From 27e27b39c11bca5f999ca8357af2037fb429c3a7 Mon Sep 17 00:00:00 2001 From: Vincent Arel-Bundock Date: Fri, 6 Oct 2023 08:35:03 -0400 Subject: [PATCH] website --- docs/articles/alternative_software.html | 54 +- docs/articles/bootstrap.html | 84 +-- .../figure-html/unnamed-chunk-13-1.png | Bin 16723 -> 16271 bytes docs/articles/brms.html | 8 +- .../figure-html/unnamed-chunk-16-1.png | Bin 32895 -> 32837 bytes .../figure-html/unnamed-chunk-21-1.png | Bin 44881 -> 44286 bytes .../figure-html/unnamed-chunk-23-1.png | Bin 14512 -> 14512 bytes .../figure-html/unnamed-chunk-27-1.png | Bin 40792 -> 40745 bytes .../figure-html/unnamed-chunk-28-1.png | Bin 40575 -> 40693 bytes .../figure-html/unnamed-chunk-30-1.png | Bin 43314 -> 43108 bytes .../figure-html/unnamed-chunk-32-1.png | Bin 67224 -> 67288 bytes .../figure-html/unnamed-chunk-33-1.png | Bin 68963 -> 68961 bytes .../figure-html/unnamed-chunk-34-1.png | Bin 62510 -> 62516 bytes .../figure-html/unnamed-chunk-35-1.png | Bin 16392 -> 17572 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docs/articles/machine_learning_files/libs/kePrint-0.0.1/kePrint.js create mode 100644 docs/articles/machine_learning_files/libs/lightable-0.0.1/lightable.css diff --git a/docs/articles/alternative_software.html b/docs/articles/alternative_software.html index 61cd12498..4f5798b9b 100644 --- a/docs/articles/alternative_software.html +++ b/docs/articles/alternative_software.html @@ -791,7 +791,7 @@

#> Term Contrast cyl Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 % #> cyl 6 - 4 4 -0.9049 1.63e+00 -0.55506 0.579 0.8 -4.100 2.29e+00 #> cyl 8 - 4 4 -19.5418 4.37e+03 -0.00447 0.996 0.0 -8579.030 8.54e+03 -#> hp dY/dX 4 -0.0326 3.39e-02 -0.96140 0.336 1.6 -0.099 3.38e-02 +#> hp dY/dX 4 -0.0326 3.39e-02 -0.96147 0.336 1.6 -0.099 3.38e-02 #> #> Columns: rowid, term, contrast, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, cyl, predicted_lo, predicted_hi, predicted, vs, hp #> Type: link @@ -908,21 +908,21 @@

head(data.frame(mar))
 #>    mpg cyl disp  hp drat    wt  qsec vs am gear carb   fitted se.fitted   dydx_cyl    dydx_hp   dydx_wt Var_dydx_cyl  Var_dydx_hp Var_dydx_wt X_weights X_at_number
-#> 1 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 22.82043 0.6876212 -0.9416168 -0.0180381 -3.166973    0.3035074 0.0001410453   0.5484524        NA           1
-#> 2 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 22.01285 0.6056817 -0.9416168 -0.0180381 -3.166973    0.3035074 0.0001410453   0.5484524        NA           1
-#> 3 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 25.96040 0.7349593 -0.9416168 -0.0180381 -3.166973    0.3035074 0.0001410453   0.5484524        NA           1
-#> 4 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 20.93608 0.5800910 -0.9416168 -0.0180381 -3.166973    0.3035074 0.0001410453   0.5484524        NA           1
-#> 5 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 17.16780 0.8322986 -0.9416168 -0.0180381 -3.166973    0.3035074 0.0001410453   0.5484524        NA           1
-#> 6 18.1   6  225 105 2.76 3.460 20.22  1  0    3    1 20.25036 0.6638322 -0.9416168 -0.0180381 -3.166973    0.3035074 0.0001410453   0.5484524        NA           1
+#> 1 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 22.82043 0.6876212 -0.9416168 -0.0180381 -3.166973    0.3035104 0.0001410451   0.5484521        NA           1
+#> 2 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 22.01285 0.6056817 -0.9416168 -0.0180381 -3.166973    0.3035104 0.0001410451   0.5484521        NA           1
+#> 3 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 25.96040 0.7349593 -0.9416168 -0.0180381 -3.166973    0.3035104 0.0001410451   0.5484521        NA           1
+#> 4 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 20.93608 0.5800910 -0.9416168 -0.0180381 -3.166973    0.3035104 0.0001410451   0.5484521        NA           1
+#> 5 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 17.16780 0.8322986 -0.9416168 -0.0180381 -3.166973    0.3035104 0.0001410451   0.5484521        NA           1
+#> 6 18.1   6  225 105 2.76 3.460 20.22  1  0    3    1 20.25036 0.6638322 -0.9416168 -0.0180381 -3.166973    0.3035104 0.0001410451   0.5484521        NA           1
 
 head(mfx)
 #> 
 #>  Term Estimate Std. Error     z Pr(>|z|)   S 2.5 % 97.5 %
 #>   cyl   -0.942      0.550 -1.71   0.0871 3.5 -2.02  0.137
 #>   cyl   -0.942      0.550 -1.71   0.0871 3.5 -2.02  0.137
-#>   cyl   -0.942      0.551 -1.71   0.0875 3.5 -2.02  0.138
 #>   cyl   -0.942      0.550 -1.71   0.0871 3.5 -2.02  0.137
-#>   cyl   -0.942      0.551 -1.71   0.0873 3.5 -2.02  0.138
+#>   cyl   -0.942      0.550 -1.71   0.0871 3.5 -2.02  0.137
+#>   cyl   -0.942      0.551 -1.71   0.0875 3.5 -2.02  0.138
 #>   cyl   -0.942      0.550 -1.71   0.0871 3.5 -2.02  0.137
 #> 
 #> Columns: rowid, term, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, predicted_lo, predicted_hi, predicted, mpg, cyl, hp, wt 
@@ -935,14 +935,14 @@ 

