effectInfo {tlm} | R Documentation |
Interpretation of Effects in Linear, Logistic and Poisson Models with Transformed Variables
Description
Provides information on interpreting effects in linear, logistic and Poisson models with transformed variables. Specifically, if a summary measure for the effect exists, the function details how to obtain and interpret it.
Usage
effectInfo(object)
## S3 method for class 'effectInfo'
print(x, ...)
Arguments
object |
object of class " |
x |
for |
... |
additional arguments for |
Value
A list with class "effectInfo
" including the following items:
- beta
regression coefficient estimate in the fitted model which is associated to the effect of the explanatory variable of interest on the response variable.
NA
corresponds to those models for which a summary effect does not exist.- Xincrease
type of change in the exploratory variable of interest (additive or realtive) for which a summary effect exists.
NA
corresponds to those models for which a summary effect does not exist.- effecttype
type of effect on the response variable for which a summary effect exists.
NA
corresponds to those models for which a summary effect is not available.- effectsize
formula for the summary effect size, if any.
NA
corresponds to those models for which a summary effect is not available.- furtherinfo
further information about how to interpret effects.
References
Barrera-Gomez J, Basagana X. Models with transformed variables: interpretation and software. Epidemiology. 2015;26(2):e16-17.
See Also
Examples
### Linear model with log transformation in the explanatory variable:
data(cotinine)
head(cotinine)
# model fitting:
modcot <- tlm(weight ~ logcotinine, data = cotinine, xpow = 0)
modcot
# information on interpreting the effect:
effectInfo(modcot)
### Linear model with no summary measure of effect:
data(glucose)
head(glucose)
# transformations Y^(-2) and X^(-1/2):
modgluco <- tlm(inv2glu ~ inv12tri, data = glucose, ypow = -2, xpow = -1/2)
modgluco
effectInfo(modgluco)