\newcommand{\bm}[1]{\boldsymbol{#1}} \newcommand{\tth}{\bm{\theta}} \newcommand{\pphi}{\bm{\phi}} \newcommand{\eeta}{\bm{\eta}} \newcommand{\Y}{\bm{Y}} \newcommand{\y}{\bm{y}} \newcommand{\B}{\bm{B}} \renewcommand{\a}{\alpha} \renewcommand{\b}{\beta} \newcommand{\g}{\gamma} \newcommand{\rv}[3][1]{#2_{#1},\ldots,#2_{#3}} \newcommand{\dr}{\bm{\Lambda}} \newcommand{\df}{\bm{\Sigma}} \newcommand{\dt}{\Delta t} \newcommand{\ud}{\mathrm{d}} \newcommand{\var}{\mathrm{var}} \newcommand{\cov}{\mathrm{cov}} \newcommand{\N}{\mathcal{N}} \newcommand{\Ym}{\Y_{\mathrm{miss}}} \newcommand{\iid}{\stackrel{\mathrm{iid}}{\sim}} \newcommand{\ind}{\stackrel{\mathrm{ind}}{\sim}}
A $d$-dimensional stochastic differential equation (SDE) $\Y_t = (Y_{1t}, \ldots, Y_{dt})$ is written as $$ \ud \Y_t = \dr_{\tth}(\Y_t)\,\ud t + \df_{\tth}(\Y_t)^{1/2}\,\ud \B_t, $$ where $\dr_{\tth}(\y)$ and $\df_{\tth}(\y)$ are the drift and diffusion functions, and $\B_t = (B_{1t}, \ldots, B_{dt})$ is $d$-dimensional Brownian motion. The msde package implements a Markov Chain Monte Carlo (MCMC) algorithm to sample from the posterior distribution $p(\tth \mid \Y)$ of the parameters given discrete observations $\Y = (\Y_0, \ldots, \Y_N)$ recorded at times $t_0, \ldots, t_N$, with some of the $d$ components of $\Y_t$ possibly latent. To do this efficiently, msde requires on-the-fly C++ compiling of user-specified models. Instructions for setting up R to compile C++ code are provided in the Installation section.
sde.model
objectThe SDE model used throughout this vignette is the so-called Lotka-Volterra predator-prey model. Let $H_t$ and $L_t$ denote the number of Hare and Lynx at time $t$ coexisting in a given habitat. The Lotka-Volterra SDE describing the interactions between these two animal populations is given by [@golightly-wilkinson10]: $$ \begin{bmatrix} \mathrm{d} H_t \ \mathrm{d} L_t \end{bmatrix} = \begin{bmatrix} \a H_t - \b H_tL_t \ \b H_tL_t - \g L_t \end{bmatrix}\, \mathrm{d} t + \begin{bmatrix} \a H_t + \b H_tL_t & -\b H_tL_t \ -\b H_tL_t & \b H_tL_t + \g L_t\end{bmatrix}^{1/2} \begin{bmatrix} \mathrm{d} B_{1t} \ \mathrm{d} B_{2t} \end{bmatrix}. $$ Thus we have $d = 2$, $\Y_t = (H_t, L_t)$, and $\tth = (\a, \b, \g)$.
