assumptions {trafo} | R Documentation |
First check of assumptions to find suitable transformations
Description
Gives a first overview if a transformation is useful and which transformation is promising to fulfill the model assumptions normality, homoscedasticity and linearity.
Usage
assumptions(object, method = "ml", std = FALSE, ...)
Arguments
object |
an object of type |
method |
a character string. Different estimation methods can be used for the estimation of the optimal transformation parameter: (i) Maximum likelihood approach ("ml"), (ii) Skewness minimization ("skew"), (iii) Kurtosis optimization ("kurt"), (iv) Divergence minimization by Kolmogorov-Smirnov ("div.ks"), by Cramer-von-Mises ("div.cvm") or by Kullback-Leibler ("div.kl"). Defaults to "ml". |
std |
logical. If |
... |
other parameters that can be passed to the function, e.g. other
lambdaranges. Self-defined lambdaranges are given to the function as an
argument that is the combination of the name of the transformation and lr and
the range needs to be a numeric vector of length 2. For instance, changing the
lambdarange for the Manly transformation would mean to add an argument
|
Value
A table with tests for normality and homoscedasticity. Furthermore, scatterplots are returned to check the linearity assumption.
See Also
bickeldoksum
, boxcox
, dual
,
glog
, gpower
, log
,
logshiftopt
, manly
, modulus
,
neglog
, sqrtshift
, yeojohnson
Examples
# Load data
data("cars", package = "datasets")
# Fit linear model
lm_cars <- lm(dist ~ speed, data = cars)
assumptions(lm_cars)
assumptions(lm_cars, method = "skew", manly_lr = c(0.000005,0.00005))