 0.7857142857 0.4285714286 0.0117565698
[251] 0.5714285714 0.8461538462 0.5714285714 0.9230769231 0.6071428571
[256] 0.2352941176 0.6785714286 0.7142857143 0.8461538462 0.8461538462
[261] 0.3913043478 0.0387811634 0.5714285714 0.6071428571 0.9230769231
[266] 0.2352941176 0.7857142857 0.9230769231 0.5357142857 0.4565217391
[271] 0.2058823529 0.8461538462 0.6071428571 0.6785714286 0.2941176471
[276] 0.8461538462 0.2826086957 0.9230769231 0.8461538462 0.8461538462
[281] 0.8461538462 0.1911764706 0.4285714286 0.6071428571 0.3260869565
[286] 0.2058823529 0.8461538462 0.4642857143 0.4642857143 0.1764705882
[291] 0.0581717452 0.2647058824 0.9230769231 0.2205882353 0.6428571429
[296] 0.7142857143 0.0103734440 0.1764705882 0.4285714286 0.0710059172
[301] 0.4130434783 0.4285714286 0.9230769231 0.4285714286 0.2826086957
[306] 0.2826086957 1.0000000000 0.0887573964 0.4285714286 0.8461538462
[311] 0.8461538462 0.3913043478 0.4347826087 0.1015625000 0.5357142857
[316] 0.5000000000 0.5714285714 0.9230769231 0.0937500000 0.0590909091
[321] 0.5357142857 0.1764705882 0.2500000000 0.6428571429 0.3695652174
[326] 0.5714285714 0.4642857143 0.2352941176 0.2058823529 0.3260869565
[331] 0.0545454545 0.2205882353 0.1250000000 0.2647058824 0.3260869565
[336] 0.0034578147 0.3260869565 0.5000000000 0.4285714286 0.1368421053
[341] 0.0772727273 0.9230769231 0.8461538462 0.4285714286 1.0000000000
[346] 0.1911764706 1.0000000000 0.6428571429 0.9230769231 0.0681818182
[351] 0.9230769231 0.2058823529 0.5000000000 0.2826086957 0.5357142857
[356] 0.0305676856 0.2826086957 0.5000000000 0.3260869565 0.7857142857
[361] 0.0027662517 0.6428571429 0.4285714286 0.8461538462 0.0041493776
[366] 0.0769230769 0.6428571429 0.7142857143 1.0000000000 0.6785714286
[371] 0.1368421053 0.4642857143 0.6071428571 0.0259067358 0.4130434783
[376] 0.2794117647 0.0174291939 0.9230769231 0.6428571429 0.4285714286
[381] 0.3260869565 0.2826086957 0.6785714286 0.9230769231 0.7142857143
[386] 0.2941176471 0.5000000000 0.9230769231 0.1473684211 0.6428571429
[391] 0.9230769231 0.1263157895 0.8461538462 0.6785714286 0.8461538462
[396] 0.5714285714 0.7142857143 0.9230769231 0.2941176471 0.3478260870
[401] 0.4285714286 0.6071428571 0.5357142857 0.3695652174 0.2352941176
[406] 0.9230769231 0.0710059172 0.0937500000 0.6428571429 0.4285714286
[411] 0.4642857143 0.7142857143 0.7500000000 0.2500000000 0.1093750000
[416] 0.9230769231 1.0000000000 0.8461538462 0.4565217391 0.7857142857
[421] 0.7142857143 0.2647058824 0.6428571429 0.0545454545 0.5434782609
[426] 0.7500000000 0.6071428571 0.9230769231 0.5714285714 0.8571428571
[431] 0.1894736842 0.0887573964 0.7142857143 0.4347826087 0.7142857143
[436] 0.6428571429 0.6428571429 1.0000000000 0.3695652174 0.1578947368
[441] 0.5357142857 0.4642857143 0.2205882353 0.2794117647 0.5357142857
[446] 0.6785714286 1.0000000000 0.3913043478 0.7142857143 0.4285714286
[451] 1.0000000000 0.3478260870 0.9230769231 0.3478260870 0.3043478261
[456] 0.4347826087 1.0000000000 0.2826086957 0.1764705882 0.5714285714
[461] 0.1911764706 0.4642857143 0.2794117647 0.1764705882 0.6071428571
[466] 0.9230769231 0.7500000000 0.1368421053 0.4285714286 0.6071428571
[471] 0.5714285714 0.1368421053 0.1015625000 0.1368421053 0.4285714286
[476] 0.8214285714 0.8461538462 0.5000000000 0.7500000000 0.4642857143
[481] 0.2826086957 0.1764705882 1.0000000000 0.7500000000 0.6428571429
[486] 0.8461538462 0.1764705882 0.0055325035 0.3260869565 0.7142857143
[491] 0.4285714286 0.6785714286 0.9230769231 0.4642857143 0.2352941176
[496] 0.1093750000 0.5357142857 0.8461538462 0.9230769231 0.8571428571