sdeModel
class definitionIn order to build this model in C++, we create a header file lotvolModel.h
containing the class definition for an sdeModel
object. The basic structure of this class is given below:
```{Rcpp, eval = FALSE} // sde model object class sdeModel { public: static const int nParams = 3; // number of model parameters static const int nDims = 2; // number of sde dimensions static const bool sdDiff = false; // whether diffusion function is on sd or var scale static const bool diagDiff = false; // whether diffusion function is diagonal void sdeDr(double dr, double x, double theta); // drift function void sdeDf(double df, double x, double theta); // diffusion function bool isValidParams(double theta); // parameter validator bool isValidData(double x, double *theta); // data validator };
The meaning of each class member is as follows: * `nParams`: The number of model parameters. For the Lotka-Volterra model we have $\tth = (\a, \b, \g)$, such that `nParams = 3`. * `nDims`: The number of dimensions in the multivariate SDE. In this case we have $\Y_t = (H_t, L_t)$, such that `nDims = 2`. * `sdeDr`: The SDE drift function. In R, this function would be implemented as ```r sde.drift <- function(x, theta) { dr <- c(theta[1]*x[1] - theta[2]*x[1]*x[2], # alpha * H - beta * H*L theta[2]*x[1]*x[2] - theta[3]*x[2]) # beta * H*L - gamma * L dr }
In C++ the same thing is accomplished with
```{Rcpp, indent = " ", eval = FALSE} void sdeDr(double dr, double x, double theta) { dr[0] = theta[0]x[0] - theta[1]x[0]x[1]; // alpha * H - beta * H * L dr[1] = theta[1]x[0]x[1] - theta[2]*x[1]; // beta * H * L - gamma * L return; }
* `sdeDf`: The SDE diffusion function. This can be specified on the standard deviation scale, or on the variance scale as above. In this case, an R implementation would be ```r sde.diff <- function(x, theta) { df <- matrix(NA, 2, 2) df[1,1] <- theta[1]*x[1] + theta[2]*x[1]*x[2] # alpha * H + beta * H*L df[1,2] <- -theta[2]*x[1]*x[2] # -beta * H*L df[2,1] <- df[1,2] # -beta * H*L df[2,2] <- theta[2]*x[1]*x[2] + theta[3]*x[2] # beta * H*L + gamma * L df }
In C++ the specification is slightly different. First we set `sdDiff = false` in order to tell **msde** to use the variance scale. Next, the diffusion function is coded as
```{Rcpp, indent = " ", eval = FALSE} void sdeDf(double df, double x, double theta) { df[0] = theta[0]x[0] + theta[1]x[0]x[1]; // matrix element (1,1) df[2] = -theta[1]x[0]x[1]; // element (1,2) df[3] = theta[1]x[0]x[1] + theta[2]*x[1]; // element (2,2) return; }
Thus there are two major differences with the R version. The first is that the `df` matrix is stored as a vector (or "array" in C++). Its elements are stored in column-major order, i.e., by stacking the columns one after the other into one long vector. The second difference is that **msde** only uses the *upper triangular* portion of the (symmetric) matrix $\df_{\tth}(\y)$. The elements below the diagonal can be set to any value or not set at all without affecting the computations. **msde** internally computes the Cholesky decomposition of the diffusion function when it is specified on the variance scale. For small problems such as this one, it is more efficient to specify the Cholesky decomposition directly, i.e., specify the diffusion on the standard deviation scale. In this case, the Cholesky decomposition of the diffusion function is $$ \begin{bmatrix} \a H_t + \b H_tL_t & -\b H_tL_t \\ -\b H_tL_t & \b H_tL_t + \g L_t\end{bmatrix}^{1/2} = \begin{bmatrix} \sqrt{\a H_t + \b H_tL_t} & -\frac{\b H_tL_t}{\sqrt{\a H_t + \b H_tL_t}} \\ 0 & \sqrt{\b H_tL_t + \g L_t - \frac{(\b H_tL_t)^2}{\a H_t + \b H_tL_t}} \end{bmatrix}, $$ which is passed to **msde** by setting `sdDiff = true` and ```{Rcpp, indent = " ", eval = FALSE} void sdeDf(double *df, double *x, double *theta) { double bHL = theta[1]*x[0]*x[1]; // beta * H*L df[0] = sqrt(theta[0]*x[0] + bHL); // sqrt(alpha * H + bHL) df[2] = -bHL/df[0]; df[3] = sqrt(bHL + theta[2]*x[1] - df[2]*df[2]); return; }
