MCMCCV: Inference and model selection for analysis of geographical...
In Sunder: Quantification of the effect of geographic versus environmental isolation on genetic differentiation
Description
Usage
Arguments
Value
Author(s)
Examples
Description
MCMC inference and model selection by cross-validation for the analysis of geographical
genetic variation
Usage
1
2
3
Arguments
gen
An array with dimensions (n,l,a) n: number of geographical locations, l:
number of loci, a: max number of alleles
D_G
A matrix of geograpical distances
D_E
A matrix of environmental distances
nit
Number of iterations
thinning
Thinning of MCMC iterations
theta.max
Upper bounds for the vector of parameters in theta. Note that
in theta, the parameters are assumed to be in this order:
(alpha,beta_G, beta_E, gamma, delta)
theta.init
Initial state of theta
run
A vector of booleans of length 3 stating which sub-model is
investigated among G+E,G,E (in this order). For example, the default value
run=c(TRUE,FALSE,FALSE) means that only one MCMC run will be performed to
estimate paremeters in the G+E model while
e.g. run=c(TRUE,FALSE,TRUE) means that MCMC runs will be
performed for models G+E and E. If n.validation.set >0
likelihoods under these two models will be returned for model selection.
ud
A vector of booleans of length 5 stating which entries in
theta=(alpha,beta_E,beta_G,gamma,delta)
will be updated in the MCMC iterations. By default all parameters in
theta will be updated. If one entry is not updated, the value used
along the MCMC simulation for this parameter is the initial value.
n.validation.set
The number of pairs (sites x locus) used as
validation set
print.pct
A boolean stating whether Fortran prints percentage
of computation achieved along each MCMC run.
Value
A list with a component named mod.lik containing likelihoods on
the validation set for the various models compared.
Author(s)
Filippo Botta, Gilles Guillot
Examples
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## Not run:
data(toydata,package='Sunder')
#### Computing options
nit <- 10^2
run <- c(1,1,1)
thinning <- 1 # max(nit/10^3,1);
ud <- c(0,1,1,0,0)
theta.init <- c(1,2,1,1,0.01)
n.validation.set <- dim(gen)[1]*dim(gen)[2]/10
theta.max <- c(10,10*max(D_G),10*max(D_E),1,0.01)
plot <- TRUE
trace <- FALSE
#### Call Sunder ####
output <- MCMCCV(gen,D_G,D_E,
nit,thinning,
theta.max,
theta.init,
run,ud,n.validation.set)
mod.lik <- output$mod.lik
tvt <- output$theta
## plotting outputs
upd=matrix(nrow=sum(run), ncol=length(theta.init), data=1)
upd[2,3]=upd[3,2]=0
plot(as.vector(D_G),as.vector(cor(t(gen[,,1]))),
bg=colorRampPalette(c("blue", "red"))(dim(D_E)[1]^2)[order(order(as.vector(D_E)))],
pch=21,
xlab='Geographic distance',
ylab='Empirical covariance genotypes')
kol=c(4,2,3)
xseq=seq(thinning,nit,thinning)
ylab=c(expression(paste(alpha)),
expression(paste(beta[D])),
expression(paste(beta[E])),
expression(paste(gamma)),
expression(paste(delta)))
par(mfrow=c(sum(run),length(theta.init)))
for (j in 1:sum(run))
{
for(k in 1:length(theta.init))
{
if (sum(upd[,k]==1)>0)
{
if(upd[j, k]==1)
{
if(exists("theta"))
ylim=c(min(tvt[k,,j],theta[k]),max(tvt[k,,j],theta[k])) else
ylim=c(min(tvt[k,,j]),max(tvt[k,,j]))
plot(0, type="n",xlab="",ylab="", xlim=c(0,nit), ylim=ylim)
lines(xseq,tvt[k,,j],col=kol[j],xlab="",ylab="")
if(exists("theta")) abline(h=theta[k],lty=2)
title(xlab="iterations")
mtext(ylab[k], side=2, line=2.3,las=1)} else plot.new()
}
}
}
print(mod.lik)
print(paste('The model achieving the highest likelihood on the validation set is:',
names(mod.lik)[order(mod.lik,decreasing=TRUE)[1]]))
theta.GE <- apply(output$theta[,,1],1,mean)
print('Posterior mean theta under model G+E:')
print(theta.GE)
theta.G <- apply(output$theta[,,2],1,mean)
theta.G[3] <- NA
print('Posterior mean theta under model G:')
print(theta.G)
theta.E <- apply(output$theta[,,3],1,mean)
theta.E[2] <- NA
print('Posterior mean theta under model E:')
print(theta.E)
## End(Not run)
Example output
Loading required package: mnormt
Computations for model 'geog+envi'
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Warning message:
In cor(t(gen[, , 1])) : the standard deviation is zero
G+E G E
-4865.776 -5043.479 -4592.177
[1] "The model achieving the highest likelihood on the validation set is: E"
[1] "Posterior mean theta under model G+E:"
alpha beta_G beta_E gamma delta
1.000000 2.044502 1.069109 1.000000 0.010000
[1] "Posterior mean theta under model G:"
alpha beta_G beta_E gamma delta
1.00000 2.08815 NA 1.00000 0.01000
[1] "Posterior mean theta under model E:"
alpha beta_G beta_E gamma delta
1.000000 NA 1.148272 1.000000 0.010000
Sunder documentation built on May 2, 2019, 2:17 p.m.
