| Type: | Package |
| Title: | Outlier Robust Specification Testing |
| Version: | 0.1 |
| Date: | 2023-05-22 |
| Author: | Mikhail Zhelonkin 
[aut, cre] |
| Maintainer: | Mikhail Zhelonkin <Mikhail.Zhelonkin@gmail.com> |
| Description: | Robust test(s) for model diagnostics in regression. The current version contains a robust test for functional specification (linearity). The test is based on the robust bounded-influence test by Heritier and Ronchetti (1994) <doi:10.1080/01621459.1994.10476822>. |
| License: | GPL-2 |
| Imports: | MASS |
| Suggests: | lmtest |
| NeedsCompilation: | no |
| Packaged: | 2023-05-22 15:44:51 UTC; M.Zhelonkin |
| Repository: | CRAN |
| Date/Publication: | 2023-05-23 08:50:05 UTC |
Outlier Robust Specification Testing
Description
The package contains an outlier robust functional miss-specification test.
Details
| Package: | lmtestrob |
| Type: | Package |
| Version: | 0.1 |
| Date: | 2023-05-22 |
| License: | GPL-2 |
Author(s)
Mikhail Zhelonkin (aut, cre).
Maintainer: Mikhail Zhelonkin <Mikhail.Zhelonkin@gmail.com>
References
Heritier, S., and Ronchetti, E. (1994) Robust Bounded-Influence Tests in General Parametric Models. Journal of the American Statistical Association, 89, p. 897-904.
Zeileis, A., and Hothorn, T. (2002) Diagnostic Checking in Regression Relationships. R News, 2, p. 7-10.
See Also
Print a robfmtest Object
Description
Print an object generated by robfmtest
Usage
## S3 method for class 'robfmtest'
print(x, digits = 4, ...)
Arguments
x |
object returned from the |
digits |
number of significant digits to be printed. |
... |
currently not used. |
Value
No return value.
Author(s)
Mikhail Zhelonkin
See Also
Robust Functional Specification Test
Description
Outlier robust test for functional miss-specification. It can be used to test linearity. The test is based on the robust Wald-type test by Heritier and Ronchetti (1994).
Usage
robfmtest(formula, power = 2:3, type = c("regressor"), data,
x.weights = c("HAT", "MCD"), testtype = "Wald", ...)
Arguments
formula |
a symbolic description of the model to be tested. |
power |
integer(s). A vector of positive integers specifying the powers of the variables that should be tested. The default option tests second and third powers. |
type |
currently, only powers of regressors can be used. |
data |
an optional data frame containing the variables in the model. If not found in data, the variables are taken from |
x.weights |
a string, indicating how the robustness weights on the covariates should be computed. The default option uses hat-matrix-based weights, second option allows to use robust Mahalanobis distance-based weights, where the Minimum Covariance Determinant is used to estimate location and scatter. |
testtype |
currently, the robust version of Wald test is implemented. |
... |
currently not used. |
Details
Since the classical tests including resettest, raintest and harvtest implemented in lmtest are not resistant to outliers and can become misleading even in the presence of one outlier, we provide a test which is resistant to outliers. The price to pay for robustness is a small loss of power, when the model holds exactly.
Value
A list with class robfmtest containing the following components:
statistic |
the value of the test statistic. |
dof |
the number of degrees of freedom. |
method |
a character string indicating what type of test was performed. |
p.value |
the p-value of the test. |
data.name |
a character string giving the name(s) of the data. |
Author(s)
Mikhail Zhelonkin
References
Heritier, S., and Ronchetti, E. (1994) Robust Bounded-Influence Tests in General Parametric Models. Journal of the American Statistical Association, 89, p. 897-904.
Examples
set.seed(123)
n <- 50
x = runif(n, -3, 3)
y = rnorm(n)
example.dat <- data.frame(x, y)
robfmtest(y ~ x, data = example.dat)
library(lmtest)
resettest(y ~ x, data = example.dat, type = "fitted")
x[50] <- -3
y[50] <- -10
example.dat <- data.frame(x, y)
robfmtest(y ~ x, data = example.dat)
resettest(y ~ x, data = example.dat, type = "fitted")