n.c.freecalc: Freecalc optimum sample size and cut-point number of...
In RSurveillance: Design and Analysis of Disease Surveillance Activities
Description
Usage
Arguments
Value
Examples
View source: R/freedom_functions_2.R
Description
Calculates optimum sample size and cut-point number of positives
to achieve specified population sensitivity, for
given population size and other parameters, using freecalc algorithm,
all paramaters must be scalars
Usage
1
2
n.c.freecalc(N, sep = 0.95, c = 1, se, sp = 1, pstar,
minSpH = 0.95)
Arguments
N
population size
sep
target population sensitivity
c
The maximum allowed cut-point number of positives to classify a cluster
as positive, default=1, if positives < c result is negative, >= c is positive
se
test unit sensitivity
sp
test unit specificity, default=1
pstar
design prevalence as a proportion or integer (number of infected units)
minSpH
minimium desired population specificity
Value
a list of 3 elements, a dataframe with 1 row and six columns for
the recommended sample size and corresponding values for population sensitivity (SeP),
population specificity (SpP), N, c and pstar, a vector of SeP values
and a vector of SpP values, for n = 1:N
Examples
1
2
3
# examples for n.c.hp
n.c.freecalc(120,0.95,c=5,se=0.9,sp=0.99,pstar=0.1, minSpH=0.9)[[1]]
n.c.freecalc(65,0.95,c=5,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)
Example output
n SeP SpP N c pstar
1 27 0.9555394 0.9999933 120 1 0.1
$`Suggested n`
n SeP SpP N c pstar
1 40 0.9547116 0.9999508 65 1 0.05
$SeP
[,1] [,2] [,3] [,4] [,5]
[1,] 0.05338462 -2.220446e-16 -2.220446e-16 -2.220446e-16 -2.220446e-16
[2,] 0.10452712 2.242115e-03 0.000000e+00 0.000000e+00 0.000000e+00
[3,] 0.15349876 6.583820e-03 7.126328e-05 -2.220446e-16 -2.220446e-16
[4,] 0.20036911 1.288771e-02 2.799267e-04 1.708800e-06 0.000000e+00
[5,] 0.24520605 2.102138e-02 6.872050e-04 8.407887e-06 3.402866e-08
[6,] 0.28807580 3.085728e-02 1.349589e-03 2.482130e-05 2.011795e-07
[7,] 0.32904300 4.227258e-02 2.319037e-03 5.699131e-05 6.937992e-07
[8,] 0.36817071 5.514905e-02 3.643163e-03 1.121596e-04 1.822968e-06
[9,] 0.40552043 6.937295e-02 5.365417e-03 1.986542e-04 4.041405e-06
[10,] 0.44115216 8.483487e-02 7.525261e-03 3.257811e-04 7.963933e-06
[11,] 0.47512441 1.014297e-01 1.015834e-02 5.037214e-04 1.438562e-05
[12,] 0.50749424 1.190563e-01 1.329664e-02 7.434332e-04 2.429773e-05
[13,] 0.53831729 1.376176e-01 1.696866e-02 1.056558e-03 3.890158e-05
[14,] 0.56764780 1.570207e-01 2.119957e-02 1.455334e-03 5.962044e-05
[15,] 0.59553865 1.771759e-01 2.601134e-02 1.952506e-03 8.810957e-05
[16,] 0.62204137 1.979978e-01 3.142290e-02 2.561253e-03 1.262645e-04
[17,] 0.64720620 2.194041e-01 3.745028e-02 3.295107e-03 1.762275e-04
[18,] 0.67108208 2.413163e-01 4.410676e-02 4.167883e-03 2.403930e-04
