Contrast Matrix for Reversed Isotonic Covariate
Return something similar to a contrast matrix for a categorical covariate that we wish to be monotonically non-decreasing in a specified order.
a vector of levels for a factor, or the number of levels.
a permutation of the levels of
a logical indicating whether constrasts should be computed.
included for compatibility reasons. Has no effect.
This function is used in creating the design matrix for categorical covariates with a specified order under a particular parameterisation. This is required if a categorical covariate is defined as monotonic.
In the order specified by
perm, the coefficient
associated with each level is the sum of increments between
the following levels. That is, if there are a total of k
levels, the first level is defined as d_2 + d_3 + d_4 + \cdots + d_k,
the second as d_3 + d_4 + \cdots + d_k,
the third as d_4 + \cdots + d_k, and so on. In fitting the model,
these increments are constrained to be non-positive.
Note that these are not ‘contrasts’ as defined in the
theory for linear models, rather this is used to define the
contrasts attribute of each variable so that
model.matrix produces the desired design
A matrix with
n rows and k columns, with
Mark W. Donoghoe firstname.lastname@example.org
model.matrix, which uses
contr.isotonic.rev to create the design matrix.
their usual use in regression models.
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