mar <- margins(mod, data = data.frame(prediction::mean_or_mode(mtcars)), unit_ses = TRUE)
 data.frame(mar)
 #>        mpg    cyl     disp       hp     drat      wt     qsec     vs      am   gear   carb   fitted se.fitted   dydx_cyl    dydx_hp   dydx_wt Var_dydx_cyl  Var_dydx_hp Var_dydx_wt SE_dydx_cyl SE_dydx_hp SE_dydx_wt X_weights X_at_number
-#> 1 20.09062 6.1875 230.7219 146.6875 3.596563 3.21725 17.84875 0.4375 0.40625 3.6875 2.8125 20.09062 0.4439832 -0.9416168 -0.0180381 -3.166973    0.3034971 0.0001410454     0.54846   0.5509057 0.01187626  0.7405808        NA           1
+#> 1 20.09062 6.1875 230.7219 146.6875 3.596563 3.21725 17.84875 0.4375 0.40625 3.6875 2.8125 20.09062 0.4439832 -0.9416168 -0.0180381 -3.166973    0.3035013 0.0001410453     0.54846   0.5509096 0.01187625  0.7405808        NA           1
 
 slopes(mod, newdata = "mean")
 #> 
 #>  Term Estimate Std. Error     z Pr(>|z|)    S   2.5 %   97.5 %
-#>   cyl   -0.942     0.5506 -1.71   0.0873  3.5 -2.0209  0.13763
+#>   cyl   -0.942     0.5510 -1.71   0.0875  3.5 -2.0216  0.13833
 #>   hp    -0.018     0.0119 -1.52   0.1290  3.0 -0.0413  0.00525
-#>   wt    -3.167     0.7406 -4.28   <0.001 15.7 -4.6186 -1.71536
+#>   wt    -3.167     0.7406 -4.28   <0.001 15.7 -4.6185 -1.71549
 #> 
 #> Columns: rowid, term, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, predicted_lo, predicted_hi, predicted, mpg, cyl, hp, wt 
 #> Type:  response
@@ -958,13 +958,13 @@

mar <- margins(mod, at = list(cyl = c(4, 6, 8))) summary(mar) #> factor cyl AME SE z p lower upper -#> cyl 4.0000 0.0381 0.5999 0.0636 0.9493 -1.1376 1.2139 -#> cyl 6.0000 0.0381 0.5999 0.0636 0.9493 -1.1376 1.2138 +#> cyl 4.0000 0.0381 0.6000 0.0636 0.9493 -1.1378 1.2141 +#> cyl 6.0000 0.0381 0.5999 0.0636 0.9493 -1.1376 1.2139 #> cyl 8.0000 0.0381 0.5999 0.0636 0.9493 -1.1376 1.2139 #> hp 4.0000 -0.0878 0.0267 -3.2937 0.0010 -0.1400 -0.0355 #> hp 6.0000 -0.0499 0.0154 -3.2397 0.0012 -0.0800 -0.0197 #> hp 8.0000 -0.0120 0.0108 -1.1065 0.2685 -0.0332 0.0092 -#> wt 4.0000 -3.1198 0.6613 -4.7176 0.0000 -4.4160 -1.8236 +#> wt 4.0000 -3.1198 0.6613 -4.7175 0.0000 -4.4160 -1.8236 #> wt 6.0000 -3.1198 0.6613 -4.7175 0.0000 -4.4160 -1.8236 #> wt 8.0000 -3.1198 0.6613 -4.7175 0.0000 -4.4160 -1.8236 @@ -974,15 +974,15 @@

newdata = datagridcf(cyl = c(4, 6, 8))) #> #> Term Contrast cyl Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 % -#> cyl mean(dY/dX) 4 0.0381 0.6000 0.0636 0.9493 0.1 -1.1377 1.21402 -#> cyl mean(dY/dX) 6 0.0381 0.5998 0.0636 0.9493 0.1 -1.1375 1.21378 -#> cyl mean(dY/dX) 8 0.0381 0.5999 0.0636 0.9493 0.1 -1.1377 1.21396 -#> hp mean(dY/dX) 4 -0.0878 0.0267 -3.2937 <0.001 10.0 -0.1400 -0.03555 -#> hp mean(dY/dX) 6 -0.0499 0.0154 -3.2398 0.0012 9.7 -0.0800 -0.01970 +#> cyl mean(dY/dX) 4 0.0381 0.5999 0.0636 0.9493 0.1 -1.1377 1.21401 +#> cyl mean(dY/dX) 6 0.0381 0.5998 0.0636 0.9493 0.1 -1.1375 1.21381 +#> cyl mean(dY/dX) 8 0.0381 0.5999 0.0636 0.9493 0.1 -1.1376 1.21389 +#> hp mean(dY/dX) 4 -0.0878 0.0267 -3.2936 <0.001 10.0 -0.1400 -0.03554 +#> hp mean(dY/dX) 6 -0.0499 0.0154 -3.2397 0.0012 9.7 -0.0800 -0.01970 #> hp mean(dY/dX) 8 -0.0120 0.0108 -1.1065 0.2685 1.9 -0.0332 0.00923 -#> wt mean(dY/dX) 4 -3.1198 0.6613 -4.7176 <0.001 18.7 -4.4160 -1.82366 -#> wt mean(dY/dX) 6 -3.1198 0.6613 -4.7176 <0.001 18.7 -4.4160 -1.82366 -#> wt mean(dY/dX) 8 -3.1198 0.6613 -4.7176 <0.001 18.7 -4.4160 -1.82366 +#> wt mean(dY/dX) 4 -3.1198 0.6613 -4.7175 <0.001 18.7 -4.4160 -1.82362 +#> wt mean(dY/dX) 6 -3.1198 0.6613 -4.7174 <0.001 18.7 -4.4160 -1.82362 +#> wt mean(dY/dX) 8 -3.1198 0.6613 -4.7174 <0.001 18.7 -4.4160 -1.82362 #> #> Columns: term, contrast, cyl, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, predicted_lo, predicted_hi, predicted #> Type: response

