robfmtest | R Documentation |
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).
robfmtest(formula, power = 2:3, type = c("regressor"), data,
x.weights = c("HAT", "MCD"), testtype = "Wald", ...)
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. |
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.
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. |
Mikhail Zhelonkin
Heritier, S., and Ronchetti, E. (1994) Robust Bounded-Influence Tests in General Parametric Models. Journal of the American Statistical Association, 89, p. 897-904.
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")
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