CondKolmY | R Documentation |
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 Kremling & Dikta (2024) https://arxiv.org/abs/2409.20262 and defined by
\tilde{\alpha}_n(t) = \frac{1}{\sqrt{n}}
\sum_{i=1}^n \left( I_{\{Y_i \le t\}} - F(t|\hat{\vartheta}_n, X_i)
\right), \quad -\infty \le t \le \infty.
gofreg::TestStatistic
-> CondKolmY
calc_stat()
Calculate the value of the test statistic for given data and a model to test for.
CondKolmY$calc_stat(data, model)
data
data.frame()
with columns x and y containing the data
model
ParamRegrModel to test for, already fitted to the data
The modified object (self
), allowing for method chaining.
clone()
The objects of this class are cloneable with this method.
CondKolmY$clone(deep = FALSE)
deep
Whether to make a deep clone.
# 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$fit(data, params_init=list(beta=c(1,1), sd=3), inplace = TRUE)
# Print value of test statistic and plot corresponding process
ts <- CondKolmY$new()
ts$calc_stat(data, model)
print(ts)
plot(ts)
# Fit a wrong model
model2 <- NormalGLM$new(linkinv = function(u) {u+10})
model2$fit(data, params_init=list(beta=c(1,1), sd=3), inplace = TRUE)
# Print value of test statistic and plot corresponding process
ts2 <- CondKolmY$new()
ts2$calc_stat(data, model2)
print(ts2)
plot(ts2)
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