From 00b3f1e27fd0ebfabfa0bd44c2283780dbd2bcfa Mon Sep 17 00:00:00 2001 From: Jim Thorson <50178738+James-Thorson-NOAA@users.noreply.github.com> Date: Thu, 28 Sep 2023 15:27:36 -0700 Subject: [PATCH] adding generics ... e.g., predict, simulate, residuals, vcov --- DESCRIPTION | 2 +- NAMESPACE | 8 ++ R/classify_variables.R | 1 + R/dsem.R | 297 ++++++++++++++++++++++++++++++++++++++++- R/make_ram.R | 21 +++ man/as_fitted_DAG.Rd | 3 +- man/dsem.Rd | 1 + man/make_ram.Rd | 2 +- man/predict.dsem.Rd | 26 ++++ man/residuals.dsem.Rd | 18 +++ man/simulate.dsem.Rd | 36 +++++ man/summary.dsem.Rd | 2 +- man/vcov.dsem.Rd | 18 +++ src/dsem.cpp | 88 ++++++++++-- vignettes/vignette.Rmd | 140 ++++++++++++++----- vignettes/vignette.pdf | Bin 0 -> 314487 bytes 16 files changed, 603 insertions(+), 60 deletions(-) create mode 100644 man/predict.dsem.Rd create mode 100644 man/residuals.dsem.Rd create mode 100644 man/simulate.dsem.Rd create mode 100644 man/vcov.dsem.Rd create mode 100644 vignettes/vignette.pdf diff --git a/DESCRIPTION b/DESCRIPTION index 8d0731c..ce62d24 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -7,7 +7,7 @@ Authors@R: c(person(given = "Anon", family = "Ymous", role = c("aut", "cre"), - email = "blah@gmail.com") + email = "James.Thorson@noaa.gov") ) Imports: TMB, diff --git a/NAMESPACE b/NAMESPACE index 40030e3..a98c022 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,8 +1,14 @@ # Generated by roxygen2: do not edit by hand +S3method(logLik,dsem) +S3method(predict,dsem) +S3method(residuals,dsem) +S3method(simulate,dsem) S3method(summary,dsem) +S3method(vcov,dsem) export(TMBAIC) export(as_fitted_DAG) +export(classify_variables) export(dsem) export(fit_tmb) export(list_parameters) @@ -13,9 +19,11 @@ importFrom(TMB,dynlib) importFrom(TMB,sdreport) importFrom(TMB,summary.sdreport) importFrom(stats,.preformat.ts) +importFrom(stats,logLik) importFrom(stats,na.omit) importFrom(stats,nlminb) importFrom(stats,optimHess) importFrom(stats,pnorm) importFrom(stats,rnorm) +importFrom(stats,vcov) useDynLib(dsem, .registration = TRUE) diff --git a/R/classify_variables.R b/R/classify_variables.R index 91fe59b..03950ef 100644 --- a/R/classify_variables.R +++ b/R/classify_variables.R @@ -8,6 +8,7 @@ #' @param model SEM model #' #' @return Tagged-list defining exogenous and endogenous variables +#' @export classify_variables <- function( model ){ diff --git a/R/dsem.R b/R/dsem.R index 80e7c39..dfcb013 100644 --- a/R/dsem.R +++ b/R/dsem.R @@ -113,6 +113,7 @@ dsem <- function( sem, tsdata, family = rep("fixed",ncol(tsdata)), + covs = colnames(tsdata), estimate_delta0 = FALSE, quiet = FALSE, run_model = TRUE, @@ -122,9 +123,17 @@ function( sem, ... ){ # (I-Rho)^-1 * Gamma * (I-Rho)^-1 - out = make_ram( sem, tsdata=tsdata, quiet=quiet ) + out = make_ram( sem, tsdata=tsdata, quiet=quiet, covs=covs ) ram = out$ram + # Error checks + if( any((out$model[,'direction']==2) & (out$model[,2]!=0)) ){ + stop("All two-headed arrows should have lag=0") + } + if( !all(c(out$model[,'first'],out$model[,'second']) %in% colnames(tsdata)) ){ + stop("Some variable in `sem` is not in `tsdata`") + } + # Data = list( "RAM" = as.matrix(na.omit(ram[,1:4])), "RAMstart" = as.numeric(ram[,5]), @@ -179,8 +188,9 @@ function( sem, if(quiet==FALSE) list_parameters(obj) out = list( "obj"=obj, "ram"=ram, - "model"=out$model, - "tmb_inputs"=list("data"=Data, "parameters"=Params, "random"=Random, "map"=Map) ) + "sem_full"=out$model, + "tmb_inputs"=list("data"=Data, "parameters"=Params, "random"=Random, "map"=Map), + "call" = match.call() ) # Export stuff if( run_model==FALSE ){ @@ -204,7 +214,7 @@ function( sem, #' @title Summarize dsem #' #' @param object Output from \code{\link{dsem}} -#' @param ... Note used +#' @param ... Not used #' #' @method summary dsem #' @export @@ -228,13 +238,290 @@ function( object, ... ){ return(coefs) } +#' Simulate dsem +#' +#' @title Simulate from a fitted \code{dsem} model +#' +#' @param object Output from \code{\link{dsem}} +#' @param nsim number of simulated data sets +#' @param variance whether to ignore uncertainty in fixed and +#' random effects, include estimation uncertainty in random effects, +#' or include estimation uncertainty in both fixed and random effects +#' @param resimulate_gmrf whether to resimulate the GMRF based on estimated or +#' simulated random effects (determined by argument \code{variance}) +#' @param seed random seed +#' @param ... Not used +#' +#' @description +#' This function conducts a parametric bootstrap, i.e., simulates new data +#' conditional upon estimated values for fixed and random effects. The user +#' can optionally simulate new random effects conditional upon their estimated +#' covariance, or simulate new fixed and random effects conditional upon their imprecision. +#' +#' Note that \code{simulate} will have no effect on states \code{x_tj} for which there +#' is a measurement and when those measurements are fitted using \code{family="fixed"} +#' +#' @method simulate dsem +#' @export +simulate.dsem <- +function( object, + nsim = 1, + seed = NULL, + variance = c("none", "random", "both"), + resimulate_gmrf = FALSE, + ... ){ + + set.seed(seed) + variance = match.arg(variance) + + # Sample from GMRF using sparse precision + rmvnorm_prec <- function(mu, prec, nsim) { + z <- matrix(rnorm(length(mu) * nsim), ncol=nsim) + L <- Matrix::Cholesky(prec, super=TRUE) + z <- Matrix::solve(L, z, system = "Lt") ## z = Lt^-1 %*% z + z <- Matrix::solve(L, z, system = "Pt") ## z = Pt %*% z + z <- as.matrix(z) + return(mu + z) + } + + # + obj = object$obj + parfull = obj$env$parList() + + par_zr = outer( obj$env$last.par.best, rep(1,nsim) ) + if( variance=="random" ){ + tmp = rmvnorm_prec( rep(0,length(obj$env$random)), obj$env$spHess(random=TRUE), nsim=nsim ) + par_zr[obj$env$random,] = par_zr[obj$env$random,,drop=FALSE] + tmp + } + if( variance=="both" ){ + if(is.null(object$opt$SD$jointPrecision)){ + stop("Please re-run `dsem` with `getsd=TRUE` and `getJointPrecision=TRUE`, or confirm that the model is converged") + } + tmp = rmvnorm_prec( rep(0,length(obj$env$last.par)), object$opt$SD$jointPrecision, nsim=nsim ) + par_zr = par_zr + tmp + } + + out = NULL + for( r in seq_len(nsim) ){ + if( resimulate_gmrf==TRUE ){ + # Simulate new fields + newrep = obj$report( par=par_zr[,r] ) + newparfull = obj$env$parList() + Q_kk = newrep$Q_kk + tmp = rmvnorm_prec( newrep$delta_k + as.vector(newrep$xhat_tj), Q_kk, nsim=1 ) + # Modify call + newcall = object$call + newcall$parameters = newparfull + newcall$parameters$x_tj[] = tmp + # Rebuild model with new GMRF values + newcall$run_model = FALSE + newfit = eval(newcall) + out[[r]] = newfit$obj$simulate()$y_tj + }else{ + out[[r]] = obj$simulate( par_zr[,r] ) + } + } + + #out = lapply( 1:nsim, FUN=\(x) obj$env$simulate(par=par_zr[,x],complete=TRUE)$y_tj ) + #mean( obj$env$simulate(par=par_zr[,1],complete=TRUE)$y_tj ) + #mean( obj$env$simulate(par=par_zr[,2]+1,complete=TRUE)$y_tj ) + #sapply(out, mean) + #apply(par_zr, MARGIN=2, mean) + + return(out) +} + +#' Extract Variance-Covariance Matrix +#' +#' extract the covariance of fixed effects, or both fixed and random effects. +#' +#' @param object output from \code{dsem} +#' @param which whether to extract the covariance among fixed effects, random effects, or both +#' @param ... ignored, for method compatibility +#' @importFrom stats vcov +#' @method vcov dsem +#' @export +vcov.dsem <- +function( object, + which = c("fixed", "random", "both"), + ...) { + + which = match.arg(which) + + if( which=="fixed" ){ + V = object$opt$SD$cov.fixed + if(is.null(V)){ + warning("Please re-run `dsem` with `getsd=TRUE`, or confirm that the model is converged") + } + } + if( which=="random" ){ + V = solve(object$obj$env$spHess(random=TRUE)) + } + if( which=="both" ){ + H = object$opt$SD$jointPrecision + if(is.null(H)){ + warning("Please re-run `dsem` with `getsd=TRUE` and `getJointPrecision=TRUE`, or confirm that the model is converged") + V = NULL + }else{ + V = solve(H) + } + } + + return( V ) +} + +#' Calculate residuals +#' +#' @title Calculate residuals for dsem +#' +#' @param object Output from \code{\link{dsem}} +#' @param type which type of residuals to compute (only option is \code{"deviance"} or \code{"response"} for now) +#' @param ... Note used +#' +#' @method residuals dsem +#' @export +residuals.dsem <- +function( object, + type = c("deviance","response"), + ... ){ + + # https://stats.stackexchange.com/questions/1432/what-do-the-residuals-in-a-logistic-regression-mean + # Normal deviance residuals + if( FALSE ){ + x = rnorm(10) + y = x + rnorm(10) + Glm = glm( y ~ 1 + x, family="gaussian") + mu = predict(Glm,type="response") + r1 = y - mu + r1 - resid(Glm) + } + # Poisson deviance residuals + if( FALSE ){ + x = rnorm(10) + y = rpois(10, exp(x)) + Glm = glm( y ~ 1 + x, family="poisson") + mu = predict(Glm,type="response") + # https://stats.stackexchange.com/questions/398098/formula-for-deviance-residuals-for-poisson-model-with-identity-link-function + r1 = sign(y - mu) * sqrt(2*(y*log((y+1e-10)/mu) - (y-mu))) + r1 - resid(Glm) + } + # Binomial deviance residuals + if( FALSE ){ + p = 0.5 + y = rbinom(10, prob=p, size=1) + Glm = glm( y ~ 1, family="binomial") + mu = predict(Glm, type="response") + r1 = sign(y - mu) * sqrt(-2*(((1-y)*log(1-mu) + y*log(mu)))) + r1 - resid(Glm) + } + # Gamma deviance residuals + if( FALSE ){ + mu = 1 + cv = 0.8 + y = rgamma( n=10, shape=1/cv^2, scale=mu*cv^2 ) + Glm = glm( y ~ 1, family=Gamma(link='log')) + mu = predict(Glm, type="response") + r1 = sign(y - mu) * sqrt(2 * ( (y-mu)/mu - log(y/mu) )) + r1 - resid(Glm) + } + + # Poisson: sign(y - mu) * sqrt(2*(ifelse(y==0, 0, y*log(y/mu)) - (y-mu))) + # Binomial: -2 * ((1-y)*log(1-mu) + y*log(mu)) + # Gamma: 2 * ( (y-mu)/mu - log(y/mu) ) + + # Easy of use + x_tj = object$obj$env$parList()$x_tj + y_tj = object$tmb_inputs$data$y_tj + familycode_j = object$tmb_inputs$data$familycode_j + report = object$obj$report() + + # + type = match.arg(type) + if( type == "deviance" ){ + resid_tj = report$devresid_tj + } + if( type == "response" ){ + resid_tj = y_tj - report$mu_tj + } + + return(resid_tj) +} + +#' predictions using dsem +#' +#' @title Predict variables given new (counterfactual) values of data, or for future or past times +#' +#' @param object Output from \code{\link{dsem}} +#' @param newdata optionally, a data frame in which to look for variables with which to predict. +#' If omitted, the fitted data are used to create predictions. If desiring predictions after the fitted data, +#' the user must append rows with NAs for those future times. Similarly, if desiring predictions given counterfactual +#' values for time-series data, then those individual observations can be edited while keeping other observations at their +#' original fitted values. +#' @param type the type of prediction required. The default is on the scale of the linear predictors; +#' the alternative "response" is on the scale of the response variable. +#' Thus for a Poisson-distributed variable the default predictions are of log-intensity and type = "response" gives the predicted intensity. +#' @param ... Note used +#' +#' @method predict dsem +#' @export +predict.dsem <- +function( object, + newdata = NULL, + type = c("link", "response"), + ... ){ + # + # newdata = eval(object$call$tsdata) + # newdata = ts( newdata[1:40,] ) + + # Easy of use + parfull = object$obj$env$parList() + type = match.arg(type) + report = object$obj$report() + + # + if( is.null(newdata) ){ + if(type=="link") out = parfull$x_tj + if(type=="response") out = report$mu_tj + }else{ + newcall = object$call + newcall$tsdata = newdata + # Rebuild model with new data + newcall$run_model = FALSE + newfit = eval(newcall) + # Optimize random effects given original MLE and newdata + newfit$obj$fn( object$opt$par ) + # Return predictor + if(type=="link") out = newfit$obj$env$parList()$x_tj + if(type=="response") out = newfit$obj$report()$mu_tj + } + + return(out) +} + +# Extract the log-likelihood of a dsem model +# +# @return object of class \code{logLik} with attributes +# \item{val}{log-likelihood} +# \item{df}{number of parameters} +#' @importFrom stats logLik +#' @export +logLik.dsem <- function(object, ...) { + val = -1 * object$opt$objective + df = length( object$opt$par ) + out = structure( val, + df = df, + class = "logLik") + return(out) +} + #' Convert dsem to phylopath output #' #' @title Convert output from package dsem to phylopath #' #' @param fit Output from \code{\link{dsem}} #' @param lag which lag to output -#' @param what whether to output estimates \code{what="Estimate"} or standard errors \code{what="Std_Error"} +#' @param what whether to output estimates \code{what="Estimate"}, standard errors \code{what="Std_Error"} +#' or p-values \code{what="Std_Error"} #' #' @return Convert output to format supplied by \code{\link[phylopath]{est_DAG}} #' diff --git a/R/make_ram.R b/R/make_ram.R index 2a121f4..7c3a131 100644 --- a/R/make_ram.R +++ b/R/make_ram.R @@ -16,6 +16,7 @@ make_ram <- function( sem, tsdata, + covs = NULL, quiet = FALSE, remove_na = TRUE ){ @@ -80,6 +81,26 @@ function( sem, model$path <- gsub("\\t", " ", model$path) model$par[model$par == ""] <- NA model <- cbind( model$path, model$lag, model$par, model$start) + + if( !is.null(covs) ){ + for (cov in covs) { + vars <- strsplit(cov, "[ ,]+")[[1]] + nvar <- length(vars) + for (i in 1:nvar) { + for (j in i:nvar) { + p1 = paste(vars[i], "<->", vars[j]) + p2 = if (i==j) paste("V[", vars[i], "]", sep = "") else paste("C[",vars[i], ",", vars[j], "]", sep = "") + p3 = NA + row <- c(p1, 0, p2, p3) + if( any((row[1]==model[,1]) & (row[2]==model[,2])) ){ + next + }else{ + model <- rbind(model, row, deparse.level = 0) + } + }} + } + } + exog.variances = endog.variances = TRUE model = add.variances() diff --git a/man/as_fitted_DAG.Rd b/man/as_fitted_DAG.Rd index 7f48a8f..fdbe23e 100644 --- a/man/as_fitted_DAG.Rd +++ b/man/as_fitted_DAG.Rd @@ -11,7 +11,8 @@ as_fitted_DAG(fit, lag = 0, what = "Estimate") \item{lag}{which lag to output} -\item{what}{whether to output estimates \code{what="Estimate"} or standard errors \code{what="Std_Error"}} +\item{what}{whether to output estimates \code{what="Estimate"}, standard errors \code{what="Std_Error"} +or p-values \code{what="Std_Error"}} } \value{ Convert output to format supplied by \code{\link[phylopath]{est_DAG}} diff --git a/man/dsem.Rd b/man/dsem.Rd index 154a8ae..7874d1f 100644 --- a/man/dsem.Rd +++ b/man/dsem.Rd @@ -8,6 +8,7 @@ dsem( sem, tsdata, family = rep("fixed", ncol(tsdata)), + covs = colnames(tsdata), estimate_delta0 = FALSE, quiet = FALSE, run_model = TRUE, diff --git a/man/make_ram.Rd b/man/make_ram.Rd index 4ef172d..8b54814 100644 --- a/man/make_ram.Rd +++ b/man/make_ram.Rd @@ -4,7 +4,7 @@ \alias{make_ram} \title{Make a RAM (Reticular Action Model)} \usage{ -make_ram(sem, tsdata, quiet = FALSE, remove_na = TRUE) +make_ram(sem, tsdata, covs = NULL, quiet = FALSE, remove_na = TRUE) } \arguments{ \item{sem}{structural equation model structure, passed to either \code{\link[sem]{specifyModel}} diff --git a/man/predict.dsem.Rd b/man/predict.dsem.Rd new file mode 100644 index 0000000..c4f0c55 --- /dev/null +++ b/man/predict.dsem.Rd @@ -0,0 +1,26 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/dsem.R +\name{predict.dsem} +\alias{predict.dsem} +\title{Predict variables given new (counterfactual) values of data, or for future or past times} +\usage{ +\method{predict}{dsem}(object, newdata = NULL, type = c("link", "response"), ...) +} +\arguments{ +\item{object}{Output from \code{\link{dsem}}} + +\item{newdata}{optionally, a data frame in which to look for variables with which to predict. +If omitted, the fitted data are used to create predictions. If desiring predictions after the fitted data, +the user must append rows with NAs for those future times. Similarly, if desiring predictions given counterfactual +values for time-series data, then those individual observations can be edited while keeping other observations at their +original fitted values.} + +\item{type}{the type of prediction required. The default is on the scale of the linear predictors; +the alternative "response" is on the scale of the response variable. +Thus for a Poisson-distributed variable the default predictions are of log-intensity and type = "response" gives the predicted intensity.} + +\item{...}{Note used} +} +\description{ +predictions using dsem +} diff --git a/man/residuals.dsem.Rd b/man/residuals.dsem.Rd new file mode 100644 index 0000000..d91c7d7 --- /dev/null +++ b/man/residuals.dsem.Rd @@ -0,0 +1,18 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/dsem.R +\name{residuals.dsem} +\alias{residuals.dsem} +\title{Calculate residuals for dsem} +\usage{ +\method{residuals}{dsem}(object, type = c("deviance", "response"), ...) +} +\arguments{ +\item{object}{Output from \code{\link{dsem}}} + +\item{type}{which type of residuals to compute (only option is \code{"deviance"} or \code{"response"} for now)} + +\item{...}{Note used} +} +\description{ +Calculate residuals +} diff --git a/man/simulate.dsem.Rd b/man/simulate.dsem.Rd new file mode 100644 index 0000000..ead19f4 --- /dev/null +++ b/man/simulate.dsem.Rd @@ -0,0 +1,36 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/dsem.R +\name{simulate.dsem} +\alias{simulate.dsem} +\title{Simulate from a fitted \code{dsem} model} +\usage{ +\method{simulate}{dsem}( + object, + nsim = 1, + seed = NULL, + parametric_uncertainty = c("none", "random", "both"), + ... +) +} +\arguments{ +\item{object}{Output from \code{\link{dsem}}} + +\item{nsim}{number of simulated data sets} + +\item{seed}{random seed} + +\item{parametric_uncertainty}{whether to ignore uncertainty in fixed and +random effects, include estimation uncertainty in random effects, +or include estimation uncertainty in both fixed and random effects} + +\item{...}{Not used} +} +\description{ +This function conducts a parametric bootstrap, i.e., simulates new data +conditional upon estimated values for fixed and random effects. The user +can optionally simulate new random effects conditional upon their estimated +covariance, or simulate new fixed and random effects conditional upon their imprecision. +} +\details{ +Simulate dsem +} diff --git a/man/summary.dsem.Rd b/man/summary.dsem.Rd index 5ecfe34..6d187d5 100644 --- a/man/summary.dsem.Rd +++ b/man/summary.dsem.Rd @@ -9,7 +9,7 @@ \arguments{ \item{object}{Output from \code{\link{dsem}}} -\item{...}{Note used} +\item{...}{Not used} } \description{ summarize dsem diff --git a/man/vcov.dsem.Rd b/man/vcov.dsem.Rd new file mode 100644 index 0000000..26ded4a --- /dev/null +++ b/man/vcov.dsem.Rd @@ -0,0 +1,18 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/dsem.R +\name{vcov.dsem} +\alias{vcov.dsem} +\title{Extract Variance-Covariance Matrix} +\usage{ +\method{vcov}{dsem}(object, which = c("fixed", "random", "both"), ...) +} +\arguments{ +\item{object}{output from \code{dsem}} + +\item{which}{whether to extract the covariance among fixed effects, random effects, or both} + +\item{...