@@ -1050,9 +1050,9 @@

avg_slopes(mod) #> #> Term Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 % -#> cyl -0.942 0.5507 -1.71 0.0873 3.5 -2.0209 0.13770 +#> cyl -0.942 0.5506 -1.71 0.0872 3.5 -2.0208 0.13753 #> hp -0.018 0.0119 -1.52 0.1288 3.0 -0.0413 0.00524 -#> wt -3.167 0.7406 -4.28 <0.001 15.7 -4.6185 -1.71549 +#> wt -3.167 0.7406 -4.28 <0.001 15.7 -4.6185 -1.71546 #> #> Columns: term, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high #> Type: response

@@ -1110,7 +1110,7 @@

#> #> Term Contrast cyl Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 % #> hp mean(dY/dX) 4 -0.0995 0.0349 -2.853 0.00433 7.9 -0.1678 -0.0311 -#> hp mean(dY/dX) 6 -0.0214 0.0388 -0.551 0.58188 0.8 -0.0975 0.0547 +#> hp mean(dY/dX) 6 -0.0214 0.0388 -0.551 0.58187 0.8 -0.0975 0.0547 #> hp mean(dY/dX) 8 -0.0134 0.0125 -1.074 0.28278 1.8 -0.0380 0.0111 #> #> Columns: term, contrast, cyl, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, predicted_lo, predicted_hi, predicted @@ -1135,7 +1135,7 @@

J_mean <- aggregate(J, by = list(mfx$cyl), FUN = mean) J_mean <- as.matrix(J_mean[, 2:ncol(J_mean)]) sqrt(diag(J_mean %*% vcov(mod) %*% t(J_mean))) -#> [1] 0.03486633 0.03882199 0.01251382 +#> [1] 0.03486654 0.03882093 0.01251377

24.3.3 Average Counterfactual Adjusted Predictions

diff --git a/docs/articles/bootstrap.html b/docs/articles/bootstrap.html index ad5f33bf5..d96c20969 100644 --- a/docs/articles/bootstrap.html +++ b/docs/articles/bootstrap.html @@ -564,9 +564,9 @@

inferences(method = "boot") #> #> Term Contrast Species Estimate Std. Error 2.5 % 97.5 % -#> Petal.Width mean(+1) setosa -0.1103 0.257 -0.581 0.408 -#> Petal.Width mean(+1) versicolor -0.0201 0.156 -0.299 0.302 -#> Petal.Width mean(+1) virginica 0.0216 0.183 -0.339 0.373 +#> Petal.Width mean(+1) setosa -0.1103 0.267 -0.625 0.443 +#> Petal.Width mean(+1) versicolor -0.0201 0.162 -0.340 0.327 +#> Petal.Width mean(+1) virginica 0.0216 0.182 -0.335 0.368 #> #> Columns: term, contrast, Species, estimate, predicted_lo, predicted_hi, predicted, std.error, conf.low, conf.high #> Type: response @@ -578,9 +578,9 @@

est #> #> Term Contrast Species Estimate Std. Error 2.5 % 97.5 % -#> Petal.Width mean(+1) setosa -0.1103 0.267 -0.643 0.384 -#> Petal.Width mean(+1) versicolor -0.0201 0.164 -0.325 0.352 -#> Petal.Width mean(+1) virginica 0.0216 0.193 -0.349 0.388 +#> Petal.Width mean(+1) setosa -0.1103 0.266 -0.662 0.404 +#> Petal.Width mean(+1) versicolor -0.0201 0.162 -0.335 0.298 +#> Petal.Width mean(+1) virginica 0.0216 0.184 -0.344 0.377 #> #> Columns: term, contrast, Species, estimate, predicted_lo, predicted_hi, predicted, std.error, conf.low, conf.high #> Type: response @@ -601,20 +601,20 @@

#> #> Bootstrap Statistics : #> original bias std. error -#> t1* -0.11025325 0.005684363 0.2671518 -#> t2* -0.02006005 0.002562855 0.1637331 -#> t3* 0.02158742 0.001121472 0.1926193 +#> t1* -0.11025325 0.003230574 0.2663606 +#> t2* -0.02006005 0.003873671 0.1615387 +#> t3* 0.02158742 0.004170627 0.1837267

Or we can extract the individual draws with the posterior_draws() function:

posterior_draws(est) |> head()
-#>   drawid        draw        term contrast    Species    estimate predicted_lo predicted_hi predicted std.error   conf.low conf.high
-#> 1      1  0.05380033 Petal.Width mean(+1)     setosa -0.11025325     5.013640     4.901389  4.957514 0.2671518 -0.6426428 0.3836699
-#> 2      1  0.10305571 Petal.Width mean(+1) versicolor -0.02006005     6.330887     6.325011  6.327949 0.1637331 -0.3247546 0.3521155
-#> 3      1  0.12579979 Petal.Width mean(+1)  virginica  0.02158742     6.997499     7.033528  7.015513 0.1926193 -0.3493749 0.3881292
-#> 4      2 -0.27902802 Petal.Width mean(+1)     setosa -0.11025325     5.013640     4.901389  4.957514 0.2671518 -0.6426428 0.3836699
-#> 5      2 -0.32378525 Petal.Width mean(+1) versicolor -0.02006005     6.330887     6.325011  6.327949 0.1637331 -0.3247546 0.3521155
-#> 6      2 -0.34445228 Petal.Width mean(+1)  virginica  0.02158742     6.997499     7.033528  7.015513 0.1926193 -0.3493749 0.3881292
+#>   drawid         draw        term contrast    Species    estimate predicted_lo predicted_hi predicted std.error   conf.low conf.high
+#> 1      1 -0.031097605 Petal.Width mean(+1)     setosa -0.11025325     5.013640     4.901389  4.957514 0.2663606 -0.6618164 0.4041951
+#> 2      1 -0.010926106 Petal.Width mean(+1) versicolor -0.02006005     6.330887     6.325011  6.327949 0.1615387 -0.3352636 0.2983942
+#> 3      1 -0.001611747 Petal.Width mean(+1)  virginica  0.02158742     6.997499     7.033528  7.015513 0.1837267 -0.3438075 0.3769131
+#> 4      2 -0.403310043 Petal.Width mean(+1)     setosa -0.11025325     5.013640     4.901389  4.957514 0.2663606 -0.6618164 0.4041951
+#> 5      2 -0.057683806 Petal.Width mean(+1) versicolor -0.02006005     6.330887     6.325011  6.327949 0.1615387 -0.3352636 0.2983942
+#> 6      2  0.101912012 Petal.Width mean(+1)  virginica  0.02158742     6.997499     7.033528  7.015513 0.1837267 -0.3438075 0.3769131
 