diagDiff
: A logical specifying whether or not the diffusion matrix $\df_\tth(\y)$ is diagonal. For the Lotka-Volterra SDE model above, we set diagDiff = false
. However, if the diffusion function were of the form
$$ \df_\tth(\Y_t) = \begin{bmatrix} \a H_t + \b H_tL_t & 0 \ 0 & \b H_tL_t + \g L_t\end{bmatrix}, $$
then we would set diagDiff = true
, and importantly, treat the df
argument of sdeDf
directly as the diagonal elements of the diffusion matrix. That is, the diffusion (on the variance scale) is encoded as
```{Rcpp, indent = " ", eval = FALSE} void sdeDf(double df, double x, double theta) { double bHL = theta[1]x[0]x[1]; // beta * HL df[0] = theta[0]x[0] + bHL; // alpha * H + bHL // note the assignment to df[1] and not df[3] df[1] = bHL + theta[2]x[1]; // bHL + gamma * L return; }
**NOTE:** The **msde** C++ library does not initialize output pointers `df` and `dr`. So, if these contain zeros they must be assigned explicitly. * `isValidParams`: A logical used to specify the parameter support. In this case we have $\a, \b, \g > 0$, such that ```{Rcpp, indent = " ", eval = FALSE} bool isValidParams(double *theta) { bool val = theta[0] > 0.0; val = val && theta[1] > 0.0; val = val && theta[2] > 0.0; return val; }
isValidData
: A logical used to specify the SDE support, which can be parameter-dependent. In this case we simply have $H_t, L_t > 0$, such that```{Rcpp, indent = " ", eval = FALSE} bool isValidData(double x, double theta) { return (x[0] > 0.0) && (x[1] > 0.0); }
Thus the whole file `lotvolModel.h` is given below: ```{Rcpp, eval = FALSE} #ifndef sdeModel_h #define sdeModel_h 1 // Lotka-Volterra Predator-Prey model // class definition class sdeModel { public: static const int nParams = 3; // number of model parameters static const int nDims = 2; // number of sde dimensions static const bool diagDiff = false; // whether diffusion function is diagonal static const bool sdDiff = true; // whether diffusion is on sd or var scale void sdeDr(double *dr, double *x, double *theta); // drift function void sdeDf(double *df, double *x, double *theta); // diffusion function bool isValidParams(double *theta); // parameter validator bool isValidData(double *x, double *theta); // data validator }; // drift function inline void sdeModel::sdeDr(double *dr, double *x, double *theta) { dr[0] = theta[0]*x[0] - theta[1]*x[0]*x[1]; // alpha * H - beta * H*L dr[1] = theta[1]*x[0]*x[1] - theta[2]*x[1]; // beta * H*L - gamma * L return; } // diffusion function (sd scale) inline void sdeModel::sdeDf(double *df, double *x, double *theta) { double bHL = theta[1]*x[0]*x[1]; // beta * H*L df[0] = sqrt(theta[0]*x[0] + bHL); // sqrt(alpha * H + bHL) df[2] = -bHL/df[0]; df[3] = sqrt(bHL + theta[2]*x[1] - df[2]*df[2]); return; } // parameter validator inline bool sdeModel::isValidParams(double *theta) { bool val = theta[0] > 0.0; val = val && theta[1] > 0.0; val = val && theta[2] > 0.0; return val; } // data validator inline bool sdeModel::isValidData(double *x, double *theta) { return (x[0] > 0.0) && (x[1] > 0.0); } #endif
The additions to the previous code sections are:
