#> Out of sample CRPS:
- #> [1] 2901.021
+ #> [1] 2852.181
And the estimated latent trend component, again using the more flexible
`plot_mvgam_...()` option to show first derivatives of the estimated
@@ -626,7 +628,7 @@ summary(mod, include_betas = FALSE)
#> 0 of 2000 iterations saturated the maximum tree depth of 12 (0%)
#> E-FMI indicated no pathological behavior
#>
-#> Samples were drawn using NUTS(diag_e) at Fri Mar 01 9:46:54 AM 2024.
+#> Samples were drawn using NUTS(diag_e) at Tue Mar 12 1:51:32 PM 2024.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split MCMC chains
#> (at convergence, Rhat = 1)
diff --git a/docs/articles/data_in_mvgam.html b/docs/articles/data_in_mvgam.html
index 6282b2f6..00004786 100644
--- a/docs/articles/data_in_mvgam.html
+++ b/docs/articles/data_in_mvgam.html
@@ -78,7 +78,7 @@
mvgam
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 5e2a8923..98d98d3c 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -9,7 +9,7 @@ articles: shared_states: shared_states.html time_varying_effects: time_varying_effects.html trend_formulas: trend_formulas.html -last_built: 2024-01-29T05:37Z +last_built: 2024-03-12T03:54Z urls: reference: https://nicholasjclark.github.io/mvgam/reference article: https://nicholasjclark.github.io/mvgam/articles diff --git a/docs/reference/figures/README-unnamed-chunk-13-1.png b/docs/reference/figures/README-unnamed-chunk-13-1.png index 97f08209..63559a6a 100644 Binary files a/docs/reference/figures/README-unnamed-chunk-13-1.png and b/docs/reference/figures/README-unnamed-chunk-13-1.png differ diff --git a/docs/reference/figures/README-unnamed-chunk-14-1.png b/docs/reference/figures/README-unnamed-chunk-14-1.png index 8e34a550..1dca9500 100644 Binary files a/docs/reference/figures/README-unnamed-chunk-14-1.png and b/docs/reference/figures/README-unnamed-chunk-14-1.png differ diff --git a/docs/reference/figures/README-unnamed-chunk-15-1.png b/docs/reference/figures/README-unnamed-chunk-15-1.png index f801d30b..8b1142de 100644 Binary files a/docs/reference/figures/README-unnamed-chunk-15-1.png and b/docs/reference/figures/README-unnamed-chunk-15-1.png differ diff --git a/docs/reference/figures/README-unnamed-chunk-16-1.png b/docs/reference/figures/README-unnamed-chunk-16-1.png index 94d4bf12..ba4439e4 100644 Binary files a/docs/reference/figures/README-unnamed-chunk-16-1.png and b/docs/reference/figures/README-unnamed-chunk-16-1.png differ diff --git a/docs/reference/figures/README-unnamed-chunk-17-1.png b/docs/reference/figures/README-unnamed-chunk-17-1.png index 85e0db39..d722ba66 100644 Binary files a/docs/reference/figures/README-unnamed-chunk-17-1.png and b/docs/reference/figures/README-unnamed-chunk-17-1.png differ diff --git a/docs/reference/figures/README-unnamed-chunk-18-1.png b/docs/reference/figures/README-unnamed-chunk-18-1.png index fbb3a6b1..c72e0706 100644 Binary files a/docs/reference/figures/README-unnamed-chunk-18-1.png and b/docs/reference/figures/README-unnamed-chunk-18-1.png differ diff --git a/docs/reference/figures/README-unnamed-chunk-19-1.png b/docs/reference/figures/README-unnamed-chunk-19-1.png index 7f2f84c9..87c0bc2e 100644 Binary files a/docs/reference/figures/README-unnamed-chunk-19-1.png and b/docs/reference/figures/README-unnamed-chunk-19-1.png differ diff --git a/docs/reference/figures/README-unnamed-chunk-20-1.png b/docs/reference/figures/README-unnamed-chunk-20-1.png index 84f2b09d..d39944b0 100644 Binary files a/docs/reference/figures/README-unnamed-chunk-20-1.png and b/docs/reference/figures/README-unnamed-chunk-20-1.png differ diff --git a/docs/reference/figures/README-unnamed-chunk-21-1.png b/docs/reference/figures/README-unnamed-chunk-21-1.png index 246e3eef..eb759d89 100644 Binary files a/docs/reference/figures/README-unnamed-chunk-21-1.png and b/docs/reference/figures/README-unnamed-chunk-21-1.png differ diff --git a/docs/reference/figures/README-unnamed-chunk-22-1.png b/docs/reference/figures/README-unnamed-chunk-22-1.png index e1b4751a..a748b548 100644 Binary files a/docs/reference/figures/README-unnamed-chunk-22-1.png and b/docs/reference/figures/README-unnamed-chunk-22-1.png differ diff --git a/docs/reference/figures/README-unnamed-chunk-23-1.png b/docs/reference/figures/README-unnamed-chunk-23-1.png index 00f1d110..45ab957c 100644 Binary files a/docs/reference/figures/README-unnamed-chunk-23-1.png and b/docs/reference/figures/README-unnamed-chunk-23-1.png differ diff --git a/docs/reference/figures/README-unnamed-chunk-24-1.png b/docs/reference/figures/README-unnamed-chunk-24-1.png index 24d051b6..40e68091 100644 Binary files a/docs/reference/figures/README-unnamed-chunk-24-1.png and b/docs/reference/figures/README-unnamed-chunk-24-1.png differ diff --git a/docs/reference/figures/mvgam_logo.png b/docs/reference/figures/mvgam_logo.png new file mode 100644 index 00000000..ce0ace9b Binary files /dev/null and b/docs/reference/figures/mvgam_logo.png differ diff --git a/docs/reference/get_mvgam_priors.html b/docs/reference/get_mvgam_priors.html index 273505f3..a514ddc0 100644 --- a/docs/reference/get_mvgam_priors.html +++ b/docs/reference/get_mvgam_priors.html @@ -402,13 +402,13 @@Examples#> 6 trend sd sigma ~ student_t(3, 0, 2.5);
