Description Usage Arguments Details Value Author(s) References See Also Examples
This function performs multivariate parameter estimation based on summary statistics using an ABC algorithm. The algorithms implemented are rejection sampling, and local linear or nonlinear (neural network) regression. A conditional heteroscedastic model is available for the latter two algorithms.
1 2 3 4 
target 
a vector of the observed summary statistics. 
param 
a vector, matrix or data frame of the simulated parameter values,
i.e. the dependent variable(s) when 
sumstat 
a vector, matrix or data frame of the simulated summary statistics,
i.e. the independent variables when 
tol 
tolerance, the required proportion of points accepted nearest the target values. 
method 
a character string indicating the type of ABC algorithm to be
applied. Possible values are 
hcorr 
logical, the conditional heteroscedastic model is applied if

transf 
a vector of character strings indicating the kind of transformation
to be applied to the parameter values. The possible values are

logit.bounds 
a matrix of bounds if 
subset 
a logical expression indicating elements or rows to keep. Missing
values in 
kernel 
a character string specifying the kernel to be used when

numnet 
the number of neural networks when 
sizenet 
the number of units in the hidden layer. Defaults to 5. Can be zero
if there are no skiplayer units. See 
lambda 
a numeric vector or a single value indicating the weight decay when

trace 
logical, if 
maxit 
numeric, the maximum number of iterations. Defaults to 500. Applies
only when 
... 
other arguments passed to 
These ABC algorithms generate random samples from the posterior
distributions of one or more parameters of interest, θ_1,
θ_2, …, θ_n. To apply any of these algorithms, (i) data
sets have to be simulated based on random draws from the prior
distributions of the θ_i's, (ii) from these data sets, a set
of summary statistics have to be calculated, S(y), (iii) the
same summary statistics have to be calculated from the observed data,
S(y_0), and (iv) a tolerance rate must be chosen
(tol
). See cv4abc
for a crossvalidation tool
that may help in choosing the tolerance rate.
When method
is "rejection"
, the simple rejection
algorithm is used. Parameter values are accepted if the Euclidean
distance between S(y) and S(y_0) is sufficiently
small. The percentage of accepted simulations is determined by
tol
. When method
is "loclinear"
, a local linear
regression method corrects for the imperfect match between S(y)
and S(y_0). The accepted parameter values are weighted by a
smooth function (kernel
) of the distance between S(y) and
S(y_0), and corrected according to a linear transform:
θ^{*} = θ  b(S(y)  S(y_0)). θ^{*}'s
represent samples form the posterior distribution. This method calls
the function lsfit
from the stats
library. When
using the "loclinear"
method, a warning about the collinearity
of the design matrix of the regression might be issued. In that
situation, we recommend to rather use the related "ridge"
method that performs locallinear ridge regression and deals with the
collinearity issue. The nonlinear regression correction method
("neuralnet"
) uses a nonlinear regression to minimize the
departure from nonlinearity using the function nnet
.
The posterior samples of parameters based on the rejection algorithm
are returned as well, even when one of the regression algorithms is
used.
Several additional arguments can be specified when method
is
"neuralnet"
. The method is based on the function
nnet
from the library nnet
, which fits
singlehiddenlayer neural networks. numnet
defines the
number of neural networks, thus the function nnet
is
called numnet
number of times. Predictions from different
neural networks can be rather different, so the median of the
predictions from all neural networks is used to provide a global
prediction. The choice of the number of neural networks is a tradeoff
between speed and accuracy. The default is set to 10 networks. The
number of units in the hidden layer can be specified via
sizenet
. Selecting the number of hidden units is similar to
selecting the independent variables in a linear or nonlinear
regression. Thus, it corresponds to the complexity of the
network. There is several rule of thumb to choose the number of hidden
units, but they are often unreliable. Generally speaking, the optimal
choice of sizenet
depends on the dimensionality, thus the
number of statistics in sumstat
. It can be zero when there are
no skiplayer units. See also nnet
for more details. The
method
"neuralnet"
is recommended when dealing with a
large number of summary statistics.
If method
is "loclinear"
, "neuralnet"
or "ridge"
, a
correction for heteroscedasticity is applied by default (hcorr =
TRUE
).
Parameters maybe transformed priori to estimation. The type of
transformation is defined by transf
. The length of
transf
is normally the same as the number of parameters. If
only one value is given, that same transformation is applied to all
parameters and the user is warned. When a parameter transformation
used, the parameters are backtransformed to their original scale
after the regression estimation. No transformations can be applied
when method
is "rejection"
.
Using names for the parameters and summary statistics is strongly
recommended. Names can be supplied as names
or
colnames
to param
and sumstat
(and
target
). If no names are supplied, P1, P2, ... is assigned to
parameters and S1, S2, ... to summary statistics and the user is
warned.
The returned value is an object of class "abc"
, containing the
following components:
adj.values 
The regression adjusted values, when 
unadj.values 
The unadjusted values that correspond to

