From 73304bfb6951a17cbbbd07a84fb96d6e41fe0b49 Mon Sep 17 00:00:00 2001 From: Nicholas Clark <nicholas.j.clark1214@gmail.com> Date: Thu, 14 Nov 2024 08:42:56 +1000 Subject: [PATCH] remove Matrix and smooth dependencies (#93) --- DESCRIPTION | 2 - NAMESPACE | 2 - R/sim_mvgam.R | 155 ++++++++++++++++++++++++----------- man/mvgam_marginaleffects.Rd | 2 + man/sim_mvgam.Rd | 45 ++++++---- src/RcppExports.o | Bin 113491 -> 118706 bytes src/trend_funs.o | Bin 102172 -> 98386 bytes tests/testthat/Rplots.pdf | Bin 1964372 -> 1961437 bytes 8 files changed, 137 insertions(+), 69 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index cc090f74..d58b162d 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -27,10 +27,8 @@ Imports: mvnfast, purrr, zoo, - smooth, dplyr, magrittr, - Matrix, rlang Encoding: UTF-8 LazyData: true diff --git a/NAMESPACE b/NAMESPACE index b2b5fbad..f3594536 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -163,7 +163,6 @@ export(te) export(ti) export(tweedie) export(variables) -importFrom(Matrix,nearPD) importFrom(Rcpp,evalCpp) importFrom(bayesplot,color_scheme_get) importFrom(bayesplot,color_scheme_set) @@ -345,7 +344,6 @@ importFrom(stats,runif) importFrom(stats,sd) importFrom(stats,setNames) importFrom(stats,start) -importFrom(stats,stl) importFrom(stats,terms) importFrom(stats,terms.formula) importFrom(stats,time) diff --git a/R/sim_mvgam.R b/R/sim_mvgam.R index 6ea2af6a..558c4ebe 100644 --- a/R/sim_mvgam.R +++ b/R/sim_mvgam.R @@ -1,24 +1,29 @@ #'Simulate a set of time series for mvgam modelling #' #'This function simulates sets of time series data for fitting a multivariate GAM that includes -#'shared seasonality and dependence on state-space latent dynamic factors. Random dependencies among series, i.e. -#'correlations in their long-term trends, are included in the form of correlated loadings on the latent dynamic factors +#'shared seasonality and dependence on state-space latent dynamic factors. Random +#'dependencies among series, i.e. correlations in their long-term trends, are included +#'in the form of correlated loadings on the latent dynamic factors #' -#'@importFrom stats rnorm rbeta rpois rlnorm rgamma cor cov2cor cov stl ts +#'@importFrom stats rnorm rbeta rpois rlnorm rgamma cor cov2cor cov ts #'@importFrom brms lognormal -#'@importFrom Matrix nearPD #'@param T \code{integer}. Number of observations (timepoints) #'@param n_series \code{integer}. Number of discrete time series -#'@param seasonality \code{character}. Either \code{shared}, meaning that all series share the exact same seasonal pattern, -#'or \code{hierarchical}, meaning that there is a global seasonality but each series' pattern can deviate slightly +#'@param seasonality \code{character}. Either \code{shared}, meaning that +#'all series share the exact same seasonal pattern, +#'or \code{hierarchical}, meaning that there is a global seasonality but +#'each series' pattern can deviate slightly #'@param use_lv \code{logical}. If \code{TRUE}, use dynamic factors to estimate series' -#'latent trends in a reduced dimension format. If \code{FALSE}, estimate independent latent trends for each series +#'latent trends in a reduced dimension format. If \code{FALSE}, estimate independent +#'latent trends for each series #'@param n_lv \code{integer}. Number of latent dynamic factors for generating the series' trends. #'Defaults to `0`, meaning that dynamics are estimated independently for each series -#'@param trend_model \code{character} specifying the time series dynamics for the latent trend. Options are: +#'@param trend_model \code{character} specifying