#> 7 inverse of NB dispsersion phi_inv ~ student_t(3, 0, 0.1);
#> example_change new_lowerbound new_upperbound
-#> 1 lambda ~ exponential(0.03); NA NA
-#> 2 mu_raw ~ normal(0.56, 0.85); NA NA
-#> 3 sigma_raw ~ exponential(0.28); NA NA
-#> 4 ar1 ~ normal(0.03, 0.94); NA NA
-#> 5 ar2 ~ normal(0.08, 0.56); NA NA
-#> 6 sigma ~ exponential(0.97); NA NA
-#> 7 phi_inv ~ normal(0.75, 0.12); NA NA
+#> 1 lambda ~ exponential(0.18); NA NA
+#> 2 mu_raw ~ normal(0.23, 0.71); NA NA
+#> 3 sigma_raw ~ exponential(0.79); NA NA
+#> 4 ar1 ~ normal(-0.57, 0.81); NA NA
+#> 5 ar2 ~ normal(0.19, 0.65); NA NA
+#> 6 sigma ~ exponential(0.12); NA NA
+#> 7 phi_inv ~ normal(-0.61, 0.11); NA NA
# Make a few changes; first, change the population mean for the series-level
# random intercepts
diff --git a/docs/reference/monotonic.html b/docs/reference/monotonic.html
index c243b75d..368ad6d9 100644
--- a/docs/reference/monotonic.html
+++ b/docs/reference/monotonic.html
@@ -64,16 +64,16 @@
Usage
# S3 method for moi.smooth.spec
-smooth.construct(object, data, knots)
+smooth.construct(object, data, knots)
# S3 method for mod.smooth.spec
-smooth.construct(object, data, knots)
+smooth.construct(object, data, knots)
# S3 method for moi.smooth
-Predict.matrix(object, data)
+Predict.matrix(object, data)
# S3 method for mod.smooth
-Predict.matrix(object, data)
+Predict.matrix(object, data)
Usage
# S3 method for moi.smooth.spec
-smooth.construct(object, data, knots)
+smooth.construct(object, data, knots)
# S3 method for mod.smooth.spec
-smooth.construct(object, data, knots)
+smooth.construct(object, data, knots)
# S3 method for moi.smooth
-Predict.matrix(object, data)
+Predict.matrix(object, data)
# S3 method for mod.smooth
-Predict.matrix(object, data)Examples
# A standard TRPS smooth doesn't capture monotonicity
mod_data <- data.frame(y = y, x = x)
-mod <- gam(y ~ s(x, k = 16),
+mod <- gam(y ~ s(x, k = 16),
data = mod_data,
family = gaussian())
@@ -166,7 +166,7 @@ Examples
# Using the 'moi' basis in mvgam rectifies this
mod_data$time <- 1:NROW(mod_data)
-mod2 <- mvgam(y ~ s(x, bs = 'moi', k = 18),
+mod2 <- mvgam(y ~ s(x, bs = 'moi', k = 18),
data = mod_data,
family = gaussian())
@@ -199,7 +199,7 @@ Examplesmod_data$time <- 1:NROW(mod_data)
# Fit a model with different smooths per factor level
-mod <- mvgam(y ~ s(x, bs = 'moi', by = fac, k = 8),
+mod <- mvgam(y ~ s(x, bs = 'moi', by = fac, k = 8),
data = mod_data,
family = gaussian())
diff --git a/docs/reference/mvgam.html b/docs/reference/mvgam.html
index a2c50701..cdc9d72f 100644
--- a/docs/reference/mvgam.html
+++ b/docs/reference/mvgam.html
@@ -91,6 +91,7 @@ Usage
prior_simulation = FALSE,
return_model_data = FALSE,
family = "poisson",
+ share_obs_params = FALSE,
use_lv = FALSE,
n_lv,
trend_map,
@@ -122,7 +123,7 @@ Usage
Arguments
- formula
A character string specifying the GAM observation model formula. These are exactly like the formula
-for a GLM except that smooth terms, s(), te(), ti(), t2(), as well as time-varying
+for a GLM except that smooth terms, s(), te(), ti(), t2(), as well as time-varying
dynamic() terms, can be added to the right hand side
to specify that the linear predictor depends on smooth functions of predictors
(or linear functionals of these). In nmix() family models, the formula is used to
@@ -201,22 +202,31 @@
Argumentsnb() for count data
poisson() for count data
gaussian() for real-valued data
betar() for proportional data on (0,1)
lognormal() for non-negative real-valued data
betar() for proportional data on (0,1)
lognormal() for non-negative real-valued data
student_t() for real-valued data
Gamma() for non-negative real-valued data
nmix() for count data with imperfect detection modeled via a
State-Space N-Mixture model. The latent states are Poisson, capturing the 'true' latent
abundance, while the observation process is Binomial to account for imperfect detection.
