predict: Prediction of a bag of models to new data

In NINAnor/oneimpact: Tools for the assessment of the cumulative impacts of anthropogenic features in ecological studies

Prediction of a bag of models to new data

Description

The function predict makes a prediction for new data based wither on a bag of models or on its formula, coefficients, and weights. The prediction can be made either for a complete new dataset with all the variables included in the formula or to predict the specific response on one single or a group of variables in the model. In this case, all the other variables are set to their median or mean value, to to zero (defined by the baseline parameter). What controls that is which columns are added in the newdata data.frame.

Usage

predict(
  x,
  newdata,
  type = c("linear", "exponential", "exp", "logit", "cloglog")[1],
  wmean = TRUE,
  wq_probs = NULL,
  include = "all",
  ...
)

## S3 method for class 'bag'
predict(
  x,
  newdata,
  data = NULL,
  type = c("linear", "exponential", "exp", "logit", "cloglog")[1],
  wmean = TRUE,
  wq_probs = NULL,
  include = "all",
  baseline = c("median", "mean", "zero")[1],
  zoi = FALSE,
  zoi_shape = c("exp_decay", "gaussian_decay", "linear_decay", "threshold_decay")[1],
  which_cumulative = "cumulative",
  type_feature = c("point", "line", "area")[1],
  type_feature_recompute = FALSE,
  n_features = 1,
  zoi_limit = 0.05,
  resolution = 100,
  line_value = 1,
  ...
)

## S3 method for class 'formula'
predict(
  x,
  newdata,
  coefs,
  weights = 1,
  type = c("linear", "exponential", "exp", "logit", "cloglog")[1],
  wmean = TRUE,
  wq_probs = NULL,
  include = "all",
  ...
)

Arguments

x	⁠[bag,list or formula]⁠ A bag of models, resulting from a call to bag_models(), or a formula used to fit the models in the bag.
newdata	New data set to be used for prediction. It can include all the variables in the formula or only those for which the user is interested in making a prediction from.
type	⁠[character="linear"]{"linear", "exponential", "exp", "logit", "cloglog"}⁠ Type of prediction. One of "linear" (default), "exp" or "exponential", "logit", or "cloglog".
wmean	⁠[logical=TRUE]⁠ Should the weighted mean values be predicted? Default is TRUE.
wq_probs	⁠[vector,numeric(3)=c(0.025, 0.5, 0.975)]⁠ A three element vector with lower, mid, and higher weighted quantiles to be computed.
include	⁠[character="all"]⁠ String of vector of strings with the terms (or unique parts of terms) to be predicted for. This does not restrict which terms we are focusing on - this is done by the definition of the newdata dataset and by which columns are in there. What the include parameters does is to set which other variables will be used for prediction, at their mean or median values, for instance.
...	Additional parameters. None implemented.
baseline	⁠[character="median"]{"median", "mean", "zero")}⁠ What values to choose for the baseline, i.e., for all other variables/terms not contained in newdata. It can be one of median, "mean", or "zero".
zoi	⁠[logical(1)=FALSE]⁠ Are the columns in newdata supposed to represent zones of influence (ZOI) variables? This parameter should be set to TRUE if you provided a set of distances from a source that need to be translated into ZOI variables (cumulative or nearest ZOI from sources).
zoi_shape	⁠[character="exp_decay"]{"exp_decay", "gaussian_decay", "linear_decay", "threshold_decay"}⁠ Shape of the zone of influence (ZOI), if zoi = TRUE. Default is ⁠exp_decay"⁠. It can assume any of the possible values for the argument type in the function dist_decay().
which_cumulative	⁠[character="cumulative"]⁠ Which string or pattern to be searched on the column names of newdata and on the original data used to fit the models to represent the cumulative ZOI. It is used to break the names of the columns/terms in the formula and get the ZOI radii as numbers, to be able to create all the ZOI radii included in the model or bag of models.
type_feature	⁠[character="point"]{"point", "line", "area"}⁠ Type of feature we are predicting for, for zone of influence-type variables. Default is "point". If type_feature = "line", a line is simulated with the function create_linear_feature_zoi() to get the values and account for the number of pixels of each single linear feature in the neighborhhod and correclty estimate the effect of each linear feature ZOI. The option "area" is still not implemented and for now is treated as a point feature at the origin.
n_features	⁠[numeric(1)=1]⁠ Number of features to be used for prediction, for ZOI variables. Default is 1.
resolution	⁠[numeric(1)=100]⁠ Resolution for the raster created in create_line_feature_zoi(), when type_feature = "line".
line_value	⁠[numeric(1)=1]⁠ Value set to the raster line created by create_line_feature_zoi(), when type_feature = "line". It could be changed to different values if we want to represent e.g. the value in the linear feature as the roads traffic or another value for spatio-temporally dynamic variables.
coefs	⁠[vector,numeric]⁠ Either a named vector of coefficients (in case there is only one model) or a matrix of coefficients, with rownames as the term names and columns as the different models/resamples. Only relevant if x is a formula.
weights	⁠[vector,numeric=1]⁠ Vector of weights for the different models/resamples, i.e. the column from the coefs object with coefficients. A single number (by default, 1) in case there is only one model (coefs is a vector). Only relevant if x is a formula.

