compute.p.values: Compute Empirical p-values
In DetSel: A Computer Program to Detect Markers Responding to Selection
Description
Usage
Arguments
Details
Value
References
Examples
Description
This command compute empirical p-values.
Usage
1
compute.p.values(x.range,y.range,n.bins,m)
Arguments
x.range
the range of values in the x-axis, respectively, which takes the default values x.range = c(-1,1)
y.range
the range of values in the y-axis, respectively, which takes the default values y.range = c(-1,1)
n.bins
the size of the 2-dimensional array of n x n square cells used to bin the F_1 and F_2 estimates, which takes the default value n.bins = c(100,100)
m
the smoothing parameters of the ASH algorithm, which takes the default value m = c(2,2)
Details
compute.p.values(x.range,y.range,n.bins,m) produces an output file, named ‘P-values_i_j.dat’, with the P-value associated with each observation. To that end, the cumulative distribution function (CDF) is evaluated empirically from the joint distribution of all the pairwise observations (F_1,F_2) within the simulated dataset. Then, the empirical P-value for a given marker locus i is calculated as one minus the CDF evaluated at locus i. For multi-allelic markers, the joint distribution of all the pairwise observations (F_1,F_2) within the simulated dataset is computed from a 2-dimensional array, where the (F_1,F_2) pairs are binned, and then smoothed using the Average Shifted Histogram (ASH) algorithm (Scott 1992) as implemented in the "ash" R package. Because the distribution of (F_1,F_2) estimates for bi-allelic markers is discontinuous with many ties, the CDF is computed instead by enumerating all (F_1,F_2) pairs in the simulated data.
Value
The output files are saved in the working directory.
References
Scott, D. W. (1992) Multivariate density estimation: theory, practice, and visualization, John Wiley, New York.
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
## This is to generate an example file in the working directory.
make.example.files()
## This will read an input file named 'data.dat' that contains co-dominant markers,
## and a maximum allele frequency of 0.99 will be applied (i.e., by removing
## marker loci in the observed and simulated datasets that have an allele with
## frequency larger than 0.99).
read.data(infile = 'data.dat',dominance = FALSE,maf = 0.99)
## The following command line executes the simulations:
run.detsel(example = TRUE)
## This compute empirical \emph{P}-values, assuming a range of values from -1 to 1
## in both dimensions, a grid of 50 x 50 bins, and a smoothing parameter m = 3
## in both dimensions.
compute.p.values(x.range = c(-1,1),y.range = c(-1,1),n.bins = c(50,50),m = c(3,3))
## This is to clean up the working directory.
remove.example.files()
Example output
[1] TRUE
Read 967 items
The data file data.dat contains 100 loci, with 2-5 alleles per locus, and 2 populations
The average values of population-specific measures of differentiation are:
----------------------------------------------
Pair F_1 F_2
1-2 0.0847 0.054
----------------------------------------------
The program will now create 1 simulation files. Please wait, this can take some time...
Simulating data in output file: `Pair_1_2_50_50.dat`...
All the simulations have been completed.
The difference between observed and simulated values of population-specific measures of differentiation are:
-------------------------------------------------------------------------
Pair F_1 (obs) F_1 (sim) F_2 (obs) F_2 (sim)
Pair_1_2 0.08468 0.08221 0.05398 0.05336
-------------------------------------------------------------------------
Computing p-values. Please wait, this can take some time...
