updates

README.Rmd

@@ -207,12 +207,12 @@ plot(contrasts, estimate_means(model)) +
 ```{r}
 model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris)
 
-estimate_contrasts(model, at = "Petal.Length", length = 3)
+estimate_contrasts(model, by = "Petal.Length", length = 3)
 ```
 
 ```{r}
 # Recompute contrasts with a higher precision (for a smoother plot)
-contrasts <- estimate_contrasts(model, at = "Petal.Length", length = 20)
+contrasts <- estimate_contrasts(model, by = "Petal.Length", length = 20)
 
 # Add Contrast column by concatenating
 contrasts$Contrast <- paste(contrasts$Level1, "-", contrasts$Level2)
@@ -302,7 +302,7 @@ model <- mgcv::gam(Sepal.Width ~ s(Petal.Length), data = iris)
 # 1. Compute derivatives
 deriv <- estimate_slopes(model,
   trend = "Petal.Length",
-  at = "Petal.Length",
+  by = "Petal.Length",
   length = 100
 )
 
@@ -363,7 +363,7 @@ For instance, the plot below shows that the effect of `hp` (the y-axis) is signi
 ```{r}
 model <- lm(mpg ~ hp * wt, data = mtcars)
 
-slopes <- estimate_slopes(model, trend = "hp", at = "wt")
+slopes <- estimate_slopes(model, trend = "hp", by = "wt")
 
 plot(slopes)
 ```

README.md

@@ -163,7 +163,10 @@ means
 ## versicolor | 2.77 | 0.05 | [2.68, 2.86]
 ## virginica  | 2.97 | 0.05 | [2.88, 3.07]
 ## 
-## Marginal means estimated at Species
+## Marginal means estimated at
+```
+
+``` r
 
 # 3. Plot
 ggplot(iris, aes(x = Species, y = Sepal.Width)) +
@@ -233,7 +236,7 @@ contrasts
 ``` r
 model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris)
 
-estimate_contrasts(model, at = "Petal.Length", length = 3)
+estimate_contrasts(model, by = "Petal.Length", length = 3)
 ## Marginal Contrasts Analysis
 ## 
 ## Level1     |     Level2 | Petal.Length | Difference |        95% CI |   SE | t(144) |      p
@@ -254,7 +257,7 @@ estimate_contrasts(model, at = "Petal.Length", length = 3)
 
 ``` r
 # Recompute contrasts with a higher precision (for a smoother plot)
-contrasts <- estimate_contrasts(model, at = "Petal.Length", length = 20)
+contrasts <- estimate_contrasts(model, by = "Petal.Length", length = 20)
 
 # Add Contrast column by concatenating
 contrasts$Contrast <- paste(contrasts$Level1, "-", contrasts$Level2)
@@ -306,6 +309,9 @@ head(pred1, n = 5)
 ## 5.00         |      2.19 | 0.10 | [1.99, 2.39] |     -0.79 |         1.40
 ## 
 ## Variable predicted: Petal.Length
+```
+
+``` r
 
 # Same for model 2
 model2 <- lm(Petal.Length ~ Sepal.Length * Species, data = iris)
@@ -354,6 +360,9 @@ random
 ## cyl   |     6 |        drat |       -0.09 | 0.54 | [-1.15,  0.98]
 ## cyl   |     8 | (Intercept) |        3.32 | 0.73 | [ 1.89,  4.74]
 ## cyl   |     8 |        drat |       -2.15 | 0.47 | [-3.07, -1.23]
+```
+
+``` r
 
 plot(random)
 ```
@@ -391,7 +400,7 @@ model <- mgcv::gam(Sepal.Width ~ s(Petal.Length), data = iris)
 # 1. Compute derivatives
 deriv <- estimate_slopes(model,
   trend = "Petal.Length",
-  at = "Petal.Length",
+  by = "Petal.Length",
   length = 100
 )
 