See mvgam_families for an example of how to use this family
Note that only nb() and poisson() are available if using JAGS as the backend.
+
Note that only nb() and poisson() are available if using JAGS as the backend.
Default is poisson().
See mvgam_families for more details
- share_obs_params
+logical. If TRUE and the family
+has additional family-specific observation parameters (e.g. variance components in
+student_t() or gaussian(), or dispersion parameters in nb() or betar()),
+these parameters will be shared across all series. This is handy if you have multiple
+time series that you believe share some properties, such as being from the same
+species over different spatial units. Default is FALSE.
- use_lv
logical. If TRUE, use dynamic factors to estimate series'
latent trends in a reduced dimension format. Only available for
@@ -453,8 +463,9 @@
Details
Observation level parameters: When more than one series is included in data and an
observation family that contains more than one parameter is used, additional observation family parameters
-(i.e. phi for nb() or sigma for gaussian()) are
-estimated independently for each series.
+(i.e. phi for nb() or sigma for gaussian()) are
+by default estimated independently for each series. But if you wish for the series to share
+the same observation parameters, set share_obs_params = TRUE
Factor regularisation: When using a dynamic factor model for the trends with JAGS factor precisions are given
regularized penalty priors to theoretically allow some factors to be dropped from the model by squeezing increasing
factors' variances to zero. This is done to help protect against selecting too many latent factors than are needed to
@@ -507,7 +518,7 @@ Examples# Formulate a model using Stan where series share a cyclic smooth for
# seasonality and each series has an independent random walk temporal process;
# Set run_model = FALSE to inspect the returned objects
-mod1 <- mvgam(formula = y ~ s(season, bs = 'cc'),
+mod1 <- mvgam(formula = y ~ s(season, bs = 'cc'),
data = dat$data_train,
trend_model = 'RW',
family = 'poisson',
@@ -538,7 +549,7 @@ Examples refresh = 100)
# Now fit the model using mvgam with the Stan backend
-mod1 <- mvgam(formula = y ~ s(season, bs = 'cc'),
+mod1 <- mvgam(formula = y ~ s(season, bs = 'cc'),
data = dat$data_train,
trend_model = 'RW',
family = poisson(),
@@ -574,7 +585,7 @@ Examplesplot(mod1, type = 'smooths', realisations = TRUE)
# Plot conditional response predictions using marginaleffects
-plot(conditional_effects(mod1), ask = FALSE)
+plot(conditional_effects(mod1), ask = FALSE)
plot_predictions(mod1, condition = 'season', points = 0.5)
# Extract observation model beta coefficient draws as a data.frame
@@ -597,7 +608,7 @@ Examples trend = c(1,1,2))
# Fit the model using AR1 trends
-mod <- mvgam(y ~ s(season, bs = 'cc'),
+mod <- mvgam(y ~ s(season, bs = 'cc'),
trend_map = trend_map,
trend_model = 'AR1',
data = mod_data,
@@ -662,9 +673,9 @@ Examples# Fit a model that includes the offset in the linear predictor as well as
# hierarchical seasonal smooths
mod <- mvgam(formula = y ~ offset(offset) +
- s(series, bs = 're') +
- s(season, bs = 'cc') +
- s(season, by = series, m = 1, k = 5),
+ s(series, bs = 're') +
+ s(season, bs = 'cc') +
+ s(season, by = series, m = 1, k = 5),
data = dat$data_train,
trend_model = 'None',
use_stan = TRUE)
@@ -765,7 +776,7 @@ Examples
# View the changepoints with ggplot2 utilities
library(ggplot2)
-mcmc_plot(mod, variable = 'delta_trend',
+mcmc_plot(mod, variable = 'delta_trend',
regex = TRUE) +
scale_y_discrete(labels = mod$trend_model$changepoints) +
labs(y = 'Potential changepoint',
diff --git a/docs/reference/update.mvgam.html b/docs/reference/update.mvgam.html
index c3b8b5a0..823f41e0 100644
--- a/docs/reference/update.mvgam.html
+++ b/docs/reference/update.mvgam.html
@@ -72,6 +72,7 @@ Usage
use_lv,
n_lv,
family,
+ share_obs_params,
priors,
lfo = FALSE,
...
@@ -175,6 +176,15 @@ Argumentsmvgam_families for more details
- share_obs_params
+logical. If TRUE and the family
+has additional family-specific observation parameters (e.g. variance components in
+student_t() or gaussian(), or dispersion parameters in nb() or betar()),
+these parameters will be shared across all series. This is handy if you have multiple
+time series that you believe share some properties, such as being from the same
+species over different spatial units. Default is FALSE.
