DominiqueMakowski
commented
2 years ago Indeed, will give this a look
Closed strengejacke closed 2 years ago
DominiqueMakowski
commented
2 years ago Indeed, will give this a look
bwiernik
commented
2 years ago It's even weirder. It says Sepal.Length but that variable isn't even in the model
bwiernik
commented
2 years ago I bet there's an extraneous ! somewhere so it's showing the columns in the data that aren't the remaining predictors, rather than the ones that are
strengejacke
commented
2 years ago One issue seems to be in insight::data_grid(). Variables adjusted for are stored as attribute, which is created with ifelse(), however, there's an "unexpected" behaviour, which caused wrong attributes here:
x <- c("a", "b")
ifelse(length(x) >= 1, x, NA)
#> [1] "a"Created on 2022-08-14 by the reprex package (v2.0.1)
strengejacke
commented
2 years ago Ok, one problem seems to be passing data grids as data argument.
str(insight::get_datagrid(iris, "Species"))
#> Classes 'datagrid', 'visualisation_matrix' and 'data.frame': 3 obs. of 5 variables:
#> $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 2 3
#> $ Sepal.Length: num 5.84 5.84 5.84
#> $ Sepal.Width : num 3.06 3.06 3.06
#> $ Petal.Length: num 3.76 3.76 3.76
#> $ Petal.Width : num 1.2 1.2 1.2
#> - attr(*, "adjusted_for")= chr [1:4] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width"
#> - attr(*, "at_specs")='data.frame': 1 obs. of 3 variables:
...As you can see, get_datagrid() correctly "adjusts for" 4 remaining variables, as we specified only "Species". estimate_expectation() just uses the attributes from get_datagrid() to get adjusted-for values, independent of the model-specification.
I'm not sure, maybe we just need read the at attribute, and then intersect "adjusted for"-variables with the remaining (non-focal) predictors of the model?
strengejacke
commented
2 years ago library(modelbased)
m <- lm(Sepal.Width ~ Petal.Length + Petal.Width + Species, data = iris)
modelbased::estimate_expectation(m, insight::get_datagrid(iris, "Species"))
#> Model-based Expectation
#>
#> Species | Predicted | SE | 95% CI | Residuals
#> --------------------------------------------------------
#> setosa | 4.38 | 0.15 | [4.08, 4.67] | -1.32
#> versicolor | 2.61 | 0.05 | [2.51, 2.71] | 0.44
#> virginica | 2.18 | 0.12 | [1.94, 2.43] | 0.88
#>
#> Variable predicted: Sepal.Width
#> Predictors modulated: Species
#> Predictors controlled: Petal.Length (3.8), Petal.Width (1.2)Created on 2022-08-14 by the reprex package (v2.0.1)
(see #200)
strengejacke
commented
2 years ago btw, I added some more options to create a reference grid to insight::get_datagrid():
m <- lm(Sepal.Width ~ Petal.Length + Species * Petal.Width, data = iris)
grid <- insight::get_datagrid(iris, "Species * Petal.Width")
modelbased::estimate_expectation(m, data = grid)
#> Model-based Expectation
#>
#> Petal.Width | Species | Predicted | SE | 95% CI | Residuals
#> ----------------------------------------------------------------------
#> 0.10 | setosa | 3.63 | 0.18 | [3.28, 3.98] | -0.58
#> 0.37 | setosa | 3.84 | 0.17 | [3.51, 4.16] | -0.78
#> 0.63 | setosa | 4.04 | 0.22 | [3.61, 4.47] | -0.98
#> 0.90 | setosa | 4.24 | 0.30 | [3.65, 4.83] | -1.18
#> 1.17 | setosa | 4.45 | 0.39 | [3.67, 5.22] | -1.39
