fastJT: Compute the Jonckheere-Terpstra Test Statistics in fastJT: Efficient Jonckheere-Terpstra Test Statistics for Robust Machine Learning and Genome-Wide Association Studies\nEfficient Jonckheere-Terpstra Test Statistics for Robust Machine Learning and Genome-Wide Association Studies\nSearch the fastJT package\nfastJT: Compute the Jonckheere-Terpstra Test Statistics\nfastJT-package: Efficient Jonckheere-Terpstra Test Statistics for Robust...\nfastJT.select: Conduct k-Fold Cross-Validation based on Jonckheere-Terpstra...\npvalues: Compute P-values Based on Jonckheere-Terpstra Test Statistics\nsummary.fastJT: Summarize Jonckheere-Terpstra Test Statistics and P-Values\nHWxtest: Exact Tests for Hardy-Weinberg Proportions\nargon2: Secure Password Hashing\nnlmrt: Functions for Nonlinear Least Squares Solutions\nIn fastJT: Efficient Jonckheere-Terpstra Test Statistics for Robust Machine Learning and Genome-Wide Association Studies\nView source: R/fastJT.R\nA method to compute the Jonckheere-Terpstra test statistics for large numbers of dependent and independent variables, with optional multi-threaded execution. The calculation of the standardized test statistics employs the null variance equation as defined by Hollander and Wolfe (1999, eq. 6.19) to account for ties in the data.\nfastJT(Y, X, outTopN=15L, numThreads=1L, standardized=TRUE)\nA matrix of continuous values, representing dependent variables, e.g. marker levels or other observed values. Row names should be sample IDs, and column names should be variable names. Required.\nA matrix of integer values, representing independent variables, e.g. SNP counts or other classification features. Row names should be sample IDs, and column names should be feature IDs. Required.\nAn integer to indicate the number of top independent variables to be reported for each dependent variable, based on the standardized Jonckheere-Terpstra test statistics. Optional. The default value is 15L. If set to NA, all results are returned.\nA integer to indicate the number of threads used in the computation. Optional. The default value is 1L (sequential computation).\nA boolean to specify whether to return standardized Jonckheere-Terpstra statistics (TRUE) or non-standardized statistics (FALSE). Optional. The default value is TRUE.\nA list with two objects\nA matrix of the standardized/non-standardized Jonckheere-Terpstra test statistics, depending on the value of the standardized argument.\nIf outTopN was specified, this object is a matrix of the column IDs of X corresponding to the top standardized Jonckheere-Terpstra test statistics for each dependent variable. Otherwise this is a vector of the column IDs of X.\nRows (samples) are assumed to be in the same order in X and Y.\nHollander, M. and Wolfe, D. A. (1999) Nonparametric Statistical Methods. New York: Wiley, 2nd edition.\nnum_sample <- 100\nnum_marker <- 10\nnum_SNP\t\t <- 500\nMark <- matrix(rnorm(num_sample*num_marker), num_sample, num_marker)\nGeno <- matrix(rbinom(num_sample*num_SNP, 2, 0.5), num_sample, num_SNP)\ncolnames(Mark) <- paste0("Mrk:",1:num_marker)\ncolnames(Geno) <- paste0("SNP:",1:num_SNP)\nres <- fastJT(Y=Mark, X=Geno, outTopN=5)\nres <- fastJT(Y=Mark, X=Geno, outTopN=NA)\nMrk:1     Mrk:2     Mrk:3     Mrk:4     Mrk:5     Mrk:6     Mrk:7\n[1,]  3.112041  3.034869 -3.144459 -3.136629 -2.955938 -3.113772  3.356456\n[2,] -2.981145  3.022007 -3.025032 -2.899474 -2.868004  2.803891 -3.208241\n[3,]  2.890056 -2.819824 -2.717341  2.824749  2.748499  2.772179 -2.779656\n[4,] -2.825261  2.627437  2.613547  2.754090  2.741551 -2.687242  2.736467\n[5,] -2.781120 -2.577385 -2.496440 -2.746955 -2.705899 -2.667335  2.550120\nMrk:8     Mrk:9    Mrk:10\n[1,] -3.154481  3.785688  3.519938\n[2,]  2.940487 -3.107839  3.199772\n[3,] -2.854041  2.765425  2.842571\n[4,] -2.683991 -2.746846 -2.826604\n[5,]  2.400626 -2.664774 -2.695368\n[1,] "SNP:452" "SNP:145" "SNP:156" "SNP:190" "SNP:410" "SNP:352" "SNP:245"\n[2,] "SNP:153" "SNP:434" "SNP:125" "SNP:127" "SNP:279" "SNP:493" "SNP:28"\n[3,] "SNP:66"  "SNP:83"  "SNP:466" "SNP:174" "SNP:497" "SNP:477" "SNP:174"\n[4,] "SNP:44"  "SNP:398" "SNP:160" "SNP:420" "SNP:369" "SNP:36"  "SNP:131"\n[5,] "SNP:396" "SNP:275" "SNP:434" "SNP:124" "SNP:117" "SNP:24"  "SNP:466"\nMrk:8     Mrk:9     Mrk:10\n[1,] "SNP:475" "SNP:228" "SNP:401"\n[2,] "SNP:321" "SNP:145" "SNP:175"\n[3,] "SNP:496" "SNP:91"  "SNP:367"\n[4,] "SNP:389" "SNP:495" "SNP:434"\n[5,] "SNP:12"  "SNP:116" "SNP:472"\nMrk:1        Mrk:2        Mrk:3        Mrk:4        Mrk:5\nSNP:1   -0.585529803 -1.295844646 -0.483142078 -0.521537475  1.219053852\nSNP:2    1.706446168 -0.205556613 -0.075044478  0.453529670 -0.447004063\nSNP:3   -0.953022364  0.249448807 -0.179091451 -1.048964213 -0.345390655\nSNP:4    1.500706773  0.103945058  0.747105104  1.955466402  2.150363385\nSNP:5   -2.387837095 -0.397425431  0.009853523 -1.928006018 -0.180647923\nSNP:6    0.865080431  1.362178887  0.019367472  0.096837362 -2.440301514\nSNP:7   -0.037729763  0.305268080  1.004983679 -1.450880875 -0.298408123\nSNP:8   -0.041560139 -0.444373795  2.349746327  0.482737000  1.601663823\nSNP:9   -0.240345526  0.003204607  0.016023035 -0.887676141 -0.842811643\nSNP:10  -0.205986294 -0.167363864  0.663018383 -0.875441748  0.341164799\nSNP:11  -0.376847915  0.576545101  0.357522381  2.000192781  1.014590541\nSNP:12   0.699764147 -0.110489076 -1.556891523  1.121631528  0.746638300\nSNP:13   0.916247931  0.249297140  1.026327188 -0.320524895 -0.035613877\nSNP:14   0.258644092 -0.939631067 -2.072426708  0.834863840  1.077138052\nSNP:15  -1.813406518 -0.281332226 -0.491484010  0.511821279  1.230404796\nSNP:16   1.812502537  1.137634571  0.102837023  1.253326223  0.392066152\nSNP:17   0.387635388  1.432008419 -0.016017991 -0.618294463  0.810510358\nSNP:18   0.411402707  0.774214542 -1.661807784  1.208292989  1.733074394\nSNP:19  -0.846786383 -0.453980532  0.408904451  0.138447964 -0.009659160\nSNP:20  -1.368723327  0.490432480 -0.958426873 -0.310927781  1.195629510\nSNP:21  -0.232495277 -0.949355716  0.516656172 -0.846024482 -0.749151450\nSNP:22  -0.603066480  0.576411608  0.229898271  2.395606623 -0.476455837\nSNP:23  -2.390201171 -2.330529019  0.619927349 -0.301675876  0.818834520\nSNP:24  -0.077127768 -0.764850362  0.700577222  1.574691921 -0.668440652\nSNP:25  -0.579851284  0.810510358 -0.816917554  1.675481886 -0.868175126\nSNP:26   1.211079238 -0.870775981  0.190169467 -0.417038305  0.243550370\nSNP:27   0.452223877 -0.211037809  1.430367375 -0.070345936  0.043547484\nSNP:28   0.497243364  1.675642020 -0.313331435  0.619851316  1.662018914\nSNP:29  -1.094966041  1.133955930 -1.250925596  1.400386836  0.724562099\nSNP:30  -1.050538886  0.619718523 -1.574151327 -0.288318243  0.301574254\nSNP:31   0.158643635  0.728465669 -0.048564378  0.016188126 -0.650762665\nSNP:32  -0.554559148  0.165047365  0.270677679 -0.336696626  0.290483363\nSNP:33   0.800129674  0.087463568  0.249433137  0.443796621 -0.871396284\nSNP:34   1.339056120  1.692645511  0.572945772  2.504591520 -0.428890835\nSNP:35  -0.592593090 -0.132766758  1.913784242 -2.088647777  0.748027343\nSNP:36  -0.010039511 -0.244294761 -1.482501082 -0.451777982 -1.127771704\nSNP:37  -0.612712148 -0.364948766 -2.105988749  1.503321062  1.349305987\nSNP:38   1.022541435 -0.897600681 -0.305776056  0.923903997  0.838418218\nSNP:39   1.164275868  1.022369668 -0.299938104  1.261030095 -0.867562904\nSNP:40  -0.239456978 -1.631500211 -0.775840609 -0.252228017  1.146200735\nSNP:41   0.504829538  0.584018485  0.973364142 -0.234267302  0.042894013\nSNP:42   0.389586131  0.080493002 -0.138447964 -1.104363990 -2.038082815\nSNP:43   1.156041309  0.783539109  0.867030982  1.323025054 -1.008324919\nSNP:44  -2.825261290  0.041595013 -0.771107555  0.329560490  0.310362792\nSNP:45  -0.440061939 -0.466732360  0.986805560  1.026811191 -0.353383072\nSNP:46  -1.627477780  0.074122750  1.807950563 -1.820841476 -0.048340924\nSNP:47   0.563865020 -1.078840009 -0.765943813 -2.512947573  1.509072278\nSNP:48   0.189057413  1.111126900  1.044790965  0.799348008 -2.278639344\nSNP:49  -2.353752131  1.034579528 -0.324770920  1.878314290 -0.746638300\nSNP:50   0.085766086 -0.752102602 -0.125350434 -0.468414779  0.910439992\nSNP:51   0.407622344  0.341342288  0.878210741 -0.288318243  0.314830265\nSNP:52   0.917328168  1.127754701  1.029117264  1.318453747 -0.844994047\nSNP:53   0.089200930 -0.318574750  0.828294350  1.108640130 -0.783693885\nSNP:54  -0.370304269 -0.786084500 -0.896526124  0.669146310 -1.156388769\nSNP:55  -1.218086560 -0.693678722  1.928360466 -0.554279170 -0.680402574\nSNP:56   1.320337558  2.017630742 -0.694064975 -0.345418383  1.720635497\nSNP:57  -1.039107521 -1.485390879 -0.133218913  0.293081608 -1.045768466\nSNP:58  -0.788684268  1.252237470 -1.619217088 -0.151298615 -1.599902371\nSNP:59  -0.826495922 -0.521826837  0.716296466 -1.792361745 -2.032207620\nSNP:60   1.709759558 -1.613705651  0.000000000 -1.312736740 -0.672377354\nSNP:61   0.026004929  0.845160208  1.410767425 -0.624118308 -1.066202109\nSNP:62   1.371582449 -1.204551331  1.063217308  1.178854236 -0.343698646\nSNP:63   0.720231018  0.102890145  1.569904476  0.175908958  0.633936057\nSNP:64   0.356259127  0.475012170 -0.131947825  0.672933907 -0.950024340\nSNP:65   0.864721535  0.923903997  0.489899273 -0.220290277 -2.206190684\nSNP:66   2.890056157  0.624252130  1.027208531  1.449982460 -0.492135277\nSNP:67  -1.405520305  1.346821082 -0.309801459 -0.557642627  0.172836604\nSNP:68  -0.384353749  0.450055244  0.482905992  0.036135823 -0.068986570\nSNP:69   1.150219361  1.543291523 -1.556179135  1.620617195  1.614173389\nSNP:70   0.204170256 -0.204170256 -0.639287196  0.953910214 -1.375638940\nSNP:71   0.244336121 -0.809165076  0.187218586 -0.574348544  0.307800048\nSNP:72  -1.240309555  2.064983460 -0.131947825  0.468414779 -1.906646071\nSNP:73   0.758309089  2.313322665  0.278366628  1.167859990 -1.052673799\nSNP:74   2.053171734  0.003285075  0.364643300  1.291034387 -0.160968664\nSNP:75   0.561819540 -0.718448017 -1.344961928  1.331342061  1.058944708\nSNP:76  -0.969790209  0.276165424  0.455994072  0.854186078 -1.175308664\nSNP:77  -0.663908414 -0.240546527  0.586933525 -0.073767602  0.477885767\nSNP:78  -0.964999746 -0.783046932 -0.939006487  0.620589062 -1.224932337\nSNP:79   1.169550779 -0.589608244  0.041884739  0.505838766 -0.570276826\nSNP:80   0.134664816  0.325166262  1.185707278  1.422191833 -0.075543677\nSNP:81  -0.881471348  0.504162579  0.621258404  0.009757985 -1.005072496\nSNP:82  -1.370684886  0.016125705  0.628902477  1.267480377 -0.158031905\nSNP:83   0.427048552 -2.819824405  0.427048552 -0.629163133  1.737533420\nSNP:84   1.055682739 -1.087384923 -0.466022110 -0.320192062  0.339213373\nSNP:85   2.263040980 -0.672795967  0.569784144 -0.930325524 -0.273625154\nSNP:86  -0.276974860 -0.563725303  0.641929969 -0.107531416 -0.772271080\nSNP:87  -0.482493828 -1.641774299  0.398300274 -0.871079460  1.680632862\nSNP:88  -0.378924651 -0.905565692  1.310180150 -1.701949705 -2.074451904\nSNP:89  -1.286605064 -1.170228726 -0.012930704  1.713318301  1.163763374\nSNP:90  -0.168377323  1.458211158 -0.155669601 -0.358993161 -0.003176931\nSNP:91  -0.380245909  1.361971710 -1.721476934 -0.062222058 -1.313576777\nSNP:92  -1.138845242  0.019635263  0.890131913 -0.222532978  0.746139986\nSNP:93   1.438152641  1.134363095  0.830573548  0.559102038 -0.313484958\nSNP:94  -0.638740906 -2.391266205 -0.118760872 -0.792809064  0.831326104\nSNP:95   0.746392904 -0.799945758  0.525487381 -0.391605246 -1.127956989\nSNP:96  -0.653237211 -0.511081963  0.098154814 -0.091385517  0.003384649\nSNP:97   1.785441577 -1.772596674  1.509276153 -0.006422452 -0.468838975\nSNP:98  -0.566584410 -0.573059660 -1.194683698  1.039277688 -0.229871389\nSNP:99  -0.048890031  1.613371010  1.672039047  0.902835899 -0.074964714\nSNP:100 -0.887273497 -0.194606455  0.662981312 -1.718473948  0.135234994\nSNP:101  1.246211315 -0.787248580  0.022310689  0.551392730  1.163343044\nSNP:102  0.249351915  0.690512996 -2.199411765  0.025574555  0.811992134\nSNP:103  0.966765064  0.603815722 -0.155078355  1.507889536 -1.065751247\nSNP:104 -1.639871115  0.854580722  0.722599143 -2.082009403 -0.023096776\nSNP:105  1.110707267 -0.780409479  1.026513714  0.385347419  0.709168780\nSNP:106 -0.816268873  0.032136570  0.494903175 -0.295656442  0.437057349\nSNP:107  0.016048766  0.420477682  0.227892484 -0.792809064 -0.125180379\nSNP:108 -0.031980616  0.812307652  0.940230117 -0.172695328  1.368770375\nSNP:109 -1.562476900 -0.531498290  1.703355965 -1.030978610 -0.416233600\nSNP:110  0.221637445 -0.337274373  0.208788897  0.838367726  0.619942418\nSNP:111 -0.229202852 -0.448721077 -0.777998414  0.203377179 -0.584305863\nSNP:112 -0.145376514  0.326289509 -1.237315664  1.650831082 -0.862567317\nSNP:113  1.100730579 -0.098279516 -0.642092838  0.910723515 -0.779684160\nSNP:114 -0.458635986  0.709400985  0.049493092  0.953566906 -0.478433223\nSNP:115  0.833450642  1.140160478 -0.706766144  0.493402780 -0.126684498\nSNP:116 -1.548981987 -0.826998857  0.945141551 -1.719632544 -1.476783674\nSNP:117 -1.709665514  0.494903175  1.073361432  0.147828221 -2.705899178\nSNP:118  0.066018946 -0.739412197 -0.336696626 -1.016691771 -0.330094731\nSNP:119  0.746356610  0.983086460  0.410989323 -0.726629122 -1.265847114\nSNP:120  0.759114467 -0.864273181 -0.233320897  0.200458799  0.108444924\nSNP:121  0.094225564  0.458131192 -0.354158156 -0.152710397 -0.711565469\nSNP:122  0.158445471  0.290483363  0.079222735 -0.330094731  0.099028419\nSNP:123 -0.839726435  0.227220094  0.780451628  0.418216695  0.582868937\nSNP:124 -2.034411227 -2.427308977  0.735850869 -2.746954603 -0.203108159\nSNP:125  0.252907093 -0.022991554 -3.025031589  2.111938449  0.463115585\nSNP:126 -0.257092558 -1.009088292  0.199246733  0.520612431  0.507757803\nSNP:127  1.112664892  0.922857351  0.746139986 -2.899473806 -0.045815613\nSNP:128 -0.116382737  0.568612803  1.865449019 -1.453121607  0.488807497\nSNP:129  1.816763934  0.019396056  0.103445633 -1.571080555  1.053852389\nSNP:130 -1.409135255 -0.217039223 -1.311953513  2.361516324 -0.022675740\nSNP:131  0.082126869  0.515756740  0.371213450  0.443485095 -0.909965713\nSNP:132 -0.205702657  0.715061618  1.309313739 -0.271005088 -0.734652347\nSNP:133 -1.633727584  0.544575861  1.468125568 -0.633746178  0.130570821\nSNP:134 -0.100421133  0.372530010  1.292517165 -0.586329842 -2.296728496\nSNP:135  0.369904974 -1.064683012 -1.997486859 -0.254108634  0.691561473\nSNP:136  1.467444300  0.288305834 -0.152251395  0.437317838 -0.184645309\nSNP:137 -1.412793864 -1.765176629 -1.008206244  0.564464985 -0.055467657\nSNP:138 -1.717881773 -1.272265824  1.931002444  0.316451905  0.051665617\nSNP:139 -0.317881313  0.131647817  0.176600730  0.420630829 -1.647203168\nSNP:140  0.390384400  1.761490586  1.228282625  0.339602690 -1.621840882\nSNP:141 -0.311645883 -0.838552323  0.298794506  0.780721129  0.093172480\nSNP:142 -0.351440605 -0.097064548  0.418381673  0.190782043 -1.783979452\nSNP:143  1.424179492 -0.690808903  0.520562159  0.036013734  0.658069144\nSNP:144  0.372888894 -0.193103177  1.098690490  1.564801607  0.559333340\nSNP:145 -1.077958933  3.034868995  0.043118357  0.082919918 -0.985088624\nSNP:146 -1.151960332  0.033293651 -0.499404768 -0.619261913  0.266349210\nSNP:147 -1.855573968 -0.675062559  0.831107170 -0.396896077  0.159436886\nSNP:148  0.246380608 -0.509186590  0.344932851  0.174108963 -1.291034387\nSNP:149  0.929367305  0.113337476 -0.981178723  0.087431767 -0.832220897\nSNP:150 -1.769981842  0.474394394 -1.213795311  0.631435297  1.619484310\nSNP:151  0.477730984 -0.755331421 -0.406716919 -1.594588555  0.516465929\nSNP:152 -0.399298532 -0.974288419  0.616516934  0.130969919  0.105414813\nSNP:153 -2.981145318 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0.355456411\nSNP:383 -1.688296279  0.182605100 -0.176197904  0.073682760  0.438892961\nSNP:384  1.015452800 -0.088158282 -0.434261165  0.153460713 -0.904438668\nSNP:385  0.191454944 -1.326980819 -0.495142096  0.382909888  1.835326704\nSNP:386  0.284765861 -1.583810123 -0.790305253  1.180658455 -0.406351284\nSNP:387  0.475031185 -0.232462069  0.414388906 -1.115144129 -0.225724038\nSNP:388 -0.047538945 -1.974450861 -0.148955362  0.402496403 -0.066554523\nSNP:389 -0.236346639 -0.301099143  2.107694004  1.486069966 -1.971713745\nSNP:390 -0.781265379 -1.605384976 -0.240642923 -1.222993483  0.352723188\nSNP:391 -0.095250723 -0.693031124 -0.003284508 -0.568219832  0.055836631\nSNP:392  1.601916841 -0.710128084  0.155237302 -0.551587861 -0.703522242\nSNP:393 -0.261205111  0.858245363  0.539371592 -1.258533715 -1.149980942\nSNP:394  0.182815360 -1.440071874  0.625420970 -0.458642044 -1.446486448\nSNP:395  0.262648068 -1.514279604 -0.314529168 -0.476657606  0.690667143\nSNP:396 -2.781120045  1.304762955 -0.961574684  0.825594425 -0.087415880\nSNP:397 -0.908209992  0.500312171 -0.589539819  1.093038692  1.271493988\nSNP:398  2.199014071  2.627436904 -0.846804505  0.378217032  0.645981303\nSNP:399  1.605602833  0.597659002  0.636426073  0.255216547  0.268138904\nSNP:400 -0.087161648 -0.668239302 -0.590762281 -0.771541996 -1.268686211\nSNP:401  0.068002486 -0.858126606 -0.307630293 -0.035620350  0.676786644\nSNP:402  0.437292639 -1.213240480  0.207138619 -0.746356610  1.588062743\nSNP:403  0.221637445 -2.071828289  0.118849065  1.005398844  0.163818981\nSNP:404  0.623638215  0.421200679  0.166521199  0.884847938  0.545275298\nSNP:405 -0.989198868  0.122841689  0.439643941 -0.362059716 -0.478436054\nSNP:406  0.483285043  0.632487136  0.898456086  0.035678762  0.230290188\nSNP:407 -0.459335574 -1.654250494 -1.596432031 -0.742003620 -2.168192396\nSNP:408  0.757719772  1.090221275 -1.192529430 -1.243683508 -0.489160865\nSNP:409 -0.580590756 -0.574103708 -1.229295511 -0.308134759 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 0.303118190\nSNP:464  0.473865945 -0.339390474  0.300968911  1.165454081  0.000000000\nSNP:465 -1.402726929 -0.785132871  1.100500050 -0.752282124 -0.200389561\nSNP:466 -0.485584101  1.501773346 -2.717341493  1.611110163  0.948657681\nSNP:467 -0.212711275  1.971547792  