- priors
An optional data.frame with prior
definitions (in JAGS or Stan syntax). if using Stan, this can also be an object of
diff --git a/index.Rmd b/index.Rmd
index e7112bb5..1e409a8d 100644
--- a/index.Rmd
+++ b/index.Rmd
@@ -5,6 +5,8 @@ always_allow_html: true
+
[](https://mc-stan.org/)
+
## mvgam
**M**ulti**V**ariate (Dynamic) **G**eneralized **A**ddivite **M**odels
diff --git a/index.md b/index.md
index 6441ce50..2fbd2c11 100644
--- a/index.md
+++ b/index.md
@@ -1,6 +1,8 @@
+[](https://mc-stan.org/)
+
## mvgam
**M**ulti**V**ariate (Dynamic) **G**eneralized **A**ddivite **M**odels
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diff --git a/man/figures/mvgam_logo.png b/man/figures/mvgam_logo.png
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index 00000000..ce0ace9b
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Examplesmod_data$time <- 1:NROW(mod_data)
# Fit a model with different smooths per factor level
-mod <- mvgam(y ~ s(x, bs = 'moi', by = fac, k = 8),
+mod <- mvgam(y ~ s(x, bs = 'moi', by = fac, k = 8),
data = mod_data,
family = gaussian())
diff --git a/docs/reference/mvgam.html b/docs/reference/mvgam.html
index a2c50701..cdc9d72f 100644
--- a/docs/reference/mvgam.html
+++ b/docs/reference/mvgam.html
@@ -91,6 +91,7 @@ Usage
prior_simulation = FALSE,
return_model_data = FALSE,
family = "poisson",
+ share_obs_params = FALSE,
use_lv = FALSE,
n_lv,
trend_map,
@@ -122,7 +123,7 @@ Usage
Arguments
- formula
A character string specifying the GAM observation model formula. These are exactly like the formula
-for a GLM except that smooth terms, s(), te(), ti(), t2(), as well as time-varying
+for a GLM except that smooth terms, s(), te(), ti(), t2(), as well as time-varying
dynamic() terms, can be added to the right hand side
to specify that the linear predictor depends on smooth functions of predictors
(or linear functionals of these). In nmix() family models, the formula is used to
@@ -201,22 +202,31 @@
Argumentsnb() for count data
poisson() for count data
gaussian() for real-valued data
betar() for proportional data on (0,1)
lognormal() for non-negative real-valued data
betar() for proportional data on (0,1)
lognormal() for non-negative real-valued data
student_t() for real-valued data
Gamma() for non-negative real-valued data
nmix() for count data with imperfect detection modeled via a
State-Space N-Mixture model. The latent states are Poisson, capturing the 'true' latent
abundance, while the observation process is Binomial to account for imperfect detection.
See mvgam_families for an example of how to use this family
Note that only nb() and poisson() are available if using JAGS as the backend.
+
Note that only nb() and poisson() are available if using JAGS as the backend.
Default is poisson().
See mvgam_families for more details
- share_obs_params
+logical. If TRUE and the family
+has additional family-specific observation parameters (e.g. variance components in
+student_t() or gaussian(), or dispersion parameters in nb() or betar()),
+these parameters will be shared across all series. This is handy if you have multiple
+time series that you believe share some properties, such as being from the same
+species over different spatial units. Default is FALSE.
- use_lv
logical. If TRUE, use dynamic factors to estimate series'
latent trends in a reduced dimension format. Only available for
@@ -453,8 +463,9 @@
Details
Observation level parameters: When more than one series is included in data and an
observation family that contains more than one parameter is used, additional observation family parameters
-(i.e. phi for nb() or sigma for gaussian()) are
-estimated independently for each series.
+(i.e. phi for nb() or sigma for gaussian()) are
+by default estimated independently for each series. But if you wish for the series to share
+the same observation parameters, set share_obs_params = TRUE
Factor regularisation: When using a dynamic factor model for the trends with JAGS factor precisions are given
regularized penalty priors to theoretically allow some factors to be dropped from the model by squeezing increasing
factors' variances to zero. This is done to help protect against selecting too many latent factors than are needed to
@@ -507,7 +518,7 @@ Examples# Formulate a model using Stan where series share a cyclic smooth for
# seasonality and each series has an independent random walk temporal process;
# Set run_model = FALSE to inspect the returned objects
-mod1 <- mvgam(formula = y ~ s(season, bs = 'cc'),
+mod1 <- mvgam(formula = y ~ s(season, bs = 'cc'),
data = dat$data_train,
trend_model = 'RW',
family = 'poisson',
@@ -538,7 +549,7 @@ Examples refresh = 100)
# Now fit the model using mvgam with the Stan backend
-mod1 <- mvgam(formula = y ~ s(season, bs = 'cc'),
+mod1 <- mvgam(formula = y ~ s(season, bs = 'cc'),
data = dat$data_train,
trend_model = 'RW',
family = poisson(),
@@ -574,7 +585,7 @@ Examplesplot(mod1, type = 'smooths', realisations = TRUE)
# Plot conditional response predictions using marginaleffects
-plot(conditional_effects(mod1), ask = FALSE)
+plot(conditional_effects(mod1), ask = FALSE)
plot_predictions(mod1, condition = 'season', points = 0.5)
# Extract observation model beta coefficient draws as a data.frame
@@ -597,7 +608,7 @@ Examples trend = c(1,1,2))
# Fit the model using AR1 trends
-mod <- mvgam(y ~ s(season, bs = 'cc'),
+mod <- mvgam(y ~ s(season, bs = 'cc'),
trend_map = trend_map,
trend_model = 'AR1',
data = mod_data,
@@ -662,9 +673,9 @@ Examples# Fit a model that includes the offset in the linear predictor as well as
# hierarchical seasonal smooths
mod <- mvgam(formula = y ~ offset(offset) +
- s(series, bs = 're') +
- s(season, bs = 'cc') +
- s(season, by = series, m = 1, k = 5),
+ s(series, bs = 're') +
+ s(season, bs = 'cc') +
+ s(season, by = series, m = 1, k = 5),
data = dat$data_train,
trend_model = 'None',
use_stan = TRUE)
@@ -765,7 +776,7 @@ Examples
# View the changepoints with ggplot2 utilities
library(ggplot2)
-mcmc_plot(mod, variable = 'delta_trend',
+mcmc_plot(mod, variable = 'delta_trend',
regex = TRUE) +
scale_y_discrete(labels = mod$trend_model$changepoints) +
labs(y = 'Potential changepoint',
diff --git a/docs/reference/update.mvgam.html b/docs/reference/update.mvgam.html
index c3b8b5a0..823f41e0 100644
--- a/docs/reference/update.mvgam.html
+++ b/docs/reference/update.mvgam.html
@@ -72,6 +72,7 @@ Usage
use_lv,
n_lv,
family,
+ share_obs_params,
priors,
lfo = FALSE,
...