#> 1.43 | setosa | 4.65 | 0.49 | [3.67, 5.62] | -1.59
#> 1.70 | setosa | 4.85 | 0.60 | [3.67, 6.03] | -1.79
#> 1.97 | setosa | 5.05 | 0.70 | [3.67, 6.44] | -2.00
#> 2.23 | setosa | 5.26 | 0.81 | [3.67, 6.85] | -2.20
#> 2.50 | setosa | 5.46 | 0.91 | [3.66, 7.26] | -2.40
#> 0.10 | versicolor | 1.72 | 0.29 | [1.14, 2.30] | 1.33
#> 0.37 | versicolor | 1.94 | 0.23 | [1.49, 2.39] | 1.12
#> 0.63 | versicolor | 2.15 | 0.16 | [1.83, 2.47] | 0.91
#> 0.90 | versicolor | 2.36 | 0.10 | [2.16, 2.56] | 0.70
#> 1.17 | versicolor | 2.57 | 0.06 | [2.46, 2.68] | 0.48
#> 1.43 | versicolor | 2.79 | 0.07 | [2.65, 2.92] | 0.27
#> 1.70 | versicolor | 3.00 | 0.12 | [2.76, 3.24] | 0.06
#> 1.97 | versicolor | 3.21 | 0.19 | [2.85, 3.58] | -0.15
#> 2.23 | versicolor | 3.42 | 0.25 | [2.93, 3.92] | -0.37
#> 2.50 | versicolor | 3.64 | 0.32 | [3.01, 4.26] | -0.58
#> 0.10 | virginica | 1.68 | 0.30 | [1.08, 2.28] | 1.37
#> 0.37 | virginica | 1.83 | 0.26 | [1.30, 2.35] | 1.23
#> 0.63 | virginica | 1.97 | 0.23 | [1.52, 2.42] | 1.09
#> 0.90 | virginica | 2.12 | 0.19 | [1.73, 2.50] | 0.94
#> 1.17 | virginica | 2.26 | 0.16 | [1.94, 2.58] | 0.80
#> 1.43 | virginica | 2.41 | 0.14 | [2.13, 2.68] | 0.65
#> 1.70 | virginica | 2.55 | 0.13 | [2.30, 2.80] | 0.51
#> 1.97 | virginica | 2.70 | 0.13 | [2.44, 2.95] | 0.36
#> 2.23 | virginica | 2.84 | 0.14 | [2.56, 3.12] | 0.22
#> 2.50 | virginica | 2.98 | 0.17 | [2.65, 3.31] | 0.07
#>
#> Variable predicted: Sepal.Width
#> Predictors modulated: Species, Petal.Width
#> Predictors controlled: Petal.Length (3.8)
grid <- insight::get_datagrid(iris, "Species * Petal.Width", range = "grid")
modelbased::estimate_expectation(m, data = grid)
#> Model-based Expectation
#>
#> Petal.Width | Species | Predicted | SE | 95% CI | Residuals
#> ----------------------------------------------------------------------
#> 0.44 | setosa | 3.89 | 0.17 | [3.55, 4.23] | -0.83
#> 1.20 | setosa | 4.47 | 0.40 | [3.67, 5.27] | -1.41
#> 1.96 | setosa | 5.05 | 0.70 | [3.67, 6.43] | -1.99
#> 0.44 | versicolor | 1.99 | 0.21 | [1.58, 2.41] | 1.06
#> 1.20 | versicolor | 2.60 | 0.05 | [2.49, 2.71] | 0.46
#> 1.96 | versicolor | 3.21 | 0.18 | [2.84, 3.57] | -0.15
#> 0.44 | virginica | 1.87 | 0.25 | [1.36, 2.37] | 1.19
#> 1.20 | virginica | 2.28 | 0.16 | [1.96, 2.59] | 0.78
#> 1.96 | virginica | 2.69 | 0.13 | [2.44, 2.94] | 0.36
#>
#> Variable predicted: Sepal.Width
#> Predictors modulated: Species, Petal.Width
#> Predictors controlled: Petal.Length (3.8)
grid <- insight::get_datagrid(iris, c("Species", "Petal.Width = [sd]"))
modelbased::estimate_expectation(m, data = grid)
#> Model-based Expectation
#>
#> Petal.Width | Species | Predicted | SE | 95% CI | Residuals
#> ----------------------------------------------------------------------
#> 0.44 | setosa | 3.89 | 0.17 | [3.55, 4.23] | -0.83
#> 1.20 | setosa | 4.47 | 0.40 | [3.67, 5.27] | -1.41
#> 1.96 | setosa | 5.05 | 0.70 | [3.67, 6.43] | -1.99
#> 0.44 | versicolor | 1.99 | 0.21 | [1.58, 2.41] | 1.06
#> 1.20 | versicolor | 2.60 | 0.05 | [2.49, 2.71] | 0.46
#> 1.96 | versicolor | 3.21 | 0.18 | [2.84, 3.57] | -0.15
#> 0.44 | virginica | 1.87 | 0.25 | [1.36, 2.37] | 1.19
#> 1.20 | virginica | 2.28 | 0.16 | [1.96, 2.59] | 0.78
#> 1.96 | virginica | 2.69 | 0.13 | [2.44, 2.94] | 0.36
#>
#> Variable predicted: Sepal.Width