[19,] 0.69371669 2.636590e-01 5.140298e-02 5.193608e-03 3.214108e-04
[20,] 0.71515650 2.863604e-01 5.934707e-02 6.386463e-03 4.221884e-04
[21,] 0.73544675 3.093515e-01 6.794479e-02 7.760715e-03 5.458919e-04
[22,] 0.75463151 3.325667e-01 7.719966e-02 9.330664e-03 6.959458e-04
[23,] 0.77275371 3.559432e-01 8.711302e-02 1.111059e-02 8.760315e-04
[24,] 0.78985512 3.794213e-01 9.768421e-02 1.311469e-02 1.090084e-03
[25,] 0.80597642 4.029440e-01 1.089106e-01 1.535705e-02 1.342291e-03
[26,] 0.82115719 4.264570e-01 1.207879e-01 1.785159e-02 1.637085e-03
[27,] 0.83543598 4.499087e-01 1.333099e-01 2.061201e-02 1.979140e-03
[28,] 0.84885027 4.732503e-01 1.464689e-01 2.365179e-02 2.373365e-03
[29,] 0.86143653 4.964350e-01 1.602556e-01 2.698409e-02 2.824899e-03
[30,] 0.87323023 5.194190e-01 1.746593e-01 3.062178e-02 3.339099e-03
[31,] 0.88426589 5.421605e-01 1.896681e-01 3.457737e-02 3.921538e-03
[32,] 0.89457705 5.646199e-01 2.052686e-01 3.886300e-02 4.577992e-03
[33,] 0.90419633 5.867602e-01 2.214465e-01 4.349039e-02 5.314431e-03
[34,] 0.91315543 6.085460e-01 2.381860e-01 4.847084e-02 6.137013e-03
[35,] 0.92148516 6.299445e-01 2.554708e-01 5.381519e-02 7.052069e-03
[36,] 0.92921546 6.509246e-01 2.732832e-01 5.953383e-02 8.066099e-03
[37,] 0.93637542 6.714572e-01 2.916050e-01 6.563663e-02 9.185752e-03
[38,] 0.94299327 6.915150e-01 3.104169e-01 7.213296e-02 1.041783e-02
[39,] 0.94909644 7.110726e-01 3.296990e-01 7.903167e-02 1.176925e-02
[40,] 0.95471157 7.301064e-01 3.494307e-01 8.634106e-02 1.324708e-02
[41,] 0.95986450 7.485943e-01 3.695909e-01 9.406891e-02 1.485846e-02
[42,] 0.96458032 7.665162e-01 3.901577e-01 1.022224e-01 1.661066e-02
[43,] 0.96888335 7.838531e-01 4.111089e-01 1.108082e-01 1.851102e-02
[44,] 0.97279719 8.005879e-01 4.324218e-01 1.198323e-01 2.056695e-02
[45,] 0.97634475 8.167049e-01 4.540733e-01 1.293003e-01 2.278594e-02
[46,] 0.97954819 8.321896e-01 4.760401e-01 1.392169e-01 2.517552e-02
[47,] 0.98242904 8.470293e-01 4.982983e-01 1.495865e-01 2.774326e-02
[48,] 0.98500812 8.612121e-01 5.208240e-01 1.604129e-01 3.049674e-02
[49,] 0.98730563 8.747278e-01 5.435930e-01 1.716991e-01 3.344358e-02
[50,] 0.98934111 8.875673e-01 5.665810e-01 1.834475e-01 3.659140e-02
[51,] 0.99113348 8.997225e-01 5.897635e-01 1.956603e-01 3.994779e-02
[52,] 0.99270106 9.111868e-01 6.131161e-01 2.083386e-01 4.352034e-02
[53,] 0.99406158 9.219543e-01 6.366140e-01 2.214833e-01 4.731660e-02
[54,] 0.99523216 9.320205e-01 6.602328e-01 2.350944e-01 5.134407e-02
[55,] 0.99622939 9.413818e-01 6.839479e-01 2.491716e-01 5.561022e-02
[56,] 0.99706928 9.500354e-01 7.077347e-01 2.637138e-01 6.012242e-02
[57,] 0.99776731 9.579796e-01 7.315689e-01 2.787196e-01 6.488800e-02
[58,] 0.99833843 9.652137e-01 7.554261e-01 2.941866e-01 6.991417e-02
