diff --git a/pkgdown.yml b/pkgdown.yml index 13e0db1..f9d5a40 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -5,7 +5,7 @@ articles: estimating-effects: estimating-effects.html installing-packages: installing-packages.html WeightIt: WeightIt.html -last_built: 2024-08-23T15:40Z +last_built: 2024-08-23T17:47Z urls: reference: https://ngreifer.github.io/WeightIt/reference article: https://ngreifer.github.io/WeightIt/articles diff --git a/reference/glm_weightit-methods.html b/reference/glm_weightit-methods.html index 0d08345..21a6520 100644 --- a/reference/glm_weightit-methods.html +++ b/reference/glm_weightit-methods.html @@ -47,6 +47,22 @@
# S3 method for class 'glm_weightit'
summary(object, ci = FALSE, level = 0.95, transform = NULL, ...)
+# S3 method for class 'multinom_weightit'
+summary(object, ci = FALSE, level = 0.95, transform = NULL, ...)
+
+# S3 method for class 'ordinal_weightit'
+summary(
+ object,
+ ci = FALSE,
+ level = 0.95,
+ transform = NULL,
+ thresholds = TRUE,
+ ...
+)
+
+# S3 method for class 'coxph_weightit'
+summary(object, ci = FALSE, level = 0.95, transform = NULL, ...)
+
# S3 method for class 'glm_weightit'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
@@ -54,9 +70,6 @@ Usage
vcov(object, complete = TRUE, ...)
# S3 method for class 'glm_weightit'
-confint(object, parm, level = 0.95, ...)
-
-# S3 method for class 'glm_weightit'
anova(object, object2, test = "Chisq", method = "Wald", tolerance = 1e-07, ...)
logical
; whether to include thresholds in the summary()
output for ordinal_weightit
objects. Default is TRUE
.
the number of significant digits to be
passed to format(coef(x), .)
when
@@ -94,12 +111,6 @@
coef()
also in this singular case.a specification of which parameters are to be given - confidence intervals, either a vector of numbers or a vector of - names. If missing, all parameters are considered.
the type of test statistic used to compare models. Currently only "Chisq"
(the chi-square statistic) is allowed.
for the Wald test, the tolerance used to determine if models are symbolically nested.
logical
; whether to include thresholds in the summary()
output for ordinal_weightit
objects. Default is TRUE
.
print(<glm_weightit>)
vcov(<glm_weightit>)
confint(<glm_weightit>)
anova(<glm_weightit>)
+ summary(<glm_weightit>)
summary(<multinom_weightit>)
summary(<ordinal_weightit>)
summary(<coxph_weightit>)
print(<glm_weightit>)
vcov(<glm_weightit>)
anova(<glm_weightit>)
glm_weightit()
objects