Predictions from a Ratio Model

Compute posterior predictions for new data from a fitted ratio model.

Usage

# S3 method for class 'ratiod_fit'
predict(
  object,
  newdata,
  type = c("response", "link"),
  component = c("ratio", "numerator", "denominator", "all"),
  summary = TRUE,
  probs = c(0.025, 0.5, 0.975),
  re_formula = NULL,
  allow_new_levels = FALSE,
  coords.0 = NULL,
  include_spatial = TRUE,
  return_spatial = FALSE,
  n_samples = NULL,
  ...
)

# S3 method for class 'ratiod_prediction'
print(x, n = 10, ...)

Arguments

object

A ratiod_fit object

newdata

Data frame with new observations. Must contain all predictor variables used in the original model.

type

Type of prediction: "response" for fitted values on the response scale, "link" for linear predictor scale.

component

Which component to predict: "ratio" (default), "numerator", "denominator", or "all".

summary

Logical; if TRUE, return summary statistics instead of full posterior draws (default TRUE).

probs

Quantiles to compute if summary = TRUE.

re_formula

Formula for random effects in prediction. Use NULL to include all random effects (if new levels match), NA or ~ 0 to exclude random effects (population-level prediction).

allow_new_levels

Logical; if TRUE, allows new group levels not in the original data (predictions use population mean).

coords.0

Matrix of spatial coordinates for new prediction locations (required for GP/HSGP spatial models). Should be N x 2 matrix with same coordinate system as training data.

include_spatial

Logical; if TRUE (default), include spatial random effects in predictions. Set to FALSE for fixed-effects-only predictions.

return_spatial

Logical; if TRUE, return spatial effect samples at new locations as w.0.samples in the output (similar to spOccupancy).

n_samples

Number of posterior samples to draw for backends that don't store samples directly (e.g., Laplace, VI). Default uses all available samples for HMC/PG.

...

Ignored

x

A ratiod_prediction object

n

Number of rows to print

Value

If summary = TRUE, a data frame with posterior summaries. If summary = FALSE, a matrix of posterior draws (rows = draws, cols = obs). If return_spatial = TRUE, returns a list with predictions and w.0.samples.

Examples

# \donttest{
# A smooth spatial field w shared by count and effort
set.seed(321)
n <- 60
x <- runif(n)
y <- runif(n)
w <- 0.6 * sin(2 * pi * x) + 0.6 * cos(2 * pi * y)
depth <- rnorm(n)
season <- factor(sample(c("summer", "winter"), n, replace = TRUE))
df <- data.frame(
  count = rpois(n, exp(2.5 + 0.3 * depth + 0.4 * (season == "winter") + w)),
  effort = rgamma(n, shape = 9, rate = exp(-w)),
  depth = depth,
  season = season,
  x = x,
  y = y
)
fit <- tratio(
  count | effort ~ depth + season,
  data = df,
  family = ratiod_poisson_gamma(),
  control = list(iter = 400, chains = 1, seed = 1)
)

# Predict for new data; levels of `season` follow the training data
new_df <- data.frame(depth = c(-1, 0, 1), season = "summer")
predict(fit, newdata = new_df)
#> tulpaRatio predictions
#> =================
#> 
#> Posterior draws: 200 
#> New observations: 3 
#> 
#>  obs component      mean        sd      q2.5       q50    q97.5
#>    1     ratio 0.9735659 0.1775233 0.6779457 0.9679804 1.453112
#>    2     ratio 1.3333026 0.2068171 0.9884281 1.3121018 1.825459
#>    3     ratio 1.8428555 0.3319191 1.2453527 1.8268176 2.525829

# Full posterior draws of the predicted ratio (draws x rows)
dim(predict(fit, newdata = new_df, summary = FALSE)$ratio)
#> [1] 200   3

# Spatial prediction at new locations (kriging) from a GP fit
fit_gp <- tratio(
  count | effort ~ depth,
  data = df,
  spatial = spatial_gp(coords = c("x", "y")),
  family = ratiod_poisson_gamma(),
  control = list(iter = 400, chains = 1, seed = 1)
)
new_coords <- matrix(runif(10), ncol = 2)
predict(fit_gp, newdata = data.frame(depth = rnorm(5)),
        coords.0 = new_coords)
#> tulpaRatio predictions
#> =================
#> 
#> Posterior draws: 200 
#> New observations: 5 
#> 
#>  obs component      mean         sd      q2.5       q50    q97.5
#>    1     ratio 1.2564437 0.09501087 1.0784341 1.2648160 1.464611
#>    2     ratio 0.9163408 0.10964141 0.7229692 0.9235379 1.141286
#>    3     ratio 0.9952051 0.10665063 0.7998387 1.0031824 1.212690
#>    4     ratio 3.1862257 0.39104624 2.4947446 3.1493899 4.004853
#>    5     ratio 1.4459924 0.08932396 1.2788499 1.4560539 1.655003
# }