the time series dynamics for the latent trend. +#'Options are: #'\itemize{ -#' \item `None` (no latent trend component; i.e. the GAM component is all that contributes to the linear predictor, -#'and the observation process is the only source of error; similarly to what is estimated by \code{\link[mgcv]{gam}}) +#' \item `None` (no latent trend component; i.e. the GAM component is all that +#' contributes to the linear predictor, and the observation process is the only +#' source of error; similarly to what is estimated by \code{\link[mgcv]{gam}}) #' \item `RW` (random walk with possible drift) #' \item `AR1` (with possible drift) #' \item `AR2` (with possible drift) @@ -27,10 +32,12 @@ #' \item `VAR1cor` (contemporaneously correlated VAR1) #' \item `GP` (Gaussian Process with squared exponential kernel)} See [mvgam_trends] for more details #'@param drift \code{logical}, simulate a drift term for each trend -#'@param prop_trend \code{numeric}. Relative importance of the trend for each series. Should be between \code{0} and \code{1} +#'@param prop_trend \code{numeric}. Relative importance of the trend for each series. +#'Should be between \code{0} and \code{1} #'@param trend_rel Deprecated. Use `prop_trend` instead #'@param freq \code{integer}. The seasonal frequency of the series -#'@param family \code{family} specifying the exponential observation family for the series. Currently supported +#'@param family \code{family} specifying the exponential observation family for the series. +#'Currently supported #'families are: `nb()`, `poisson()`, `bernoulli()`, `tweedie()`, `gaussian()`, #'`betar()`, `lognormal()`, `student()` and `Gamma()` #'@param phi \code{vector} of dispersion parameters for the series @@ -53,13 +60,18 @@ #'series (i.e. `nu` for `student()`) #'If \code{length(nu) < n_series}, the first element of `nu` will #'be replicated `n_series` times. Defaults to \code{3} -#'@param mu \code{vector} of location parameters for the series. If \code{length(mu) < n_series}, the first element of `mu` will -#'be replicated `n_series` times. Defaults to small random values between `-0.5` and `0.5` on the link scale -#'@param prop_missing \code{numeric} stating proportion of observations that are missing. Should be between +#'@param mu \code{vector} of location parameters for the series. +#'If \code{length(mu) < n_series}, the first element of `mu` will +#'be replicated `n_series` times. Defaults to small random values +#'between `-0.5` and `0.5` on the link scale +#'@param prop_missing \code{numeric} stating proportion of observations that are missing. +#'Should be between #'\code{0} and \code{0.8}, inclusive -#'@param prop_train \code{numeric} stating the proportion of data to use for training. Should be between \code{0.2} and \code{1} -#'@return A \code{list} object containing outputs needed for \code{\link{mvgam}}, including 'data_train' and 'data_test', -#'as well as some additional information about the simulated seasonality and trend dependencies +#'@param prop_train \code{numeric} stating the proportion of data to use for training. +#'Should be between \code{0.2} and \code{1} +#'@return A \code{list} object containing outputs needed for \code{\link{mvgam}}, +#'including 'data_train' and 'data_test', as well as some additional information +#'about the simulated seasonality and trend dependencies #'@examples #'# Simulate series with observations bounded at 0 and 1 (Beta responses) #'sim_data <- sim_mvgam(family = betar(), trend_model = RW(), prop_trend = 0.6) @@ -282,21 +294,6 @@ sim_mvgam = function(T = 100, if(trend_model == 