@@ -465,7 +474,7 @@ is significantly negative only when `wt` is low (`< ~4`).
 ``` r
 model <- lm(mpg ~ hp * wt, data = mtcars)
 
-slopes <- estimate_slopes(model, trend = "hp", at = "wt")
+slopes <- estimate_slopes(model, trend = "hp", by = "wt")
 
 plot(slopes)
 ```

tests/testthat/test-attributes_visualisation.R

@@ -3,15 +3,15 @@ test_that("attributes_means", {
   model <- lm(Sepal.Length ~ Species * Sepal.Width, data = iris)
 
   estim <- suppressMessages(estimate_means(model))
-  expect_identical(attributes(estim)$at, "Species")
-  expect_identical(attributes(estim)$fixed, NULL)
+  expect_identical(attributes(estim)$by, "Species")
+  expect_null(attributes(estim)$fixed)
 
   estim <- suppressMessages(estimate_means(model, fixed = "Sepal.Width"))
-  expect_identical(attributes(estim)$at, "Species")
+  expect_identical(attributes(estim)$by, "Species")
   expect_identical(attributes(estim)$fixed, "Sepal.Width")
 
-  estim <- suppressMessages(estimate_means(model, at = "all"))
-  expect_identical(attributes(estim)$at, c("Species", "Sepal.Width"))
+  estim <- suppressMessages(estimate_means(model, by = "all"))
+  expect_identical(attributes(estim)$by, c("Species", "Sepal.Width"))
 })
 
 
@@ -22,13 +22,13 @@ test_that("attributes_contrasts", {
 
   estim <- suppressMessages(estimate_contrasts(model))
   expect_identical(attributes(estim)$contrast, "Species")
-  expect_identical(attributes(estim)$at, NULL)
-  expect_identical(attributes(estim)$fixed, NULL)
+  expect_null(attributes(estim)$by)
+  expect_null(attributes(estim)$fixed)
 
   estim <- suppressMessages(estimate_contrasts(model, fixed = "Sepal.Width"))
   expect_identical(attributes(estim)$contrast, "Species")
   expect_identical(attributes(estim)$fixed, "Sepal.Width")
-  expect_identical(attributes(estim)$modulate, NULL)
+  expect_null(attributes(estim)$modulate)
 })
 
 

tests/testthat/test-brms.R

@@ -5,15 +5,15 @@ test_that("estimate_means - brms", {
   skip_if_not_installed("emmeans")
   model <- brms::brm(Sepal.Length ~ Species * Sepal.Width, data = iris, refresh = 0, iter = 1000)
   estim <- estimate_means(model)
-  expect_equal(dim(estim), c(3, 5))
+  expect_identical(dim(estim), c(3L, 5L))
 })
 
 test_that("estimate_relation - brms", {
   skip_if_not_installed("brms")
   skip_if_not_installed("emmeans")
   model <- brms::brm(Sepal.Length ~ Species * Sepal.Width, data = iris, refresh = 0, iter = 1000)
   estim <- estimate_relation(model, preserve_range = FALSE)
-  expect_equal(dim(estim), c(30, 6))
+  expect_identical(dim(estim), c(30L, 6L))
 
   # estim <- estimate_relation(model, preserve_range=FALSE, iterations = 10)
   # expect_equal(dim(estim), c(30, 6))
@@ -23,6 +23,6 @@ test_that("estimate_means - brms", {
   skip_if_not_installed("brms")
   skip_if_not_installed("emmeans")
   model <- brms::brm(Sepal.Length ~ Species * Sepal.Width, data = iris, refresh = 0, iter = 1000)
-  estim <- estimate_slopes(model, at = "Species")
-  expect_equal(dim(estim), c(3, 5))
+  estim <- estimate_slopes(model, by = "Species")
+  expect_identical(dim(estim), c(3L, 5L))
 })

tests/testthat/test-estimate_means.R

@@ -13,51 +13,51 @@ test_that("estimate_means() - core", {
   # Simple
   model <- lm(vs ~ cyl, data = dat)
   estim1 <- suppressMessages(estimate_means(model))
-  expect_equal(dim(estim1), c(3, 5))
+  expect_identical(dim(estim1), c(3L, 5L))
   estim2 <- suppressMessages(estimate_means(model, backend = "marginaleffects"))
-  expect_equal(dim(estim2), c(3, 5))
-  expect_true(max(estim1$Mean - estim2$Mean) < 1e-10)
+  expect_identical(dim(estim2), c(3L, 5L))
+  expect_lt(max(estim1$Mean - estim2$Mean), 1e-10)
 
