docs

Author: strengejacke

R/estimate_means.R

@@ -211,12 +211,12 @@
 #' @section Predictions and contrasts at meaningful values (data grids):
 #'
 #' To define representative values for focal predictors (specified in `by`,
-#' `contrast`, and `trend`), you can use several methods. These values are
+#' `contrast`, and `slope`), you can use several methods. These values are
 #' internally generated by `insight::get_datagrid()`, so consult its
 #' documentation for more details.
 #'
 #' * You can directly specify values as strings or lists for `by`, `contrast`,
-#'   and `trend`.
+#'   and `slope`.
 #'   * For numeric focal predictors, use examples like `by = "gear = c(4, 8)"`,
 #'     `by = list(gear = c(4, 8))` or `by = "gear = 5:10"`
 #'   * For factor or character predictors, use `by = "Species = c('setosa', 'virginica')"`

README.Rmd

@@ -45,7 +45,7 @@ The two probably most popular R packages for extracting these quantities of inte
 
 The package's approach simplifies estimation by focusing on three key questions:
 
-1. Predictor of Interest: Which variable's effect do you want to analyze? This is specified with the `by`, `contrast`, or `trend` arguments.
+1. Predictor of Interest: Which variable's effect do you want to analyze? This is specified with the `by`, `contrast`, or `slope` arguments.
 
 2. Evaluation Points: At which specific values should the predictor be evaluated? This is also defined in the `by` argument. For a more refined control over the evaluation points, see the [data grids](https://easystats.github.io/insight/reference/get_datagrid.html) vignette.
 

README.md

@@ -34,7 +34,7 @@ The package’s approach simplifies estimation by focusing on three key
 questions:
 
 1.  Predictor of Interest: Which variable’s effect do you want to
-    analyze? This is specified with the `by`, `contrast`, or `trend`
+    analyze? This is specified with the `by`, `contrast`, or `slope`
     arguments.
 
 2.  Evaluation Points: At which specific values should the predictor be
@@ -175,13 +175,13 @@ model <- lm(Sepal.Width ~ Species, data = iris)
 means <- estimate_means(model, by = "Species")
 means
 ## Estimated Marginal Means
-## 
+##
 ## Species    | Mean |   SE |       95% CI | t(147)
 ## ------------------------------------------------
 ## setosa     | 3.43 | 0.05 | [3.33, 3.52] |  71.36
 ## versicolor | 2.77 | 0.05 | [2.68, 2.86] |  57.66
 ## virginica  | 2.97 | 0.05 | [2.88, 3.07] |  61.91
-## 
+##
 ## Variable predicted: Sepal.Width
 ## Predictors modulated: Species
 
@@ -234,13 +234,13 @@ model <- lm(Sepal.Width ~ Species, data = iris)
 contrasts <- estimate_contrasts(model, contrast = "Species")
 contrasts
 ## Marginal Contrasts Analysis
-## 
+##
 ## Level1     | Level2     | Difference |   SE |         95% CI | t(147) |      p
 ## ------------------------------------------------------------------------------
 ## versicolor | setosa     |      -0.66 | 0.07 | [-0.79, -0.52] |  -9.69 | < .001
 ## virginica  | setosa     |      -0.45 | 0.07 | [-0.59, -0.32] |  -6.68 | < .001
 ## virginica  | versicolor |       0.20 | 0.07 | [ 0.07,  0.34] |   3.00 |  0.003
-## 
+##
 ## Variable predicted: Sepal.Width
 ## Predictors contrasted: Species
 ## p-values are uncorrected.
@@ -269,7 +269,7 @@ difference <- estimate_contrasts(
 # no line break for table
 print(difference, table_width = Inf)
 ## Marginal Contrasts Analysis
-## 
+##
 ## Level1     | Level2     | Petal.Length | Difference |   SE |         95% CI | t(144) |      p
 ## ---------------------------------------------------------------------------------------------
 ## versicolor | setosa     |         1.00 |      -1.70 | 0.34 | [-2.37, -1.02] |  -4.97 | < .001
@@ -281,7 +281,7 @@ print(difference, table_width = Inf)
 ## versicolor | setosa     |         6.90 |      -1.78 | 1.44 | [-4.62,  1.06] |  -1.24 |  0.218
 ## virginica  | setosa     |         6.90 |      -2.25 | 1.42 | [-5.06,  0.56] |  -1.58 |  0.116
 ## virginica  | versicolor |         6.90 |      -0.47 | 0.28 | [-1.03,  0.09] |  -1.65 |  0.101
-## 
+##
 ## Variable predicted: Sepal.Width
 ## Predictors contrasted: Species
 ## p-values are uncorrected.
@@ -383,15 +383,15 @@ pred1$Petal.Length <- iris$Petal.Length # Add true response
 # Print first 5 lines of output
 head(pred1, n = 5)
 ## Model-based Predictions
-## 
+##
 ## Sepal.Length | Predicted |   SE |       95% CI | Residuals | Petal.Length
 ## -------------------------------------------------------------------------
 ## 5.10         |      2.38 | 0.10 | [2.19, 2.57] |     -0.98 |         1.40
 ## 4.90         |      2.00 | 0.11 | [1.79, 2.22] |     -0.60 |         1.40
 ## 4.70         |      1.63 | 0.12 | [1.39, 1.87] |     -0.33 |         1.30
 ## 4.60         |      1.45 | 0.13 | [1.19, 1.70] |      0.05 |         1.50
 ## 5.00         |      2.19 | 0.10 | [1.99, 2.39] |     -0.79 |         1.40
-## 
+##
 ## Variable predicted: Petal.Length
 
 # Same for model 2