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Returns a data.frame with all combos of predictors

Source: R/interpolate_newdata.R
interpolate_newdata.Rd

[Experimental]

Usage

interpolate_newdata(fit, by = NULL, x_values = NULL, at = NULL, arma = NULL)

Arguments

fit

An mcpfit object.

by

Character vector of categorical or group-level columns to evaluate separately. Categorical model predictors are always included.

x_values

Numeric vector of x-values to evaluate at.

at

Named list setting additional continuous predictors to fixed values. They default to their observed means. Family response auxiliaries can also be supplied as explicit scalar design values; e.g., at = list(N = 20).

arma

Logical. If TRUE, preserve the observed response history for conditional AR/MA evaluation. Defaults to TRUE when the fit includes ar() or ma() terms. Set to FALSE to interpolate unconditional trends.

Value

data.frame with

  • Cols for par_x

  • unique levels combos of factorial vars

  • fixed values for additional continuous predictors

Details

This function synthesizes predictors for all combinations of predictor values. It is used internally in plot.mcpfit() and may be useful if you want to build your own custom plot.

The par_x variable will be interpolated with higher resolution around the change points where the values can change abruptly, but lower resolution in between to speed up the computation.

Categorical variables and requested grouping factors are combined factorially (all level combinations). Additional continuous predictors are held at their observed means, or at values supplied through at. Family-specific response auxiliaries are not interpolated. Supply binomial trial counts as a scalar in at or use newdata for a varying design.

Likelihood weights are needed only when evaluating log_lik() and therefore need not be supplied here.

Author

Jonas Kristoffer Lindeløv jonas@lindeloev.dk

Examples

# \donttest{
# Get predictors for a fit
newdata = interpolate_newdata(demo_fit)

# Fit summary
head(fitted(demo_fit, newdata))
#>        time   fitted        sd     Q2.5    Q97.5
#> 1 0.6186323 10.04702 0.6479304 8.782501 11.32416
#> 2 1.6198715 10.04702 0.6479304 8.782501 11.32416
#> 3 2.6211108 10.04702 0.6479304 8.782501 11.32416
#> 4 3.6223501 10.04702 0.6479304 8.782501 11.32416
#> 5 4.6235893 10.04702 0.6479304 8.782501 11.32416
#> 6 5.6248286 10.04702 0.6479304 8.782501 11.32416

# Predictions for each draw
prediction = predict(demo_fit, newdata, summary = FALSE)
head(prediction)
#> # A tibble: 6 × 13
#>   .chain .iteration .draw  cp_1  cp_2 Intercept_1 time_2 Intercept_3 time_3
#>    <int>      <int> <int> <dbl> <dbl>       <dbl>  <dbl>       <dbl>  <dbl>
#> 1      1          1     1  30.8  72.1        10.2  0.513        15.7 0.0773
#> 2      1          1     1  30.8  72.1        10.2  0.513        15.7 0.0773
#> 3      1          1     1  30.8  72.1        10.2  0.513        15.7 0.0773
#> 4      1          1     1  30.8  72.1        10.2  0.513        15.7 0.0773
#> 5      1          1     1  30.8  72.1        10.2  0.513        15.7 0.0773
#> 6      1          1     1  30.8  72.1        10.2  0.513        15.7 0.0773
#> # ℹ 4 more variables: sigma_1 <dbl>, time <dbl>, .prediction <dbl>,
#> #   data_row <int>

# Custom plot
library(ggplot2)
plotdata = fitted(demo_fit, newdata)
ggplot(plotdata, aes(x = time, y = fitted)) +
  geom_ribbon(aes(ymin = `Q2.5`, ymax = `Q97.5`), alpha = 0.3) +
  geom_line(lwd = 2) +
  geom_point(aes(y = response), data = demo_fit$data)

# }