[59,] 0.99879706 9.717376e-01 7.792822e-01 3.101124e-01 7.520806e-02
[60,] 0.99915715 9.775522e-01 8.031132e-01 3.264935e-01 8.077670e-02
[61,] 0.99943211 9.826592e-01 8.268953e-01 3.433262e-01 8.662699e-02
[62,] 0.99963492 9.870610e-01 8.506049e-01 3.606062e-01 9.276570e-02
[63,] 0.99977805 9.907608e-01 8.742187e-01 3.783287e-01 9.919948e-02
[64,] 0.99987354 9.937623e-01 8.977137e-01 3.964883e-01 1.059348e-01
[65,] 0.99993297 9.960701e-01 9.210669e-01 4.150792e-01 1.129781e-01
$SpP
[,1] [,2] [,3] [,4] [,5]
[1,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[2,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[3,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[4,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[5,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[6,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[7,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[8,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[9,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[10,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[11,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[12,] 0.9999999 0.9999999 0.9999999 0.9999999 0.9999999
[13,] 0.9999999 0.9999999 0.9999999 0.9999999 0.9999999
[14,] 0.9999998 0.9999998 0.9999998 0.9999998 0.9999998
[15,] 0.9999997 0.9999997 0.9999997 0.9999997 0.9999997
[16,] 0.9999996 0.9999996 0.9999996 0.9999996 0.9999996
[17,] 0.9999994 0.9999994 0.9999994 0.9999994 0.9999994
[18,] 0.9999992 0.9999992 0.9999992 0.9999992 0.9999992
[19,] 0.9999990 0.9999990 0.9999990 0.9999990 0.9999990
[20,] 0.9999986 0.9999986 0.9999986 0.9999986 0.9999986
[21,] 0.9999982 0.9999982 0.9999982 0.9999982 0.9999982
[22,] 0.9999977 0.9999977 0.9999977 0.9999977 0.9999977
[23,] 0.9999971 0.9999971 0.9999971 0.9999971 0.9999971
[24,] 0.9999964 0.9999964 0.9999964 0.9999964 0.9999964
[25,] 0.9999955 0.9999955 0.9999955 0.9999955 0.9999955
[26,] 0.9999945 0.9999945 0.9999945 0.9999945 0.9999945
[27,] 0.9999933 0.9999933 0.9999933 0.9999933 0.9999933
[28,] 0.9999919 0.9999919 0.9999919 0.9999919 0.9999919
[29,] 0.9999903 0.9999903 0.9999903 0.9999903 0.9999903
[30,] 0.9999884 0.9999884 0.9999884 0.9999884 0.9999884
[31,] 0.9999863 0.9999863 0.9999863 0.9999863 0.9999863
[32,] 0.9999839 0.9999839 0.9999839 0.9999839 0.9999839
[33,] 0.9999812 0.9999812 0.9999812 0.9999812 0.9999812
[34,] 0.9999782 0.9999782 0.9999782 0.9999782 0.9999782
[35,] 0.9999747 0.9999747 0.9999747 0.9999747 0.9999747
[36,] 0.9999709 0.9999709 0.9999709 0.9999709 0.9999709
[37,] 0.9999666 0.9999666 0.9999666 0.9999666 0.9999666
[38,] 0.9999619 0.9999619 0.9999619 0.9999619 0.9999619
[39,] 0.9999566 0.9999566 0.9999566 0.9999566 0.9999566
[40,] 0.9999508 0.9999508 0.9999508 0.9999508 0.9999508
[41,] 0.9999445 0.9999445 0.9999445 0.9999445 0.9999445