#ifndef
/#define
/#endif
).sdeModel::
identifier is prepended to the class member definitions when these are written outside of the class declaration itself.inline
keyword before the class member definitions, both of which ensure that only one instance of these functions is passed to the C++ compiler.sde.model
objectOne the sdeModel
class is created as the C++ level, it is compiled in R using the following commands:
require(msde) # put lotvolModel.h in the working directory data.names <- c("H", "L") param.names <- c("alpha", "beta", "gamma") lvmod <- sde.make.model(ModelFile = "lotvolModel.h", data.names = data.names, param.names = param.names)
Before using the model for inference, it is useful to make sure that the C++ entrypoints are error-free. To facilitate this, msde provides R wrappers to the internal C++ drift, diffusion, and validator functions, which can then be checked against R versions as follows:
# helper functions # random matrix of size nreps x length(x) from vector x jit.vec <- function(x, nreps) { apply(t(replicate(n = nreps, expr = x, simplify = "matrix")), 2, jitter) } # maximum absolute and relative error between two arrays max.diff <- function(x1, x2) { c(abs = max(abs(x1-x2)), rel = max(abs(x1-x2)/max(abs(x1), 1e-8))) } # R sde functions # drift and diffusion lv.drift <- function(x, theta) { dr <- c(theta[1]*x[1] - theta[2]*x[1]*x[2], # alpha * H - beta * H*L theta[2]*x[1]*x[2] - theta[3]*x[2]) # beta * H*L - gamma * L dr } lv.diff <- function(x, theta) { df <- matrix(NA, 2, 2) df[1,1] <- theta[1]*x[1] + theta[2]*x[1]*x[2] # alpha * H + beta * H*L df[1,2] <- -theta[2]*x[1]*x[2] # -beta * H*L df[2,1] <- df[1,2] # -beta * H*L df[2,2] <- theta[2]*x[1]*x[2] + theta[3]*x[2] # beta * H*L + gamma * L chol(df) # always use sd scale in R } # validators lv.valid.data <- function(x, theta) all(x > 0) lv.valid.params <- function(theta) all(theta > 0) # generate some test values nreps <- 12 x0 <- c(H = 71, L = 79) theta0 <- c(alpha = .5, beta = .0025, gamma = .3) X <- jit.vec(x0, nreps) Theta <- jit.vec(theta0, nreps) # drift and diffusion check # R versions dr.R <- matrix(NA, nreps, lvmod$ndims) # drift df.R <- matrix(NA, nreps, lvmod$ndims^2) # diffusion for(ii in 1:nreps) { dr.R[ii,] <- lv.drift(x = X[ii,], theta = Theta[ii,]) # flattens diffusion matrix into a row df.R[ii,] <- c(lv.diff(x = X[ii,], theta = Theta[ii,])) } # C++ versions dr.cpp <- sde.drift(model = lvmod, x = X, theta = Theta) df.cpp <- sde.diff(model = lvmod, x = X, theta = Theta) # compare max.diff(dr.R, dr.cpp) max.diff(df.R, df.cpp) # validator check # generate invalid data and parameters X.bad <- X X.bad[c(1,3,5),1] <- -X.bad[c(1,3,5),1] Theta.bad <- Theta Theta.bad[c(2,4,6),3] <- -Theta.bad[c(2,4,6),3] # R versions x.R <- rep(NA, nreps) theta.R <- rep(NA, nreps) for(ii in 1:nreps) { x.R[ii] <- lv.valid.data(x = X.bad[ii,], theta = Theta.bad[ii,]) theta.R[ii] <- lv.valid.params(theta = Theta.bad[ii,]) } # C++ versions x.cpp <- sde.valid.data(model = lvmod, x = X.bad, theta = Theta.bad) theta.cpp <- sde.valid.params(model = lvmod, theta = Theta.bad) # compare c(x = all(x.R == x.cpp), theta = all(theta.R == theta.cpp))
The basis for both simulation and inference with SDEs is the Euler-Maruyama approximation [@maruyama55], which states that over a small time interval $\dt$, the (intractable) transition density of the SDE can be approximated by
$$ \Y_{t+\dt} \mid \Y_t \approx \N\Big(\Y_t + \dr_\tth(\Y_t)\dt, \df_\tth(\Y_t)\dt\Big), $$
with convergence to the true SDE dynamics as $\dt \to 0$.
