Runs residual, influence, collinearity, goodness-of-fit, and
specification tests, then summarises the overall assessment.
Usage
full_diagnostics(
y,
X,
y_hat = NULL,
model_type = "linear",
column_names = NULL
)
Arguments
- y
Response.
- X
Design matrix.
- y_hat
Optional fitted values (OLS used if NULL).
- model_type
"linear", "logistic", "poisson".
- column_names
Optional column names for X.
Value
A morie_diagnostic_report.
Examples
set.seed(15)
X <- cbind(1, matrix(rnorm(100 * 2), 100, 2))
y <- drop(X %*% c(0.5, 1, -0.5) + rnorm(100, sd = 0.5))
r <- full_diagnostics(y, X)
r$overall_assessment
#> [1] "Issues detected: non-normal residuals; 1 outlier(s); 11 influential point(s); 1 collinear variable(s)"
