Monte Carlo sampling for metrology applications

Description

Creates Monte Carlo sampling designs for metrology applications

Usage

  MCdesign(N,k,distrib,distrib.pars,x)

Arguments

Details

This function creates a sampling design based on a Monte Carlo simulation.

If distrib or members of it are missing, an error message is displayed. Distributions have to be chosen among uniform(unif), triangular(triang), normal(norm), truncated normal(tnorm), student(t), location-scale student(t.scaled).

If distrib.pars is missing or misspecified, the standardized parameters of the associated distributions in distrib are used for all the variables in x:

unif : min=0, max=1

triang : min=0, max=1, mode=0.5

norm : mean=0, sd=1

tnorm : mean=0, sd=1, lower=0, upper=+Inf

t : nu=100

t.scaled : nu=100, mean=0, sd=1

If x or members of it are missing, arbitrary names of the form 'Xn' are applied to the columns of the output table. Names are automatically abbreviated to 15 characters.

Value

A table containing the MC design with margins in distrib.

Author(s)

Severine Demeyer severine.demeyer@lne.fr

Examples

  
  N<- 100
  k<- 4
  x<- list("X1","X2","X3","X4")
  distrib<- list("norm","norm","unif","t.scaled")
  distrib.pars<- list(list(0,2),list(0,1),list(20,150),list(2,0,1))
  MCdesign(N,k,distrib,distrib.pars,x)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(ATmet)
Loading required package: DiceDesign
Loading required package: lhs
Loading required package: metRology

Attaching package: 'metRology'

The following objects are masked from 'package:base':