In order to simulate data from the Lotka-Volterra SDE model, we use the function sde.sim()
. Here we'll generate $N = 50$ observations of the process with initial values $\Y_0 = (71, 79)$, and parameter values $\tth = (.5, .0025,.3)$, with time between observations of $\dt = 1$ year. The dt.sim
argument to sde.sim()
specifies the internal observation time used by the Euler-Maruyama approximation.
# simulation parameters theta0 <- c(alpha = .5, beta = .0025, gamma = .3) # true parameter values x0 <- c(H = 71, L = 79) # initial SDE values N <- 50 # number of observations dT <- 1 # time between observations (years) # simulate data lvsim <- sde.sim(model = lvmod, x0 = x0, theta = theta0, nobs = N-1, # N-1 steps forward dt = dT, dt.sim = dT/100) # internal observation time # plot data Xobs <- rbind(c(x0), lvsim$data) # include first observation tseq <- (1:N-1)*dT # observation times clrs <- c("black", "red") par(mar = c(4, 4, 1, 0)+.1) plot(x = 0, type = "n", xlim = range(tseq), ylim = range(Xobs), xlab = "Time (years)", ylab = "Population") lines(tseq, Xobs[,"H"], type = "o", pch = 16, col = clrs[1]) lines(tseq, Xobs[,"L"], type = "o", pch = 16, col = clrs[2]) legend("topleft", legend = c("Hare", "Lynx"), fill = clrs)
Parameter inference is conducted used a well-known data augmentation scheme due to @pedersen95. Assume that, as above, the SDE observations are evenly spaced with interobservation time $\dt$. For any integer $m > 0$, let $\Y_{(m)} = (\Y_{m,0}, \ldots, \Y_{m,Nm})$ denote the value of the SDE at equally spaced intervals of $\dt_m = \dt/m$. Thus, $\Y_{(1)} = \Y$ corresponds to the observed data, and for $m > 1$, we have $\Y_{m,nm} = \Y_n$. Thus we shall refer to the "missing data" as $\Ym = \Y_{(m)} \setminus \Y$. The Euler-Maruyama approximation to the complete likelihood is
$$ \mathcal L(\tth \mid \Y_{(m)}) = \prod_{n=0}^{Nm-1} \varphi\Big(\Y_{m,n+1} \mid \Y_{m,n} + \dr_\tth(\Y_{m,n})\dt_m, \df_\tth(\Y_{m,n}\dt)\Big), $$
where $\varphi(\y \mid \mathbf \mu, \mathbf \Sigma)$ is the PDF of $\y \sim \N(\mathbf \mu, \mathbf \Sigma)$. The Bayesian data augmentation scheme then consists of chosing a prior $\pi(\tth)$ and sampling from the posterior distribution
$$ p_m(\tth \mid \Y) = \int \mathcal L(\tth \mid \Y_{(m)}) \times \pi(\tth) \, \ud \Ym. $$
As $m \to \infty$ this approximate posterior converges to the true SDE posterior $p(\tth \mid \Y)$.
Posterior sampling from the Euler-Maruyama posterior is accomplished with the function sde.post()
. In the following example we use $m = 1$, i.e., there is no missing data. We'll use a Lebesgue prior $\pi(\tth) \propto 1$; more information on the default and custom prior specifications can be found in the following sections.