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Description Usage Arguments Value Author(s) Examples
Description
MCMC inference and model selection by cross-validation for the analysis of geographical genetic variation
Usage
1 2 3 |
Arguments
gen |
An array with dimensions (n,l,a) n: number of geographical locations, l: number of loci, a: max number of alleles |
D_G |
A matrix of geograpical distances |
D_E |
A matrix of environmental distances |
nit |
Number of iterations |
thinning |
Thinning of MCMC iterations |
theta.max |
Upper bounds for the vector of parameters in theta. Note that in theta, the parameters are assumed to be in this order: (alpha,beta_G, beta_E, gamma, delta) |
theta.init |
Initial state of theta |
run |
A vector of booleans of length 3 stating which sub-model is
investigated among G+E,G,E (in this order). For example, the default value
|
ud |
A vector of booleans of length 5 stating which entries in theta=(alpha,beta_E,beta_G,gamma,delta) will be updated in the MCMC iterations. By default all parameters in theta will be updated. If one entry is not updated, the value used along the MCMC simulation for this parameter is the initial value. |
n.validation.set |
The number of pairs (sites x locus) used as validation set |
print.pct |
A boolean stating whether Fortran prints percentage of computation achieved along each MCMC run. |
Value
A list with a component named mod.lik containing likelihoods on the validation set for the various models compared.
Author(s)
Filippo Botta, Gilles Guillot
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | ## Not run:
data(toydata,package='Sunder')
#### Computing options
nit <- 10^2
run <- c(1,1,1)
thinning <- 1 # max(nit/10^3,1);
ud <- c(0,1,1,0,0)
theta.init <- c(1,2,1,1,0.01)
n.validation.set <- dim(gen)[1]*dim(gen)[2]/10
theta.max <- c(10,10*max(D_G),10*max(D_E),1,0.01)
plot <- TRUE
trace <- FALSE
#### Call Sunder ####
output <- MCMCCV(gen,D_G,D_E,
nit,thinning,
theta.max,
theta.init,
run,ud,n.validation.set)
mod.lik <- output$mod.lik
tvt <- output$theta
## plotting outputs
upd=matrix(nrow=sum(run), ncol=length(theta.init), data=1)
upd[2,3]=upd[3,2]=0
plot(as.vector(D_G),as.vector(cor(t(gen[,,1]))),
bg=colorRampPalette(c("blue", "red"))(dim(D_E)[1]^2)[order(order(as.vector(D_E)))],
pch=21,
xlab='Geographic distance',
ylab='Empirical covariance genotypes')
kol=c(4,2,3)
xseq=seq(thinning,nit,thinning)
ylab=c(expression(paste(alpha)),
expression(paste(beta[D])),
expression(paste(beta[E])),
expression(paste(gamma)),
expression(paste(delta)))
par(mfrow=c(sum(run),length(theta.init)))
for (j in 1:sum(run))
{
for(k in 1:length(theta.init))
{
if (sum(upd[,k]==1)>0)
{
if(upd[j, k]==1)
{
if(exists("theta"))
ylim=c(min(tvt[k,,j],theta[k]),max(tvt[k,,j],theta[k])) else
ylim=c(min(tvt[k,,j]),max(tvt[k,,j]))
plot(0, type="n",xlab="",ylab="", xlim=c(0,nit), ylim=ylim)
lines(xseq,tvt[k,,j],col=kol[j],xlab="",ylab="")
if(exists("theta")) abline(h=theta[k],lty=2)
title(xlab="iterations")
mtext(ylab[k], side=2, line=2.3,las=1)} else plot.new()
}
}
}
print(mod.lik)
print(paste('The model achieving the highest likelihood on the validation set is:',
names(mod.lik)[order(mod.lik,decreasing=TRUE)[1]]))
theta.GE <- apply(output$theta[,,1],1,mean)
print('Posterior mean theta under model G+E:')
print(theta.GE)
theta.G <- apply(output$theta[,,2],1,mean)
theta.G[3] <- NA
print('Posterior mean theta under model G:')
print(theta.G)
theta.E <- apply(output$theta[,,3],1,mean)
theta.E[2] <- NA
print('Posterior mean theta under model E:')
print(theta.E)
## End(Not run)
|
Example output
Loading required package: mnormt
Computations for model 'geog+envi'
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[1] 20
% of computations
[1] 21
% of computations
[1] 22
% of computations
[1] 23
% of computations
[1] 24
% of computations
[1] 25
% of computations
[1] 26
% of computations
[1] 27
% of computations
[1] 28
% of computations
[1] 29
% of computations
[1] 30
% of computations
[1] 31
% of computations
[1] 32
% of computations
[1] 33
% of computations
[1] 34
% of computations
[1] 35
% of computations
[1] 36
% of computations
[1] 37
% of computations
[1] 38
% of computations
[1] 39
% of computations
[1] 40
% of computations
[1] 41
% of computations
[1] 42
% of computations
[1] 43
% of computations
[1] 44
% of computations
[1] 45
% of computations
[1] 46
% of computations
[1] 47
% of computations
[1] 48