[42,] 0.9999375 0.9999375 0.9999375 0.9999375 0.9999375
[43,] 0.9999299 0.9999299 0.9999299 0.9999299 0.9999299
[44,] 0.9999215 0.9999215 0.9999215 0.9999215 0.9999215
[45,] 0.9999124 0.9999124 0.9999124 0.9999124 0.9999124
[46,] 0.9999026 0.9999026 0.9999026 0.9999026 0.9999026
[47,] 0.9998919 0.9998919 0.9998919 0.9998919 0.9998919
[48,] 0.9998803 0.9998803 0.9998803 0.9998803 0.9998803
[49,] 0.9998678 0.9998678 0.9998678 0.9998678 0.9998678
[50,] 0.9998543 0.9998543 0.9998543 0.9998543 0.9998543
[51,] 0.9998398 0.9998398 0.9998398 0.9998398 0.9998398
[52,] 0.9998242 0.9998242 0.9998242 0.9998242 0.9998242
[53,] 0.9998075 0.9998075 0.9998075 0.9998075 0.9998075
[54,] 0.9997896 0.9997896 0.9997896 0.9997896 0.9997896
[55,] 0.9997705 0.9997705 0.9997705 0.9997705 0.9997705
[56,] 0.9997501 0.9997501 0.9997501 0.9997501 0.9997501
[57,] 0.9997283 0.9997283 0.9997283 0.9997283 0.9997283
[58,] 0.9997051 0.9997051 0.9997051 0.9997051 0.9997051
[59,] 0.9996804 0.9996804 0.9996804 0.9996804 0.9996804
[60,] 0.9996542 0.9996542 0.9996542 0.9996542 0.9996542
[61,] 0.9996265 0.9996265 0.9996265 0.9996265 0.9996265
[62,] 0.9995970 0.9995970 0.9995970 0.9995970 0.9995970
[63,] 0.9995659 0.9995659 0.9995659 0.9995659 0.9995659
[64,] 0.9995330 0.9995330 0.9995330 0.9995330 0.9995330
[65,] 0.9994982 0.9994982 0.9994982 0.9994982 0.9994982
RSurveillance documentation built on July 2, 2020, 2:33 a.m.
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Description Usage Arguments Value Examples
View source: R/freedom_functions_2.R
Description
Calculates optimum sample size and cut-point number of positives to achieve specified population sensitivity, for given population size and other parameters, using freecalc algorithm, all paramaters must be scalars
Usage
1 2 | n.c.freecalc(N, sep = 0.95, c = 1, se, sp = 1, pstar,
minSpH = 0.95)
|
Arguments
N |
population size |
sep |
target population sensitivity |
c |
The maximum allowed cut-point number of positives to classify a cluster as positive, default=1, if positives < c result is negative, >= c is positive |
se |
test unit sensitivity |
sp |
test unit specificity, default=1 |
pstar |
design prevalence as a proportion or integer (number of infected units) |
minSpH |
minimium desired population specificity |
Value
a list of 3 elements, a dataframe with 1 row and six columns for the recommended sample size and corresponding values for population sensitivity (SeP), population specificity (SpP), N, c and pstar, a vector of SeP values and a vector of SpP values, for n = 1:N
Examples
1 2 3 | # examples for n.c.hp
n.c.freecalc(120,0.95,c=5,se=0.9,sp=0.99,pstar=0.1, minSpH=0.9)[[1]]
n.c.freecalc(65,0.95,c=5,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)
|
Example output
n SeP SpP N c pstar