@@ -175,6 +176,15 @@ Argumentsmvgam_families for more details
- share_obs_params
+logical. If TRUE and the family
+has additional family-specific observation parameters (e.g. variance components in
+student_t() or gaussian(), or dispersion parameters in nb() or betar()),
+these parameters will be shared across all series. This is handy if you have multiple
+time series that you believe share some properties, such as being from the same
+species over different spatial units. Default is FALSE.
- priors
An optional data.frame with prior
definitions (in JAGS or Stan syntax). if using Stan, this can also be an object of
diff --git a/index.Rmd b/index.Rmd
index e7112bb5..1e409a8d 100644
--- a/index.Rmd
+++ b/index.Rmd
@@ -5,6 +5,8 @@ always_allow_html: true
+[](https://mc-stan.org/)
+
## mvgam
**M**ulti**V**ariate (Dynamic) **G**eneralized **A**ddivite **M**odels
diff --git a/index.md b/index.md
index 6441ce50..2fbd2c11 100644
--- a/index.md
+++ b/index.md
@@ -1,6 +1,8 @@
+[](https://mc-stan.org/)
+
## mvgam
**M**ulti**V**ariate (Dynamic) **G**eneralized **A**ddivite **M**odels
diff --git a/man/figures/README-unnamed-chunk-13-1.png b/man/figures/README-unnamed-chunk-13-1.png
index 97f08209..63559a6a 100644
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index f801d30b..8b1142de 100644
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index 00f1d110..45ab957c 100644
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diff --git a/man/figures/README-unnamed-chunk-24-1.png b/man/figures/README-unnamed-chunk-24-1.png
index 24d051b6..40e68091 100644
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diff --git a/man/figures/mvgam_logo.png b/man/figures/mvgam_logo.png
new file mode 100644
index 00000000..ce0ace9b
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Usage
Arguments
- formula
A character string specifying the GAM observation model formula. These are exactly like the formula
-for a GLM except that smooth terms, s(), te(), ti(), t2(), as well as time-varying
+for a GLM except that smooth terms, s(), te(), ti(), t2(), as well as time-varying
dynamic() terms, can be added to the right hand side
to specify that the linear predictor depends on smooth functions of predictors
(or linear functionals of these). In nmix() family models, the formula is used to
@@ -201,22 +202,31 @@
Argumentsnb() for count data
poisson() for count data
gaussian() for real-valued data
betar() for proportional data on (0,1)
lognormal() for non-negative real-valued data
betar() for proportional data on (0,1)
lognormal() for non-negative real-valued data
student_t() for real-valued data
Gamma() for non-negative real-valued data
nmix() for count data with imperfect detection modeled via a
State-Space N-Mixture model. The latent states are Poisson, capturing the 'true' latent
abundance, while the observation process is Binomial to account for imperfect detection.
See mvgam_families for an example of how to use this family
Note that only nb() and poisson() are available if using JAGS as the backend.
+
Note that only nb() and poisson() are available if using JAGS as the backend.
Default is poisson().
See mvgam_families for more details
- share_obs_params
+logical. If TRUE and the family
+has additional family-specific observation parameters (e.g. variance components in
+student_t() or gaussian(), or dispersion parameters in nb() or betar()),
+these parameters will be shared across all series. This is handy if you have multiple
+time series that you believe share some properties, such as being from the same
+species over different spatial units. Default is FALSE.
- use_lv
logical. If TRUE, use dynamic factors to estimate series'
latent trends in a reduced dimension format. Only available for
@@ -453,8 +463,9 @@
Details
Observation level parameters: When more than one series is included in data and an
observation family that contains more than one parameter is used, additional observation family parameters
-(i.e. phi for nb() or sigma for gaussian()) are
-estimated independently for each series.
