Colr | R Documentation |
A proportional-odds model for continuous variables
Colr(formula, data, subset, weights, offset, cluster, na.action = na.omit, ...)
formula |
an object of class |
data |
an optional data frame, list or environment (or object
coercible by |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
weights |
an optional vector of weights to be used in the fitting
process. Should be |
offset |
this can be used to specify an _a priori_ known component to
be included in the linear predictor during fitting. This
should be |
cluster |
optional factor with a cluster ID employed for computing clustered covariances. |
na.action |
a function which indicates what should happen when the data
contain |
... |
additional arguments to |
Simultaneous estimation of all possible binary logistic models obtained by dichotomisation of a continuous response. The regression coefficients can be constant allowing for an interpretation as log-odds ratios.
The model is defined with a positive shift term, thus exp(coef())
is
the multiplicative change of the odds ratio (conditional odds of treatment
or for a one unit increase in a numeric variable divided by conditional odds
of reference). Large values of the linear predictor correspond to small
values of the conditional expectation response (but this relationship is
nonlinear).
An object of class Colr
, with corresponding coef
,
vcov
, logLik
, estfun
, summary
,
print
, plot
and predict
methods.
Tina Lohse, Sabine Rohrmann, David Faeh and Torsten Hothorn (2017), Continuous Outcome Logistic Regression for Analyzing Body Mass Index Distributions, F1000Research, 6(1933), \Sexpr[results=rd]{tools:::Rd_expr_doi("10.12688/f1000research.12934.1")}.
Torsten Hothorn, Lisa Moest, Peter Buehlmann (2018), Most Likely Transformations, Scandinavian Journal of Statistics, 45(1), 110–134, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/sjos.12291")}.
data("BostonHousing2", package = "mlbench")
lm(cmedv ~ crim + zn + indus + chas + nox + rm + age + dis +
rad + tax + ptratio + b + lstat, data = BostonHousing2)
Colr(cmedv ~ chas + crim + zn + indus + nox +
rm + age + dis + rad + tax + ptratio + b + lstat,
data = BostonHousing2)
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