1 27 0.9555394 0.9999933 120 1 0.1
$`Suggested n`
n SeP SpP N c pstar
1 40 0.9547116 0.9999508 65 1 0.05
$SeP
[,1] [,2] [,3] [,4] [,5]
[1,] 0.05338462 -2.220446e-16 -2.220446e-16 -2.220446e-16 -2.220446e-16
[2,] 0.10452712 2.242115e-03 0.000000e+00 0.000000e+00 0.000000e+00
[3,] 0.15349876 6.583820e-03 7.126328e-05 -2.220446e-16 -2.220446e-16
[4,] 0.20036911 1.288771e-02 2.799267e-04 1.708800e-06 0.000000e+00
[5,] 0.24520605 2.102138e-02 6.872050e-04 8.407887e-06 3.402866e-08
[6,] 0.28807580 3.085728e-02 1.349589e-03 2.482130e-05 2.011795e-07
[7,] 0.32904300 4.227258e-02 2.319037e-03 5.699131e-05 6.937992e-07
[8,] 0.36817071 5.514905e-02 3.643163e-03 1.121596e-04 1.822968e-06
[9,] 0.40552043 6.937295e-02 5.365417e-03 1.986542e-04 4.041405e-06
[10,] 0.44115216 8.483487e-02 7.525261e-03 3.257811e-04 7.963933e-06
[11,] 0.47512441 1.014297e-01 1.015834e-02 5.037214e-04 1.438562e-05
[12,] 0.50749424 1.190563e-01 1.329664e-02 7.434332e-04 2.429773e-05
[13,] 0.53831729 1.376176e-01 1.696866e-02 1.056558e-03 3.890158e-05
[14,] 0.56764780 1.570207e-01 2.119957e-02 1.455334e-03 5.962044e-05
[15,] 0.59553865 1.771759e-01 2.601134e-02 1.952506e-03 8.810957e-05
[16,] 0.62204137 1.979978e-01 3.142290e-02 2.561253e-03 1.262645e-04
[17,] 0.64720620 2.194041e-01 3.745028e-02 3.295107e-03 1.762275e-04
[18,] 0.67108208 2.413163e-01 4.410676e-02 4.167883e-03 2.403930e-04
[19,] 0.69371669 2.636590e-01 5.140298e-02 5.193608e-03 3.214108e-04
[20,] 0.71515650 2.863604e-01 5.934707e-02 6.386463e-03 4.221884e-04
[21,] 0.73544675 3.093515e-01 6.794479e-02 7.760715e-03 5.458919e-04
[22,] 0.75463151 3.325667e-01 7.719966e-02 9.330664e-03 6.959458e-04
[23,] 0.77275371 3.559432e-01 8.711302e-02 1.111059e-02 8.760315e-04
[24,] 0.78985512 3.794213e-01 9.768421e-02 1.311469e-02 1.090084e-03
[25,] 0.80597642 4.029440e-01 1.089106e-01 1.535705e-02 1.342291e-03
[26,] 0.82115719 4.264570e-01 1.207879e-01 1.785159e-02 1.637085e-03
[27,] 0.83543598 4.499087e-01 1.333099e-01 2.061201e-02 1.979140e-03
[28,] 0.84885027 4.732503e-01 1.464689e-01 2.365179e-02 2.373365e-03
[29,] 0.86143653 4.964350e-01 1.602556e-01 2.698409e-02 2.824899e-03
[30,] 0.87323023 5.194190e-01 1.746593e-01 3.062178e-02 3.339099e-03
[31,] 0.88426589 5.421605e-01 1.896681e-01 3.457737e-02 3.921538e-03
[32,] 0.89457705 5.646199e-01 2.052686e-01 3.886300e-02 4.577992e-03
[33,] 0.90419633 5.867602e-01 2.214465e-01 4.349039e-02 5.314431e-03
[34,] 0.91315543 6.085460e-01 2.381860e-01 4.847084e-02 6.137013e-03
[35,] 0.92148516 6.299445e-01 2.554708e-01 5.381519e-02 7.052069e-03
[36,] 0.92921546 6.509246e-01 2.732832e-01 5.953383e-02 8.066099e-03
[37,] 0.93637542 6.714572e-01 2.916050e-01 6.563663e-02 9.185752e-03
[38,] 0.94299327 6.915150e-01 3.104169e-01 7.213296e-02 1.041783e-02
