Rainbow Test
Rainbow test for linearity.
raintest(formula, fraction = 0.5, order.by = NULL, center = NULL, data=list())
formula |
a symbolic description for the model to be tested
(or a fitted |
fraction |
numeric. The percentage of observations in the subset is
determined by
|
order.by |
Either a vector |
center |
numeric. If If the Mahalanobis distance is chosen |
data |
an optional data frame containing the variables in the model.
By default the variables are taken from the environment which
|
The basic idea of the Rainbow test is that even if the true
relationship is
non-linear, a good linear fit can be achieved on a subsample in the "middle" of
the data. The null hypothesis is rejected whenever the overall fit is
significantly worse than the fit for the subsample. The test statistic under
H_0 follows an F distribution with parameter
degrees of freedom.
Examples can not only be found on this page, but also on the help pages of the
data sets bondyield
, currencysubstitution
,
growthofmoney
, moneydemand
,
unemployment
, wages
.
A list with class "htest"
containing the following components:
statistic |
the value of the test statistic. |
p.value |
the p-value of the test. |
parameter |
degrees of freedom. |
method |
a character string indicating what type of test was performed. |
data.name |
a character string giving the name(s) of the data. |
J.M. Utts (1982), The Rainbow Test for Lack of Fit in Regression. Communications in Statistics – Theory and Methods 11, 2801–2815.
W. Krämer & H. Sonnberger (1986), The Linear Regression Model under Test. Heidelberg: Physica
x <- c(1:30) y <- x^2 + rnorm(30,0,2) rain <- raintest(y ~ x) rain ## critical value qf(0.95, rain$parameter[1], rain$parameter[2])
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