ss 
The summary statistics for the accepted simulations. 
weights 
The regression weights, when 
residuals 
The residuals from the regression when 
dist 
The Euclidean distances for the accepted simulations. 
call 
The original call. 
na.action 
A logical vector indicating the elements or rows that
were excluded, including both 
region 
A logical expression indicting the elements or rows that were accepted. 
transf 
The parameter transformations that have been used. 
logit.bounds 
The bounds, if transformation was 
kernel 
The kernel used. 
method 
Character string indicating the 
lambda 
A numeric vector of length 
numparam 
Number of parameters used. 
numstat 
Number of summary statistics used. 
aic 
The sum of the AIC of the 
bic 
The same but with the BIC. 
names 
A list with two elements: 
Katalin Csillery, Olivier Francois and Michael Blum with some initial code from Mark Beaumont.
Pritchard, J.K., and M.T. Seielstad and A. PerezLezaun and M.W. Feldman (1999) Population growth of human Y chromosomes: a study of Y chromosome microsatellites. Molecular Biology and Evolution, 16, 1791–1798.
Beaumont, M.A., Zhang, W., and Balding, D.J. (2002) Approximate Bayesian Computation in Population Genetics, Genetics, 162, 20252035.
Blum, M.G.B. and Francois, O. (2010) Nonlinear regression models for Approximate Bayesian Computation. Statistics and Computing 20, 6373.
Csillery, K., M.G.B. Blum, O.E. Gaggiotti and O. Francois (2010) Approximate Bayesian Computation (ABC) in practice. Trends in Ecology and Evolution, 25, 410418.
summary.abc
, hist.abc
,
plot.abc
, lsfit
, nnet
,
cv4abc
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  require(abc.data)
data(musigma2)
?musigma2
## The rejection algorithm
##
rej < abc(target=stat.obs, param=par.sim, sumstat=stat.sim, tol=.1, method =
"rejection")
## ABC with local linear regression correction without/with correction
## for heteroscedasticity
##
lin < abc(target=stat.obs, param=par.sim, sumstat=stat.sim, tol=.1, hcorr =
FALSE, method = "loclinear", transf=c("none","log"))
linhc < abc(target=stat.obs, param=par.sim, sumstat=stat.sim, tol=.1, method =
"loclinear", transf=c("none","log"))
## posterior summaries
##
linsum < summary(linhc, intvl = .9)
linsum
## compare with the rejection sampling
summary(linhc, unadj = TRUE, intvl = .9)
## posterior histograms
##
hist(linhc, breaks=30, caption=c(expression(mu),
expression(sigma^2)))
## or send histograms to a pdf file
hist(linhc, file="linhc", breaks=30, caption=c(expression(mu),
expression(sigma^2)))
## diagnostic plots: compare the 2 'abc' objects: "loclinear",
## "loclinear" with correction for heteroscedasticity
##
plot(lin, param=par.sim)
plot(linhc, param=par.sim)
## example illustrates how to add "true" parameter values to a plot
##
postmod < c(post.mu[match(max(post.mu[,2]), post.mu[,2]),1],
post.sigma2[match(max(post.sigma2[,2]), post.sigma2[,2]),1])
plot(linhc, param=par.sim, true=postmod)
## artificial example to show how to use the logit tranformations
##
myp < data.frame(par1=runif(1000,1,1),par2=rnorm(1000),par3=runif(1000,0,2))
mys < myp+rnorm(1000,sd=.1)
myt < c(0,0,1.5)
lin2 < abc(target=myt, param=myp, sumstat=mys, tol=.1, method =
"loclinear", transf=c("logit","none","logit"),logit.bounds = rbind(c(1,
1), c(NA, NA), c(0, 2)))
summary(lin2)

