From 1d8257dbacfda3cd2342080cd6108a105a498388 Mon Sep 17 00:00:00 2001 From: James-Thorson-NOAA <50178738+James-Thorson-NOAA@users.noreply.github.com> Date: Tue, 17 Sep 2024 00:18:48 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20James-Th?= =?UTF-8?q?orson-NOAA/dsem@298cbb73c8a456d6198e2cb27ee971cfa72d0664=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- articles/dynamic_factor_analysis.html | 2 +- articles/vignette.html | 94 +++++------------- .../figure-html/unnamed-chunk-10-1.png | Bin 189883 -> 139117 bytes .../figure-html/unnamed-chunk-11-1.png | Bin 139117 -> 84347 bytes .../figure-html/unnamed-chunk-7-1.png | Bin 35990 -> 150048 bytes authors.html | 2 +- index.html | 5 + news/index.html | 2 +- pkgdown.yml | 2 +- reference/TMBAIC.html | 2 +- reference/as_fitted_DAG.html | 2 +- reference/as_sem.html | 2 +- reference/bering_sea.html | 2 +- reference/classify_variables.html | 2 +- reference/dsem.html | 2 +- reference/dsem_control.html | 2 +- reference/index.html | 8 +- reference/isle_royale.html | 2 +- reference/list_parameters.html | 2 +- reference/logLik.dsem.html | 6 +- reference/make_dfa.html | 2 +- reference/make_dsem_ram.html | 2 +- reference/parse_path.html | 2 +- reference/plot.dsem.html | 2 +- reference/predict.dsem.html | 2 +- reference/print.dsem.html | 2 +- reference/residuals.dsem.html | 2 +- reference/sea_otter.html | 2 +- reference/simulate.dsem.html | 2 +- reference/summary.dsem.html | 2 +- reference/vcov.dsem.html | 2 +- search.json | 2 +- sitemap.xml | 1 - 33 files changed, 57 insertions(+), 107 deletions(-) diff --git a/articles/dynamic_factor_analysis.html b/articles/dynamic_factor_analysis.html index ed4ab03..171dc28 100644 --- a/articles/dynamic_factor_analysis.html +++ b/articles/dynamic_factor_analysis.html @@ -62,7 +62,7 @@

James Thorson

- Source: vignettes/dynamic_factor_analysis.Rmd + Source: vignettes/dynamic_factor_analysis.Rmd
dynamic_factor_analysis.Rmd
diff --git a/articles/vignette.html b/articles/vignette.html index 467889e..97f27b7 100644 --- a/articles/vignette.html +++ b/articles/vignette.html @@ -62,7 +62,7 @@

James Thorson

- Source: vignettes/vignette.Rmd + Source: vignettes/vignette.Rmd
vignette.Rmd
@@ -80,64 +80,12 @@

James specification of simultaneous and lagged effects in a non-recursive structure. We here highlight a few features in particular.

-

Comparison with linear models -

-

We first show that dsem is identical to a linear model. -To do so, we simulate data with a single response and single -predictor:

-
-# simulate normal distribution
-x = rnorm(100)
-y = 1 + 0.5 * x + rnorm(100)
-data = data.frame(x=x, y=y)
-
-# Fit as linear model
-Lm = lm( y ~ x, data=data )
-
-# Fit as DSEM
-fit = dsem( sem = "x -> y, 0, beta",
-            tsdata = ts(data),
-            control = dsem_control(quiet=TRUE) )
-#> 1 regions found.
-#> Using 1 threads
-#> 1 regions found.
-#> Using 1 threads
-
-# Display output
-m1 = rbind(
-  "lm" = summary(Lm)$coef[2,1:2],
-  "dsem" = summary(fit)[1,9:10]
-)
-knitr::kable( m1, digits=3)
- - - - - - - - - - - - - - - - - - -
EstimateStd_Error
lm0.4740.097
dsem0.4740.097
-

This shows that linear and dynamic structural equation models give -identical estimates of the single path coefficient.

-
-

Comparison with dynamic linear models

We first demonstrate that dsem gives identical results to dynlm for a well-known econometric model, the Klein-1 model.