# initialize the posterior sampler init <- sde.init(model = lvmod, x = Xobs, dt = dT, m = 1, theta = c(.1, .1, .1)) nsamples <- 2e4 burn <- 2e3 lvpost <- sde.post(model = lvmod, init = init, hyper = NULL, #prior specification nsamples = nsamples, burn = burn) # posterior histograms tnames <- expression(alpha, beta, gamma) par(mfrow = c(1,3)) for(ii in 1:lvmod$nparams) { hist(lvpost$params[,ii], breaks = 25, freq = FALSE, xlab = tnames[ii], main = parse(text = paste0("p[1](", tnames[ii], "*\" | \"*bold(Y))"))) # superimpose true parameter value abline(v = theta0[ii], lwd = 4, lty = 2) }
sde.init()
In the example above there was no missing data, i.e., $m = 1$ and both components of the SDE are observed at each time $t_0, \ldots, t_N$. In order to refine the Euler-Maruyama approximation, we simply pass a larger value of $m$ to sde.init()
:
# 3 missing data points between each observation, so dt_m = dt/4 m <- 4 init <- sde.init(model = lvmod, x = Xobs, dt = dT, m = m, theta = c(.1, .1, .1))
We can also assume that only the first $q < d$ components of the SDE are observed, with the last $d-q$ being latent. In this case with $q = 1$, the lynx population would be unobserved, and this is specified with the nvar.obs
argument:
init <- sde.init(model = lvmod, x = Xobs, dt = dT, nvar.obs = 1, # number of "observed" variables per timepoint m = m, theta = c(.1, .1, .1))
Note that the initial data x
must still be supplied as an $(N+1) \times d$ matrix, with the missing values corresponding to initial values for the MCMC sampler.
Since msde allows for some of the $q$ components of $\Y_t$ to be latent, a prior must be specified not only for $\tth$ but also for the latent variables in the initial observation $\Y_0$.
In the example above, we assumed a Lebesgue prior $\pi(\tth, \Y_0) \propto 1$, with the restriction that $\tth, \Y_0 > 0$ (as specified in the sdeModel
class definition via the isValidData
and isValidParams
validators).
The default prior in msde is a multivariate normal, for which the (fixed) hyper-parameters are supplied via the hyper
argument to sde.post()
. The hyper
argument can either be NULL
, or a list with elements mu
and Sigma
. These consist of a named vector named matrix specifying the mean and variance of the named elements. Unnamed elements are given a Lebesgue prior. So for example, posterior inference for the dataset above with $m = 1$, latent variable $L$, and prior distribution
$$ L_0, \alpha, \gamma \iid \N(1, 1), \qquad \pi(H_0, \beta \mid H_0, \alpha, \gamma) \propto 1 $$
is obtained as follows:
# prior specification pnames <- c("L", "alpha", "gamma") hyper <- list(mu = rep(1, 3), Sigma = diag(3)) names(hyper$mu) <- pnames dimnames(hyper$Sigma) <- list(pnames, pnames) # initialize the posterior sampler init <- sde.init(model = lvmod, x = Xobs, dt = dT, m = 1, nvar.obs = 1, # L is latent theta = c(.1, .1, .1)) nsamples <- 2e4 burn <- 2e3 lvpost <- sde.post(model = lvmod, init = init, hyper = hyper, #prior specification nsamples = nsamples, burn = burn) # posterior histograms tnames <- expression(alpha, beta, gamma) par(mfrow = c(1,3)) for(ii in 1:lvmod$nparams) { hist(lvpost$params[,ii], breaks = 25, freq = FALSE, xlab = tnames[ii], main = parse(text = paste0("p[1](", tnames[ii], "*\" | \"*bold(Y))"))) # superimpose true parameter value abline(v = theta0[ii], lwd = 4, lty = 2) }
msde provides a two-stage mechanism for specifying user-defined priors. This is illustrated below with a simple log-normal prior on $\eeta = (\alpha, \gamma, \beta, L_0) = (\rv \eta 4)$, namely
$$ \pi(\eeta) \iff \log(\eta_i) \iid \N(\mu_i, \sigma_i^2), $$
where the hyperparameters are $\bm \mu = (\rv \mu 4)$ and $\bm \sigma = (\rv \sigma 4)$. Ultimately, the hyperparameters will be passed to sde.post()
as a two-element list, e.g.,
hyper <- list(mu = c(0,0,0,0), sigma = c(1,1,1,1))
sdePrior
class definitionThe first step of the prior specification is to define it at the C++ level, through the sdPrior
class. The class corresponding to the log-normal prior above is defined in the file lotvolPrior.h
pasted below.
