model_predict {OptHoldoutSize}

Make predictions

Description

Make predictions according to a given model

Usage

model_predict(
  data_test,
  trained_model,
  return_type,
  threshold = NULL,
  model_family = NULL,
  ...
)

Arguments

data_test

Data for which predictions are to be computed

trained_model

Model for which predictions are to be made

return_type

??

threshold

??

model_family

??

...

Passed to function predict.glm() or predict.ranger()

Value

Vector of predictions

Examples

## Set seed for reproducibility
seed=1234
set.seed(seed)

# Initialisation of patient data
n_iter <- 500           # Number of point estimates to be calculated
nobs <- 5000            # Number of observations, i.e patients
npreds <- 7             # Number of predictors

# Model family
family="log_reg"

# Baseline behaviour is an oracle Bayes-optimal predictor on only one variable
max_base_powers <- 1
base_vars=1

# Check the following holdout size fractions
frac_ho = 0.1


# Set ground truth coefficients, and the accuracy at baseline
coefs_general <- rnorm(npreds,sd=1/sqrt(npreds))
coefs_base <- gen_base_coefs(coefs_general, max_base_powers = max_base_powers)

# Generate dataset
X <- gen_preds(nobs, npreds)

# Generate labels
newdata <- gen_resp(X, coefs = coefs_general)
Y <- newdata$classes

# Combined dataset
pat_data <- cbind(X, Y)
pat_data$Y = factor(pat_data$Y)

# For each holdout size, split data into intervention and holdout set
mask <- split_data(pat_data, frac_ho)
data_interv <- pat_data[!mask,]
data_hold <- pat_data[mask,]

# Train model
trained_model <- model_train(data_hold, model_family = family)
thresh <- 0.5

# Make predictions
class_pred <- model_predict(data_interv, trained_model,
                            return_type = "class",
                            threshold = 0.5, model_family = family)


# Simulate baseline predictions
base_pred <- oracle_pred(data_hold,coefs_base[base_vars, ], num_vars = base_vars)


# Contingency table for model-based predictor (on intervention set)
print(table(class_pred,data_interv$Y))

# Contingency table for model-based predictor (on holdout set)
print(table(base_pred,data_hold$Y))