% of computations
[1] 49
% of computations
[1] 50
% of computations
[1] 51
% of computations
[1] 52
% of computations
[1] 53
% of computations
[1] 54
% of computations
[1] 55
% of computations
[1] 56
% of computations
[1] 57
% of computations
[1] 58
% of computations
[1] 59
% of computations
[1] 60
% of computations
[1] 61
% of computations
[1] 62
% of computations
[1] 63
% of computations
[1] 64
% of computations
[1] 65
% of computations
[1] 66
% of computations
[1] 67
% of computations
[1] 68
% of computations
[1] 69
% of computations
[1] 70
% of computations
[1] 71
% of computations
[1] 72
% of computations
[1] 73
% of computations
[1] 74
% of computations
[1] 75
% of computations
[1] 76
% of computations
[1] 77
% of computations
[1] 78
% of computations
[1] 79
% of computations
[1] 80
% of computations
[1] 81
% of computations
[1] 82
% of computations
[1] 83
% of computations
[1] 84
% of computations
[1] 85
% of computations
[1] 86
% of computations
[1] 87
% of computations
[1] 88
% of computations
[1] 89
% of computations
[1] 90
% of computations
[1] 91
% of computations
[1] 92
% of computations
[1] 93
% of computations
[1] 94
% of computations
[1] 95
% of computations
[1] 96
% of computations
[1] 97
% of computations
[1] 98
% of computations
[1] 99
% of computations
[1] 100
Computations for model 'envi'
% of computations
[1] 1
% of computations
[1] 2
% of computations
[1] 3
% of computations
[1] 4
% of computations
[1] 5
% of computations
[1] 6
% of computations
[1] 7
% of computations
[1] 8
% of computations
[1] 9
% of computations
[1] 10
% of computations
[1] 11
% of computations
[1] 12
% of computations
[1] 13
% of computations
[1] 14
% of computations
[1] 15
% of computations
[1] 16
% of computations
[1] 17
% of computations
[1] 18
% of computations
[1] 19
% of computations
[1] 20
% of computations
[1] 21
% of computations
[1] 22
% of computations
[1] 23
% of computations
[1] 24
% of computations
[1] 25
% of computations
[1] 26
% of computations
[1] 27
% of computations
[1] 28
% of computations
[1] 29
% of computations
[1] 30
% of computations
[1] 31
% of computations
[1] 32
% of computations
[1] 33
% of computations
[1] 34
% of computations
[1] 35
% of computations
[1] 36
% of computations
[1] 37
% of computations
[1] 38
% of computations
[1] 39
% of computations
[1] 40
% of computations
[1] 41
% of computations
[1] 42
% of computations
[1] 43
% of computations
[1] 44
% of computations
[1] 45
% of computations
[1] 46
% of computations
[1] 47
% of computations
[1] 48
% of computations
[1] 49
% of computations
[1] 50
% of computations
[1] 51
% of computations
[1] 52
% of computations
[1] 53
% of computations
[1] 54
% of computations
[1] 55
% of computations
[1] 56
% of computations
[1] 57
% of computations
[1] 58
% of computations
[1] 59
% of computations
[1] 60
% of computations
[1] 61
% of computations
[1] 62
% of computations
[1] 63
% of computations
[1] 64
% of computations
[1] 65
% of computations
[1] 66
% of computations
[1] 67
% of computations
[1] 68
% of computations
[1] 69
% of computations
[1] 70
% of computations
[1] 71
% of computations
[1] 72
% of computations
[1] 73
% of computations
[1] 74
% of computations
[1] 75
% of computations
[1] 76
% of computations
[1] 77
% of computations
[1] 78
% of computations
[1] 79
% of computations
[1] 80
% of computations
[1] 81
% of computations
[1] 82
% of computations
[1] 83
% of computations
[1] 84
% of computations
[1] 85
% of computations
[1] 86
% of computations
[1] 87
% of computations
[1] 88
% of computations
[1] 89
% of computations
[1] 90
% of computations
[1] 91
% of computations
[1] 92
% of computations
[1] 93
% of computations
[1] 94
% of computations
[1] 95
% of computations
[1] 96
% of computations
[1] 97
% of computations
[1] 98
% of computations
[1] 99
% of computations
[1] 100
Warning message:
In cor(t(gen[, , 1])) : the standard deviation is zero
G+E G E
-4865.776 -5043.479 -4592.177
[1] "The model achieving the highest likelihood on the validation set is: E"
[1] "Posterior mean theta under model G+E:"
alpha beta_G beta_E gamma delta
1.000000 2.044502 1.069109 1.000000 0.010000
[1] "Posterior mean theta under model G:"
alpha beta_G beta_E gamma delta
1.00000 2.08815 NA 1.00000 0.01000
[1] "Posterior mean theta under model E:"
alpha beta_G beta_E gamma delta
1.000000 NA 1.148272 1.000000 0.010000
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