[39,] 0.94909644 7.110726e-01 3.296990e-01 7.903167e-02 1.176925e-02
[40,] 0.95471157 7.301064e-01 3.494307e-01 8.634106e-02 1.324708e-02
[41,] 0.95986450 7.485943e-01 3.695909e-01 9.406891e-02 1.485846e-02
[42,] 0.96458032 7.665162e-01 3.901577e-01 1.022224e-01 1.661066e-02
[43,] 0.96888335 7.838531e-01 4.111089e-01 1.108082e-01 1.851102e-02
[44,] 0.97279719 8.005879e-01 4.324218e-01 1.198323e-01 2.056695e-02
[45,] 0.97634475 8.167049e-01 4.540733e-01 1.293003e-01 2.278594e-02
[46,] 0.97954819 8.321896e-01 4.760401e-01 1.392169e-01 2.517552e-02
[47,] 0.98242904 8.470293e-01 4.982983e-01 1.495865e-01 2.774326e-02
[48,] 0.98500812 8.612121e-01 5.208240e-01 1.604129e-01 3.049674e-02
[49,] 0.98730563 8.747278e-01 5.435930e-01 1.716991e-01 3.344358e-02
[50,] 0.98934111 8.875673e-01 5.665810e-01 1.834475e-01 3.659140e-02
[51,] 0.99113348 8.997225e-01 5.897635e-01 1.956603e-01 3.994779e-02
[52,] 0.99270106 9.111868e-01 6.131161e-01 2.083386e-01 4.352034e-02
[53,] 0.99406158 9.219543e-01 6.366140e-01 2.214833e-01 4.731660e-02
[54,] 0.99523216 9.320205e-01 6.602328e-01 2.350944e-01 5.134407e-02
[55,] 0.99622939 9.413818e-01 6.839479e-01 2.491716e-01 5.561022e-02
[56,] 0.99706928 9.500354e-01 7.077347e-01 2.637138e-01 6.012242e-02
[57,] 0.99776731 9.579796e-01 7.315689e-01 2.787196e-01 6.488800e-02
[58,] 0.99833843 9.652137e-01 7.554261e-01 2.941866e-01 6.991417e-02
[59,] 0.99879706 9.717376e-01 7.792822e-01 3.101124e-01 7.520806e-02
[60,] 0.99915715 9.775522e-01 8.031132e-01 3.264935e-01 8.077670e-02
[61,] 0.99943211 9.826592e-01 8.268953e-01 3.433262e-01 8.662699e-02
[62,] 0.99963492 9.870610e-01 8.506049e-01 3.606062e-01 9.276570e-02
[63,] 0.99977805 9.907608e-01 8.742187e-01 3.783287e-01 9.919948e-02
[64,] 0.99987354 9.937623e-01 8.977137e-01 3.964883e-01 1.059348e-01
[65,] 0.99993297 9.960701e-01 9.210669e-01 4.150792e-01 1.129781e-01
$SpP
[,1] [,2] [,3] [,4] [,5]
[1,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[2,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[3,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[4,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[5,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[6,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[7,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[8,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[9,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[10,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[11,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[12,] 0.9999999 0.9999999 0.9999999 0.9999999 0.9999999
[13,] 0.9999999 0.9999999 0.9999999 0.9999999 0.9999999
[14,] 0.9999998 0.9999998 0.9999998 0.9999998 0.9999998
[15,] 0.9999997 0.9999997 0.9999997 0.9999997 0.9999997
[16,] 0.9999996 0.9999996 0.9999996 0.9999996 0.9999996