```{Rcpp, eval = FALSE}
// Prior for Lotka-Volterra Model
class sdePrior { private: static const int nHyper = 4; // (alpha, beta, gamma, L) double mean, sd; // log-normal mean and standard deviation vectors public: double logPrior(double theta, double x); // log-prior function sdePrior(double *phi, int nArgs, int nEachArg); // constructor ~sdePrior(); // destructor };
// constructor inline sdePrior::sdePrior(double *phi, int nArgs, int nEachArg) { // allocate memory for hyperparameters mean = new double[nHyper]; sd = new double[nHyper]; // hard-copy hyperparameters into Prior object for(int ii=0; ii<nHyper; ii++) { mean[ii] = phi[0][ii]; sd[ii] = phi[1][ii]; } }
// destructor inline sdePrior::~sdePrior() { // deallocate memory to avoid memory leaks delete [] mean; delete [] sd; }
// log-prior function itself: // independent log-normal densities for (alpha,beta,gamma,L) inline double sdePrior::logPrior(double theta, double x) { double lpi = 0.0; // alpha,beta,gamma for(int ii=0; ii < 3; ii++) { lpi += R::dlnorm(theta[ii], mean[ii], sd[ii], 1); } // L lpi += R::dlnorm(x[1], mean[3], sd[3], 1); return lpi; }
The meaning of each class member is as follows: * `sdePrior`: The class constructor. This is how the C++ code collects the hyperparameters from R. Its argument signature must be matched *exactly* and has the following meaning: * `phi`: A double-pointers of type `double`. This is a simple mechanism to give the address of the elements of a list of numeric vectors, as the hyperparameters are defined on the R side. So with the example above, $\sigma_2$ is pointed to by `phi[1][1]`. * `nArgs`: The number of elements in the hyperparameter vector, i.e., `length(hyper)`, which in this case is 2. In this case this number is known in advance, but for greater flexibility it is determined at runtime automatically in the C++ code from the specific value of `hyper`. * `nEachArg`: The length of each hyperparameter element, i.e., `sapply(hyper, length)`. Again this is determined automatically from the C++ code. Note that for speed considerations, the contents of `phi` should be copied directly into the `sdePrior` object, held here in the private members `mean` and `sd`. * `~sdePrior`: The class destructor, which is needed to deallocate the dynamic memory to prevent memory leaks. * `logPrior`: The log-prior function itself, of which the signature must be matched *exactly*. Its arguments correspond to $\tth$ and $\Y_0$. In this case, we use the C++ version of R's `dlnorm` function, which is accessible by including `Rcpp.h`. ### Formatting the R input to the C++ code C++ is much less forgiving that R when it comes to accepting incorrect inputs. Thus, if a user accidently passed the hyperparameters to `sde.post()` as e.g., ```r bad.hyper <- list(mean = c(0,0,0,0))
at best this would cause garbage MCMC output and at worst, the R session to terminate abruptly. For this reason msde provides an input-checking mechanism, which can also used to format the hyperparameters into the list-of-numeric-vectors input expected by the C++ code. This is done by passing an appropriate input checking function to sde.make.model()
through the argument hyper.check
. This argument accepts a function with the exact signature of the example below. In this example, hyper
must be a list with elements mu
and sigma
, which are either:
# must match argument signature _exactly_ lvcheck <- function(hyper, param.names, data.names) { if(is.null(names(hyper)) || !identical(sort(names(hyper)), c("mu", "sigma"))) { stop("hyper must be a list with elements mu and sigma.") } mu <- hyper$mu if(length(mu) == 1) mu <- rep(mu, 4) if(!is.numeric(mu) || length(mu) != 4) { stop("mu must be a numeric scalar or vector of length four.") } sig <- hyper$sigma if(length(sig) == 1) sig <- rep(sig, 4) if(!is.numeric(sig) || length(sig) != 4 || !all(sig > 0)) { stop("sigma must be a positive scalar or vector of length four.") } list(mu, sig) } #lvcheck <- mvn.hyper.check
Now we are ready to create the sde.model
object:
data.names <- c("H", "L") param.names <- c("alpha", "beta", "gamma") lvmod2 <- sde.make.model(ModelFile = "lotvolModel.h", PriorFile = "lotvolPrior.h", # prior specification hyper.check = lvcheck, # prior input checking data.names = data.names, param.names = param.names)
We can also test the C++ implementation of the prior against one written in R, using the msde function sde.prior()
.