See Also

plot_response(), create_line_feature_zoi().

Examples

#---
# fit a bag to be tested

# load packages
library(glmnet)

# load data
data("reindeer_rsf")
# rename it just for convenience
dat <- reindeer_rsf

# formula initial structure
f <- use ~ private_cabins_XXX + public_cabins_high_XXX +
  NORUTreclass +
  # poly(norway_pca_klima_axis1, 2, raw = TRUE) +
  # poly(norway_pca_klima_axis2, 2, raw = TRUE) +
  norway_pca_klima_axis1 + norway_pca_klima_axis1_sq +
  norway_pca_klima_axis2 + norway_pca_klima_axis2_sq +
  norway_pca_klima_axis3 + norway_pca_klima_axis4

# add ZOI terms to the formula
zois <- c(100, 250, 500, 1000, 2500, 5000, 10000, 20000)
f <- add_zoi_formula(f, zoi_radius = zois, pattern = "XXX",
                     type = c("cumulative_exp_decay"),
                     separator = "_", predictor_table = TRUE)$formula

# sampling - random sampling
set.seed(1234)
samples <- create_resamples(y = dat$use,
                            p = c(0.2, 0.2, 0.2),
                            times = 10,
                            colH0 = NULL)

# fit multiple models
fittedl <- bag_fit_net_logit(f,
                             data = dat,
                             samples = samples,
                             standardize = "internal", # glmnet does the standardization of covariates
                             metric = "AUC",
                             method = "AdaptiveLasso",
                             parallel = "mclapply",
                             mc.cores = 2)

# bag models in a single object
bag_object <- bag_models(fittedl, dat, score_threshold = 0.7)

#---
# prediction using formula

# new data, looking only at PCA1
dfvar = data.frame(norway_pca_klima_axis1 = seq(min(bag_object$data_summary$norway_pca_klima_axis1),
                                                max(bag_object$data_summary$norway_pca_klima_axis1),
                                                length.out = 100))
dfvar$norway_pca_klima_axis1_sq = dfvar$norway_pca_klima_axis1**2

# one model only
bag_predict(x = f,
            newdata = dfvar,
            coefs = bag_object$coef[,1],
            include = "axis1")

# whole bag, weighted mean - here all weights = 1
bag_predict(x = f,
            newdata = dfvar,
            coefs = bag_object$coef,
            include = names(dfvar))

# whole bag, for each model separately
bag_predict(x = f,
            newdata = dfvar,
            coefs = bag_object$coef,
            wmean = FALSE,
            include = names(dfvar))

#---
# prediction using bag

# prediction for the very same dataset, linear scale
bag_predict(x = bag_object,
            newdata = dat,
            data = dat)

# non ZOI variable
# new data, looking only at PCA3
dfvar = data.frame(norway_pca_klima_axis3 = seq(min(bag_object$data_summary$norway_pca_klima_axis3),
                                                max(bag_object$data_summary$norway_pca_klima_axis3),
                                                length.out = 100))

bag_predict(x = bag_object,
            newdata = dfvar,
            data = dat)

# ZOI variable
# new data, looking only at private cabins
dfvar = data.frame(private_cabins = 1e3*seq(0.2, 20, length.out = 100))

# prediction for 1 feature, linear scale
bag_predict(x = bag_object,
            newdata = dfvar,
            data = dat,
            zoi = TRUE,
            baseline = "zero")

# prediction for 30 features, exp scale, with weighted confidence intervals
bag_predict(x = bag_object,
            newdata = dfvar,
            data = dat,
            type = "exp",
            wq_probs = c(0.025, 0.975),
            zoi = TRUE,
            n_features = 30,
            baseline = "zero")

# plot
plot(dfvar[,1],
     bag_predict(x = bag_object,
                 newdata = dfvar,
                 data = dat,
                 type = "exp",
                 zoi = TRUE,
                 n_features = 30,
                 baseline = "zero")[,1])

NINAnor/oneimpact documentation built on June 14, 2025, 12:27 a.m.