+(i.e. phi for nb() or sigma for gaussian()) are
+by default estimated independently for each series. But if you wish for the series to share
+the same observation parameters, set share_obs_params = TRUE
Factor regularisation: When using a dynamic factor model for the trends with JAGS factor precisions are given
regularized penalty priors to theoretically allow some factors to be dropped from the model by squeezing increasing
factors' variances to zero. This is done to help protect against selecting too many latent factors than are needed to
@@ -507,7 +518,7 @@ Examples# Formulate a model using Stan where series share a cyclic smooth for
# seasonality and each series has an independent random walk temporal process;
# Set run_model = FALSE to inspect the returned objects
-mod1 <- mvgam(formula = y ~ s(season, bs = 'cc'),
+mod1 <- mvgam(formula = y ~ s(season, bs = 'cc'),
data = dat$data_train,
trend_model = 'RW',
family = 'poisson',
@@ -538,7 +549,7 @@ Examples refresh = 100)
# Now fit the model using mvgam with the Stan backend
-mod1 <- mvgam(formula = y ~ s(season, bs = 'cc'),
+mod1 <- mvgam(formula = y ~ s(season, bs = 'cc'),
data = dat$data_train,
trend_model = 'RW',
family = poisson(),
@@ -574,7 +585,7 @@ Examplesplot(mod1, type = 'smooths', realisations = TRUE)
# Plot conditional response predictions using marginaleffects
-plot(conditional_effects(mod1), ask = FALSE)
+plot(conditional_effects(mod1), ask = FALSE)
plot_predictions(mod1, condition = 'season', points = 0.5)
# Extract observation model beta coefficient draws as a data.frame
@@ -597,7 +608,7 @@ Examples trend = c(1,1,2))
# Fit the model using AR1 trends
-mod <- mvgam(y ~ s(season, bs = 'cc'),
+mod <- mvgam(y ~ s(season, bs = 'cc'),
trend_map = trend_map,
trend_model = 'AR1',
data = mod_data,
@@ -662,9 +673,9 @@ Examples# Fit a model that includes the offset in the linear predictor as well as
# hierarchical seasonal smooths
mod <- mvgam(formula = y ~ offset(offset) +
- s(series, bs = 're') +
- s(season, bs = 'cc') +
- s(season, by = series, m = 1, k = 5),
+ s(series, bs = 're') +
+ s(season, bs = 'cc') +
+ s(season, by = series, m = 1, k = 5),
data = dat$data_train,
trend_model = 'None',
use_stan = TRUE)
@@ -765,7 +776,7 @@ Examples
# View the changepoints with ggplot2 utilities
library(ggplot2)
-mcmc_plot(mod, variable = 'delta_trend',
+mcmc_plot(mod, variable = 'delta_trend',
regex = TRUE) +
scale_y_discrete(labels = mod$trend_model$changepoints) +
labs(y = 'Potential changepoint',
diff --git a/docs/reference/update.mvgam.html b/docs/reference/update.mvgam.html
index c3b8b5a0..823f41e0 100644
--- a/docs/reference/update.mvgam.html
+++ b/docs/reference/update.mvgam.html
@@ -72,6 +72,7 @@ Usage
use_lv,
n_lv,
family,
+ share_obs_params,
priors,
lfo = FALSE,
...
@@ -175,6 +176,15 @@ Argumentsmvgam_families for more details
- share_obs_params
+logical. If TRUE and the family
+has additional family-specific observation parameters (e.g. variance components in
+student_t() or gaussian(), or dispersion parameters in nb() or betar()),
+these parameters will be shared across all series. This is handy if you have multiple
+time series that you believe share some properties, such as being from the same
+species over different spatial units. Default is FALSE.
- priors
An optional data.frame with prior
definitions (in JAGS or Stan syntax). if using Stan, this can also be an object of
diff --git a/index.Rmd b/index.Rmd
index e7112bb5..1e409a8d 100644
--- a/index.Rmd
+++ b/index.Rmd
@@ -5,6 +5,8 @@ always_allow_html: true
+[](https://mc-stan.org/)
+
## mvgam
**M**ulti**V**ariate (Dynamic) **G**eneralized **A**ddivite **M**odels
diff --git a/index.md b/index.md
index 6441ce50..2fbd2c11 100644
--- a/index.md
+++ b/index.md
@@ -1,6 +1,8 @@
+[](https://mc-stan.org/)
+
## mvgam
**M**ulti**V**ariate (Dynamic) **G**eneralized **A**ddivite **M**odels
diff --git a/man/figures/README-unnamed-chunk-13-1.png b/man/figures/README-unnamed-chunk-13-1.png
index 97f08209..63559a6a 100644
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index 8e34a550..1dca9500 100644
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index f801d30b..8b1142de 100644
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index 00f1d110..45ab957c 100644
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index 24d051b6..40e68091 100644
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diff --git a/man/figures/mvgam_logo.png b/man/figures/mvgam_logo.png
new file mode 100644
index 00000000..ce0ace9b
Binary files /dev/null and b/man/figures/mvgam_logo.png differ
A character string specifying the GAM observation model formula. These are exactly like the formula
-for a GLM except that smooth terms, s(), te(), ti(), t2(), as well as time-varying
+for a GLM except that smooth terms, s(), te(), ti(), t2(), as well as time-varying
dynamic() terms, can be added to the right hand side
to specify that the linear predictor depends on smooth functions of predictors
(or linear functionals of these). In nmix() family models, the formula is used to
@@ -201,22 +202,31 @@
Argumentsnb() for count data
poisson() for count data
gaussian() for real-valued data
betar() for proportional data on (0,1)
lognormal() for non-negative real-valued data
betar() for proportional data on (0,1)
lognormal() for non-negative real-valued data
student_t() for real-valued data
Gamma() for non-negative real-valued data
nmix() for count data with imperfect detection modeled via a
State-Space N-Mixture model. The latent states are Poisson, capturing the 'true' latent
abundance, while the observation process is Binomial to account for imperfect detection.