-
+
 data(KleinI, package="AER")
 TS = ts(data.frame(KleinI, "time"=time(KleinI) - 1931))
 
@@ -170,6 +118,10 @@ 

Comparison with dynamic linear mo control = dsem_control( quiet = TRUE, newton_loops = 0) ) +#> 1 regions found. +#> Using 1 threads +#> 1 regions found. +#> Using 1 threads # Compile m1 = rbind( summary(fm_cons)$coef[-1,], @@ -201,13 +153,13 @@

Comparison with dynamic linear mo expand_limits(x = c(-0.2,1), y = c(-0.2,0) ) p1

-

-
+

+
 p2
-

-
+

+
 grid.arrange( arrangeGrob(p3, p4, nrow=2) )
-

+

Results show that both packages provide (almost) identical estimates and standard errors.

We can also compare results using the Laplace approximation against @@ -216,13 +168,13 @@

Comparison with dynamic linear mo variance parameters (presumably because those follow a chi-squared distribution with positive skewness), but otherwise the two produce similar estimates.

-
+
 library(tmbstan)
 
 # MCMC for both fixed and random effects
 mcmc = tmbstan( fit$obj, init="last.par.best" )
 summary_mcmc = summary(mcmc)
-
+
 # long-form data frame
 m1 = summary_mcmc$summary[1:17,c('mean','sd')]
 rownames(m1) = paste0( "b", seq_len(nrow(m1)) )
@@ -239,7 +191,7 @@ 

Comparison with dynamic linear mo 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)) #

-

+

Comparison with vector autoregressive models @@ -259,7 +211,7 @@

Comparison with vector aut

Here, we compare fits using dsem with dynlm, as well as a vector autoregressive model package vars, and finally with MARSS.

-
+
 data(isle_royale)
 data = ts( log(isle_royale[,2:3]), start=1959)
 
@@ -377,7 +329,7 @@ 

Comparison with vector aut 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 @@ -388,7 +340,7 @@

Multi-causal ecosystem synthesis

We next replicate an analysis involving climate, forage fishes, stomach contents, and recruitment of a predatory fish.

-
+
 data(bering_sea)
 Z = ts( bering_sea )
 family = rep('fixed', ncol(bering_sea))
@@ -422,7 +374,7 @@ 

Multi-causal ecosystem synthesis control = dsem_control(use_REML=FALSE, quiet=TRUE) ) ParHat = fit$obj$env$parList() # summary( fit )

-
+
 # Timeseries plot
 oldpar <- par(no.readonly = TRUE)
 par( mfcol=c(3,3), mar=c(2,2,2,0), mgp=c(2,0.5,0), tck=-0.02 )
@@ -441,8 +393,8 @@ 

Multi-causal ecosystem synthesis y=c(tmp[,3],rev(tmp[,4])), col=rgb(0,0,1,0.2), border=NA ) } par(oldpar)

-

-
+

+
 #
 library(phylopath)
 library(ggplot2)
@@ -471,7 +423,7 @@ 

Multi-causal ecosystem synthesis# 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.

@@ -480,7 +432,7 @@

Site-replicated trophic cascadeFinally, we replicate an analysis involving a trophic cascade involving sea stars predators, sea urchin consumers, and kelp producers.

-
+
 data(sea_otter)
 Z = ts( sea_otter[,-1] )
 
@@ -634,7 +586,7 @@ 

Site-replicated trophic cascade p2 + scale_x_continuous(expand = c(0.3, 0)), labels = c("Simultaneous effects", "Two-sided p-value"), ncol = 1, nrow = 2)

-

+

Again, these results are further discussed in the paper describing dsem.

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  • Citation

    -

    Source: inst/CITATION

    +

    Source: inst/CITATION

    Thorson, J. T., Andrews, A. G., Essington, T., & Large, S. (2024). Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms. Methods in Ecology and Evolution.

    @Article{,
    diff --git a/index.html b/index.html
    index f1acfdb..77d370e 100644
    --- a/index.html
    +++ b/index.html
    @@ -83,6 +83,11 @@ 

    Dynamic structural equation models

    Please see package vignettes for more details regarding syntax and features.

    +
    +

    Citation +

    +

    Thorson, J. T., Andrews, A. G., Essington, T., & Large, S. (2024). Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms. Methods in Ecology and Evolution 15(4): 744-755. https://doi.org/10.1111/2041-210X.14289

    +