 posterior_draws(est, shape = "DxP") |> dim()
 #> [1] 500   3
@@ -629,9 +629,9 @@

est #> #> Term Contrast Species Estimate 2.5 % 97.5 % -#> Petal.Width mean(+1) setosa -0.1103 -0.559 0.470 -#> Petal.Width mean(+1) versicolor -0.0201 -0.357 0.309 -#> Petal.Width mean(+1) virginica 0.0216 -0.411 0.370 +#> Petal.Width mean(+1) setosa -0.1103 -0.650 0.510 +#> Petal.Width mean(+1) versicolor -0.0201 -0.371 0.264 +#> Petal.Width mean(+1) virginica 0.0216 -0.262 0.322 #> #> Columns: term, contrast, Species, estimate, predicted_lo, predicted_hi, predicted, conf.low, conf.high #> Type: response @@ -641,28 +641,28 @@

#> # A tibble: 101 × 3 #> splits id estimates #> <list> <chr> <list> -#> 1 <split [150/56]> Bootstrap001 <tibble [3 × 7]> -#> 2 <split [150/56]> Bootstrap002 <tibble [3 × 7]> -#> 3 <split [150/54]> Bootstrap003 <tibble [3 × 7]> -#> 4 <split [150/63]> Bootstrap004 <tibble [3 × 7]> -#> 5 <split [150/54]> Bootstrap005 <tibble [3 × 7]> -#> 6 <split [150/56]> Bootstrap006 <tibble [3 × 7]> -#> 7 <split [150/58]> Bootstrap007 <tibble [3 × 7]> -#> 8 <split [150/53]> Bootstrap008 <tibble [3 × 7]> +#> 1 <split [150/47]> Bootstrap001 <tibble [3 × 7]> +#> 2 <split [150/48]> Bootstrap002 <tibble [3 × 7]> +#> 3 <split [150/57]> Bootstrap003 <tibble [3 × 7]> +#> 4 <split [150/58]> Bootstrap004 <tibble [3 × 7]> +#> 5 <split [150/55]> Bootstrap005 <tibble [3 × 7]> +#> 6 <split [150/53]> Bootstrap006 <tibble [3 × 7]> +#> 7 <split [150/55]> Bootstrap007 <tibble [3 × 7]> +#> 8 <split [150/63]> Bootstrap008 <tibble [3 × 7]> #> 9 <split [150/54]> Bootstrap009 <tibble [3 × 7]> -#> 10 <split [150/54]> Bootstrap010 <tibble [3 × 7]> +#> 10 <split [150/55]> Bootstrap010 <tibble [3 × 7]> #> # ℹ 91 more rows

Or we can extract the individual draws with the posterior_draws() function:

posterior_draws(est) |> head()
-#>   drawid        draw        term contrast    Species    estimate predicted_lo predicted_hi predicted   conf.low conf.high
-#> 1      1 -0.02715873 Petal.Width mean(+1)     setosa -0.11025325     5.013640     4.901389  4.957514 -0.5590238 0.4703151
-#> 2      1 -0.20167877 Petal.Width mean(+1) versicolor -0.02006005     6.330887     6.325011  6.327949 -0.3570730 0.3088383
-#> 3      1 -0.28226487 Petal.Width mean(+1)  virginica  0.02158742     6.997499     7.033528  7.015513 -0.4111337 0.3704871
-#> 4      2 -0.17211812 Petal.Width mean(+1)     setosa -0.11025325     5.013640     4.901389  4.957514 -0.5590238 0.4703151
-#> 5      2 -0.34497272 Petal.Width mean(+1) versicolor -0.02006005     6.330887     6.325011  6.327949 -0.3570730 0.3088383
-#> 6      2 -0.42478979 Petal.Width mean(+1)  virginica  0.02158742     6.997499     7.033528  7.015513 -0.4111337 0.3704871
+#>   drawid       draw        term contrast    Species    estimate predicted_lo predicted_hi predicted   conf.low conf.high
+#> 1      1  0.2677437 Petal.Width mean(+1)     setosa -0.11025325     5.013640     4.901389  4.957514 -0.6497296 0.5100013
+#> 2      1  0.1692484 Petal.Width mean(+1) versicolor -0.02006005     6.330887     6.325011  6.327949 -0.3712161 0.2635059
+#> 3      1  0.1237673 Petal.Width mean(+1)  virginica  0.02158742     6.997499     7.033528  7.015513 -0.2617070 0.3222299
+#> 4      2 -0.9913947 Petal.Width mean(+1)     setosa -0.11025325     5.013640     4.901389  4.957514 -0.6497296 0.5100013
+#> 5      2 -0.4053751 Petal.Width mean(+1) versicolor -0.02006005     6.330887     6.325011  6.327949 -0.3712161 0.2635059
+#> 6      2 -0.1347756 Petal.Width mean(+1)  virginica  0.02158742     6.997499     7.033528  7.015513 -0.2617070 0.3222299
 