    cbind, rbind

Loading required package: msm
Loading required package: sensitivity
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/ATmet/MCdesign.Rd_%03d_medium.png", width=480, height=480)
> ### Name: MCdesign
> ### Title: Monte Carlo sampling for metrology applications
> ### Aliases: MCdesign
> ### Keywords: ~kwd1 ~kwd2
> 
> ### ** Examples
> 
>   
>   N<- 100
>   k<- 4
>   x<- list("X1","X2","X3","X4")
>   distrib<- list("norm","norm","unif","t.scaled")
>   distrib.pars<- list(list(0,2),list(0,1),list(20,150),list(2,0,1))
>   MCdesign(N,k,distrib,distrib.pars,x)
             X1          X2        X3           X4
1    1.24218491  1.76287517  53.24972 -1.250148131
2   -0.15329330 -1.07814849  56.04057 -0.005311875
3    0.34676988  0.77851239  89.20934  1.826269593
4   -3.40727968  2.15972232  59.77379  2.099142255
5    2.31760865  0.14724025  70.69417 -1.596816323
6   -3.08167751  0.32742703  65.86774  0.499030102
7    1.32851898 -0.92415868  83.02511 -1.945695637
8    4.09418216 -1.79005843  75.18160  0.620637608
9    7.12814008  0.53956513 138.11064 -0.278075525
10   1.88332711 -1.56543725  29.56898  0.521127012
11   1.97439582  0.68136332 127.23958  0.163288424
12  -2.33006117 -0.68289126  99.80069  2.908074126
13  -0.16709511 -0.86368605 119.30860 -0.369914378
14  -2.13010925 -1.99428894  75.14097 -0.619601195
15  -0.20688803 -0.85903664 107.64385  0.045505476
16  -2.33163986 -0.76057900  99.89781 -0.251509814
17   0.27425766 -2.37365630 135.71798  0.069336794
18   1.08861047  0.33869923  89.73289 -1.056601798
19   1.87344634 -0.62629791  85.82851  1.368662348
20   0.42412197 -1.17512081 147.88274 -1.224961947
21   3.31241243  1.51327364  64.24597  0.891704018
22   2.14986208 -0.04037350  64.37908 -0.801856294
23   0.97385315 -0.57031394  40.76443  0.003181979
24   2.26437921  0.61772520 119.03465  1.498861641
25   0.98407890  0.36050004 134.34145 -1.053926007
26   0.57271575  0.65192473 108.70197  0.159895622
27   2.44199764  0.39732418  73.13044 -0.209042283
28  -2.11756118  0.30920543  54.85679  0.084968333
29  -1.20445086 -0.54365054  42.67627 -0.209559508
30  -1.82122733  0.85867754 123.22291  0.494558695
31   0.95482005  2.09026906 144.90931 -1.246785767
32  -1.36014961 -1.16755590  46.44859 -2.649633754
33  -1.39928047 -1.17690235 112.35829 -1.377770308
34   3.23603916  0.04456069 107.37909 -1.648504397
35   0.20183967 -1.11279115  52.49385 -0.207227301
36  -1.89581663  0.10742765  55.09534  0.480838792
37  -1.39910817  0.26900180  42.00210 -0.963727781
38  -4.61134901  0.95523108  35.39366 -0.905428991
39   0.32871105 -1.03910307  49.47020 -0.318421454
40  -0.71794307 -0.57358048 116.80289 -1.500513173
41  -1.22710254 -0.60629380 121.32624  0.164892589
42   4.71820944  0.51319277  35.39856 -0.596403087
43   1.14323849  0.13262180  62.41865 -1.497816003
44   4.17271306  0.37389371 114.76926 -0.135019765
45   0.78046522  2.27457524  60.76311 -0.484552710
46   1.80275860 -0.42648072 144.22668  0.480712476
47  -1.16803534 -0.53698191  26.57289 -0.094289721
48   1.76277174  0.92448150  44.60110 -0.741947666
49   0.43430538  1.08656104  54.05102 -0.078694893
50   1.00128277 -1.69169602 146.64005  0.510603415
51   3.87615030 -0.79570399  51.64869 13.100392984
52   0.57794692 -1.96767618  67.94757 -1.306180861
53  -0.42278949 -0.08134621 111.42918  1.604240203
54  -2.44765110  0.14175425  21.34965 -2.605357157
55  -1.36033209  0.59726446 145.82158 -2.600765166
56   2.55457076  0.98989529  79.95639 -0.149008074
57  -1.29369759 -1.00029550  28.24514 -0.273634578
58  -0.28181882  0.04231831  59.71688 -1.716862988
59   0.20191851 -1.22053351  50.38145  1.452621840
60  -2.76660613 -1.11505558 139.91846 -0.428736992
61  -1.35691329  0.89341470  20.58763  2.918520869
62  -3.77929979  1.20396947 121.34902  0.441150095
63   0.86244058 -1.71656663 114.75942 -2.137618018
64   1.56135730 -0.15748854 142.88113  3.307845190
65   1.14157757 -0.65891701  91.50367  6.001980109
66   0.95683248  0.98843379  57.05834 -0.021428968
67  -0.00596739 -1.99022289  65.34190  0.987271269
68  -2.26672087  0.60093673 127.30009 -0.404135952
69  -2.96041651  0.10310664  58.97433  0.994750916
70   0.78660437 -0.23214962  83.45037 -2.682315397
71  -0.08965698  1.49362282 121.09289 -0.556886176
72  -0.23394961  0.13223087 130.84245  0.307234341
73  -3.53492048  0.88583371  51.13679 -0.144560342
74  -3.74064151  1.27940240 117.82103  0.978776187
75  -3.47895467  0.08558895 133.38249  0.720147341
76   1.59379562 -0.14619593  96.68147  0.405684795
77   0.86984597  1.17729676  27.36262  1.239708911
78   3.16612024  0.08197560 112.81203  0.975557114
79  -2.25642562  0.66411612  50.62852  1.966950568
80  -0.01753205 -1.46440673  21.07896 -0.364887437
81   1.49119707  0.47607476 138.60675 -5.295548431
82  -1.92596048 -1.65646456  53.64103 -0.126890879
83  -2.20525376  0.01677258 146.29843  1.974077525
84   0.88499899 -1.06220964  41.86338 -2.174961150
85   0.78180702  0.25764924  46.12225 -0.279012018
86  -1.44131133 -0.62906304 104.93276 -0.995994582
87   1.33336916 -1.25356846  64.21855  0.446415442
88   2.49500227  1.70002051  73.50679  1.341713681
89   0.87054993  0.08520848  58.01354 -0.837667394
90   0.44219181 -0.91777134  83.47053  2.142719855
91   2.62073183 -0.59251151 115.65755  3.711711077
92   1.49963575  0.51041193  47.49121  0.270258924
93  -0.73953612  0.50450319 121.84258  0.438234241
94   1.11695783  0.33217526 136.31430  0.669972111
95  -0.96140106 -0.82875876 144.71523 -1.835189469
96  -3.46147585 -1.08962611 128.29275  0.336367711
97   3.18417318  1.49984351 110.73497 -0.084675334
98  -3.15807611  1.66066926  90.01682 -0.263943527
99  -2.09097478 -0.50346570  89.31546 -0.171195318
100  1.74261831 -1.24155894  31.33275 -0.056365007
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>