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ggplot2 plot_mvgam_resids() #84

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ggplot2 plot_mvgam_resids() #84

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4adaf0c
ggplot2 plot_mvgam_resids()
mhollanders Oct 24, 2024
1b4177f
ggplot2 plot_mvgam_factors()
mhollanders Oct 26, 2024
51ebcbc
updated DESCRIPTION and three plotting functions
mhollanders Nov 6, 2024
5832ba5
Merge branch 'master' into matt
nicholasjclark Nov 13, 2024
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3 changes: 2 additions & 1 deletion DESCRIPTION
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,8 @@ Imports:
dplyr,
magrittr,
Matrix,
rlang
rlang,
patchwork (>= 1.3.0)
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
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105 changes: 50 additions & 55 deletions R/plot_mvgam_factors.R
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Expand Up @@ -28,7 +28,7 @@
#'plot_mvgam_factors(mod)
#'}
#'@export
plot_mvgam_factors = function(object, plot = TRUE){
plot_mvgam_factors = function(object, plot = TRUE) {

# Check arguments
if (!(inherits(object, "mvgam"))) {
Expand All @@ -47,6 +47,7 @@ plot_mvgam_factors = function(object, plot = TRUE){
starts <- c(1, starts[-c(1, object$n_lv + 1)])
ends <- ends[-1]
probs <- c(0.05, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.95)
probs <- c(0.2, 0.4, 0.6, 0.9)

# Set up plot environment
if(plot){
Expand All @@ -68,62 +69,56 @@ plot_mvgam_factors = function(object, plot = TRUE){
}
}

# Loop across each lv and calculate probability that the lv was dropped
lv_estimates <- do.call(rbind, lapply(1:object$n_lv, function(x){
lv_estimates <- lapply(1:object$n_lv,
\(x) {

if(object$fit_engine == 'stan'){
inds_lv <- seq(x, dim(mcmc_chains(object$model_output, 'LV'))[2], by = object$n_lv)
preds <- mcmc_chains(object$model_output, 'LV')[,inds_lv]
} else {
preds <- mcmc_chains(object$model_output, 'LV')[,starts[x]:ends[x]]
}

# Keep only the in-sample observations for testing against the null of white noise
preds <- preds[,1:(length(object$obs_data$y) / NCOL(object$ytimes))]
if(object$fit_engine == 'stan'){
inds_lv <- seq(x, dim(mcmc_chains(object$model_output, 'LV'))[2], by = object$n_lv)
preds <- mcmc_chains(object$model_output, 'LV')[,inds_lv]
} else {
preds <- mcmc_chains(object$model_output, 'LV')[,starts[x]:ends[x]]
}

cred <- sapply(1:NCOL(preds),
function(n) quantile(preds[,n],
probs = probs))
# If plot = TRUE, plot the LVs
if(plot){
preds_last <- preds[1,]
ylim <- range(cred)
ylab <- paste0('Factor ', x)
pred_vals <- seq(1:length(preds_last))
plot(1, type = "n", bty = 'L',
xlab = 'Time',
ylab = ylab,
xlim = c(0, length(preds_last)),
ylim = ylim)
polygon(c(pred_vals, rev(pred_vals)), c(cred[1,], rev(cred[9,])),
col = c_light, border = NA)
polygon(c(pred_vals, rev(pred_vals)), c(cred[2,], rev(cred[8,])),
col = c_light_highlight, border = NA)
polygon(c(pred_vals, rev(pred_vals)), c(cred[3,], rev(cred[7,])),
col = c_mid, border = NA)
polygon(c(pred_vals, rev(pred_vals)), c(cred[4,], rev(cred[6,])),
col = c_mid_highlight, border = NA)
lines(pred_vals, cred[5,], col = c_dark, lwd = 2.5)
box(bty = 'L', lwd = 2)

}
# Keep only the in-sample observations for testing against the null of white noise
preds <- preds[,1:(length(object$obs_data$y) / NCOL(object$ytimes))]
n_draw <- nrow(preds)
n_obs <- ncol(preds)
factor_name <- paste0("Factor ", x)

# Calculate second derivatives of empirical medians and upper / lower intervals;
# factors with small second derivatives are moving in roughly a straight line and not
# likely contributing much (or at all) to the latent trend estimates
meds <- cred[5,]
uppers <- cred[8,]
lowers <- cred[2,]
data.frame('Contribution' = sum(abs(diff(diff(meds)) +
diff(diff(uppers)) +
diff(diff(lowers)))))
}))

rownames(lv_estimates) <- paste0('Factor', 1:object$n_lv)

if(plot){
layout(1)
}
if (plot) {
fig <- data.frame(preds = c(preds),
obs = rep(1:n_obs, each = n_draw),
.draw = rep(1:n_draw, n_obs)) |>
ggplot2::ggplot(ggplot2::aes(obs, preds)) +
ggdist::stat_lineribbon(point_interval = ggdist::median_qi,
.width = probs,
colour = c_dark,
show.legend = FALSE) +
ggplot2::scale_fill_manual(values = c(c_light, c_light_highlight, c_mid, c_mid_highlight)) +
ggplot2::scale_x_continuous(expand = c(0, 0)) +
ggplot2::scale_y_continuous(expand = c(0, 0)) +
ggplot2::labs(x = "Time",
y = factor_name)
}

lv_estimates / sum(lv_estimates)
# Calculate second derivatives of empirical medians and upper / lower intervals;
# factors with small second derivatives are moving in roughly a straight line and not
# likely contributing much (or at all) to the latent trend estimates
meds <- apply(preds, 2, median)
uppers <- apply(preds, 2, \(p) quantile(p, 0.8))
lowers <- apply(preds, 2, \(p) quantile(p, 0.2))
contribution <- data.frame('Contribution' = sum(abs(diff(diff(meds)) +
diff(diff(uppers)) +
diff(diff(lowers)))),
row.names = factor_name)
if (plot) {
list(fig = fig, contribution = contribution)
} else {
list(contribution = contribution)
}
})
if (plot)
lapply(lv_estimates, \(x) print(x$fig))
contributions <- do.call(rbind, lapply(lv_estimates, \(x) x$contribution))
contributions / sum(contributions)
}
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