MEP {gofreg} | R Documentation |
Marked empirical process test statistic for a given GLM
Description
This class inherits from TestStatistic and implements a function to calculate the test statistic (and x-y-values that can be used to plot the underlying process).
The process underlying the test statistic is given in Dikta & Scheer (2021) doi: 10.1007/978-3-030-73480-0 and defined by
\bar{R}^1_n(u)
= \frac{1}{\sqrt{n}} \sum_{i=1}^n \left( Y_i - m(X_i, \hat{\beta}_n)
\right) I_{\{\hat{\beta}_n X_i \le u\}}, \quad -\infty \le u \le \infty.
Super class
gofreg::TestStatistic
-> MEP
Methods
Public methods
Inherited methods
Method calc_stat()
Calculate the value of the test statistic for given data and a model to test for.
Usage
MEP$calc_stat(data, model)
Arguments
data
data.frame()
with columns x and y containing the datamodel
ParamRegrModel to test for
Returns
The modified object (self
), allowing for method chaining.
Method clone()
The objects of this class are cloneable with this method.
Usage
MEP$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
Examples
# Create an example dataset
n <- 100
x <- cbind(runif(n), rbinom(n, 1, 0.5))
model <- NormalGLM$new()
y <- model$sample_yx(x, params=list(beta=c(2,3), sd=1))
data <- dplyr::tibble(x = x, y = y)
# Fit the correct model
model$fit(data, params_init=list(beta=c(1,1), sd=3), inplace = TRUE)
# Print value of test statistic and plot corresponding process
ts <- MEP$new()
ts$calc_stat(data, model)
print(ts)
plot(ts)
# Fit a wrong model
model2 <- NormalGLM$new(linkinv = function(u) {u+10})
model2$fit(data, params_init=list(beta=c(1,1), sd=3), inplace = TRUE)
# Print value of test statistic and plot corresponding process
ts2 <- MEP$new()
ts2$calc_stat(data, model2)
print(ts2)
plot(ts2)