 posterior_draws(est, shape = "PxD") |> dim()
 #> [1]   3 100
@@ -678,10 +678,10 @@

avg_comparisons(mod) |> inferences(method = "fwb")
 #> 
 #>          Term            Contrast Estimate Std. Error  2.5 % 97.5 %
-#>  Petal.Length +1                    0.8929     0.0789  0.738  1.047
-#>  Petal.Width  +1                   -0.0362     0.1550 -0.326  0.266
-#>  Species      versicolor - setosa  -1.4629     0.3313 -2.158 -0.779
-#>  Species      virginica - setosa   -1.9842     0.3952 -2.783 -1.190
+#>  Petal.Length +1                    0.8929     0.0798  0.723  1.040
+#>  Petal.Width  +1                   -0.0362     0.1620 -0.342  0.286
+#>  Species      versicolor - setosa  -1.4629     0.3393 -2.131 -0.831
+#>  Species      virginica - setosa   -1.9842     0.3973 -2.732 -1.231
 #> 
 #> Columns: term, contrast, estimate, std.error, conf.low, conf.high 
 #> Type:  response
@@ -703,9 +703,9 @@

inferences(method = "simulation") #> #> Term Contrast Species Estimate Std. Error 2.5 % 97.5 % -#> Petal.Width mean(+1) setosa -0.1103 0.291 -0.668 0.505 -#> Petal.Width mean(+1) versicolor -0.0201 0.162 -0.327 0.302 -#> Petal.Width mean(+1) virginica 0.0216 0.174 -0.324 0.327 +#> Petal.Width mean(+1) setosa -0.1103 0.272 -0.636 0.435 +#> Petal.Width mean(+1) versicolor -0.0201 0.160 -0.338 0.285 +#> Petal.Width mean(+1) virginica 0.0216 0.172 -0.333 0.350 #> #> Columns: term, contrast, Species, estimate, std.error, conf.low, conf.high, predicted_lo, predicted_hi, predicted, tmp_idx #> Type: response

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by = "x") #> #> x Estimate 2.5 % 97.5 % -#> 0 0.450 0.343 0.565 -#> 1 0.549 0.441 0.664 +#> 0 0.454 0.338 0.565 +#> 1 0.552 0.436 0.664 #> #> Columns: x, estimate, conf.low, conf.high #> Type: response @@ -1093,7 +1093,7 @@

sample_new_levels = "gaussian") #> #> Term Contrast Estimate 2.5 % 97.5 % -#> x 1 - 0 0.0967 0.0465 0.162 +#> x 1 - 0 0.0965 0.0494 0.162 #> #> Columns: term, contrast, estimate, conf.low, conf.high #> Type: response @@ -1111,7 +1111,7 @@

effects = "integrateoutRE") bm$ContrastSummary |> data.frame() #> M Mdn LL UL PercentROPE PercentMID CI CIType ROPE MID Label -#> 1 0.09898374 0.09694253 0.04870074 0.1611877 NA NA 0.95 ETI <NA> <NA> AME x +#> 1 0.09864399 0.09651684 0.04835076 0.1610664 NA NA 0.95 ETI <NA> <NA> AME x

See the alternative software vignette for more information on brmsmargins.

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    slopes(mod, variables = "mpg", newdata = datagrid(mpg = 25, hp = 110))
     #> 
     #>  Group Term mpg  hp Estimate Std. Error       z Pr(>|z|)    S    2.5 %  97.5 %
    -#>      3  mpg  25 110 -0.06041     0.0169 -3.5802   <0.001 11.5 -0.09348 -0.0273
    +#>      3  mpg  25 110 -0.06041     0.0169 -3.5813   <0.001 11.5 -0.09347 -0.0273
     #>      4  mpg  25 110 -0.00321     0.0335 -0.0958   0.9237  0.1 -0.06896  0.0625
    -#>      5  mpg  25 110  0.06362     0.0301  2.1129   0.0346  4.9  0.00461  0.1226
    +#>      5  mpg  25 110  0.06362     0.0301  2.1132   0.0346  4.9  0.00461  0.1226
     #> 
     #> Columns: rowid, term, group, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, mpg, hp, predicted_lo, predicted_hi, predicted, gear 
     #> Type:  probs
    @@ -554,12 +554,12 @@

    avg_slopes(mod)
     #> 
     #>  Group Term Estimate Std. Error     z Pr(>|z|)    S     2.5 %   97.5 %
    -#>      3  hp  -0.00377   0.001514 -2.49  0.01285  6.3 -0.006735 -0.00080
    -#>      3  mpg -0.07014   0.015484 -4.53  < 0.001 17.4 -0.100488 -0.03979
    -#>      4  hp   0.00201   0.000958  2.10  0.03555  4.8  0.000136  0.00389
    +#>      3  hp  -0.00377   0.001514 -2.49  0.01284  6.3 -0.006735 -0.00080
    +#>      3  mpg -0.07014   0.015485 -4.53  < 0.001 17.4 -0.100490 -0.03979
    +#>      4  hp   0.00201   0.000957  2.10  0.03553  4.8  0.000136  0.00389
     #>      4  mpg  0.03747   0.013861  2.70  0.00687  7.2  0.010303  0.06464
     #>      5  hp   0.00175   0.000833  2.11  0.03519  4.8  0.000122  0.00339
    -#>      5  mpg  0.03267   0.009571  3.41  < 0.001 10.6  0.013909  0.05143
    +#>      5  mpg  0.03267   0.009572  3.41  < 0.001 10.6  0.013907  0.05143
     #> 
     #> Columns: term, group, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high 
     #> Type:  probs
    diff --git a/docs/articles/comparisons_files/figure-html/unnamed-chunk-48-1.png b/docs/articles/comparisons_files/figure-html/unnamed-chunk-48-1.png index 48c2ad899f0e4fbd8d5e012bc36fcb5571a9c94b..b6f5be6f7d3f7b8604597674ef0fb32ee6e0041b 100644 GIT binary patch delta 50 zcmaFW$oQ&}al=JUmIRg~ft#;#mP)eZ^|hyM_EYYV74ewL*cduz&ol-g@O1TaS?83{ F1OWKt5?ufQ delta 50 zcmaFW$oQ&}al=JUmH?I`L7T5~mP)cTyf$C7*-yDcR%Ge}#vESdmunb+z|+;wWt~$( F695AK60QIM diff --git a/docs/articles/elasticity.html b/docs/articles/elasticity.html index 6eb68efc8..fe0a9b8d6 100644 --- a/docs/articles/elasticity.html +++ b/docs/articles/elasticity.html @@ -530,7 +530,7 @@