The p-values for each locus in population pair 1-2 are:
-------------------
Locus P-value
1 1 2.28003e-03
2 2 8.62436e-01
3 3 9.41367e-01
4 4 2.38057e-01
5 5 8.62436e-01
6 6 8.48481e-01
7 7 5.75242e-01
8 8 4.26309e-01
9 9 3.45494e-01
10 10 8.48481e-01
11 11 9.70565e-01
12 12 4.96856e-01
13 13 3.03687e-01
14 14 8.14226e-01
15 15 7.91609e-01
16 16 7.69765e-01
17 17 4.87926e-01
18 18 6.06311e-01
19 19 7.91479e-01
20 20 6.73716e-01
21 21 8.62436e-01
22 22 8.62436e-01
23 23 6.69686e-01
24 24 7.43397e-01
25 25 2.04455e-01
26 26 7.57298e-01
27 27 4.45516e-01
28 28 0.00000e+00
29 29 1.22689e-01
30 30 4.16806e-01
31 31 8.65356e-01
32 32 7.17112e-01
33 33 8.62436e-01
34 34 6.38958e-01
35 35 1.62445e-01
36 36 4.35837e-01
37 37 2.83931e-01
38 38 8.62436e-01
39 39 5.47798e-01
40 40 3.14233e-01
41 41 9.00706e-01
42 42 8.14226e-01
43 43 8.88106e-01
44 44 0.00000e+00
45 45 8.65068e-01
46 46 5.10808e-01
47 47 7.49136e-01
48 48 8.32038e-01
49 49 5.47798e-01
50 50 3.45494e-01
51 51 6.69686e-01
52 52 6.56119e-01
53 53 8.88106e-01
54 54 9.79976e-02
55 55 9.38388e-01
56 56 0.00000e+00
57 57 2.36165e-02
58 58 9.70565e-01
59 59 7.10336e-01
60 60 9.67022e-01
61 61 6.19352e-01
62 62 2.05534e-02
63 63 2.65552e-01
64 64 8.82540e-01
65 65 7.29645e-01
66 66 0.00000e+00
67 67 3.34841e-01
68 68 7.69765e-01
69 69 6.38958e-01
70 70 7.29645e-01
71 71 5.22876e-01
72 72 8.92420e-01
73 73 8.48481e-01
74 74 4.45516e-01
75 75 5.60599e-01
76 76 8.48481e-01
77 77 3.08051e-01
78 78 8.65356e-01
79 79 9.38388e-01
80 80 9.41367e-01
81 81 9.13864e-01
82 82 4.26309e-01
83 83 1.99762e-01
84 84 8.62436e-01
85 85 9.41367e-01
86 86 6.91531e-01
87 87 9.13864e-01
88 88 7.91479e-01
89 89 5.75242e-01
90 90 7.10336e-01
91 91 0.00000e+00
92 92 6.91531e-01
93 93 1.69613e-02
94 94 4.65838e-01
95 95 9.41367e-01
96 96 3.67965e-03
97 97 -2.22045e-16
98 98 9.00915e-03
99 99 2.66200e-03
100 100 -2.22045e-16
-------------------
The above results are saved in file: P-values_1_2.dat
DetSel documentation built on Feb. 17, 2020, 5:09 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value References Examples
Description
This command compute empirical p-values.
Usage
1 | compute.p.values(x.range,y.range,n.bins,m)
|
Arguments
x.range |
the range of values in the x-axis, respectively, which takes the default values x.range = c(-1,1) |
y.range |
the range of values in the y-axis, respectively, which takes the default values y.range = c(-1,1) |
n.bins |
the size of the 2-dimensional array of n x n square cells used to bin the F_1 and F_2 estimates, which takes the default value n.bins = c(100,100) |
m |
the smoothing parameters of the ASH algorithm, which takes the default value m = c(2,2) |
Details
compute.p.values(x.range,y.range,n.bins,m) produces an output file, named ‘P-values_i_j.dat’, with the P-value associated with each observation. To that end, the cumulative distribution function (CDF) is evaluated empirically from the joint distribution of all the pairwise observations (F_1,F_2) within the simulated dataset. Then, the empirical P-value for a given marker locus i is calculated as one minus the CDF evaluated at locus i. For multi-allelic markers, the joint distribution of all the pairwise observations (F_1,F_2) within the simulated dataset is computed from a 2-dimensional array, where the (F_1,F_2) pairs are binned, and then smoothed using the Average Shifted Histogram (ASH) algorithm (Scott 1992) as implemented in the "ash" R package. Because the distribution of (F_1,F_2) estimates for bi-allelic markers is discontinuous with many ties, the CDF is computed instead by enumerating all (F_1,F_2) pairs in the simulated data.
Value
The output files are saved in the working directory.
