subgroup_effect | R Documentation |
This function calculate the OR, CI, an p.value within each value of specified covariable.
subgroup_effect(
formula,
model,
method = c("lik.ratio", "wald"),
transpose = FALSE,
stat_digits = 2,
p_digits = 4,
sstable = FALSE,
flextable = FALSE,
simplify = TRUE,
...
)
formula |
A formula in the the form of A + B ~ X, where - X is the covariable to be concerned. X must be of type factor. - A, B are the covarables having interaction with X whose effects will be calculated. If . or blank. All covariables having interaction with X would be included. |
model |
A fitted glm model or a formula |
method |
a string whose value is either "lik.ratio" for CI and tests based on likelihood ratio statistics (preferred) or "wald" for CI and tests based on Wald statistics |
transpose |
logical value, default is FALSE, whether to return a transposed summary. See also t.subgroup_logist_summary |
stat_digits |
Number of decimal digits for statistics |
p_digits |
Number of decimal digits for the p-values |
sstable |
logical value specifying whether to return in sstable format. Default is FALSE. Set to TRUE forces verbose to FALSE |
flextable |
logical value specifying whether to build flextable object. Default it FALSE. Set to TRUE forces sstable to TRUE. |
simplify |
by default, if there is only one variable on each side of the formula and no LHS is a binary, then the function will combine tables in each state of X to one. Set to FALSE to avoid this behaviour. |
... |
additional parameters passed to glm to fit "model" (if model is a formula) |
Under certain circumstances defined in param simplify, a flextable, an sstable, a data.frame of class 'subgroup_logist_summary', or a 'subgroup_logist_summary'/list of logist_summary, each represents one state of X.
Trinh Dong Huu Khanh
logist_summary, print.subgroup_logist_summary, t.subgroup_logist_summary
y = sample(0:1, 1000, replace = T)
x1 = sample(1:100, 1000, replace = T)
x2 = sample(c("A", "B"), 1000, replace = T)
x3 = sample(c("C", "D", "E"), 1000, replace = T)
x4 = sample(c("F", "G"), 1000, replace = T)
fakefit = glm(y ~ x1*x3 + x2*x3 + x2*x4, family = binomial())
C306::subgroup_effect(~x3, fakefit)
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