# generate some test values nreta <- 12 x0 <- c(H = 71, L = 79) theta0 <- c(alpha = .5, beta = .0025, gamma = .3) X <- jit.vec(x0, nreta) Theta <- jit.vec(theta0, nreta) Eta <- cbind(Theta, L = X[,"L"]) nrphi <- 5 Phi <- lapply(1:nrphi, function(ii) list(mu = rnorm(4), sigma = rexp(4))) # prior check # R version lpi.R <- matrix(NA, nreta, nrphi) for(ii in 1:nrphi) { lpi.R[,ii] <- colSums(dlnorm(x = t(Eta), meanlog = Phi[[ii]]$mu, sdlog = Phi[[ii]]$sigma, log = TRUE)) } # C++ version lpi.cpp <- matrix(NA, nreta, nrphi) for(ii in 1:nrphi) { lpi.cpp[,ii] <- sde.prior(model = lvmod2, theta = Theta, x = X, hyper = Phi[[ii]]) } # compare max.diff(lpi.R, lpi.cpp)
The msde package requires on-the-fly C++ compiling of user-specified models which is handled through the R package Rcpp [@eddelbuettel.francois11].
Path
.xcode-select --install
. You can simply press "Install" without obtaining the entire Xcode suite.build-essential
and a recent version of g++ or clang++.To make sure the C++ compiler is set up correctly, install the Rcpp package and from within R run the following:
Rcpp::cppFunction("double AddTest(double x, double y) {return x + y;}") AddTest(5.2, 3.4)
If the code compiles and outputs r 5.2+3.4
then the C++ compiler is interfaced with R correctly.
It's possible to speed up msde by a reasonable amount by passing a few flags to the C++ compiler. This can be done by creating a Makevars
file (or Makevars.win
on Windows). To do this, find your home folder by running the R command Sys.getenv("HOME")
, and in that folder create a subfolder called .R
containing the Makevars
file (if it doesn't exist already). Now add the following lines to th .R/Makevars
file:
```{bash, eval = FALSE} CXXFLAGS=-O3 -ffastmath CXX=clang++
The first two options make the C++ code faster and the third uses the clang++ compiler instead of g++, which has better error messages (and is the default compiler on OS X). ### Enable OpenMP support (optional) On a multicore machine, **msde** can parallelize some of its computations with [OpenMP](https://en.wikipedia.org/wiki/OpenMP) directives. This can't be done through R on Windows and is supported by default on recent versions of g++/clang++ on Linux. For OS X, the default version of clang++ does not support OpenMP but it is supported by that of the [LLVM Project](http://llvm.org/). This can be installed through [Homebrew](https://brew.sh/). After installing Homebrew using the instructions from the previous link, in Terminal run `brew install llvm`. Then have R use LLVM's clang++ compiler by setting the following in `.R/Makevars`: ```{bash, eval = FALSE} CXXFLAGS=-I/usr/local/opt/llvm/include -O3 -ffast-math LDFLAGS=-L/usr/local/opt/llvm/lib CXX=/usr/local/opt/llvm/bin/clang++
Note that these compiler directives alone will not enable OpenMP support. This happens at compile time by linking against -fopenmp
, which is done internally by msde.
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