   # Interaction (factor * continuous)
   model <- lm(mpg ~ wt * gear, data = dat)
   estim1 <- suppressMessages(estimate_means(model))
-  expect_equal(dim(estim1), c(3, 5))
+  expect_identical(dim(estim1), c(3L, 5L))
   estim2 <- suppressMessages(estimate_means(model, backend = "marginaleffects"))
-  expect_equal(dim(estim2), c(3, 6))
-  expect_true(max(estim1$Mean - estim2$Mean) < 1e-10)
+  expect_identical(dim(estim2), c(3L, 6L))
+  expect_lt(max(estim1$Mean - estim2$Mean), 1e-10)
 
   # At specific levels
   model <- lm(Sepal.Width ~ Species, data = iris)
-  estim1 <- suppressMessages(estimate_means(model, at = "Species=c('versicolor', 'virginica')"))
-  expect_equal(dim(estim1), c(2, 5))
-  estim2 <- suppressMessages(estimate_means(model, at = "Species=c('versicolor', 'virginica')", backend = "marginaleffects"))
-  expect_equal(dim(estim2), c(2, 5))
-  expect_true(max(estim1$Mean - estim2$Mean) < 1e-10)
+  estim1 <- suppressMessages(estimate_means(model, by = "Species=c('versicolor', 'virginica')"))
+  expect_identical(dim(estim1), c(2L, 5L))
+  estim2 <- suppressMessages(estimate_means(model, by = "Species=c('versicolor', 'virginica')", backend = "marginaleffects"))
+  expect_identical(dim(estim2), c(2L, 5L))
+  expect_lt(max(estim1$Mean - estim2$Mean), 1e-10)
 
   # Interactions between factors
   dat <- iris
   dat$Petal.Length_factor <- ifelse(dat$Petal.Length < 4.2, "A", "B")
   dat <<- dat
 
   model <- lm(Sepal.Width ~ Species * Petal.Length_factor, data = dat)
-  estim1 <- suppressMessages(estimate_means(model, at = "all"))
-  expect_equal(dim(estim1), c(6, 6))
-  estim2 <- suppressWarnings(suppressMessages(estimate_means(model, at = "all", backend = "marginaleffects")))
-  expect_equal(dim(estim2), c(6, 6))
+  estim1 <- suppressMessages(estimate_means(model, by = "all"))
+  expect_identical(dim(estim1), c(6L, 6L))
+  estim2 <- suppressWarnings(suppressMessages(estimate_means(model, by = "all", backend = "marginaleffects")))
+  expect_identical(dim(estim2), c(6L, 6L))
 
   # No interaction (two factors)
   model <- lm(Petal.Length ~ Sepal.Width + Species, data = iris)
   estim1 <- suppressMessages(estimate_means(model))
-  expect_equal(dim(estim1), c(3, 5))
+  expect_identical(dim(estim1), c(3L, 5L))
   estim2 <- suppressMessages(estimate_means(model, backend = "marginaleffects"))
-  expect_equal(dim(estim2), c(3, 6))
-  expect_true(max(estim1$Mean - estim2$Mean) < 1e-10)
+  expect_identical(dim(estim2), c(3L, 6L))
+  expect_lt(max(estim1$Mean - estim2$Mean), 1e-10)
 
 
   # At specific levels of continuous
-  estim1 <- suppressMessages(estimate_means(model, at = "Sepal.Width"))
+  estim1 <- suppressMessages(estimate_means(model, by = "Sepal.Width"))
   expect_equal(dim(estim1), c(10, 5))
-  estim2 <- suppressMessages(estimate_means(model, at = "Sepal.Width", backend = "marginaleffects"))
+  estim2 <- suppressMessages(estimate_means(model, by = "Sepal.Width", backend = "marginaleffects"))
   expect_equal(dim(estim2), c(10, 6))
   # Note that the absolute values are different here... for unclear reasons
   expect_true(max(diff(estim1$Mean) - diff(estim2$Mean)) < 1e-10)
@@ -69,57 +69,57 @@ test_that("estimate_means() - core", {
   model <- glm(y ~ Species, family = "binomial", data = dat)
 