See mvgam_families for an example of how to use this family
Note that only nb() and poisson() are available if using JAGS as the backend.
+
Note that only nb() and poisson() are available if using JAGS as the backend.
Default is poisson().
See mvgam_families for more details
logical. If TRUE and the family
+has additional family-specific observation parameters (e.g. variance components in
+student_t() or gaussian(), or dispersion parameters in nb() or betar()),
+these parameters will be shared across all series. This is handy if you have multiple
+time series that you believe share some properties, such as being from the same
+species over different spatial units. Default is FALSE.
logical. If TRUE, use dynamic factors to estimate series'
latent trends in a reduced dimension format. Only available for
@@ -453,8 +463,9 @@
Details
Observation level parameters: When more than one series is included in data and an
observation family that contains more than one parameter is used, additional observation family parameters
-(i.e. phi for nb() or sigma for gaussian()) are
-estimated independently for each series.
+(i.e. phi for nb() or sigma for gaussian()) are
+by default estimated independently for each series. But if you wish for the series to share
+the same observation parameters, set share_obs_params = TRUE
Factor regularisation: When using a dynamic factor model for the trends with JAGS factor precisions are given
regularized penalty priors to theoretically allow some factors to be dropped from the model by squeezing increasing
factors' variances to zero. This is done to help protect against selecting too many latent factors than are needed to
@@ -507,7 +518,7 @@ Examples# Formulate a model using Stan where series share a cyclic smooth for
# seasonality and each series has an independent random walk temporal process;
# Set run_model = FALSE to inspect the returned objects
-mod1 <- mvgam(formula = y ~ s(season, bs = 'cc'),
+mod1 <- mvgam(formula = y ~ s(season, bs = 'cc'),
data = dat$data_train,
trend_model = 'RW',
family = 'poisson',
@@ -538,7 +549,7 @@ Examples refresh = 100)
# Now fit the model using mvgam with the Stan backend
-mod1 <- mvgam(formula = y ~ s(season, bs = 'cc'),
+mod1 <- mvgam(formula = y ~ s(season, bs = 'cc'),
data = dat$data_train,
trend_model = 'RW',
family = poisson(),
@@ -574,7 +585,7 @@ Examplesplot(mod1, type = 'smooths', realisations = TRUE)
# Plot conditional response predictions using marginaleffects
-plot(conditional_effects(mod1), ask = FALSE)
+plot(conditional_effects(mod1), ask = FALSE)
plot_predictions(mod1, condition = 'season', points = 0.5)
# Extract observation model beta coefficient draws as a data.frame
@@ -597,7 +608,7 @@ Examples trend = c(1,1,2))
# Fit the model using AR1 trends
-mod <- mvgam(y ~ s(season, bs = 'cc'),
+mod <- mvgam(y ~ s(season, bs = 'cc'),
trend_map = trend_map,
trend_model = 'AR1',
data = mod_data,
@@ -662,9 +673,9 @@ Examples# Fit a model that includes the offset in the linear predictor as well as
# hierarchical seasonal smooths
mod <- mvgam(formula = y ~ offset(offset) +
- s(series, bs = 're') +
- s(season, bs = 'cc') +
- s(season, by = series, m = 1, k = 5),
+ s(series, bs = 're') +
+ s(season, bs = 'cc') +
+ s(season, by = series, m = 1, k = 5),
data = dat$data_train,
trend_model = 'None',
use_stan = TRUE)
@@ -765,7 +776,7 @@ Examples
# View the changepoints with ggplot2 utilities
library(ggplot2)
-mcmc_plot(mod, variable = 'delta_trend',
+mcmc_plot(mod, variable = 'delta_trend',
regex = TRUE) +
scale_y_discrete(labels = mod$trend_model$changepoints) +
labs(y = 'Potential changepoint',
diff --git a/docs/reference/update.mvgam.html b/docs/reference/update.mvgam.html
index c3b8b5a0..823f41e0 100644
--- a/docs/reference/update.mvgam.html
+++ b/docs/reference/update.mvgam.html
@@ -72,6 +72,7 @@ Usage
use_lv,
n_lv,
family,
+ share_obs_params,
priors,
lfo = FALSE,
...
