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 @@
vignettes/dynamic_factor_analysis.Rmd
+ Source: vignettes/dynamic_factor_analysis.Rmd
dynamic_factor_analysis.Rmd
vignettes/vignette.Rmd
+ Source: vignettes/vignette.Rmd
vignette.Rmd
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)$coef[2,1:2],
- "dsem" = summary(fit)[1,9:10]
-)
-knitr::kable( m1, digits=3)
- | Estimate | -Std_Error | -
---|---|---|
lm | -0.474 | -0.097 | -
dsem | -0.474 | -0.097 | -
This shows that linear and dynamic structural equation models give -identical estimates of the single path coefficient.
-We first demonstrate that dsem
gives identical results
to dynlm
for a well-known econometric model, the Klein-1
model.
+- -data(KleinI, package="AER") TS = ts(data.frame(KleinI, "time"=time(KleinI) - 1931)) @@ -170,6 +118,10 @@
Comparison with dynamic linear mo control = dsem_control( quiet = TRUE, newton_loops = 0) ) +#> 1 regions found. +#> Using 1 threads +#> 1 regions found. +#> Using 1 threads # Compile m1 = rbind( summary(fm_cons)$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
withdynlm
, as well as a vector autoregressive model packagevars
, and finally withMARSS
.+- +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 fromvars
, usingdynlm
, 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. -
diff --git a/articles/vignette_files/figure-html/unnamed-chunk-10-1.png b/articles/vignette_files/figure-html/unnamed-chunk-10-1.png index 0a3024801e3af9a9ba60d69e781d91a6bad72fe1..a9cb2dc4838d45dabced3b344c9e912fc936fce1 100644 GIT binary patch literal 139117 zcmeFZ^;=cj_XfJKPyz9%2nJw)v>+)+8FWfF3L;(7jdBzf1Zioc8|e;3L+- +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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