   estim <- suppressMessages(estimate_means(model))
-  expect_equal(dim(estim), c(3, 5))
+  expect_identical(dim(estim), c(3L, 5L))
   estim <- suppressMessages(estimate_means(model, transform = "response"))
-  expect_equal(dim(estim), c(3, 5))
+  expect_identical(dim(estim), c(3L, 5L))
   expect_true(all(estim$Probability >= 0) & all(estim$Probability <= 1))
 
 
   model <- lm(Petal.Length ~ Sepal.Width + Species, data = iris)
   estim <- suppressMessages(estimate_means(model))
-  expect_equal(dim(estim), c(3, 5))
+  expect_identical(dim(estim), c(3L, 5L))
 
-  estim <- suppressMessages(estimate_means(model, at = "all"))
+  estim <- suppressMessages(estimate_means(model, by = "all"))
   expect_equal(dim(estim), c(30, 6))
 
   # In formula modification
   # FIXME: this got broken but it seems just to tedious to fix. Don't use in formula transforms.
   # model <- lm(mpg ~ wt * as.factor(gear), data = mtcars)
   # estim <- suppressMessages(estimate_means(model))
-  # expect_equal(dim(estim), c(3, 5))
+  # expect_equal(dim(estim), c(3L, 5L))
 
   # One continuous and one factor
   model <- lm(Petal.Length ~ Species * Sepal.Width, data = iris)
 
   estim <- suppressMessages(estimate_means(model))
-  expect_equal(dim(estim), c(3, 5))
+  expect_identical(dim(estim), c(3L, 5L))
   estim <- suppressMessages(estimate_means(model, fixed = "Sepal.Width"))
-  expect_equal(dim(estim), c(3, 6))
-  estim <- suppressMessages(estimate_means(model, at = c("Species", "Sepal.Width"), length = 2))
-  expect_equal(dim(estim), c(6, 6))
-  estim <- suppressMessages(estimate_means(model, at = "Species=c('versicolor', 'setosa')"))
-  expect_equal(dim(estim), c(2, 5))
-  estim <- suppressMessages(estimate_means(model, at = "Sepal.Width=c(2, 4)"))
-  expect_equal(dim(estim), c(2, 5))
-  estim <- suppressMessages(estimate_means(model, at = c("Species", "Sepal.Width=0")))
-  expect_equal(dim(estim), c(3, 6))
-  estim <- suppressMessages(estimate_means(model, at = "Sepal.Width", length = 5))
+  expect_identical(dim(estim), c(3L, 6L))
+  estim <- suppressMessages(estimate_means(model, by = c("Species", "Sepal.Width"), length = 2))
+  expect_identical(dim(estim), c(6L, 6L))
+  estim <- suppressMessages(estimate_means(model, by = "Species=c('versicolor', 'setosa')"))
+  expect_identical(dim(estim), c(2L, 5L))
+  estim <- suppressMessages(estimate_means(model, by = "Sepal.Width=c(2, 4)"))
+  expect_identical(dim(estim), c(2L, 5L))
+  estim <- suppressMessages(estimate_means(model, by = c("Species", "Sepal.Width=0")))
+  expect_identical(dim(estim), c(3L, 6L))
+  estim <- suppressMessages(estimate_means(model, by = "Sepal.Width", length = 5))
   expect_equal(dim(estim), c(5, 5))
-  estim <- suppressMessages(estimate_means(model, at = "Sepal.Width=c(2, 4)"))
-  expect_equal(dim(estim), c(2, 5))
+  estim <- suppressMessages(estimate_means(model, by = "Sepal.Width=c(2, 4)"))
+  expect_identical(dim(estim), c(2L, 5L))
 
   # Two factors
   dat <- iris
   dat$Petal.Length_factor <- ifelse(dat$Petal.Length < 4.2, "A", "B")
   dat <<- dat
   model <- lm(Petal.Length ~ Species * Petal.Length_factor, data = dat)
 