#> Predictors modulated: Species, Petal.Width = [sd]
#> Predictors controlled: Petal.Length (3.8)
grid <- insight::get_datagrid(iris, c("Species", "Petal.Width = [3,9]"))
modelbased::estimate_expectation(m, data = grid)
#> Model-based Expectation
#>
#> Petal.Width | Species | Predicted | SE | 95% CI | Residuals
#> -----------------------------------------------------------------------
#> 3.00 | setosa | 5.84 | 1.11 | [3.65, 8.04] | -2.79
#> 3.67 | setosa | 6.35 | 1.38 | [3.63, 9.08] | -3.29
#> 4.33 | setosa | 6.86 | 1.65 | [3.60, 10.11] | -3.80
#> 5.00 | setosa | 7.37 | 1.92 | [3.58, 11.15] | -4.31
#> 5.67 | setosa | 7.87 | 2.18 | [3.56, 12.19] | -4.82
#> 6.33 | setosa | 8.38 | 2.45 | [3.53, 13.23] | -5.33
#> 7.00 | setosa | 8.89 | 2.72 | [3.51, 14.27] | -5.83
#> 7.67 | setosa | 9.40 | 2.99 | [3.49, 15.31] | -6.34
#> 8.33 | setosa | 9.91 | 3.26 | [3.46, 16.35] | -6.85
#> 9.00 | setosa | 10.41 | 3.53 | [3.44, 17.39] | -7.36
#> 3.00 | versicolor | 4.03 | 0.44 | [3.17, 4.90] | -0.98
#> 3.67 | versicolor | 4.57 | 0.60 | [3.37, 5.76] | -1.51
#> 4.33 | versicolor | 5.10 | 0.77 | [3.57, 6.62] | -2.04
#> 5.00 | versicolor | 5.63 | 0.94 | [3.78, 7.48] | -2.57
#> 5.67 | versicolor | 6.16 | 1.10 | [3.98, 8.34] | -3.10
#> 6.33 | versicolor | 6.69 | 1.27 | [4.18, 9.20] | -3.63
#> 7.00 | versicolor | 7.22 | 1.44 | [4.38, 10.06] | -4.16
#> 7.67 | versicolor | 7.75 | 1.60 | [4.58, 10.92] | -4.69
#> 8.33 | versicolor | 8.28 | 1.77 | [4.79, 11.78] | -5.23
#> 9.00 | versicolor | 8.81 | 1.94 | [4.99, 12.64] | -5.76
#> 3.00 | virginica | 3.26 | 0.23 | [2.80, 3.71] | -0.20
#> 3.67 | virginica | 3.62 | 0.32 | [2.98, 4.26] | -0.56
#> 4.33 | virginica | 3.98 | 0.42 | [3.14, 4.82] | -0.92
#> 5.00 | virginica | 4.34 | 0.53 | [3.30, 5.38] | -1.28
#> 5.67 | virginica | 4.70 | 0.63 | [3.45, 5.95] | -1.64
#> 6.33 | virginica | 5.06 | 0.74 | [3.61, 6.52] | -2.01
#> 7.00 | virginica | 5.43 | 0.84 | [3.76, 7.09] | -2.37
#> 7.67 | virginica | 5.79 | 0.95 | [3.91, 7.66] | -2.73
#> 8.33 | virginica | 6.15 | 1.06 | [4.06, 8.23] | -3.09
#> 9.00 | virginica | 6.51 | 1.16 | [4.21, 8.81] | -3.45
#>
#> Variable predicted: Sepal.Width
#> Predictors modulated: Species, Petal.Width = [3,9]
#> Predictors controlled: Petal.Length (3.8)
grid <- insight::get_datagrid(iris, c("Species", "Petal.Width = [terciles]"))
modelbased::estimate_expectation(m, data = grid)
#> Model-based Expectation
#>
#> Petal.Width | Species | Predicted | SE | 95% CI | Residuals
#> ----------------------------------------------------------------------
#> 0.87 | setosa | 4.22 | 0.29 | [3.65, 4.78] | -1.16
#> 1.60 | setosa | 4.78 | 0.56 | [3.67, 5.88] | -1.72
#> 0.87 | versicolor | 2.34 | 0.11 | [2.12, 2.55] | 0.72
#> 1.60 | versicolor | 2.92 | 0.10 | [2.72, 3.12] | 0.14
#> 0.87 | virginica | 2.10 | 0.20 | [1.71, 2.49] | 0.96
#> 1.60 | virginica | 2.50 | 0.13 | [2.24, 2.75] | 0.56
#>
#> Variable predicted: Sepal.Width
#> Predictors modulated: Species, Petal.Width = [terciles]
#> Predictors controlled: Petal.Length (3.8)Created on 2022-08-14 by the reprex package (v2.0.1)
See example.
Sepal.Widthis predicted, and appears in the output column. The footer saysPredictors controlled: Sepal.Length, but missesPetal.Width.Created on 2022-08-10 by the reprex package (v2.0.1)