References
Scott, D. W. (1992) Multivariate density estimation: theory, practice, and visualization, John Wiley, New York.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ## This is to generate an example file in the working directory.
make.example.files()
## This will read an input file named 'data.dat' that contains co-dominant markers,
## and a maximum allele frequency of 0.99 will be applied (i.e., by removing
## marker loci in the observed and simulated datasets that have an allele with
## frequency larger than 0.99).
read.data(infile = 'data.dat',dominance = FALSE,maf = 0.99)
## The following command line executes the simulations:
run.detsel(example = TRUE)
## This compute empirical \emph{P}-values, assuming a range of values from -1 to 1
## in both dimensions, a grid of 50 x 50 bins, and a smoothing parameter m = 3
## in both dimensions.
compute.p.values(x.range = c(-1,1),y.range = c(-1,1),n.bins = c(50,50),m = c(3,3))
## This is to clean up the working directory.
remove.example.files()
|
Example output
[1] TRUE
Read 967 items
The data file data.dat contains 100 loci, with 2-5 alleles per locus, and 2 populations
The average values of population-specific measures of differentiation are:
----------------------------------------------
Pair F_1 F_2
1-2 0.0847 0.054
----------------------------------------------
The program will now create 1 simulation files. Please wait, this can take some time...
Simulating data in output file: `Pair_1_2_50_50.dat`...
All the simulations have been completed.
The difference between observed and simulated values of population-specific measures of differentiation are:
-------------------------------------------------------------------------
Pair F_1 (obs) F_1 (sim) F_2 (obs) F_2 (sim)
Pair_1_2 0.08468 0.08221 0.05398 0.05336
-------------------------------------------------------------------------
Computing p-values. Please wait, this can take some time...
The p-values for each locus in population pair 1-2 are:
-------------------
Locus P-value
1 1 2.28003e-03
2 2 8.62436e-01
3 3 9.41367e-01
4 4 2.38057e-01
5 5 8.62436e-01
6 6 8.48481e-01
7 7 5.75242e-01
8 8 4.26309e-01
9 9 3.45494e-01
10 10 8.48481e-01
11 11 9.70565e-01
12 12 4.96856e-01
13 13 3.03687e-01
14 14 8.14226e-01
15 15 7.91609e-01
16 16 7.69765e-01
17 17 4.87926e-01
18 18 6.06311e-01
19 19 7.91479e-01
20 20 6.73716e-01
21 21 8.62436e-01
22 22 8.62436e-01
23 23 6.69686e-01
24 24 7.43397e-01
25 25 2.04455e-01
26 26 7.57298e-01
27 27 4.45516e-01
28 28 0.00000e+00
29 29 1.22689e-01
30 30 4.16806e-01
31 31 8.65356e-01
32 32 7.17112e-01
33 33 8.62436e-01
34 34 6.38958e-01
35 35 1.62445e-01
36 36 4.35837e-01
37 37 2.83931e-01
38 38 8.62436e-01
39 39 5.47798e-01
40 40 3.14233e-01
41 41 9.00706e-01
42 42 8.14226e-01
43 43 8.88106e-01
44 44 0.00000e+00
45 45 8.65068e-01
46 46 5.10808e-01
47 47 7.49136e-01
48 48 8.32038e-01
49 49 5.47798e-01
50 50 3.45494e-01
51 51 6.69686e-01
52 52 6.56119e-01
53 53 8.88106e-01
54 54 9.79976e-02
55 55 9.38388e-01
56 56 0.00000e+00
57 57 2.36165e-02
58 58 9.70565e-01
59 59 7.10336e-01
60 60 9.67022e-01
61 61 6.19352e-01
62 62 2.05534e-02
63 63 2.65552e-01
64 64 8.82540e-01
65 65 7.29645e-01
66 66 0.00000e+00
67 67 3.34841e-01
68 68 7.69765e-01
69 69 6.38958e-01
70 70 7.29645e-01
71 71 5.22876e-01
72 72 8.92420e-01
73 73 8.48481e-01
74 74 4.45516e-01
75 75 5.60599e-01
76 76 8.48481e-01
77 77 3.08051e-01
78 78 8.65356e-01
79 79 9.38388e-01
80 80 9.41367e-01
81 81 9.13864e-01
82 82 4.26309e-01
83 83 1.99762e-01
84 84 8.62436e-01
85 85 9.41367e-01
86 86 6.91531e-01
87 87 9.13864e-01
88 88 7.91479e-01
89 89 5.75242e-01
90 90 7.10336e-01
91 91 0.00000e+00
92 92 6.91531e-01
93 93 1.69613e-02
94 94 4.65838e-01
95 95 9.41367e-01
96 96 3.67965e-03
97 97 -2.22045e-16
98 98 9.00915e-03
99 99 2.66200e-03
100 100 -2.22045e-16
-------------------
The above results are saved in file: P-values_1_2.dat
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.