-  estim <- suppressMessages(estimate_means(model, at = "all"))
-  expect_equal(dim(estim), c(6, 6))
-  estim <- suppressMessages(estimate_means(model, at = "Petal.Length_factor"))
-  expect_equal(dim(estim), c(2, 5))
-  estim <- suppressMessages(estimate_means(model, at = "Petal.Length_factor='B'"))
-  expect_true(as.character(unique(estim$Petal.Length_factor)) == "B")
+  estim <- suppressMessages(estimate_means(model, by = "all"))
+  expect_identical(dim(estim), c(6L, 6L))
+  estim <- suppressMessages(estimate_means(model, by = "Petal.Length_factor"))
+  expect_identical(dim(estim), c(2L, 5L))
+  estim <- suppressMessages(estimate_means(model, by = "Petal.Length_factor='B'"))
+  expect_identical(as.character(unique(estim$Petal.Length_factor)), "B")
 
 
   # Three factors
@@ -132,7 +132,7 @@ test_that("estimate_means() - core", {
   expect_equal(dim(estim), c(12, 7))
   estim <- suppressMessages(estimate_means(model, fixed = "am"))
   expect_equal(dim(estim), c(6, 7))
-  estim <- suppressMessages(estimate_means(model, at = c("gear='5'", "vs")))
+  estim <- suppressMessages(estimate_means(model, by = c("gear='5'", "vs")))
   expect_equal(dim(estim), c(2, 7))
 
   dat <- iris
@@ -156,13 +156,13 @@ test_that("estimate_means() - core", {
   model <- glm(Petal.Length_factor ~ Species, data = dat, family = "binomial")
 
   estim <- suppressMessages(estimate_means(model))
-  expect_equal(dim(estim), c(3, 5))
+  expect_identical(dim(estim), c(3L, 5L))
   estim <- suppressMessages(estimate_means(model, transform = "none"))
-  expect_equal(dim(estim), c(3, 5))
+  expect_identical(dim(estim), c(3L, 5L))
 
   model <- glm(Petal.Length ~ Species, data = iris, family = "Gamma")
   estim <- suppressMessages(estimate_means(model))
-  expect_equal(dim(estim), c(3, 5))
+  expect_identical(dim(estim), c(3L, 5L))
 })
 
 test_that("estimate_means() - mixed models", {
@@ -176,15 +176,15 @@ test_that("estimate_means() - mixed models", {
   model <- lme4::lmer(Sepal.Width ~ Species + (1 | Petal.Length_factor), data = dat)
 
   estim1 <- suppressMessages(estimate_means(model))
-  expect_equal(dim(estim1), c(3, 5))
+  expect_identical(dim(estim1), c(3L, 5L))
   estim2 <- suppressMessages(estimate_means(model, backend = "marginaleffects"))
-  expect_equal(dim(estim2), c(3, 5))
-  expect_true(max(estim1$Mean - estim2$Mean) < 1e-10)
+  expect_identical(dim(estim2), c(3L, 5L))
+  expect_lt(max(estim1$Mean - estim2$Mean), 1e-10)
 
   model <- lme4::glmer(Sepal.Width ~ Species + (1 | Petal.Length_factor), data = dat, family = "Gamma")
   estim1 <- suppressMessages(estimate_means(model))
-  expect_equal(dim(estim1), c(3, 5))
+  expect_identical(dim(estim1), c(3L, 5L))
   estim2 <- suppressMessages(estimate_means(model, backend = "marginaleffects"))
-  expect_equal(dim(estim2), c(3, 5))
-  expect_true(max(estim1$Mean - estim2$Mean) < 1e-10)
+  expect_identical(dim(estim2), c(3L, 5L))
+  expect_lt(max(estim1$Mean - estim2$Mean), 1e-10)
 })

tests/testthat/test-estimate_predicted.R

@@ -137,7 +137,7 @@ test_that("estimate_response - Frequentist", {
   expect_equal(dim(estim), c(32, 4))
 
   model <- glm(vs ~ wt + cyl, data = mtcars, family = "binomial")
-  estim <- estimate_link(model, at = "wt")
+  estim <- estimate_link(model, by = "wt")
   expect_equal(dim(estim), c(10, 6))