1.311190399 -2.136637140 -0.098418650\nSNP:468 -0.321122586  0.128449034  1.740484415  0.648667623 -1.156041309\nSNP:469  0.222119243  0.762931312  0.383075216  1.213608036  1.638531805\nSNP:470 -0.857640548 -1.262369795  0.992550297 -0.035333506 -0.485032669\nSNP:471  0.229710920  0.857371743 -0.812076632  0.255593840 -0.165003618\nSNP:472  0.295160664  1.067119322 -0.561129613  0.132984475  1.203347321\nSNP:473 -1.183138741  0.407416922 -1.456922912 -0.211856799 -1.430848229\nSNP:474  0.513654619  1.907392632  0.238833040 -0.703412377 -0.795019570\nSNP:475 -0.100296317  0.287947491  1.342676503 -0.126179238  1.329735043\nSNP:476  0.099576243  0.073879148 -0.767700715  0.228061719 -1.069641582\nSNP:477  1.278228875  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1.503088963  0.089545725 -0.901853378\nSNP:4    1.877507608  0.740608538 -1.500706773  0.474249327 -1.058940277\nSNP:5   -1.796625710  0.417132477  1.796625710 -0.128095800 -1.441898879\nSNP:6   -1.026476033 -0.232409668  2.246626790 -0.451907688  0.961917792\nSNP:7   -1.327401652  0.250388425 -0.984403809  1.032423507 -1.210782385\nSNP:8   -0.316496444 -1.352302988  0.872762921  0.233376166 -0.290920974\nSNP:9   -0.182662599  0.990223565 -2.323340080  0.298028452 -0.490304872\nSNP:10   0.650144239 -1.525585987  0.714514956 -0.939812465 -0.019311215\nSNP:11  -0.602312480 -0.125615972 -0.041871991  1.143427435  0.293103934\nSNP:12  -0.010044461  0.706460455  2.400626285 -0.318074612  0.164059537\nSNP:13   0.281673392  0.741416170 -1.013376687  0.113316882  0.255772391\nSNP:14  -0.441986739 -0.723548661  0.946179019  0.441986739  0.468178545\nSNP:15  -1.420219311 -0.877892127  0.227099508  0.932124846  0.647403075\nSNP:16  -0.327793012  0.012854628 -1.780365968  1.034797548  0.951242466\nSNP:17  -1.220570934 -0.797695965  1.137277379  1.316678882  0.458114550\nSNP:18   0.353093662  1.104632464  0.806608456  2.141237710  0.463232969\nSNP:19  -0.982014627  1.065727349 -1.381259917  1.432775439 -0.350949490\nSNP:20   0.849441877  1.670034785  0.407091013 -1.195629510 -0.323749546\nSNP:21  -0.536030779 -1.349764250 -0.258328086  0.135622245 -1.995584465\nSNP:22  -0.303199169  0.229898271  0.356508913 -1.496004693  0.596402762\nSNP:23  -0.905027628 -1.057523125 -1.276321013 -0.069617510  0.885136911\nSNP:24  -2.667335294 -0.893396641 -0.205674047 -0.372784210  1.227616967\nSNP:25   0.490150533  0.855360733  0.592665677  0.406856978 -0.573444087\nSNP:26  -0.510454886  0.597198853  1.057609142  0.497109660  0.370330015\nSNP:27  -0.599615363  0.760406075  0.063646323  1.068588273 -0.003349806\nSNP:28   1.239702633 -3.208241429 -0.572170446  1.069413810 -0.313331435\nSNP:29  -0.828535135  1.094966041  0.250185119  0.536110970 -0.627087377\nSNP:30   0.917978774 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 1.669837446\nSNP:44  -0.246370464  0.105587342  1.263848482 -0.182378135  1.091069196\nSNP:45  -0.633422488  0.073343657 -1.106822453 -0.846785852  2.080292803\nSNP:46  -0.196586425  0.029004554 -0.061231837 -1.363214061 -0.447959230\nSNP:47   1.333068168 -0.140151421  1.411292217  0.935429252  1.339586839\nSNP:48  -0.872317536 -0.580439425  0.832515976  0.222225380  1.097859713\nSNP:49   0.599319533 -1.148416758 -0.806905069  0.565837995  0.672978917\nSNP:50  -0.653141734 -2.064983460 -0.442025214 -0.890647818  0.105558260\nSNP:51  -1.600663350 -0.745650629 -0.281690238  0.904722763  1.176470992\nSNP:52  -1.377636209  0.693749977  1.496001134  0.996238118  0.266321081\nSNP:53   0.006371495 -1.242441525  1.204212555  0.375918205  0.471490630\nSNP:54  -0.344318004  0.727615405  0.051972529 -0.571697818 -0.214386682\nSNP:55   0.979115899 -0.215737402 -0.142718589 -0.368413101 -0.348498879\nSNP:56   0.823193343 -1.720635497 -0.080705230 -0.196920760 -2.443754355\nSNP:57  -0.985819955  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2.312813886\nSNP:71   0.396649547  2.186332302  0.218950550  0.092022695  1.551693027\nSNP:72  -0.079168695 -0.686128690  0.329869562  1.279893902  1.477815639\nSNP:73  -1.526217028  0.764708322  0.131184273 -0.335959723 -1.263848482\nSNP:74  -0.285801505  0.601168684 -2.086022482  0.850834367  0.482905992\nSNP:75   0.915936098 -0.943175833 -0.650348679  0.759307620 -0.568629473\nSNP:76   0.597288010 -0.661512527 -0.892720789 -2.324927521  1.355137312\nSNP:77  -0.234131953  0.356008860  1.420828151  0.484300341 -0.792199895\nSNP:78  -0.523114340  1.627827854 -0.204696916 -0.900016598 -0.009747472\nSNP:79  -0.679821527  0.125654216  0.731371974 -1.092225107  2.529193832\nSNP:80   1.244828417 -0.883532570 -0.784997339  2.019972233  1.225121371\nSNP:81   1.362865294 -0.256960282  1.135178968 -0.328518842  1.662110180\nSNP:82   0.796609804  0.145131341  0.454744868 -0.125780495 -0.177382750\nSNP:83  -0.257533096 -1.085550898 -1.385462858  0.374889950  0.107577116\nSNP:84  -0.656235216 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0.184007924  0.048423138  0.668239302 -0.022597464\nSNP:112 -1.004713242 -0.991790885  1.301927448 -0.681654322 -0.578275467\nSNP:113 -1.303841579  1.605232094  0.989347127  0.845203837  0.425877903\nSNP:114  0.293659012  0.267262697 -0.458635986  0.669806512 -0.735797301\nSNP:115 -0.413391518 -1.773582966  2.220312510 -1.406864684 -0.840118247\nSNP:116  0.308483701  0.853252789 -0.032817415 -2.664774095 -0.216594939\nSNP:117  0.809841559  0.308511070  0.629876768  0.038563884 -0.057845826\nSNP:118 -1.934355124 -0.785625460  0.851644406 -2.356876379 -1.043099350\nSNP:119  2.245645659  0.338655202 -0.700325806  0.193986960 -0.213714448\nSNP:120  0.463355584 -0.818266244  0.069010406  0.404203807  0.489645262\nSNP:121 -0.672575580 -0.354158156 -1.387390207  1.179444134 -0.581599173\nSNP:122  0.264075785 -0.666791357 -0.607374305  0.019805684  1.795715337\nSNP:123 -1.122928292 -1.010964767 -0.787037718  0.088912211  0.503835861\nSNP:124  0.988903657  1.495009233 -0.762488005 -0.695895166  1.221978593\nSNP:125 -0.541943770 -0.771859309  0.561650816  1.126586140 -0.259476108\nSNP:126  0.732713792 -1.433291014 -1.343308618  1.516846095  