Loading required package: abc.data
Loading required package: nnet
Loading required package: quantreg
Loading required package: SparseM
Attaching package: 'SparseM'
The following object is masked from 'package:base':
backsolve
Loading required package: MASS
Loading required package: locfit
locfit 1.59.1 20130322
musigma2 package:abc.data R Documentation
_A _s_e_t _o_f _o_b_j_e_c_t_s _u_s_e_d _t_o _e_s_t_i_m_a_t_e _t_h_e _p_o_p_u_l_a_t_i_o_n _m_e_a_n _a_n_d _v_a_r_i_a_n_c_e _i_n _a
_G_a_u_s_s_i_a_n _m_o_d_e_l _w_i_t_h _A_B_C (_s_e_e _t_h_e _v_i_g_n_e_t_t_e _o_f _t_h_e '_a_b_c' _p_a_c_k_a_g_e _f_o_r _m_o_r_e
_d_e_t_a_i_l_s).
_D_e_s_c_r_i_p_t_i_o_n:
'musigma2' loads in five R objects: 'par.sim' is a data frame and
contains the parameter values of the simulated data sets, 'stat'
is a data frame and contains the simulated summary statistics,
'stat.obs' is a data frame and contains the observed summary
statistics, 'post.mu' and 'post.sigma2' are data frames and
contain the true posterior distributions for the two parameters of
interest, mu and sigma^2, respectively.
_U_s_a_g_e:
data(musigma2)
_F_o_r_m_a_t:
The 'par.sim' data frame contains the following columns:
'mu' The population mean.
'sigma2' The population variance.
The 'stat.sim' and 'stat.obs' data frames contain the following
columns:
'mean' The sample mean.
'var' The logarithm of the sample variance.
The 'post.mu' and 'post.sigma2' data frames contain the following
columns:
'x' the coordinates of the points where the density is estimated.
'y' the posterior density values.
_D_e_t_a_i_l_s:
The prior of sigma^2 is an inverse chi^2 distribution with one
degree of freedom. The prior of mu is a normal distribution with
variance of sigma^2. For this simple example, the closed form of
the posterior distribution is available.
_S_o_u_r_c_e:
The observed statistics are the mean and variance of the sepal of
_Iris setosa_, estimated from part of the 'iris' data.
The data were collected by Anderson, Edgar.
_R_e_f_e_r_e_n_c_e_s:
Anderson, E. (1935). The irises of the Gaspe Peninsula, _Bulletin
of the American Iris Society_, *59*, 25.
Call:
abc(target = stat.obs, param = par.sim, sumstat = stat.sim, tol = 0.1,
method = "loclinear", transf = c("none", "log"))
Data:
abc.out$adj.values (1000 posterior samples)
Weights:
abc.out$weights
mu sigma2
Min.: 3.2091 0.0855
Weighted 5 % Perc.: 3.3250 0.1187
Weighted Median: 3.4198 0.1636
Weighted Mean: 3.4194 0.1674
Weighted Mode: 3.4225 0.1531
Weighted 95 % Perc.: 3.5144 0.2287
Max.: 3.5982 0.3108
mu sigma2
Min.: 3.20912108 0.08552166
Weighted 5 % Perc.: 3.32503009 0.11871687
Weighted Median: 3.41976457 0.16356863
Weighted Mean: 3.41943316 0.16743565
Weighted Mode: 3.42252573 0.15309016
Weighted 95 % Perc.: 3.51441169 0.22874721
Max.: 3.59822201 0.31079180
Call:
abc(target = stat.obs, param = par.sim, sumstat = stat.sim, tol = 0.1,
method = "loclinear", transf = c("none", "log"))
Data:
abc.out$unadj.values (1000 posterior samples)
mu sigma2
Min.: 2.4798 0.0651
5% Perc.: 2.6745 0.1245
Median: 3.1856 0.2759
Mean: 3.2173 0.2882
Mode: 3.1079 0.2468
95% Perc.: 3.8752 0.5007
Max.: 4.2307 0.6950
Call:
abc(target = myt, param = myp, sumstat = mys, tol = 0.1, method = "loclinear",
transf = c("logit", "none", "logit"), logit.bounds = rbind(c(1,
1), c(NA, NA), c(0, 2)))
Data:
abc.out$adj.values (100 posterior samples)
Weights:
abc.out$weights
par1 par2 par3
Min.: 0.3497 0.3278 1.1899
Weighted 2.5 % Perc.: 0.2335 0.2351 1.2656
Weighted Median: 0.0056 0.0100 1.5337
Weighted Mean: 0.0076 0.0077 1.5395
Weighted Mode: 0.0199 0.0194 1.5358
Weighted 97.5 % Perc.: 0.2359 0.2268 1.8149
Max.: 0.3094 0.2588 1.8408
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