'VAR1cor'){ # Use the LKJ distribution to sample correlation matrices # with nice properties - lkj_corr <- function(n_series, eta = 0.8) { - alpha <- eta + (n_series - 2)/2 - r12 <- 2 * rbeta(1, alpha, alpha) - 1 - R <- matrix(0, n_series, n_series) - R[1,1] <- 1; R[1,2] <- r12; R[2,2] <- sqrt(1 - r12^2) - if(n_series > 2) for (m in 2:(n_series - 1)){ - alpha <- alpha - 0.5 - y <- rbeta(1, m / 2, alpha) - z <- rnorm(m, 0, 1) - z <- z / sqrt(crossprod(z)[1]) - R[1:m,m+1] <- sqrt(y) * z - R[m+1,m+1] <- sqrt(1 - y) - } - return(crossprod(R)) - } # Sample trend SD parameters and construct Sigma sigma <- runif(n_lv, 0.4, 1.2) Sigma <- outer(sigma, sigma) * lkj_corr(n_series = n_lv) @@ -337,20 +334,9 @@ sim_mvgam = function(T = 100, } if(use_lv){ - # Loadings on the trends for each series, with sparse but strong correlations - sparse = function(n){ - x <- rep(0, n) - x[sample(seq(1, length(x)), ceiling(0.15*length(x)))] <- - sample(c(-0.75, -0.25, 0.25, 0.75), ceiling(0.15*length(x)), T) - x - } - corMat <- matrix(sparse(n_series ^ 2), n_series) - corMat[lower.tri(corMat)] = t(corMat)[lower.tri(corMat)] - diag(corMat) <- 1 - stddev <- rep(1, n_series) - Sigma <- stddev %*% t(stddev) * corMat + Sigma <- random_Sigma(n_series) loadings <- as.matrix(matrix(mvnfast::rmvn(n = n_lv, mu = rep(0, n_series), - sigma = as.matrix(Matrix::nearPD(Sigma)$mat)), + sigma = Sigma), ncol = n_series)) } else { @@ -359,9 +345,7 @@ sim_mvgam = function(T = 100, } # Simulate the global seasonal pattern - stl_season <- stl(smooth::sim.es(model = "ANA" ,frequency = freq, obs = T + 5, - randomizer = "rnorm")$data, 'periodic')$time.series[,1] - glob_season <- as.vector(scale(zoo::rollmean(stl_season, k = 6, na.pad = F))) + glob_season <- periodic_gp(T, period = freq, rho = runif(1, 0.5, 1.2)) # Simulate observed series as dependent on seasonality and trend obs_trends <- matrix(NA, nrow = T, ncol = n_series) @@ -503,3 +487,76 @@ sim_mvgam = function(T = 100, return(out) } + +#' Simulate a fixed seasonal pattern +#' @noRd +sim_seasonal = function(T, freq = 12){ + beta1 <- runif(1, 0.2, 0.6) + beta2 <- runif(1, -0.5, 0.5) + cov1 <- sin(2 * pi * (1:T) / freq) + cov2 <- cos(2 * pi * (1:T) / freq) + rnorm(T, + mean = beta1 * cov1 + beta2 * cov2, + sd = 0.1) +} + +#' Simulate from a periodic GP +#' @noRd +periodic_gp <- function(T, period = 12, rho = 1){ + time <- 1:T + cov_matrix = array(0, c(length(time), length(time))); + for(i in 1:length(time)) { + cov_matrix[i, i] = 1 + 0.00000001; + if (i < length(time)) { + for(j in (i+1):length(time)) { + covariance = exp(-2*(sin(pi * abs(time[i] - time[j]) / period)^2) / (rho ^ 2)) + cov_matrix[i, j] = covariance + cov_matrix[j, i] = covariance + } + } + } + chol_cov <- t(chol(cov_matrix)); + values <- as.vector(scale(chol_cov %*% rnorm(length(time)))) + return(values) +} + +#' Simulate from the LKJ distribution +#' @noRd +lkj_corr <- function(n_series, eta = 0.8) { + alpha <- eta + (n_series - 2) / 2 + r12 <- 2 * rbeta(1, alpha, alpha) - 1 + R <- matrix(0, n_series, n_series) + R[1, 1] <- 1; R[1, 2] <- r12 + R[2, 2] <- sqrt(1 - r12 ^ 2) + if(n_series > 2) for (m in 2:(n_series - 1)){ + alpha <- alpha - 0.5 + y <- rbeta(1, m / 2, alpha) + z <- rnorm(m, 0, 1) + z <- z / sqrt(crossprod(z)[1]) + R[1 : m, m + 1] <- sqrt(y) * z + R[m + 1, m + 1] <- sqrt(1 - y) + } + return(crossprod(R)) +} + +#' Generate a random covariance matrix +#' @noRd +random_Sigma = function(N){ + L_Omega <- matrix(0, N, N); + L_Omega[1, 1] <- 1; + for (i in 2 : N) { + bound <- 1; + for (j in 1 : (i - 1)) { + is_sparse <- rbinom(1, 1, 0.6) + if(is_sparse){ + L_Omega[i, j] <- runif(1, -0.05, 0.05); + } else { + L_Omega[i, j] <- runif(1, -sqrt(bound), sqrt(bound)); + } + bound <- bound - L_Omega[i, j] ^ 2; + } + L_Omega[i, i] <- sqrt(bound); + } + Sigma <- L_Omega %*% t(L_Omega); + return(Sigma) +} diff --git a/man/mvgam_marginaleffects.Rd b/man/mvgam_marginaleffects.Rd index 5564cfdb..16e6648e 100644 --- a/man/mvgam_marginaleffects.Rd +++ b/man/mvgam_marginaleffects.Rd @@ -86,6 +86,8 @@ arguments.