}{ignored, for method compatibility} +} +\description{ +extract the covariance of fixed effects, or both fixed and random effects. +} diff --git a/src/dsem.cpp b/src/dsem.cpp index 217d7f1..7c9f3d5 100644 --- a/src/dsem.cpp +++ b/src/dsem.cpp @@ -2,6 +2,13 @@ #define TMB_LIB_INIT R_init_dsem #include +// get sign of double, only for REPORT use +template +Type sign(Type x){ + return x / pow(pow(x,2),0.5); +} + + template Type objective_function::operator() () { @@ -9,6 +16,7 @@ Type objective_function::operator() () using namespace density; // Data + //DATA_INTEGER( resimulate_gmrf ); DATA_IMATRIX( RAM ); DATA_VECTOR( RAMstart ); DATA_IVECTOR( familycode_j ); @@ -66,6 +74,7 @@ Type objective_function::operator() () Linv_kk = Gammainv_kk * ( I_kk - Rho_kk ); Q_kk = Linv_kk.transpose() * Linv_kk; + // Calculate effect of initial condition -- SPARSE version vector delta_k( n_k ); delta_k.setZero(); @@ -103,28 +112,72 @@ Type objective_function::operator() () xhat_tj(t,j) = mu_j(j); }} jnll_gmrf = GMRF(Q_kk)( x_tj - xhat_tj - delta_k ); + //SIMULATE{ + // if( resimulate_gmrf >= 1 ){ + // //x_tj = GMRF(Q_kk).simulate(x_tj); + // //x_tj += xhat_tj + delta_k; + // } + // REPORT( x_tj ); + //} // Distribution for data + array devresid_tj( n_t, n_j ); + array mu_tj( n_t, n_j ); for(int t=0; t::operator() () // Reporting //REPORT( V_kk ); REPORT( Q_kk ); + REPORT( xhat_tj ); // needed to simulate new GMRF in R + REPORT( delta_k ); // needed to simulate new GMRF in R REPORT( Rho_kk ); + REPORT( mu_tj ); + REPORT( devresid_tj ); //REPORT( Gammainv_kk ); REPORT( jnll ); REPORT( loglik_tj ); REPORT( jnll_gmrf ); + SIMULATE{ + REPORT( y_tj ); + } return jnll; } diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 8707374..8750ec0 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -14,9 +14,10 @@ knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) -# use devtools::build_rmd("vignettes/comparison.Rmd") to test build -# library(rmarkdown) -# render( "vignette.Rmd", pdf_document()) # to build PDF, and perhaps tinytex::latexmk() to install dependencies +# Build +# setwd(R'(C:\Users\James.Thorson\Desktop\Git\dsem)'); devtools::build_rmd("vignettes/vignette.Rmd") +# PDF +# library(rmarkdown); render( "vignettes/vignette.Rmd", pdf_document()) ``` ```{r setup, echo=TRUE} @@ -33,12 +34,12 @@ We first demonstrate that `dsem` gives identical results to `dynlm` for a well-k library(dynlm) data(KleinI, package="AER") -KleinI = ts(data.frame(KleinI, "time"=time(KleinI) - 1931)) +TS = ts(data.frame(KleinI, "time"=time(KleinI) - 1931)) # dynlm -fm_cons <- dynlm(consumption ~ cprofits + L(cprofits) + I(pwage + gwage), data = KleinI) -fm_inv <- dynlm(invest ~ cprofits + L(cprofits) + capital, data = KleinI) # -fm_pwage <- dynlm(pwage ~ gnp + L(gnp) + time, data = KleinI) +fm_cons <- dynlm(consumption ~ cprofits + L(cprofits) + I(pwage + gwage), data = TS) +fm_inv <- dynlm(invest ~ cprofits + L(cprofits) + capital, data = TS) # +fm_pwage <- dynlm(pwage ~ gnp + L(gnp) + time, data = TS) # dsem sem = " @@ -55,9 +56,13 @@ sem = " gnp -> pwage, -1, c3 time -> pwage, 0, c1 " -tsdata = KleinI[,c("time","gnp","pwage","cprofits",'consumption',"gwage","invest","capital")] -tsdata[1,c('consumption','pwage','invest')] = NA -fit = dsem( sem=sem, tsdata=tsdata, newtonsteps=0, quiet=TRUE ) # +tsdata = TS[,c("time","gnp","pwage","cprofits",'consumption', + "gwage","invest","capital")] +fit = dsem( sem=sem, + tsdata = tsdata, + newtonsteps = 0, + quiet = TRUE, + estimate_delta0 = TRUE ) # Compile m1 = rbind( summary(fm_cons)$coef[-1,], summary(fm_inv)$coef[-1,], summary(fm_pwage)$coef[-1,] )[,1:2] @@ -70,10 +75,27 @@ m = cbind(m, "lower"=m$Estimate-m$Std..Error, "upper"=m$Estimate+m$Std..Error ) # ggplot estimates library(ggplot2) -ggplot(data=m, aes(x=interaction(var,eq), y=Estimate, color=method)) + +library(reshape) +library(gridExtra) +library(phylopath) + +longform = melt( as.data.frame(KleinI) ) + longform$year = rep( time(KleinI), 9 ) +p1 = ggplot( data=longform, aes(x=year, y=value) ) + + facet_grid( rows=vars(variable), scales="free" ) + + geom_line( ) + +p2 = ggplot(data=m, aes(x=interaction(var,eq), y=Estimate, color=method)) + geom_point( position=position_dodge(0.9) ) + geom_errorbar( aes(ymax=as.numeric(upper),ymin=as.numeric(lower)), width=0.25, position=position_dodge(0.9)) # + +p3 = plot( as_fitted_DAG(fit) ) + + expand_limits(x = c(-0.2,1) ) +p4 = plot( as_fitted_DAG(fit, lag=-1) ) + + expand_limits(x = c(-0.2,1), y = c(-0.2,0) ) + +grid.arrange( arrangeGrob(p1, arrangeGrob(p2, p3, p4), ncol=2) ) ``` Results show that both packages provide (almost) identical estimates and standard errors. @@ -90,16 +112,21 @@ Here, we compare fits using `dsem` with `dynlm`, as well as a vector autoregress ```{r, echo=TRUE, message=FALSE, fig.width=7, fig.height=5} data(isle_royale) data = ts( log(isle_royale[,2:3]), start=1959) +family = c("fixed","fixed") sem = " wolves -> wolves, -1, arW moose -> wolves, -1, MtoW wolves -> moose, -1, WtoM moose -> moose, -1, arM - moose <-> moose, 0, sdM - wolves <-> wolves, 0, SDW " -fit = dsem( sem=sem, tsdata=data, estimate_delta0=TRUE, upper=0.99, lower=-0.99, quiet=TRUE ) +# initial first without delta0 (to improve starting values) +fit0 = dsem( sem=sem, tsdata=data, estimate_delta0=FALSE, + quiet=TRUE, getsd=FALSE, family=family ) +# Refit with delta0 +fit = dsem( sem=sem, tsdata=data, estimate_delta0=TRUE, + quiet=TRUE, parameters=fit0$obj$env$parList(), + family=family, getJointPrecision = TRUE ) # dynlm fm_wolf = dynlm( wolves ~ 1 + L(wolves) + L(moose), data=data ) # @@ -122,29 +149,46 @@ SE <- MARSSparamCIs( kem.1 ) library(vars) var = VAR( data, type="const" ) -## Compile -library(TMB) # needed to do summary for m2 +### Compile m1 = rbind( summary(fm_wolf)$coef[-1,], summary(fm_moose)$coef[-1,] )[,1:2] m2 = summary(fit$opt$SD)[1:4,] +#m2 = cbind( "Estimate"=fit$opt$par, "Std. Error"=fit$opt$SD$par.fixed )[1:4,] m3 = cbind( SE$parMean[c(1,3,2,4)], SE$par.se$B[c(1,3,2,4)] ) colnames(m3) = colnames(m2) m4 = rbind( summary(var$varresult$wolves)$coef[-3,], summary(var$varresult$moose)$coef[-3,] )[,1:2] # Bundle m = rbind( - data.frame("var"=rownames(m1),m1,"method"="dynlm","eq"=rep(c("Wolf","Moose"),each=2)), - data.frame("var"=rownames(m1),m2,"method"="dsem","eq"=rep(c("Wolf","Moose"),each=2)), - data.frame("var"=rownames(m1),m3,"method"="MARSS","eq"=rep(c("Wolf","Moose"),each=2)), - data.frame("var"=rownames(m1),m4,"method"="vars","eq"=rep(c("Wolf","Moose"),each=2)) + data.frame("var"=rownames(m1), m1, "method"="dynlm", "eq"=rep(c("Wolf","Moose"),each=2)), + data.frame("var"=rownames(m1), m2, "method"="dsem", "eq"=rep(c("Wolf","Moose"),each=2)), + data.frame("var"=rownames(m1), m3, "method"="MARSS", "eq"=rep(c("Wolf","Moose"),each=2)), + data.frame("var"=rownames(m1), m4, "method"="vars", "eq"=rep(c("Wolf","Moose"),each=2)) ) +#knitr::kable( m1, digits=3) +#knitr::kable( m2, digits=3) + m = cbind(m, "lower"=m$Estimate-m$Std..Error, "upper"=m$Estimate+m$Std..Error ) -# + # ggplot estimates ... interaction(x,y) causes an error sometimes library(ggplot2) -ggplot(data=m, aes(x=interaction(var,eq), y=Estimate, color=method)) + +library(ggpubr) +library(ggraph) +longform = reshape( isle_royale, idvar = "year", direction="long", varying=list(2:3), v.names="abundance", timevar="species", times=c("wolves","moose") ) +p1 = ggplot( data=longform, aes(x=year, y=abundance) ) + + facet_grid( rows=vars(species), scales="free" ) + + geom_point( ) + +p2 = ggplot(data=m, aes(x=interaction(var,eq), y=Estimate, color=method)) + geom_point( position=position_dodge(0.9) ) + geom_errorbar( aes(ymax=as.numeric(upper),ymin=as.numeric(lower)), width=0.25, position=position_dodge(0.9)) # +p3 = plot( as_fitted_DAG(fit, lag=-1), rotation=0 ) + + geom_edge_loop( aes( label=round(weight,2), direction=0)) + #arrow=arrow(), , angle_calc="along", label_dodge=grid::unit(10,"points") ) + expand_limits(x = c(-0.1,0) ) + +ggarrange( p1, p2, p3, + labels = c("Time-series data", "Estimated effects", "Fitted