[17,] 0.9999994 0.9999994 0.9999994 0.9999994 0.9999994
[18,] 0.9999992 0.9999992 0.9999992 0.9999992 0.9999992
[19,] 0.9999990 0.9999990 0.9999990 0.9999990 0.9999990
[20,] 0.9999986 0.9999986 0.9999986 0.9999986 0.9999986
[21,] 0.9999982 0.9999982 0.9999982 0.9999982 0.9999982
[22,] 0.9999977 0.9999977 0.9999977 0.9999977 0.9999977
[23,] 0.9999971 0.9999971 0.9999971 0.9999971 0.9999971
[24,] 0.9999964 0.9999964 0.9999964 0.9999964 0.9999964
[25,] 0.9999955 0.9999955 0.9999955 0.9999955 0.9999955
[26,] 0.9999945 0.9999945 0.9999945 0.9999945 0.9999945
[27,] 0.9999933 0.9999933 0.9999933 0.9999933 0.9999933
[28,] 0.9999919 0.9999919 0.9999919 0.9999919 0.9999919
[29,] 0.9999903 0.9999903 0.9999903 0.9999903 0.9999903
[30,] 0.9999884 0.9999884 0.9999884 0.9999884 0.9999884
[31,] 0.9999863 0.9999863 0.9999863 0.9999863 0.9999863
[32,] 0.9999839 0.9999839 0.9999839 0.9999839 0.9999839
[33,] 0.9999812 0.9999812 0.9999812 0.9999812 0.9999812
[34,] 0.9999782 0.9999782 0.9999782 0.9999782 0.9999782
[35,] 0.9999747 0.9999747 0.9999747 0.9999747 0.9999747
[36,] 0.9999709 0.9999709 0.9999709 0.9999709 0.9999709
[37,] 0.9999666 0.9999666 0.9999666 0.9999666 0.9999666
[38,] 0.9999619 0.9999619 0.9999619 0.9999619 0.9999619
[39,] 0.9999566 0.9999566 0.9999566 0.9999566 0.9999566
[40,] 0.9999508 0.9999508 0.9999508 0.9999508 0.9999508
[41,] 0.9999445 0.9999445 0.9999445 0.9999445 0.9999445
[42,] 0.9999375 0.9999375 0.9999375 0.9999375 0.9999375
[43,] 0.9999299 0.9999299 0.9999299 0.9999299 0.9999299
[44,] 0.9999215 0.9999215 0.9999215 0.9999215 0.9999215
[45,] 0.9999124 0.9999124 0.9999124 0.9999124 0.9999124
[46,] 0.9999026 0.9999026 0.9999026 0.9999026 0.9999026
[47,] 0.9998919 0.9998919 0.9998919 0.9998919 0.9998919
[48,] 0.9998803 0.9998803 0.9998803 0.9998803 0.9998803
[49,] 0.9998678 0.9998678 0.9998678 0.9998678 0.9998678
[50,] 0.9998543 0.9998543 0.9998543 0.9998543 0.9998543
[51,] 0.9998398 0.9998398 0.9998398 0.9998398 0.9998398
[52,] 0.9998242 0.9998242 0.9998242 0.9998242 0.9998242
[53,] 0.9998075 0.9998075 0.9998075 0.9998075 0.9998075
[54,] 0.9997896 0.9997896 0.9997896 0.9997896 0.9997896
[55,] 0.9997705 0.9997705 0.9997705 0.9997705 0.9997705
[56,] 0.9997501 0.9997501 0.9997501 0.9997501 0.9997501
[57,] 0.9997283 0.9997283 0.9997283 0.9997283 0.9997283
[58,] 0.9997051 0.9997051 0.9997051 0.9997051 0.9997051
[59,] 0.9996804 0.9996804 0.9996804 0.9996804 0.9996804
[60,] 0.9996542 0.9996542 0.9996542 0.9996542 0.9996542
[61,] 0.9996265 0.9996265 0.9996265 0.9996265 0.9996265
[62,] 0.9995970 0.9995970 0.9995970 0.9995970 0.9995970
[63,] 0.9995659 0.9995659 0.9995659 0.9995659 0.9995659
[64,] 0.9995330 0.9995330 0.9995330 0.9995330 0.9995330
[65,] 0.9994982 0.9994982 0.9994982 0.9994982 0.9994982
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