} \item \code{newdata = datagrid(cyl = c(4, 6))}: \code{cyl} variable equal to 4 and 6 and other regressors fixed at their means or modes. \item See the Examples section and the \code{\link[marginaleffects:datagrid]{datagrid()}} documentation. } +\item \code{\link[=subset]{subset()}} call with a single argument to select a subset of the dataset used to fit the model, ex: \code{newdata = subset(treatment == 1)} +\item \code{\link[dplyr:filter]{dplyr::filter()}} call with a single argument to select a subset of the dataset used to fit the model, ex: \code{newdata = filter(treatment == 1)} \item string: \itemize{ \item "mean": Marginal Effects at the Mean. Slopes when each predictor is held at its mean or mode. diff --git a/man/sim_mvgam.Rd b/man/sim_mvgam.Rd index 8bced2e3..b760dedf 100644 --- a/man/sim_mvgam.Rd +++ b/man/sim_mvgam.Rd @@ -30,19 +30,24 @@ sim_mvgam( \item{n_series}{\code{integer}. Number of discrete time series} -\item{seasonality}{\code{character}. Either \code{shared}, meaning that all series share the exact same seasonal pattern, -or \code{hierarchical}, meaning that there is a global seasonality but each series' pattern can deviate slightly} +\item{seasonality}{\code{character}. Either \code{shared}, meaning that +all series share the exact same seasonal pattern, +or \code{hierarchical}, meaning that there is a global seasonality but +each series' pattern can deviate slightly} \item{use_lv}{\code{logical}. If \code{TRUE}, use dynamic factors to estimate series' -latent trends in a reduced dimension format. If \code{FALSE}, estimate independent latent trends for each series} +latent trends in a reduced dimension format. If \code{FALSE}, estimate independent +latent trends for each series} \item{n_lv}{\code{integer}. Number of latent dynamic factors for generating the series' trends. Defaults to \code{0}, meaning that dynamics are estimated independently for each series} -\item{trend_model}{\code{character} specifying the time series dynamics for the latent trend. Options are: +\item{trend_model}{\code{character} specifying the time series dynamics for the latent trend. +Options are: \itemize{ -\item \code{None} (no latent trend component; i.e. the GAM component is all that contributes to the linear predictor, -and the observation process is the only source of error; similarly to what is estimated by \code{\link[mgcv]{gam}}) +\item \code{None} (no latent trend component; i.e. the GAM component is all that +contributes to the linear predictor, and the observation process is the only +source of error; similarly to what is estimated by \code{\link[mgcv]{gam}}) \item \code{RW} (random walk with possible drift) \item \code{AR1} (with possible drift) \item \code{AR2} (with possible drift) @@ -53,13 +58,15 @@ and the observation process is the only source of error; similarly to what is es \item{drift}{\code{logical}, simulate a drift term for each trend} -\item{prop_trend}{\code{numeric}. Relative importance of the trend for each series. Should be between \code{0} and \code{1}} +\item{prop_trend}{\code{numeric}. Relative importance of the trend for each series. +Should be between \code{0} and \code{1}} \item{trend_rel}{Deprecated. Use \code{prop_trend} instead} \item{freq}{\code{integer}. The seasonal frequency of the series} -\item{family}{\code{family} specifying the exponential observation family for the series. Currently supported +\item{family}{\code{family} specifying the exponential observation family for the series. +Currently supported families are: \code{nb()}, \code{poisson()}, \code{bernoulli()}, \code{tweedie()}, \code{gaussian()}, \code{betar()}, \code{lognormal()}, \code{student()} and \code{Gamma()}} @@ -87,22 +94,28 @@ series (i.e. \code{nu} for \code{student()}) If \code{length(nu) < n_series}, the first element of \code{nu} will be replicated \code{n_series} times. Defaults to \code{3}} -\item{mu}{\code{vector} of location parameters for the series. If \code{length(mu) < n_series}, the first element of \code{mu} will -be replicated \code{n_series} times. Defaults to small random values between \code{-0.5} and \code{0.5} on the link scale} +\item{mu}{\code{vector} of location parameters for the series. +If \code{length(mu) < n_series}, the first element of \code{mu} will +be replicated \code{n_series} times. Defaults to small random values +between \code{-0.5} and \code{0.5} on the link scale} -\item{prop_missing}{\code{numeric} stating proportion of observations that are missing. Should be between +\item{prop_missing}{\code{numeric} stating proportion of observations that are missing. +Should be between \code{0} and \code{0.8}, inclusive} -\item{prop_train}{\code{numeric} stating the proportion of data to use for training. Should be between \code{0.2} and \code{1}} +\item{prop_train}{\code{numeric} stating the proportion of data to use for training. +Should be between \code{0.2} and \code{1}} } \value{ -A \code{list} object containing outputs needed for \code{\link{mvgam}}, including 'data_train' and 'data_test', -as well as some additional information about the simulated seasonality and trend dependencies +A \code{list} object containing outputs needed for \code{\link{mvgam}}, +including 'data_train' and 'data_test', as well as some additional information +about the simulated seasonality and trend dependencies } \description{ This function simulates sets of time series data for fitting a multivariate GAM that includes -shared seasonality and dependence on state-space latent dynamic factors. Random dependencies among series, i.e. -correlations in their long-term trends, are included in the form of correlated loadings on the latent dynamic factors +shared seasonality and dependence on state-space latent dynamic factors. Random +dependencies among series, i.e. correlations in their long-term trends, are included +in the form of correlated loadings on the latent dynamic factors } \examples{ # Simulate series with observations bounded at 0 and 1 (Beta responses) diff --git a/src/RcppExports.o b/src/RcppExports.o index 5cf5ae212a40f74725a9674bfa1be2b221d56b80..03b011192531eff382f7b4b3aad3c852fa2b68e4 100644 GIT binary patch literal 118706 zcmeEv3wRVo)^<%YorK&b2q>(#Q3efOaB@!scPA3GqZ7>{uI$Q6FocWCCBj6}MGYn> z;}D68E~|bkt9W-^R#^pa8!iGWs}WJr^%4b@aZo{7MZEC8r%rWE_spRAF8}xK|NLJ+ zPfx#Hb?Tg|Q&s0wRaf^+ye7*faGY_lOZ+6AkXsB3HO&bTR7dOKLW~(Ig#T#{7?CD~ zSJXEQn>Zmf!6L%di*w6qLM(WN1Kqd;M19w=3un*f5cMnNI6_3LXy`hr)EQQQ+dbfC zKfDF?4JVaU*n^N`<oa|+kO&m`11D7!IRbNWmj$}zDu0v>Nmt}AvlEis{t5h~+@g|7 zI~~dOCD1Xyieft*$t?ukiwSav&2lQRHB*RE(Ct)ook$lD4S|!2{pEIkl)n{c39;}E z4y6i|`716hvC~oh)`8A`$ov&oIut|s+X%W_6hEz(lA<bmT9UgZfgjgLS(0A1C(uc` zB~=bOlDh+RD-z|FI`vF)_ayL>a!acm^(NcF+4dZ(`YkJS=tZ^z&}~kTJ8X`#J_@0T 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