Change remaining FUN args in hypotheses

book/articles/reference/hypotheses.qmd

@@ -37,7 +37,7 @@ package website, or scroll down this page for a full list of vignettes:
   equivalence = NULL,
   joint = FALSE,
   joint_test = "f",
-  FUN = NULL,
+  hypoth_fun = NULL,
   numderiv = "fdforward",
   ...
 )
@@ -190,7 +190,7 @@ package website, or scroll down this page for a full list of vignettes:
 <td>
 <p>A character string specifying the type of test, either &quot;f&quot; or &quot;chisq&quot;. The null hypothesis is set by the <code>hypothesis</code> argument, with default null equal to 0 for all parameters.</p>
 </td></tr>
-<tr style="vertical-align: top;"><td style = "white-space: nowrap; font-family: monospace; vertical-align: top"><code>FUN</code></td>
+<tr style="vertical-align: top;"><td style = "white-space: nowrap; font-family: monospace; vertical-align: top"><code>hypoth_fun</code></td>
 <td>
 <p><code>NULL</code> or function.
 </p>
@@ -305,7 +305,7 @@ library(marginaleffects)
 library(marginaleffects)
 mod <- lm(mpg ~ hp + wt + factor(cyl), data = mtcars)
 
-# When `FUN` and `hypotheses` are `NULL`, `hypotheses()` returns a data.frame of parameters
+# When `hypoth_fun` and `hypotheses` are `NULL`, `hypotheses()` returns a data.frame of parameters
 hypotheses(mod)
 
 # Test of equality between coefficients
@@ -318,7 +318,7 @@ hypotheses(mod, hypothesis = "exp(hp + wt) = 0.1")
 hypotheses(mod, hypothesis = "hp = wt", vcov = "HC3")
 
 # b1, b2, ... shortcuts can be used to identify the position of the
-# parameters of interest in the output of FUN
+# parameters of interest in the output of hypoth_fun
 hypotheses(mod, hypothesis = "b2 = b3")
 
 # wildcard
@@ -341,15 +341,15 @@ hypotheses(cmp, hypothesis = "b2 = 0.2")
 pre <- predictions(mod, newdata = datagrid(hp = 110, mpg = c(30, 35)))
 hypotheses(pre, hypothesis = "b1 = b2")
 
-# The `FUN` argument can be used to compute standard errors for fitted values
+# The `hypoth_fun` argument can be used to compute standard errors for fitted values
 mod <- glm(am ~ hp + mpg, data = mtcars, family = binomial)
 
 f <- function(x) predict(x, type = "link", newdata = mtcars)
-p <- hypotheses(mod, FUN = f)
+p <- hypotheses(mod, hypoth_fun = f)
 head(p)
 
 f <- function(x) predict(x, type = "response", newdata = mtcars)
-p <- hypotheses(mod, FUN = f)
+p <- hypotheses(mod, hypoth_fun = f)
 head(p)
 
 # Complex aggregation
@@ -370,7 +370,7 @@ aggregation_fun <- function(model) {
         rename(term = cyl)
 }
 
-hypotheses(mod, FUN = aggregation_fun)
+hypotheses(mod, hypoth_fun = aggregation_fun)
 
 # Equivalence, non-inferiority, and non-superiority tests
 mod <- lm(mpg ~ hp + factor(gear), data = mtcars)

inst/tinytest/test-equivalence.R

@@ -64,7 +64,7 @@ FUN <- function(model, ...) {
 e1 <- tost(dat$y[dat$x == 0], dat$y[dat$x == 1], epsilon = .05)
 e2 <- hypotheses(
     mod,
-    FUN = FUN,
+    hypoth_fun = FUN,
     equivalence = c(-.05, .05),
     df = e1$parameter)
 expect_true(e1$tost.p.value > .5 && e1$tost.p.value < .9)

inst/tinytest/test-hypotheses.R

@@ -5,7 +5,7 @@ requiet("car")
 
 
 
-# When `FUN` and `hypotheses` are `NULL`, `hypotheses()` returns a data.frame of parameters
+# When `hypoth_fun` and `hypotheses` are `NULL`, `hypotheses()` returns a data.frame of parameters
 dat <- mtcars
 mod <- lm(mpg ~ hp + wt + factor(cyl), data = dat)
 dmm <- hypotheses(mod)
@@ -25,32 +25,32 @@ expect_inherits(dmm, "data.frame")
 dmm <- hypotheses(mod, "hp = wt", vcov = "HC3")
 expect_inherits(dmm, "data.frame")
 
-# b1, b2, ... shortcuts can be used to identify rows in the output of FUN
+# b1, b2, ... shortcuts can be used to identify rows in the output of hypoth_fun
 dmm <- hypotheses(mod, "b2 = b3")
 expect_inherits(dmm, "data.frame")
 
 # term names with special characters have to be enclosed in backticks
 dmm <- hypotheses(mod, "`factor(cyl)6` = `factor(cyl)8`")
 expect_inherits(dmm, "data.frame")
 
-# The `FUN` argument can be used to compute standard errors for fitted values
+# The `hypoth_fun` argument can be used to compute standard errors for fitted values
 mod <- glm(am ~ hp + mpg, data = mtcars, family = binomial)
 
 f <- function(x) predict(x, type = "link", newdata = mtcars)
-p <- hypotheses(mod, FUN = f)
+p <- hypotheses(mod, hypoth_fun = f)
 expect_inherits(p, "data.frame")
 expect_true(all(p$std.error > 0))
 
 f <- function(x) predict(x, type = "response", newdata = mtcars)
-p <- hypotheses(mod, FUN = f)
+p <- hypotheses(mod, hypoth_fun = f)
 expect_inherits(p, "data.frame")
 expect_true(all(p$std.error > 0))
 
 # equality between predictions: 1 and 2 equal, 2 and 3 different
 f <- function(x) predict(x, type = "link", newdata = mtcars)
-dmm <- hypotheses(mod, FUN = f, hypothesis = "b1 = b2")
+dmm <- hypotheses(mod, hypoth_fun = f, hypothesis = "b1 = b2")
 expect_equivalent(dmm$estimate, 0)
-dmm <- hypotheses(mod, FUN = f, hypothesis = "b3 = b2")
+dmm <- hypotheses(mod, hypoth_fun = f, hypothesis = "b3 = b2")
 expect_equivalent(dmm$estimate, 1.33154848763268)
 
 # named matrix