@@ -175,6 +176,15 @@ Argumentsmvgam_families for more details
Examples refresh = 100)
# Now fit the model using mvgam with the Stan backend
-mod1 <- mvgam(formula = y ~ s(season, bs = 'cc'),
+mod1 <- mvgam(formula = y ~ s(season, bs = 'cc'),
data = dat$data_train,
trend_model = 'RW',
family = poisson(),
@@ -574,7 +585,7 @@ Examplesplot(mod1, type = 'smooths', realisations = TRUE)
# Plot conditional response predictions using marginaleffects
-plot(conditional_effects(mod1), ask = FALSE)
+plot(conditional_effects(mod1), ask = FALSE)
plot_predictions(mod1, condition = 'season', points = 0.5)
# Extract observation model beta coefficient draws as a data.frame
@@ -597,7 +608,7 @@ Examples trend = c(1,1,2))
# Fit the model using AR1 trends
-mod <- mvgam(y ~ s(season, bs = 'cc'),
+mod <- mvgam(y ~ s(season, bs = 'cc'),
trend_map = trend_map,
trend_model = 'AR1',
data = mod_data,
@@ -662,9 +673,9 @@ Examples# Fit a model that includes the offset in the linear predictor as well as
# hierarchical seasonal smooths
mod <- mvgam(formula = y ~ offset(offset) +
- s(series, bs = 're') +
- s(season, bs = 'cc') +
- s(season, by = series, m = 1, k = 5),
+ s(series, bs = 're') +
+ s(season, bs = 'cc') +
+ s(season, by = series, m = 1, k = 5),
data = dat$data_train,
trend_model = 'None',
use_stan = TRUE)
@@ -765,7 +776,7 @@ Examples
# View the changepoints with ggplot2 utilities
library(ggplot2)
-mcmc_plot(mod, variable = 'delta_trend',
+mcmc_plot(mod, variable = 'delta_trend',
regex = TRUE) +
scale_y_discrete(labels = mod$trend_model$changepoints) +
labs(y = 'Potential changepoint',
diff --git a/docs/reference/update.mvgam.html b/docs/reference/update.mvgam.html
index c3b8b5a0..823f41e0 100644
--- a/docs/reference/update.mvgam.html
+++ b/docs/reference/update.mvgam.html
@@ -72,6 +72,7 @@ Usage
use_lv,
n_lv,
family,
+ share_obs_params,
priors,
lfo = FALSE,
...
@@ -175,6 +176,15 @@ Argumentsmvgam_families for more details
Examples trend = c(1,1,2))
# Fit the model using AR1 trends
-mod <- mvgam(y ~ s(season, bs = 'cc'),
+mod <- mvgam(y ~ s(season, bs = 'cc'),
trend_map = trend_map,
trend_model = 'AR1',
data = mod_data,
@@ -662,9 +673,9 @@ Examples# Fit a model that includes the offset in the linear predictor as well as
# hierarchical seasonal smooths
mod <- mvgam(formula = y ~ offset(offset) +
- s(series, bs = 're') +
- s(season, bs = 'cc') +
- s(season, by = series, m = 1, k = 5),
+ s(series, bs = 're') +
+ s(season, bs = 'cc') +
+ s(season, by = series, m = 1, k = 5),
data = dat$data_train,
trend_model = 'None',
use_stan = TRUE)
@@ -765,7 +776,7 @@ Examples
# View the changepoints with ggplot2 utilities
library(ggplot2)
-mcmc_plot(mod, variable = 'delta_trend',
+mcmc_plot(mod, variable = 'delta_trend',
regex = TRUE) +
scale_y_discrete(labels = mod$trend_model$changepoints) +
labs(y = 'Potential changepoint',
diff --git a/docs/reference/update.mvgam.html b/docs/reference/update.mvgam.html
index c3b8b5a0..823f41e0 100644
--- a/docs/reference/update.mvgam.html
+++ b/docs/reference/update.mvgam.html
@@ -72,6 +72,7 @@ Usage
use_lv,
n_lv,
family,
+ share_obs_params,
priors,
lfo = FALSE,
...
@@ -175,6 +176,15 @@ Argumentsmvgam_families for more details
Examples
# View the changepoints with ggplot2 utilities
library(ggplot2)
-mcmc_plot(mod, variable = 'delta_trend',
+mcmc_plot(mod, variable = 'delta_trend',
regex = TRUE) +
scale_y_discrete(labels = mod$trend_model$changepoints) +
labs(y = 'Potential changepoint',
diff --git a/docs/reference/update.mvgam.html b/docs/reference/update.mvgam.html
index c3b8b5a0..823f41e0 100644
--- a/docs/reference/update.mvgam.html
+++ b/docs/reference/update.mvgam.html
@@ -72,6 +72,7 @@ Usage
use_lv,
n_lv,
family,
+ share_obs_params,
priors,
lfo = FALSE,
...
@@ -175,6 +176,15 @@ Argumentsmvgam_families for more details
Argumentsmvgam_families for more details
logical. If TRUE and the family
+has additional family-specific observation parameters (e.g. variance components in
+student_t() or gaussian(), or dispersion parameters in nb() or betar()),
+these parameters will be shared across all series. This is handy if you have multiple
+time series that you believe share some properties, such as being from the same
+species over different spatial units. Default is FALSE.
An optional data.frame with prior
definitions (in JAGS or Stan syntax). if using Stan, this can also be an object of
diff --git a/index.Rmd b/index.Rmd
index e7112bb5..1e409a8d 100644
--- a/index.Rmd
+++ b/index.Rmd
@@ -5,6 +5,8 @@ always_allow_html: true
+[](https://mc-stan.org/)
+
## mvgam
**M**ulti**V**ariate (Dynamic) **G**eneralized **A**ddivite **M**odels
diff --git a/index.md b/index.md
index 6441ce50..2fbd2c11 100644
--- a/index.md
+++ b/index.md
@@ -1,6 +1,8 @@
+[](https://mc-stan.org/)
+
## mvgam
**M**ulti**V**ariate (Dynamic) **G**eneralized **A**ddivite **M**odels
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