megabot
tests for correlation between a variable, X, and another variable, Y,
conditional on other variables, Z. The basic approach involves fitting an specified model of X on
Z, a specified model of Y on Z, and then determining whether there is any
remaining information between X and Y. This is done by
computing residuals for both models, calculating their correlation, and
testing the null of no residual correlation. The test statistic output
is the correlation between probability-scale residuals. X and Y can
be continous or ordered discrete variables. megabot
replicates the functionality
of cobot
, cocobot
, and countbot
megabot(
formula,
data,
fit.x,
fit.y,
link.x = c("logit", "probit", "cloglog", "loglog", "cauchit", "logistic"),
link.y = c("logit", "probit", "cloglog", "loglog", "cauchit", "logistic"),
subset,
na.action = getOption("na.action"),
fisher = TRUE,
conf.int = 0.95
)
an object of class Formula
(or one
that can be coerced to that class): a symbolic description of the
model to be fitted. The details of model specification are given
under ‘Details’.
an optional data frame, list or environment (or object
coercible by as.data.frame
to a data frame)
containing the variables in the model. If not found in
data
, the variables are taken from
environment(formula)
, typically the environment from which
megabot
is called.
The fitting function used for the model of X or Y on Z. Options are ordinal, lm, lm.emp, poisson, nb, and orm.
The link family to be used for the ordinal model of X on Z. Defaults to logit. Other options are probit, cloglog,loglog, cauchit, and logistic(equivalent with logit). Used only when fit.x is either ordinal or orm.
an optional vector specifying a subset of observations to be used in the fitting process.
action to take when NA
present in data.
logical indicating whether to apply fisher transformation to compute confidence intervals and p-values for the correlation.
numeric specifying confidence interval coverage.
object of cocobot class.
Formula is specified as X | Y ~ Z
.
This indicates that models of X ~ Z
and
Y ~ Z
will be fit. The null hypothesis to be
tested is \(H_0 : X\) independent of Y conditional
on Z.
Li C and Shepherd BE (2012) A new residual for ordinal outcomes. Biometrika. 99: 473--480.
Shepherd BE, Li C, Liu Q (2016) Probability-scale residuals for continuous, discrete, and censored data. The Canadian Journal of Statistics. 44: 463--479.
# NOT RUN {
data(PResidData)
megabot(y|w ~ z, fit.x="ordinal", fit.y="lm.emp", data=PResidData)
# }
Run the code above in your browser using DataLab