0.192819419\nSNP:127 -2.186059258  0.746139986  1.079939454 -0.484336482 -0.569422621\nSNP:128 -1.340064091 -0.142984506 -0.275993349 -0.595214571  1.180453479\nSNP:129  1.196090134 -0.730584785  0.155168450  1.150832670 -1.499961682\nSNP:130 -0.871396284  0.379008793  0.275348268  1.000971940  0.774214542\nSNP:131  0.489476142  2.736467288 -1.304174686  0.088697019  1.185911994\nSNP:132  0.884847938  0.375488978 -0.042446580 -0.564866027 -0.610577729\nSNP:133  1.493602801 -0.589161020 -1.691051359  0.429928312  0.366235228\nSNP:134 -1.091674899  0.152251395  0.748299411 -2.348558759  1.162941509\nSNP:135 -0.890988502  0.633663303  0.833090333 -1.103281792  0.788058423\nSNP:136  0.774214542 -1.143505161  0.741820629  0.411402707 -0.307742182\nSNP:137  0.851591682  1.099564739  0.773284401  0.427427243 -0.094621298\nSNP:138 -1.091436164 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 1.393616194\nSNP:422 -0.737678486  1.668792663  0.344249960 -0.324578534  0.540964223\nSNP:423  0.790704627 -0.163479016 -0.590526240  0.350312176 -0.890793820\nSNP:424 -0.827313701 -0.209242765  1.767296583 -1.870308406 -1.168540364\nSNP:425 -1.194339600 -0.365211283 -0.411273967  0.134897861 -0.871900810\nSNP:426 -0.116247639 -0.322910108  2.389534796  0.096873032 -0.846024482\nSNP:427 -0.080477986  1.078405019 -0.035410314  0.183489809  0.634166534\nSNP:428  0.759781118 -0.418381673 -0.471934527 -0.679451836 -0.244334897\nSNP:429  0.395873942  0.271549894 -0.336983604 -1.030580924 -0.101422250\nSNP:430 -2.077300312  0.877227290  0.035871748  2.207743032  1.509874481\nSNP:431 -1.122613326  0.893224844 -1.609194953 -0.330180390  0.281522227\nSNP:432  1.237058075  0.114778584 -1.747185117  0.586646098  0.714177858\nSNP:433  0.521519087 -0.151408767  0.285994338  0.541706923  0.709938887\nSNP:434 -0.731076362  0.252676162  0.400912844 -2.476226389 -2.826604000\nSNP:435  0.201085182  0.926310428 -1.836138464  0.662592157  0.062633089\nSNP:436  0.860192719 -0.137229189  0.625898982  1.348862513 -0.090370441\nSNP:437  0.022546456 -0.389731605 -0.718265684 -0.357522381 -0.402615294\nSNP:438 -1.226882053  0.225746298  0.513654619 -1.841958922  0.611805184\nSNP:439 -0.878008920 -1.388850473 -0.494877755 -1.280296643 -0.047891396\nSNP:440 -1.336270520 -1.975634405  0.095904583  1.317089603  0.147053694\nSNP:441  0.336966951 -0.143461177 -0.103425500 -0.597198853  0.884121207\nSNP:442 -1.023911301 -0.959716235 -0.497511761 -1.171559953 -0.253570510\nSNP:443 -1.382852379 -0.063433595  0.266421101 -0.824636740 -0.881726976\nSNP:444 -0.009574506 -0.252128658 -0.373405734  0.009574506  0.341490714\nSNP:445 -0.411627381  0.197710789  1.124682686  1.234882142  0.314392566\nSNP:446 -0.666252633 -1.460371933 -0.403789474  0.444168422  0.928715791\nSNP:447 -0.417249124  0.100947369  0.773929826  0.222084211 -0.915256142\nSNP:448 -0.525094696  0.211318597  0.198511409  1.664934401  0.653166573\nSNP:449  0.113337476 -0.935843733 -0.696215926  0.521352391 -0.437158837\nSNP:450 -0.228277510  1.025545230 -0.514476178 -1.741041902 -0.664389767\nSNP:451  0.878690328 -0.463185599 -0.769705481  0.000000000  1.573468726\nSNP:452  0.837983503  1.120597547 -1.790984349  0.351624450 -0.870845601\nSNP:453 -1.243169818  1.945975155 -0.029836076  0.076247749 -0.328196832\nSNP:454 -0.465130637  0.393572078  0.927008613 -0.764375523  0.198412370\nSNP:455 -0.352912703 -2.124134948 -0.552674610  1.351722240  2.403801618\nSNP:456 -1.549990254 -0.242289697  0.116166293  0.687040648  1.390676481\nSNP:457 -1.279035479  0.964097094  1.953903445 -0.289229128 -0.546321687\nSNP:458  0.740000162  0.811718239  0.114096941 -0.870396666 -0.570484706\nSNP:459 -0.086592293 -0.406317684  0.006660946 -0.093253239  1.285562509\nSNP:460  0.656863494 -1.803971448  0.695314039 -0.656863494  0.765806707\nSNP:461  1.678892974 -0.874079839  0.016492072 -0.689368628 -0.234187429\nSNP:462 -0.716000064 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 0.074413397\nSNP:476  0.761276441 -0.298728730 -0.189516076 -0.375820015  0.260183087\nSNP:477  2.772178995 -0.756634221 -0.537693255  0.473298853  0.202842366\nSNP:478 -0.948624183  0.773792422 -0.029138627 -2.023515748  0.061514879\nSNP:479  1.487692660 -0.995036267  0.956142341  0.217157752 -0.917248415\nSNP:480 -0.257807844 -1.335913372  0.525660149 -0.130577999 -0.244415228\nSNP:481  0.381301247  0.964467861 -1.015735256 -0.599187675  1.701436659\nSNP:482 -0.594927257  1.135770218 -0.423130081  0.283147197 -1.123044501\nSNP:483  0.546015880 -0.119857144  0.945539695 -0.472769847 -0.259690480\nSNP:484 -1.547337956  1.579373938  0.714402410  0.932247091  1.810033012\nSNP:485 -0.809641738 -0.449800965 -0.323190323  0.389827503  0.942916098\nSNP:486  0.288416989  0.858129560  0.879493782 -0.907979410 -0.979193482\nSNP:487  0.654708914  0.267989659 -0.946444492 -0.132298692 -1.244964618\nSNP:488  2.312097208 -0.544598355 -0.570686899 -0.264146508 -0.498943403\nSNP:489 -0.528935433  0.400912844  0.144867666  0.778242579 -0.198771914\nSNP:490 -0.113237777  0.702074220  0.488540125  1.504444756 -0.126179238\nSNP:491 -0.482406154  0.598960662  1.136406444 -0.424128901  0.929198432\nSNP:492  0.036626061  0.968925806 -1.568261355  1.614876343 -0.216426726\nSNP:493  2.803891280  0.917037383 -0.059376521  0.178129564 -0.250700867\nSNP:494  1.590541173  0.080984785 -0.553935928  1.175899075 -1.039844637\nSNP:495  1.076869579  0.533464622  0.036447893 -2.746845786 -0.765405762\nSNP:496  0.068115529 -0.258839011 -2.854040677 -1.001298280  2.247812466\nSNP:497 -0.237444311  0.276476253 -0.354540136  0.269970929  0.829428759\nSNP:498 -0.499595721 -0.757665033 -0.069480200 -1.088523126  1.889199712\nSNP:499  1.329748048  0.208273791 -0.086513728  0.176231669 -1.387423867\nSNP:500 -1.039844637 -1.545189694  0.482669317  0.754778194  0.042112088\n "SNP:1"   "SNP:2"   "SNP:3"   "SNP:4"   "SNP:5"   "SNP:6"   "SNP:7"\n "SNP:8"   "SNP:9"   "SNP:10"  "SNP:11"  "SNP:12"  "SNP:13"  "SNP:14"\n "SNP:15"  "SNP:16"  "SNP:17"  "SNP:18"  "SNP:19"  "SNP:20"  "SNP:21"\n "SNP:22"  "SNP:23"  "SNP:24"  "SNP:25"  "SNP:26"  "SNP:27"  "SNP:28"\n "SNP:29"  "SNP:30"  "SNP:31"  "SNP:32"  "SNP:33"  "SNP:34"  "SNP:35"\n "SNP:36"  "SNP:37"  "SNP:38"  "SNP:39"  "SNP:40"  "SNP:41"  "SNP:42"\n "SNP:43"  "SNP:44"  "SNP:45"  "SNP:46"  "SNP:47"  "SNP:48"  "SNP:49"\n "SNP:50"  "SNP:51"  "SNP:52"  "SNP:53"  "SNP:54"  "SNP:55"  "SNP:56"\n "SNP:57"  "SNP:58"  "SNP:59"  "SNP:60"  "SNP:61"  "SNP:62"  "SNP:63"\n "SNP:64"  "SNP:65"  "SNP:66"  "SNP:67"  "SNP:68"  "SNP:69"  "SNP:70"\n "SNP:71"  "SNP:72"  "SNP:73"  "SNP:74"  "SNP:75"  "SNP:76"  "SNP:77"\n "SNP:78"  "SNP:79"  "SNP:80"  "SNP:81"  "SNP:82"  "SNP:83"  "SNP:84"\n "SNP:85"  "SNP:86"  "SNP:87"  "SNP:88"  "SNP:89"  "SNP:90"  "SNP:91"\n "SNP:92"  "SNP:93"  "SNP:94"  "SNP:95"  "SNP:96"  "SNP:97"  "SNP:98"\n "SNP:99"  "SNP:100" "SNP:101" "SNP:102" "SNP:103" "SNP:104" "SNP:105"\n "SNP:106" "SNP:107" "SNP:108" "SNP:109" "SNP:110" "SNP:111" "SNP:112"\n "SNP:113" "SNP:114" "SNP:115" "SNP:116" "SNP:117" "SNP:118" "SNP:119"\n "SNP:120" "SNP:121" "SNP:122" "SNP:123" "SNP:124" "SNP:125" "SNP:126"\n "SNP:127" "SNP:128" "SNP:129" "SNP:130" "SNP:131" "SNP:132" "SNP:133"\n "SNP:134" "SNP:135" "SNP:136" "SNP:137" "SNP:138" "SNP:139" "SNP:140"\n "SNP:141" "SNP:142" "SNP:143" "SNP:144" "SNP:145" "SNP:146" "SNP:147"\n "SNP:148" "SNP:149" "SNP:150" "SNP:151" "SNP:152" "SNP:153" "SNP:154"\n "SNP:155" "SNP:156" "SNP:157" "SNP:158" "SNP:159" "SNP:160" "SNP:161"\n "SNP:162" "SNP:163" "SNP:164" "SNP:165" "SNP:166" "SNP:167" "SNP:168"\n "SNP:169" "SNP:170" "SNP:171" "SNP:172" "SNP:173" "SNP:174" "SNP:175"\n "SNP:176" "SNP:177" "SNP:178" "SNP:179" "SNP:180" "SNP:181" "SNP:182"\n "SNP:183" "SNP:184" "SNP:185" "SNP:186" "SNP:187" "SNP:188" "SNP:189"\n "SNP:190" "SNP:191" "SNP:192" "SNP:193" "SNP:194" "SNP:195" "SNP:196"\n "SNP:197" "SNP:198" "SNP:199" "SNP:200" "SNP:201" "SNP:202" "SNP:203"\n "SNP:204" "SNP:205" "SNP:206" "SNP:207" "SNP:208" "SNP:209" "SNP:210"\n "SNP:211" "SNP:212" "SNP:213" "SNP:214" "SNP:215" "SNP:216" "SNP:217"\n "SNP:218" "SNP:219" "SNP:220" "SNP:221" "SNP:222" "SNP:223" "SNP:224"\n "SNP:225" "SNP:226" "SNP:227" "SNP:228" "SNP:229" "SNP:230" "SNP:231"\n "SNP:232" "SNP:233" "SNP:234" "SNP:235" "SNP:236" "SNP:237" "SNP:238"\n "SNP:239" "SNP:240" "SNP:241" "SNP:242" "SNP:243" "SNP:244" "SNP:245"\n "SNP:246" "SNP:247" "SNP:248" "SNP:249" "SNP:250" "SNP:251" "SNP:252"\n "SNP:253" "SNP:254" "SNP:255" "SNP:256" "SNP:257" "SNP:258" "SNP:259"\n "SNP:260" "SNP:261" "SNP:262" "SNP:263" "SNP:264" "SNP:265" "SNP:266"\n "SNP:267" "SNP:268" "SNP:269" "SNP:270" "SNP:271" "SNP:272" "SNP:273"\n "SNP:274" "SNP:275" "SNP:276" "SNP:277" "SNP:278" "SNP:279" "SNP:280"\n "SNP:281" "SNP:282" "SNP:283" "SNP:284" "SNP:285" "SNP:286" "SNP:287"\n "SNP:288" "SNP:289" "SNP:290" "SNP:291" "SNP:292" "SNP:293" "SNP:294"\n "SNP:295" "SNP:296" "SNP:297" "SNP:298" "SNP:299" "SNP:300" "SNP:301"\n "SNP:302" "SNP:303" "SNP:304" "SNP:305" "SNP:306" "SNP:307" "SNP:308"\n "SNP:309" "SNP:310" "SNP:311" "SNP:312" "SNP:313" "SNP:314" "SNP:315"\n "SNP:316" "SNP:317" "SNP:318" "SNP:319" "SNP:320" "SNP:321" "SNP:322"\n "SNP:323" "SNP:324" "SNP:325" "SNP:326" "SNP:327" "SNP:328" "SNP:329"\n "SNP:330" "SNP:331" "SNP:332" "SNP:333" "SNP:334" "SNP:335" "SNP:336"\n "SNP:337" "SNP:338" "SNP:339" "SNP:340" "SNP:341" "SNP:342" "SNP:343"\n "SNP:344" "SNP:345" "SNP:346" "SNP:347" "SNP:348" "SNP:349" "SNP:350"\n "SNP:351" "SNP:352" "SNP:353" "SNP:354" "SNP:355" "SNP:356" "SNP:357"\n "SNP:358" "SNP:359" "SNP:360" "SNP:361" "SNP:362" "SNP:363" "SNP:364"\n "SNP:365" "SNP:366" "SNP:367" "SNP:368" "SNP:369" "SNP:370" "SNP:371"\n "SNP:372" "SNP:373" "SNP:374" "SNP:375" "SNP:376" "SNP:377" "SNP:378"\n "SNP:379" "SNP:380" "SNP:381" "SNP:382" "SNP:383" "SNP:384" "SNP:385"\n "SNP:386" "SNP:387" "SNP:388" "SNP:389" "SNP:390" "SNP:391" "SNP:392"\n "SNP:393" "SNP:394" "SNP:395" "SNP:396" "SNP:397" "SNP:398" "SNP:399"\n "SNP:400" "SNP:401" "SNP:402" "SNP:403" "SNP:404" "SNP:405" "SNP:406"\n "SNP:407" "SNP:408" "SNP:409" "SNP:410" "SNP:411" "SNP:412" "SNP:413"\n "SNP:414" "SNP:415" "SNP:416" "SNP:417" "SNP:418" "SNP:419" "SNP:420"\n "SNP:421" "SNP:422" "SNP:423" "SNP:424" "SNP:425" "SNP:426" "SNP:427"\n "SNP:428" "SNP:429" "SNP:430" "SNP:431" "SNP:432" "SNP:433" "SNP:434"\n "SNP:435" "SNP:436" "SNP:437" "SNP:438" "SNP:439" "SNP:440" "SNP:441"\n "SNP:442" "SNP:443" "SNP:444" "SNP:445" "SNP:446" "SNP:447" "SNP:448"\n "SNP:449" "SNP:450" "SNP:451" "SNP:452" "SNP:453" "SNP:454" "SNP:455"\n "SNP:456" "SNP:457" "SNP:458" "SNP:459" "SNP:460" "SNP:461" "SNP:462"\n "SNP:463" "SNP:464" "SNP:465" "SNP:466" "SNP:467" "SNP:468" "SNP:469"\n "SNP:470" "SNP:471" "SNP:472" "SNP:473" "SNP:474" "SNP:475" "SNP:476"\n "SNP:477" "SNP:478" "SNP:479" "SNP:480" "SNP:481" "SNP:482" "SNP:483"\n "SNP:484" "SNP:485" "SNP:486" "SNP:487" "SNP:488" "SNP:489" "SNP:490"\n "SNP:491" "SNP:492" "SNP:493" "SNP:494" "SNP:495" "SNP:496" "SNP:497"\n "SNP:498" "SNP:499" "SNP:500"\nfastJT documentation built on Aug. 15, 2017, 1:01 a.m.\nRelated to fastJT in fastJT...\nPackage overview fastJT


Diese Titel könnten dich interessieren. Link zu den Titeln: https://stadtbibliothek.magdeburg.de/Permalink.aspx?searchhash=OCLC_3b4e80995ba9c3bc05a8a6f96e0f45d73cbc4869&detail=8\nLink zum Titel: https://stadtbibliothek.magdeburg.de/Permalink.aspx?id=0177892\nZentrale Ter N 91 Gueri Monum\n9 von 1613\nÜbergeordnetes Werk: Monumenta Guerickiana / Otto-von-Guericke-Gesellschaft\nZweigstelle: Zentrale Mediengruppe: Zeitschrift MEKZ: 4. OG Magdeburg & Umgebung Signatur: Ter N 91 Gueri Monum Status: Verfügbar Frist: Barcode: 73$0273649 Vorbestellungen: 0\nZweigstelle: Zentrale Mediengruppe: Zeitschrift MEKZ: 4. OG Magdeburg & Umgebung Signatur: Ter N 91 Gueri Monum Status: Verfügbar Frist: Barcode: 73$0495238 Vorbestellungen: 0\nZweigstelle: Zentrale Mediengruppe: Zeitschrift MEKZ: 2. OG Signatur: Ter N 91 Gueri Monum Status: Präsenzbestand Frist: Barcode: 73$0273648 Vorbestellungen: 0\nSystematik: Ter N 91 Guericke, Otto von (1602-1686)\nBeschreibung: 113 S. : Ill.\nSchlagwörter: Biografie, Guericke, Otto von, Grab, Historische Persönlichkeit, Magdeburg, Architektur, Kirchenbau, Marktkirche Sankt Johannes <Magdeburg>, Epidemie, Pest, Schönebeck (Elbe), Carnot, Lazare Nicolas Marguérite, Johanniskirche <Magdeburg>, Schönebeck, Seuche Mehr...\nFußnote: Enth.: Artikel 32-42


