| search.bin | R Documentation |
Use this function to create a binary choice model set and search for the best models (and other information) based on in-sample and out-of-sample evaluation metrics.
search.bin( data, combinations, metrics = get.search.metrics(), modelChecks = get.search.modelchecks(), items = get.search.items(), options = get.search.options(), costMatrices = NULL, searchLogit = TRUE, searchProbit = FALSE, optimOptions = get.options.newton(), aucOptions = get.options.roc() )
data |
A list that determines data and other required information for the search process.
Use |
combinations |
A list that determines the combinations of the exogenous variables in the search process.
Use |
metrics |
A list of options for measuring performance. Use get.search.metrics function to get them. |
modelChecks |
A list of options for excluding a subset of the model set. Use get.search.modelchecks function to get them. |
items |
A list of options for specifying the purpose of the search. Use get.search.items function to get them. |
options |
A list of extra options for performing the search. Use get.search.options function to get them. |
costMatrices |
A list of numeric matrices where each one determines how to score the calculated probabilities.
Given the number of choices |
searchLogit |
If |
searchProbit |
If |
optimOptions |
A list for Newton optimization options. Use get.options.newton function to get the options. |
aucOptions |
A list for AUC calculation options. Use get.options.roc function to get the options. |
A nested list with the following members:
counts |
Information about the expected number of models, number of estimated models, failed estimations, and some details about the failures. |
results |
A data frame with requested information in |
info |
The arguments and some general information about the search process such as the elapsed time. |
Note that the output does not contain any estimation results, but minimum required data to estimate the models (Use summary() function to get the estimation).
estim.bin
# We simulate some data for this example:
# sample data:
n = 50 # number of observations
num_x_r <- 3L # number of relevant explanatory variables
num_x_ir <- 20 # (relatively large) number of irrelevant explanatory variables
set.seed(340)
sample <- sim.bin(num_x_r, n)
x_ir <- lapply(1:num_x_ir, function(x) rnorm(n))
# prepare data:
data <- data.frame(sample$y, sample$x, x_ir)
colnames(data) <- c("Y", colnames(sample$x), paste0("z", 1:num_x_ir))
# Use glm function to estimate and analyse:
fit <- glm(Y ~ . - Y, data = data, family = binomial())
summary(fit)
# You can also use this package estimation function:
data0 <- get.data(data,
equations = list(Y ~ . - Y),
addIntercept = FALSE)
fit <- estim.bin(data = data0)
# format and print coefficients:
print(fit)
# Alternatively, You can define a binary choice model set:
x_sizes = c(1:3) # assuming we know the number of relevant explanatory variables is less than 3
metric_options <- get.search.metrics(typesIn = c("sic")) # We use SIC for searching
search_res <- search.bin(data = data0,
combinations = get.combinations(sizes = x_sizes),
metrics = metric_options)
print(search_res)
# Use summary function to estimate the best model:
search_sum <- summary(search_res, y = sample$y, x = data[,3:ncol(data)])
# format and print coefficients:
s_fit <- summary(search_res)
print(s_fit$results[[1]]$value)
# Try a step-wise search for creating a larger model set:
search_res <- search.bin(data = data0,
combinations = get.combinations(
sizes = list(c(1, 2, 3), c(4)),
stepsNumVariables = c(NA, 7)),
metrics = metric_options)
# combinations argument is different
print(search_res)
# Use summary like before.