From ffad27d69249926b4771fc804d1adab9ae9fd65e Mon Sep 17 00:00:00 2001 From: Jim Thorson <50178738+James-Thorson-NOAA@users.noreply.github.com> Date: Mon, 1 Apr 2024 07:30:18 -0700 Subject: [PATCH] small fixes in docs --- README.md | 2 +- vignettes/dynamic_factor_analysis.Rmd | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 2ee358f..b63b185 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,7 @@ The model has several advantages: * It is rapidly fitted as a Gaussian Markov random field (GMRF) in a Generalized Linear Mixed Model (GLMM), with speed and asymptotics associated with each * It allows granular control over the number of parameters (and restrictions on parameters) used to structure the covariance among variables and over time, -_phylosem_ is specifically intended as a minimal implementation, and uses standard packages to simplify input/output formatting: +_dsem_ is specifically intended as a minimal implementation, and uses standard packages to simplify input/output formatting: * Input: time-series defined using class _ts_, with `NA` for missing values * Input: structural trade-offs specified using syntax defined by package _sem_ diff --git a/vignettes/dynamic_factor_analysis.Rmd b/vignettes/dynamic_factor_analysis.Rmd index b751bb5..d9402f5 100644 --- a/vignettes/dynamic_factor_analysis.Rmd +++ b/vignettes/dynamic_factor_analysis.Rmd @@ -140,7 +140,7 @@ These estimated states follow the data more closely and have smaller estimated c ## Reduced-rank factor model with measurement errors -Next, we can specify three factors factors while eliminating additional process error and estimating measurement errors. This requires us to switch to `gmrf_parameterization = "projection"`, so that we can fit a rank-deficient Gaussian Markov random field: +Next, we can specify two factors factors while eliminating additional process error and estimating measurement errors. This requires us to switch to `gmrf_parameterization = "projection"`, so that we can fit a rank-deficient Gaussian Markov random field: ```{r, echo=TRUE, message=FALSE, fig.width=7, fig.height=7} # Add factors to data