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Obtain counterfactual predictions for a dynamitefit object.

Usage

# S3 method for dynamitefit
predict(
  object,
  newdata = NULL,
  type = c("response", "mean", "link"),
  funs = list(),
  impute = c("none", "locf", "nocb"),
  new_levels = c("none", "bootstrap", "gaussian", "original"),
  global_fixed = FALSE,
  n_draws = NULL,
  thin = 1,
  expand = TRUE,
  df = TRUE,
  ...
)

Arguments

object

[dynamitefit]
The model fit object.

newdata

[data.frame]
Data used in predictions. Predictions are computed for missing (NA) values in the response variable columns, and non-missing values are assumed fixed. If NULL (default), the data used in model estimation is used for predictions as well, after all values in the response variable columns after the first fixed time point are converted to NA values. Missing values in predictor columns can be imputed (argument impute). There should be no new time points that were not present in the data that were used to fit the model. New group levels can be included, but if the model contains random effects, an option for the random effects for the new levels must be chosen (argument new_levels). If the grouping variable of the original data is missing, it is assumed that all observations in newdata belong to the first group in the original data. New group levels are not allowed for models using latent factors.

type

[character(1)]
Type of prediction, "response" (default), "mean", or "link".

funs

[list()]
A named list whose names should correspond to the response variables of the model. Each element of funs should be a a named list of functions that will be applied to the corresponding predicted type of the channel over the individuals for each combination of the posterior draws and time points. In other words, the resulting predictions will be averages over the individuals. The functions should take the corresponding type variable values as their only argument. If funs is empty, the full individual level values are returned instead. Note that this argument can only be used if there are multiple individuals (i.e., group was not NULL in the dynamite call).

impute

[character(1)]
Which imputation scheme to use for missing exogenous predictor values. Currently supported options are no imputation: "none" (default), last observation carried forward: "locf", and next observation carried backward: "nocb".

new_levels

[character(1)]
Defines if and how to sample the random effects for observations whose group level was not present in the original data. The options are:

  • "none" (the default) which will signal an error if new levels are encountered.

  • "bootstrap" which will randomly draw from the posterior samples of the random effects across all original levels.

  • "gaussian" which will randomly draw from a gaussian distribution using the posterior samples of the random effects standard deviation (and correlation matrix if applicable).

  • "original" which will randomly match each new level to one of the original levels. The posterior samples of the random effects of the matched levels will then be used for the new levels.

This argument is ignored if the model does not contain random effects.

global_fixed

[logical(1)]
If FALSE (the default), the first non-fixed time point is counted from the the first non-NA observation for each group member separately. Otherwise, the first non-fixed time point is counted from the first time point globally. If there are no groups, then the options are equivalent.

n_draws

[integer(1)]
Number of posterior samples to use, default is NULL which uses all samples without permuting (with chains concatenated). If n_drawsis smaller than ndraws(object), a random subset of n_draws posterior samples are used.

thin

[integer(1)]
Use only every thin posterior sample. This can be beneficial with when the model object contains large number of samples. Default is 1 meaning that all samples are used.

expand

[logical(1)]
If TRUE (the default), the output is a single data.frame containing the original newdata and the predicted values. Otherwise, a list is returned with two components, simulated and observed, where the first contains only the predicted values, and the second contains the original newdata. Setting expand to FALSE can help conserve memory because newdata is not replicated n_draws times in the output. This argument is ignored if funs are provided.

df

[logical(1)]
If TRUE (default) the output consists of data.frame objects, and data.table objects otherwise.

...

Ignored.

Value

A data.frame containing the predicted values or a list of two data.frames. See the expand argument for details. Note that the .draw column is not the same as .draw from as.data.frame and as_draws methods as predict uses permuted samples. A mapping between these variables can be done using information in object$stanfit@sim$permutation.

Details

Note that forecasting (i.e., predictions for time indices beyond the last time index in the original data) is not supported by the dynamite package. However, such predictions can be obtained by augmenting the original data with NA values before model estimation.

See also

Obtaining predictions fitted.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN
out <- predict(gaussian_example_fit, type = "response", n_draws = 2L)
head(out)
#>   id time .draw     y_new          x z         y
#> 1  1    1     1 0.4046907 -0.5753470 0 0.4046907
#> 2  1    2     1 2.1157969  1.0527115 1 1.3665618
#> 3  1    3     1 0.4849242  0.1192452 0 0.1047957
#> 4  1    4     1 0.4413383 -0.5211173 0 0.9295666
#> 5  1    5     1 1.0301223  1.3011760 1 0.8605904
#> 6  1    6     1 2.8054966 -0.2052993 1 2.1160820

# using summary functions
sumr <- predict(multichannel_example_fit, type = "mean",
  funs = list(g = list(m = mean, s = sd), b = list(sum = sum)),
  n_draws = 2L)
head(sumr$simulated)
#>         m_g       s_g    sum_b time .draw
#> 1        NA        NA       NA    1     1
#> 2 0.3840585 0.6902912 29.91914    2     1
#> 3 0.6142540 0.5847917 31.30550    3     1
#> 4 0.8888016 0.5100763 32.28057    4     1
#> 5 1.0689886 0.5192106 32.95679    5     1
#> 6 1.2603872 0.5050212 34.00574    6     1
# \donttest{
# Please update your rstan and StanHeaders installation before running
# on Windows
if (!identical(.Platform$OS.type, "windows")) {
  # Simulate from the prior predictive distribution

  f <- obs(y ~ lag(y) + varying(~ -1 + x), "gaussian") +
    splines(df = 10, noncentered = TRUE)

  # Create data with missing observations
  # Note that due to the lagged term in the model,
  # we need to fix the first time point
  d <- data.frame(y = c(0, rep(NA, 49)), x = rnorm(50), time = 1:50)

  # Suppress warnings due to the lack of data
  suppressWarnings(
    priors <- get_priors(f, data = d, time = "time")
  )

  # Modify default priors which can produce exploding behavior when used
  # without data
  priors$prior <- c(
    "normal(0, 1)",
    "normal(0.6, 0.1)",
    "normal(-0.2, 0.5)",
    "normal(0.2, 0.1)",
    "normal(0.5, 0.1)"
  )

  # Samples from the prior conditional on the first time point and x
  fit <- dynamite(
    dformula = f,
    data = d,
    time = "time",
    verbose = FALSE,
    priors = priors,
    chains = 1
  )

  # Simulate new data
  pp <- predict(fit)

  ggplot2::ggplot(pp, ggplot2::aes(time, y_new, group = .draw)) +
    ggplot2::geom_line(alpha = 0.1) +
    ggplot2::theme_bw()
}
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 1: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 1: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 1: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 1: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.176 seconds (Warm-up)
#> Chain 1:                0.115 seconds (Sampling)
#> Chain 1:                0.291 seconds (Total)
#> Chain 1: 

# }