Aktivitäten 2020 | Dieblicher Pfadfinder e.V. – Stamm Treverer\nArchiv der Kategorie: Aktivitäten 2020\nAlle Akivitäten der Treverer im Jahr 2020.\nDieser Beitrag wurde unter Aktivitäten 1980, Aktivitäten 1981, Aktivitäten 1982, Aktivitäten 1983, Aktivitäten 1984, Aktivitäten 1985, Aktivitäten 1986, Aktivitäten 1987, Aktivitäten 1988, Aktivitäten 1989, Aktivitäten 1990, Aktivitäten 1991, Aktivitäten 1992, Aktivitäten 1993, Aktivitäten 1994, Aktivitäten 1995, Aktivitäten 1996, Aktivitäten 1997, Aktivitäten 1998, Aktivitäten 1999, Aktivitäten 2000, Aktivitäten 2001, Aktivitäten 2002, Aktivitäten 2003, Aktivitäten 2004, Aktivitäten 2005, Aktivitäten 2006, Aktivitäten 2007, Aktivitäten 2008, Aktivitäten 2009, Aktivitäten 2010, Aktivitäten 2011, Aktivitäten 2012, Aktivitäten 2013, Aktivitäten 2014, Aktivitäten 2015, Aktivitäten 2016, Aktivitäten 2017, Aktivitäten 2018, Aktivitäten 2019, Aktivitäten 2020, Aktivitäten 2021 abgelegt am 24. November 2018 von Rainer Heinz.\nDieser Beitrag wurde unter Aktivitäten 2020, Aktivitäten 2021 abgelegt am 2. November 2020 von Matthew Cowen.\nDieser Beitrag wurde unter Aktivitäten 2020 abgelegt am 19. Juli 2020 von Rainer Heinz.\nDieser Beitrag wurde unter Aktivitäten 2020 abgelegt am 11. Januar 2020 von Rainer Heinz.\nDieser Beitrag wurde unter Aktivitäten 2020 abgelegt am 25. Januar 2020 von Rainer Heinz.\nDieser Beitrag wurde unter Aktivitäten 2020 abgelegt am 14. März 2020 von Rainer Heinz.\nDieser Beitrag wurde unter Aktivitäten 2020 abgelegt am 29. März 2020 von Rainer Heinz.\nFrohe Ostern! 🥳🐰🥚\nDieser Beitrag wurde unter Aktivitäten 2020 abgelegt am 12. April 2020 von Rainer Heinz.\nDieser Beitrag wurde unter Aktivitäten 2020 abgelegt am 6. September 2020 von Rainer Heinz.\nDieser Beitrag wurde unter Aktivitäten 2020 abgelegt am 18. April 2020 von Rainer Heinz.\nNoch vor ein paar Monaten dachten wir, wir würden am gestrigen Abend am Moselvorgelände mit großem Lagerfeuer einen Tanz in den Mai feiern. Am nächsten Morgen wären wir zum Klang von paukenden Topfdeckeln im stinkenden Mief der Kothe oder des Felixzeltes aufgewacht. Draußen würden vermutlich schon die ersten Wös toben und ein Purzelbaum-Contest auf der Wiese veranstalten. Morgenrunde, Yankee Doodle oder Muffin Man, Frühstück, Porridge und Kakao – gefolgt von ganz viel spannendem Programm. So hätten wir uns das diesige Wochenende erhofft… Und eins sei klar: Wir holen das alles nach!!! 😊\nBis bald und Gut Pfad 😊\nDieser Beitrag wurde unter Aktivitäten 2020 abgelegt am 20. April 2020 von Rainer Heinz.\nDieser Beitrag wurde unter Aktivitäten 2020 abgelegt am 29. Juni 2020 von Rainer Heinz.\nDieser Beitrag wurde unter Aktivitäten 2020 abgelegt am 15. Juli 2020 von Rainer Heinz.\nDieser Beitrag wurde unter Aktivitäten 2020 abgelegt am 18. Juli 2020 von Rainer Heinz.\nDieser Beitrag wurde unter Aktivitäten 2020 abgelegt am 31. Juli 2020 von Rainer Heinz.\nDieser Beitrag wurde unter Aktivitäten 2020 abgelegt am 26. August 2020 von Rainer Heinz.\nDieser Beitrag wurde unter Aktivitäten 2020 abgelegt am 4. September 2020 von Rainer Heinz.\nDieser Beitrag wurde unter Aktivitäten 2020 abgelegt am 11. Oktober 2020 von Rainer Heinz.\nAnmeldung Sippenaktion 12 20\nAnmeldung zur Sippenaktion\n(Nur im geschützten Bereich sichtbar)\nDieser Beitrag wurde am 17. November 2020 von Matthew Cowen in Aktivitäten 2020 veröffentlicht. Schlagworte: Online-Escaperoom, Sippe, Sippenaktion.\nDieser Beitrag wurde unter Aktivitäten 2020 abgelegt am 27. November 2020 von Rainer Heinz.\nDieser Beitrag wurde unter Aktivitäten 2020 abgelegt am 13. Dezember 2020 von Kai Krall.\nSTELLUNGNAHME DER DIEBLICHER PFADFINDER ZUM ANBAU EINES BÜRGERSAALES (Stand: 10.12.2020)\nSTELLUNGNAHME DER DIEBLICHER PFADFINDER ZUM ANBAU EINES BÜRGERSAALES\nInfo an unsere Mitglieder vom 01. April 2020 (Stand: 10.12.2020)\nDiese Information ist nur im “geschützten Bereich” sichtbar – LogIn erforderlich!\nDieser Beitrag wurde unter Aktivitäten 2020 abgelegt am 5. April 2020 von Rainer Heinz.


Can I retire at age 80 with 2,800,000? - RetirementSimulation.com\nCan I retire at age 80 with 2,800,000?\n1 years $231,221 $229,162 $227,152 $225,191 $223,276 $221,313\n2 years $114,466 $112,334 $110,268 $108,265 $106,322 $104,344\n3 years $75,553 $73,412 $71,351 $69,366 $67,455 $65,523\n4 years $56,100 $53,965 $51,925 $49,975 $48,109 $46,238\n5 years $44,431 $42,309 $40,296 $38,385 $36,572 $34,768\n6 years $36,655 $34,549 $32,565 $30,697 $28,938 $27,203\n7 years $31,102 $29,014 $27,062 $25,238 $23,535 $21,869\n8 years $26,940 $24,870 $22,951 $21,171 $19,524 $17,928\n9 years $23,704 $21,654 $19,767 $18,033 $16,442 $14,915\n10 years $21,117 $19,087 $17,234 $15,545 $14,010 $12,550\n11 years $19,001 $16,992 $15,172 $13,529 $12,049 $10,657\n12 years $17,240 $15,251 $13,465 $11,866 $10,441 $9,115\n13 years $15,750 $13,783 $12,030 $10,476 $9,105 $7,843\n14 years $14,475 $12,528 $10,809 $9,300 $7,981 $6,782\n15 years $13,370 $11,445 $9,759 $8,294 $7,027 $5,889\n16 years $12,405 $10,500 $8,848 $7,426 $6,211 $5,131\n17 years $11,554 $9,670 $8,051 $6,672 $5,507 $4,485\n18 years $10,799 $8,936 $7,350 $6,013 $4,896 $3,930\n19 years $10,123 $8,282 $6,728 $5,433 $4,364 $3,451\n20 years $9,516 $7,696 $6,175 $4,921 $3,898 $3,036\n21 years $8,968 $7,168 $5,680 $4,466 $3,489 $2,676\n22 years $8,470 $6,691 $5,235 $4,061 $3,127 $2,361\n23 years $8,016 $6,258 $4,833 $3,698 $2,807 $2,087\n24 years $7,601 $5,864 $4,470 $3,373 $2,523 $1,846\n25 years $7,219 $5,503 $4,140 $3,080 $2,270 $1,634\n26 years $6,867 $5,172 $3,840 $2,816 $2,045 $1,449\n27 years $6,542 $4,867 $3,566 $2,578 $1,844 $1,285\n28 years $6,241 $4,586 $3,315 $2,362 $1,664 $1,140\n29 years $5,961 $4,327 $3,085 $2,166 $1,502 $1,012\n30 years $5,700 $4,086 $2,873 $1,988 $1,357 $899\n31 years $5,456 $3,863 $2,679 $1,826 $1,227 $799\n32 years $5,228 $3,655 $2,499 $1,678 $1,110 $711\n33 years $5,014 $3,461 $2,334 $1,543 $1,005 $632\n34 years $4,814 $3,280 $2,180 $1,420 $910 $563\n35 years $4,625 $3,111 $2,038 $1,307 $824 $501\n36 years $4,447 $2,953 $1,907 $1,204 $746 $446\n37 years $4,279 $2,805 $1,785 $1,109 $677 $397\n38 years $4,120 $2,666 $1,671 $1,022 $613 $354\n39 years $3,970 $2,535 $1,566 $942 $556 $315\n40 years $3,828 $2,412 $1,468 $869 $504 $281\n41 years $3,693 $2,296 $1,376 $802 $458 $250\n42 years $3,565 $2,186 $1,291 $740 $415 $223\n43 years $3,443 $2,083 $1,211 $683 $377 $199\n44 years $3,327 $1,986 $1,137 $631 $342 $177\n45 years $3,216 $1,893 $1,068 $583 $311 $158\n46 years $3,111 $1,806 $1,003 $538 $282 $141\n47 years $3,010 $1,724 $942 $497 $256 $125\n48 years $2,914 $1,646 $885 $459 $233 $112\n49 years $2,822 $1,571 $832 $425 $211 $100\n50 years $2,734 $1,501 $782 $392 $192 $89\nHow long will my money last in retirement? Person retiring at age 80 starting with $2,800,000, adding $4,301 every year, while hoping to spend $22,006 every year in retirement. These numbers increase with inflation.\n79 $2,800,000 $2,800,000 $2,800,000 $2,800,000\n80 $3,090,036 $2,941,636 $2,898,236 $3,309,601\n81 $3,381,806 $3,062,362 $2,971,913 $3,576,223\n82 $3,702,598 $3,188,266 $3,047,321 $3,865,236\n83 $4,055,351 $3,319,579 $3,124,493 $4,181,430\n84 $4,443,299 $3,456,541 $3,203,466 $4,527,506\n85 $4,870,009 $3,599,405 $3,284,276 $4,906,438\n86 $5,339,408 $3,748,434 $3,366,957 $5,321,502\n87 $5,855,825 $3,903,904 $3,451,547 $5,776,303\n88 $6,424,028 $4,066,104 $3,538,083 $6,274,815\n89 $7,049,271 $4,235,336 $3,626,601 $6,821,412\n90 $7,737,344 $4,411,914 $3,717,139 $7,420,911\n91 $8,494,625 $4,596,170 $3,809,735 $8,078,619\n92 $9,328,143 $4,788,448 $3,904,428 $8,800,381\n93 $10,245,642 $4,989,111 $4,001,255 $9,592,635\n94 $11,255,656 $5,198,536 $4,100,256 $10,462,473\n95 $12,367,588 $5,417,120 $4,201,469 $11,417,706\n96 $13,591,799 $5,645,275 $4,304,935 $12,466,939\n97 $14,939,704 $5,883,435 $4,410,692 $13,619,652\n98 $16,423,884 $6,132,054 $4,518,781 $14,886,286\n99 $18,058,202 $6,391,606 $4,629,241 $16,278,345\n100 $19,857,930 $6,662,586 $4,742,112 $17,808,504


Mac OS High Sierra läuft Dreibeinblog\nPosted on 27. September 2017 17. September 2020 by Peter Wilhelm\nPrevious PostDöpfner dreht durch\nNext PostHorst leidet


Bis 50 € | Accessoires von Katana für Frauen\nAccessoires von Katana für Frauen\nKatana Scheckbuchhülle, königsblau (Blau) - 953118\nKatana Scheckbuchhülle, Orange (Orange) - 953125\nKatana, Damen-Geldbörse Choco\nKatana, Damen-Geldbörse Braun Schokoladenbraun\nKatana Scheckbuchhülle, rot (Rot) - 953125\nKatana , Damen-Geldbörse Blau blau\nKatana , Damen-Geldbörse Schwarz schwarz\nKatana, Damen-Geldbörse Taupe Taupe\nKatana, Damen Damen-Geldbörse, braun (Braun) - 753105\nKatana , Münzbörse rot rot\nKatana Münzbörse, gelb (Gelb) - 953109\nKatana, Damen-Geldbörse Fushia MAT\nKatana, Damen Damen-Geldbörse, rot (Rot) - 753105\nKatana , Damen-Geldbörse orange ORANGE\nKatana Scheckbuchhülle, Schwarz (Schwarz) - 553008\nKatana, Damen Rucksackhandtasche Orange Orange one Size\nKatana Münzbörse, violett (Violett) - 953109\nKatana , Damen-Geldbörse violett violett\nKatana Scheckbuchhülle, Choco (Braun) - 553050\nVanity – Geldbörse für Damen, Katana, Rindsleder, weich, 10 x 19 cm\nKatana Münzbörse, braun (Braun) - 753108\nKatana Münzbörse, rot (Rot) - 953109\nKatana Münzbörse, Marineblau (Blau) - 953109\nKatana , Damen-Geldbörse Rot rot\nKatana Scheckbuchhülle, gelb (Gelb) - 953118\nKatana Münzbörse, Schwarz (Schwarz) - 753116\nKatana 553118 Reisebegleiter aus Leder, Ref. Rot, Schwarz oder Schokoladenbraun, Choco (Braun) - 553118\nKatana Scheckbuchhülle, rot (Rot) - 553008\nKatana Münzbörse, braun (Braun) - 753116\nKatana Scheckbuchhülle, Orange (Orange) - 953118\nKatana Münzbörse, Schwarz (Schwarz) - 553109\nKatana Münzbörse, braun (Braun) - 953109\nKatana Münzbörse, Fuchsia (Pink) - 953109\nKatana Scheckbuchhülle, rot (Rot) - 953118\nKatana, Damen Damen-Geldbörse, Schwarz (Schwarz) - 753105\nKatana , Damen-Geldbörse\nKatana Scheckbuchhülle, Taupe (Grau) - 953125\nKatana Münzbörse, Taupe (Grau) - 953109\nKatana Scheckbuchhülle, Fuchsia (Pink) - 953125\nKatana Scheckbuchhülle, Fuchsia (Pink) - 953118\nKatana, Damen Rucksackhandtasche Schwarz Schwarz one Size\nKatana Scheckbuchhülle, Marineblau (Blau) - 953125\nKatana, Damen Rucksackhandtasche Braun Hellbraun one Size\nVanity – Geldbörse mit italienischem Stil, Kartenhalter, Katana, Rindsleder, schokoladenbraun (Braun) - .\nKatana , Damen-Geldbörse Gelb gelb\nKatana Münzbörse, Schwarz (Schwarz) - 753108\nKatana Scheckbuchhülle, gelb (Gelb) - 953125\nVanity – Geldbörse, Damen, Katana, weiches Rindsleder, glatt, 9 x 13 cm\nKatana , Münzbörse braun braun\nKatana Münzbörse, königsblau (Blau) - 953109\nKatana Scheckbuchhülle, königsblau (Blau) - 953125\nKatana, Damen Rucksackhandtasche Rot rot one Size\nKatana Münzbörse, rot (Rot) - 753108\nKatana, Damen Damen-Geldbörse, Choco (Braun) - 753105\nKatana Scheckbuchhülle, violett (Violett) - 953125\nKatana, Münzbörse Choco\nKatana, Damen Rucksackhandtasche Beige Sand one Size\nKatana Münzbörse, Orange (Orange) - 953109\nKatana Scheckbuchhülle, violett (Violett) - 953118


Vaud - LEO: Übersetzung im Englisch ⇔ Deutsch Wörterbuch\n816.234 Einträge, 64.771 Anfragen\nVaud - canton in Switzerland [GEOG.] die Waadt kein Pl.\nVaud - canton in Switzerland [GEOG.] das Waadtland kein Pl.\nauld, baud, laud, vade, Vlad Baud, Vlad\nVaud - Waadt Letzter Beitrag: 02 Sep. 08, 18:55\nWaadt [vat, vaːt] die, Waadtland, amtlich französisch Vaud, Kanton der Sc 0 Antworten\n- canton in Switzerland [GEOG.]VaudVauddie Waadt kein Pl.die WaadtWaadtVaud - canton in Switzerland [GEOG.]VaudVauddas Waadtland kein Pl.das WaadtlandWaadtland\nauldbaudlaudvadeVladBaudVladVaud - WaadtWaadt [vat, vaːt] die, Waadtland, amtlich französisch Vaud, Kanton der ScVaud - Waadt\nNutzungsbedingungen / DatenschutzImpressumWerbung auf LEOSuchtipps FAQ - Frequently Asked QuestionsCopyright © LEO Dictionary Team 2006 - 2019leomuc8. CPU µs: 14002/1269