Description Usage Arguments Value See Also Examples
Please note: There is a faster option: 'chest_speedglm'
. 'chest_glm'
is used to assess confounding effects using Generalized Linear Models, such as
logistic regression and Poisson regression with 'glm'
.
It presents odds ratios or rate ratios for the association
between exposure and outcome variables by adding other variables (potential
confounders) to the model sequentially. The order of variables to be added is
based on the magnitudes of the changes in effect estimates.
1 2 3 4 5 6 7 8 9 10 11 |
crude |
An object of formula for initial model, generally crude model. However, any other variables can also be included here as the initial model. |
xlist |
A vector of characters with all variable names of potential confounders. |
data |
Data frame. |
family |
Description of the error distribution. Default is "binomial". |
method |
Method to detect for singularity. |
na_omit |
Remove all missing values, default: 'na_omit = TRUE'. |
indicate |
indicate progress. |
plus |
Change the |
... |
Further optional arguments for forestplot. |
A table with effect estimates and their changes at all steps.
'glm'
'stats'
1 2 3 4 |
$data
variables est lb ub Change p n
1 Crude 2.307947 1.841000 2.913558 NA 8.839157e-13 2367
2 + Age 3.342051 2.506369 4.498958 44.8062080 5.899645e-16 2367
3 + Married 3.314066 2.484515 4.462517 -0.8373444 1.002763e-15 2367
4 + Sex 3.337314 2.500947 4.495835 0.7014981 7.521353e-16 2367
$fun
[1] "chest_glm"
$family
[1] "binomial"
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