predict.cosimmr_output {cosimmr}

Predicts proportion of each source in a mixture, based on values provided for covariates

Description

Predicts proportion of each source in a mixture, based on values provided for covariates

Usage

## S3 method for class 'cosimmr_output'
predict(object, x_pred, n_output = 3600, ...)

Arguments

object

An object of class cosimmr_output created via the function cosimmr_ffvb

x_pred

A data.frame of covariate values that the user wishes to predict source proportions for, provided in the same order that the original covariance matrix was. Important for this to be a data.frame otherwise numeric values can be set as characters and this causes incorrect calculations.

n_output

the number of posterior samples to generate. Defaults to 3600.

...

Other arguments (not used)

Value

object of class 'cosimmr_pred_out'

Author(s)

Emma Govan <[e-mail]> Andrew Parnell

References

Andrew C. Parnell, Donald L. Phillips, Stuart Bearhop, Brice X. Semmens, Eric J. Ward, Jonathan W. Moore, Andrew L. Jackson, Jonathan Grey, David J. Kelly, and Richard Inger. Bayesian stable isotope mixing models. Environmetrics, 24(6):387–399, 2013.

See Also

cosimmr_load for creating objects suitable for this function, and plot.cosimmr_output for plotting output.

Examples

## See the package vignette for a detailed run through of these 4 examples

# Data set 1: 10 obs on 2 isos, 4 sources, with tefs and concdep
data(geese_data_day1)
cov_1 = c(1,2,3,2,3,1,1,1,2)
simmr_1 <- with(
  geese_data_day1,
  cosimmr_load(
    formula = mixtures ~ cov_1,
    source_names = source_names,
    source_means = source_means,
    source_sds = source_sds,
    correction_means = correction_means,
    correction_sds = correction_sds,
    concentration_means = concentration_means
  )
)

# Plot
plot(simmr_1)

# Print
simmr_1

# FFVB run
simmr_1_out <- cosimmr_ffvb(simmr_1)

# Print it
print(simmr_1_out)


# Plot
plot(simmr_1_out, type = "isospace")
plot(simmr_1_out, type = "beta_histogram", cov_name = "cov_1")

x_pred = data.frame(cov_1 = c(1,5))

pred_array<-predict(simmr_1_out, x_pred)