    #> #> Term Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 % #> hp -0.0318 0.00903 -3.52 <0.001 11.2 -0.0495 -0.0141 -#> wt -3.8778 0.63273 -6.13 <0.001 30.1 -5.1180 -2.6377 +#> wt -3.8778 0.63276 -6.13 <0.001 30.1 -5.1180 -2.6376 #> #> Columns: term, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high #> Type: response @@ -548,7 +548,7 @@

    #> #> Term Contrast Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 % #> hp eY/dX -0.00173 0.000502 -3.46 <0.001 10.8 -0.00272 -0.000751 -#> wt eY/dX -0.21165 0.037849 -5.59 <0.001 25.4 -0.28583 -0.137464 +#> wt eY/dX -0.21165 0.037851 -5.59 <0.001 25.4 -0.28583 -0.137461 #> #> Columns: term, contrast, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high #> Type: response diff --git a/docs/articles/experiments.html b/docs/articles/experiments.html index d69374971..443bf47ff 100644 --- a/docs/articles/experiments.html +++ b/docs/articles/experiments.html @@ -534,7 +534,7 @@

    library(tidyverse)
     library(marginaleffects)
    -library(modelsummary)
    +library(modelsummary)
     
     ## See ?mtcars for variable definitions
     fit <- lm(mpg ~ vs + am + vs:am, data=mtcars) # equivalent to ~ vs*am
    diff --git a/docs/articles/gam.html b/docs/articles/gam.html index 8d22e47a1..c1f757d6c 100644 --- a/docs/articles/gam.html +++ b/docs/articles/gam.html @@ -600,11 +600,11 @@

    #> #> Term Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 % #> Time 0.0261 0.00137 19.1 <0.001 267.8 0.0234 0.0288 -#> Time 0.0261 0.00136 19.2 <0.001 270.3 0.0234 0.0288 -#> Time 0.0261 0.00133 19.5 <0.001 280.1 0.0235 0.0287 -#> Time 0.0260 0.00128 20.3 <0.001 301.1 0.0235 0.0285 -#> Time 0.0259 0.00120 21.6 <0.001 339.8 0.0235 0.0282 -#> Time 0.0257 0.00109 23.5 <0.001 404.6 0.0236 0.0279 +#> Time 0.0261 0.00136 19.2 <0.001 270.4 0.0234 0.0288 +#> Time 0.0261 0.00133 19.5 <0.001 280.0 0.0235 0.0287 +#> Time 0.0260 0.00128 20.3 <0.001 301.4 0.0235 0.0285 +#> Time 0.0259 0.00120 21.6 <0.001 340.0 0.0235 0.0282 +#> Time 0.0257 0.00109 23.5 <0.001 404.4 0.0236 0.0279 #> #> Columns: rowid, term, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, predicted_lo, predicted_hi, predicted, Y, Group, Time, Subject #> Type: response

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z(%W6qgoPXcJO_mOmMYy`c>kgUgnOxXH7)E8uWF0sB>E;0P0CeE@!7)YUkgkfwoek07n$E913G6Z>gRW_5^#m|% z-`}Jc(Q_xzf*l+m;_>NGB;RKFnXXo)*wc0Y=O;D)uioGMzZ$&D>CJtd)k$C11bCkm Oq^oUoG5u*2^*g diff --git a/docs/articles/gformula.html b/docs/articles/gformula.html index e36d9daa7..8f996ab6a 100644 --- a/docs/articles/gformula.html +++ b/docs/articles/gformula.html @@ -731,7 +731,7 @@

    hypothesis = "median = mean" ) #> -#> Term Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 % -#> median = mean -0.000969 0.00702 -0.138 0.89 0.2 -0.0147 0.0128 +#> Term Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 % +#> median = mean -0.0012 0.00594 -0.201 0.841 0.3 -0.0128 0.0104 #> #> Columns: term, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high diff --git a/docs/articles/logit.html b/docs/articles/logit.html index 1c82cad75..2aa592354 100644 --- a/docs/articles/logit.html +++ b/docs/articles/logit.html @@ -529,7 +529,7 @@

    Load libraries, data and fit a covariate-adjusted logistic regression model.

    library(marginaleffects)
    -library(modelsummary)
    +library(modelsummary)
     library(ggplot2)
     library(rms)
     
    diff --git a/docs/articles/machine_learning.html b/docs/articles/machine_learning.html
    index a822f240c..03ca65318 100644
    --- a/docs/articles/machine_learning.html
    +++ b/docs/articles/machine_learning.html
    @@ -103,7 +103,7 @@
     function gtag(){dataLayer.push(arguments);}
     gtag('js', new Date());
     gtag('config', 'G-V6MC2RPFXR', { 'anonymize_ip': true});
    -
    +
     
     
     
    @@ -487,9 +487,19 @@
             
         
    @@ -520,70 +530,424 @@

    library("fmeffects") library("ggplot2") library("mlr3verse") +library("modelsummary") library("tidymodels") |> suppressPackageStartupMessages() options(width = 10000)