path digram"), + ncol = 1, nrow = 3) ``` Results again show that `dsem` can estimate parameters for a vector autoregressive model (VAM), and it exactly matches results from `vars`, using `dynlm`, or using `MARSS`. @@ -169,15 +213,15 @@ sem = " log_Cfall -> log_PercentCop, 0, Cfall_to_Scop log_SSB -> log_RperS, 0, SSB_to_RperS - log_seaice <-> log_seaice, 0, var1, 0.001 - log_CP <-> log_CP,0, var2, 0.001 - log_Cspring <-> log_Cspring, 0, var3, 0.001 - log_Cfall <-> log_Cfall, 0, var4, 0.001 - log_Esummer <-> log_Esummer, 0, var5, 0.001 - log_SSB <-> log_SSB, 0, var6, 0.001 - log_RperS <-> log_RperS, 0, var7, 0.001 - log_PercentEuph <-> log_PercentEuph, 0, var8, 0.001 - log_PercentCop <-> log_PercentCop, 0, var9, 0.001 + #log_seaice <-> log_seaice, 0, var1, 0.001 + #log_CP <-> log_CP,0, var2, 0.001 + #log_Cspring <-> log_Cspring, 0, var3, 0.001 + #log_Cfall <-> log_Cfall, 0, var4, 0.001 + #log_Esummer <-> log_Esummer, 0, var5, 0.001 + #log_SSB <-> log_SSB, 0, var6, 0.001 + #log_RperS <-> log_RperS, 0, var7, 0.001 + #log_PercentEuph <-> log_PercentEuph, 0, var8, 0.001 + #log_PercentCop <-> log_PercentCop, 0, var9, 0.001 log_seaice -> log_seaice, -1, AR1, 0.001 log_CP -> log_CP, -1, AR2, 0.001 @@ -216,19 +260,30 @@ for(i in 1:ncol(bering_sea)){ library(phylopath) library(ggplot2) library(ggpubr) +library(reshape) +library(gridExtra) +longform = melt( bering_sea ) + longform$year = rep( 1963:2023, ncol(bering_sea) ) +p0 = ggplot( data=longform, aes(x=year, y=value) ) + + facet_grid( rows=vars(variable), scales="free" ) + + geom_point( ) + p1 = plot( (as_fitted_DAG(fit)), edge.width=1, type="width", text_size=4, show.legend=FALSE, - arrow = grid::arrow(type='closed', 18, grid::unit(10,'points')) ) + arrow = grid::arrow(type='closed', 18, grid::unit(10,'points')) ) + + scale_x_continuous(expand = c(0.4, 0.1)) p1$layers[[1]]$mapping$edge_width = 1 p2 = plot( (as_fitted_DAG(fit, what="p_value")), edge.width=1, type="width", text_size=4, show.legend=FALSE, colors=c('black', 'black'), - arrow = grid::arrow(type='closed', 18, grid::unit(10,'points')) ) + arrow = grid::arrow(type='closed', 18, grid::unit(10,'points')) ) + + scale_x_continuous(expand = c(0.4, 0.1)) p2$layers[[1]]$mapping$edge_width = 0.5 -ggarrange( p1 + scale_x_continuous(expand = c(0.4, 0.1)), - p2 + scale_x_continuous(expand = c(0.4, 0.1)), - labels = c("Simultaneous effects", "Two-sided p-value"), - ncol = 1, nrow = 2) +#grid.arrange( arrangeGrob( p0+ggtitle("timeseries"), +# arrangeGrob( p1+ggtitle("Estimated path diagram"), +# p2+ggtitle("Estimated p-values"), nrow=2), ncol=2 ) ) +ggarrange(p1, p2, labels = c("Simultaneous effects", "Two-sided p-value"), + ncol = 1, nrow = 2) ``` These results are further discussed in the paper describing dsem. @@ -374,6 +429,17 @@ p2 = plot( get_part(as_fitted_DAG(fit, what="p_value" )), type="width", show.legend=FALSE, colors=c('black', 'black')) p2$layers[[1]]$mapping$edge_width = 0.1 +longform = melt( sea_otter[,-1], as.is=TRUE ) +longform$X1 = 1999:2019[longform$X1] +longform$Site = gsub( "log_Kelp_", "", + gsub( "log_Urchins_", "", + gsub( "Pycno_", "", + gsub( "log_Otter_Count_", "", longform$X2)))) +longform$Species = sapply( seq_len(nrow(longform)), FUN=\(i)gsub(longform$Site[i],"",longform$X2[i]) ) +p3 = ggplot( data=longform, aes(x=X1, y=value, col=Species) ) + + facet_grid( rows=vars(Site), scales="free" ) + + geom_line( ) + ggarrange(p1 + scale_x_continuous(expand = c(0.3, 0)), p2 + scale_x_continuous(expand = c(0.3, 0)), labels = c("Simultaneous effects", "Two-sided p-value"), diff --git a/vignettes/vignette.pdf b/vignettes/vignette.pdf new file mode 100644 index 0000000000000000000000000000000000000000..74f37f31a0c4ffc55840ced57c1920711351b6d5 GIT binary patch literal 314487 zcma%i18^6Wg|JpV+o-?bykQZQD4pZQHhO<0N_c?!Eteuj>6(|J&8IyL+uQ 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