    -

    -19.1 mlr3 +

    +19.1 tidymodels +

    +

    marginaleffects also supports the tidymodels machine learning framework. When the underlying engine used by tidymodels to train the model is itself supported as a standalone package by marginaleffects, we can obtain both estimates and their standard errors:

    +
    +
    library(tidymodels)
    +
    +penguins <- modeldata::penguins |> 
    +  na.omit() |>
    +  select(sex, island, species, bill_length_mm)
    +
    +mod <- linear_reg(mode = "regression") |>
    +    set_engine("lm") |>
    +    fit(bill_length_mm ~ ., data = penguins)
    +
    +avg_comparisons(mod, type = "numeric", newdata = penguins)
    +
    +
    
    +    Term           Contrast Estimate Std. Error     z Pr(>|z|)     S  2.5 % 97.5 %
    + island  Dream - Biscoe       -0.489      0.470 -1.04    0.299   1.7 -1.410  0.433
    + island  Torgersen - Biscoe    0.103      0.488  0.21    0.833   0.3 -0.853  1.059
    + sex     male - female         3.697      0.255 14.51   <0.001 156.0  3.198  4.197
    + species Chinstrap - Adelie   10.347      0.422 24.54   <0.001 439.4  9.521 11.174
    + species Gentoo - Adelie       8.546      0.410 20.83   <0.001 317.8  7.742  9.350
    +
    +Columns: term, contrast, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high 
    +Type:  numeric 
    +
    +
    avg_predictions(mod, type = "numeric", newdata = penguins, by = "island")
    +
    +
    
    +    island Estimate Std. Error   z Pr(>|z|)   S 2.5 % 97.5 %
    + Biscoe        45.2      0.182 248   <0.001 Inf  44.9   45.6
    + Dream         44.2      0.210 211   <0.001 Inf  43.8   44.6
    + Torgersen     39.0      0.339 115   <0.001 Inf  38.4   39.7
    +
    +Columns: island, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high 
    +Type:  numeric 
    +
    +
    +

    When the underlying engine that tidymodels uses to fit the model is not supported by marginaleffects as a standalone model, we can also obtain correct results, but no uncertainy estimates. Here is a random forest model:

    +
    +
    mod <- rand_forest(mode = "regression") |>
    +    set_engine("ranger") |>
    +    fit(bill_length_mm ~ ., data = penguins)
    +
    +avg_comparisons(mod, newdata = penguins, type = "numeric")
    +
    +
    
    +           Term           Contrast Estimate
    + bill_length_mm +1                    0.000
    + island         Dream - Biscoe        0.244
    + island         Torgersen - Biscoe   -2.059
    + sex            male - female         2.711
    + species        Chinstrap - Adelie    5.915
    + species        Gentoo - Adelie       5.975
    +
    +Columns: term, contrast, estimate 
    +Type:  numeric 
    +
    +
    +

    +19.1.1 Workflows

    +

    tidymodels “workflows” are a convenient way to train a model while applying a series of pre-processing steps to the data. marginaleffects supports workflows out of the box. First, let’s consider a simple regression task:

    +
    +
    penguins <- modeldata::penguins |> 
    +  na.omit() |>
    +  select(sex, island, species, bill_length_mm)
    +
    +mod <- penguins |>
    +    recipe(bill_length_mm ~ island + species + sex, data = _) |>
    +    step_dummy(all_nominal_predictors()) |>
    +    workflow(spec = linear_reg(mode = "regression", engine = "glm")) |>
    +    fit(penguins)
    +
    +avg_comparisons(mod, newdata = penguins, type = "numeric")
    +
    +
    
    +           Term           Contrast Estimate Std. Error     z Pr(>|z|)     S  2.5 % 97.5 %
    + bill_length_mm +1                    0.000         NA    NA       NA    NA     NA     NA
    + island         Dream - Biscoe       -0.489      0.470 -1.04    0.299   1.7 -1.410  0.433
    + island         Torgersen - Biscoe    0.103      0.488  0.21    0.833   0.3 -0.853  1.059
    + sex            male - female         3.697      0.255 14.51   <0.001 156.0  3.198  4.197
    + species        Chinstrap - Adelie   10.347      0.422 24.54   <0.001 439.4  9.521 11.174
    + species        Gentoo - Adelie       8.546      0.410 20.83   <0.001 317.8  7.742  9.350
    +
    +Columns: term, contrast, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high 
    +Type:  numeric 
    +
    +
    +

    Now, we run a classification task instead, and plot the predicted probabilities:

    +
    +
    mod <- penguins |>
    +    recipe(sex ~ island + species + bill_length_mm, data = _) |>
    +    step_dummy(all_nominal_predictors()) |>
    +    workflow(spec = logistic_reg(mode = "classification", engine = "glm")) |>
    +    fit(penguins)
    +
    +plot_predictions(
    +  mod,
    +  condition = c("bill_length_mm", "group"),
    +  newdata = penguins,
    +  type = "prob")
    +
    +

    +
    +
    +

    Finally, let’s consider a more complex task, where we train several models and summarize them in a table using modelsummary:

    +
    +
    library(modelsummary)
    +
    +recipe <- penguins |>
    +    recipe(sex ~ ., data = _) |>
    +    step_ns(bill_length_mm, deg_free = 4) |>
    +    step_dummy(all_nominal_predictors())
    +
    +models <- list(
    +  logit = logistic_reg(mode = "classification", engine = "glm"),
    +  forest = rand_forest(mode = "classification", engine = "ranger"),
    +  xgb = boost_tree(mode = "classification", engine = "xgboost")
    +)
    +
    +lapply(models, \(x) {
    +  recipe |>
    +  workflow(spec = x) |>
    +  fit(penguins) |>
    +  avg_comparisons(newdata = penguins, type = "prob") }) |>
    +  modelsummary(shape = term + contrast + group ~ model)
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    logitforestxgb
    bill_length_mm+1female−0.101−0.060−0.075
    +1(0.004)
    +1male0.1010.0600.075
    +1(0.004)
    islandDream - Biscoefemale−0.0440.000−0.004
    Dream - Biscoe(0.069)
    Dream - Biscoemale0.0440.0000.004
    Dream - Biscoe(0.069)
    Torgersen - Biscoefemale0.015−0.0580.008
    Torgersen - Biscoe(0.074)
    Torgersen - Biscoemale−0.0150.058−0.008
    Torgersen - Biscoe(0.074)
    sexmale - femalefemale0.0000.0000.000
    male - femalemale0.0000.0000.000
    speciesChinstrap - Adeliefemale0.5620.1690.441
    Chinstrap - Adelie(0.036)
    Chinstrap - Adeliemale−0.562−0.169−0.441
    Chinstrap - Adelie(0.036)
    Gentoo - Adeliefemale0.4530.1210.361
    Gentoo - Adelie(0.025)
    Gentoo - Adeliemale−0.453−0.121−0.361
    Gentoo - Adelie(0.025)
    Num.Obs.333
    AIC302.2
    BIC336.4
    Log.Lik.−142.082
    +
    +
    +

    +19.2 mlr3

    mlr3 is a machine learning framework for R. It makes it possible for users to train a wide range of models, including linear models, random forests, gradient boosting machines, and neural networks.

    In this example, we use the bikes dataset supplied by the fmeffects package to train a random forest model predicting the number of bikes rented per hour. We then use marginaleffects to interpret the results of the model.

    -
    data("bikes", package = "fmeffects")
    +
    data("bikes", package = "fmeffects")
     
     task <- as_task_regr(x = bikes, id = "bikes", target = "count")
     forest <- lrn("regr.ranger")$train(task)

    As described in other vignettes, we can use the avg_comparisons() function to compute the average change in predicted outcome that is associated with a change in each feature:

    -
    avg_comparisons(forest, newdata = bikes)
    +
    avg_comparisons(forest, newdata = bikes)
    
            Term      Contrast Estimate
      count      +1               0.000
    - holiday    False - True    13.835
    - humidity   +1             -23.142
    - month      +1               3.874
    - season     spring - fall  -30.230
    - season     summer - fall   -7.471
    - season     winter - fall    4.135
    - temp       +1               3.558
    - weather    misty - clear   -7.759
    - weather    rain - clear   -60.609
    - weekday    Fri - Sun       68.991
    - weekday    Mon - Sun       77.713
    - weekday    Sat - Sun       22.763
    - weekday    Thu - Sun       84.597
    - weekday    Tue - Sun       83.098
    - weekday    Wed - Sun       85.500
    - windspeed  +1               0.254
    - workingday False - True  -192.565
    - year       1 - 0           98.296
    + holiday    False - True     8.697
    + humidity   +1             -24.723
    + month      +1               4.364
    + season     spring - fall  -34.671
    + season     summer - fall  -10.571
    + season     winter - fall    0.165
    + temp       +1               3.301
    + weather    misty - clear   -7.633
    + weather    rain - clear   -59.395
    + weekday    Fri - Sun       65.917
    + weekday    Mon - Sun       74.898
    + weekday    Sat - Sun       20.971
    + weekday    Thu - Sun       81.958
    + weekday    Tue - Sun       80.634
    + weekday    Wed - Sun       82.814
    + windspeed  +1               0.185
    + workingday False - True  -194.817
    + year       1 - 0           99.321
     
     Columns: term, contrast, estimate 
     Type:  response 
    -

    These results are easy to interpret: An increase of 1 degree Celsius in the temperature is associated with an increase of 3.558 bikes rented per hour.

    +

    These results are easy to interpret: An increase of 1 degree Celsius in the temperature is associated with an increase of 3.301 bikes rented per hour.

    We could obtain the same result manually as follows:

    -
    lo <- transform(bikes, temp = temp - 0.5)
    +
    lo <- transform(bikes, temp = temp - 0.5)
     hi <- transform(bikes, temp = temp + 0.5)
     mean(predict(forest, newdata = hi) - predict(forest, newdata = lo))
    -
    [1] 3.558093
    +
    [1] 3.301041
    +

    +19.2.1 fmeffects: Forward or centered effects

    As the code above makes clear, the avg_comparisons() computes the effect of a “centered” change on the outcome. If we want to compute a “Forward Marginal Effect” instead, we can call:

    -
    avg_comparisons(
    +
    avg_comparisons(
         forest,
         variables = list("temp" = \(x) data.frame(x, x + 1)),
         newdata = bikes)
    
      Term Contrast Estimate
    - temp   custom     2.41
    + temp   custom     2.25
     
     Columns: term, contrast, estimate 
     Type:  response 
    @@ -591,19 +955,19 @@

    This is equivalent to using the fmeffects package:

    -
    fmeffects::fme(
    +
    fmeffects::fme(
         model = forest,
         data = bikes,
         target = "count",
         feature = "temp",
         step.size = 1)$ame 
    -
    [1] 2.412783
    +
    [1] 2.245841

    With marginaleffects::avg_comparisons(), we can also compute the average effect of a simultaneous change in multiple predictors, using the variables and cross arguments. In this example, we see what happens (on average) to the predicted outcome when the temp, season, and weather predictors all change together:

    -
    avg_comparisons(
    +
    avg_comparisons(
         forest,
         variables = c("temp", "season", "weather"),
         cross = TRUE,
    @@ -611,111 +975,47 @@ 

    
      Estimate     C: season C: temp    C: weather
    -  -33.443 spring - fall      +1 misty - clear
    -  -76.611 spring - fall      +1 rain - clear 
    -  -11.686 summer - fall      +1 misty - clear
    -  -62.018 summer - fall      +1 rain - clear 
    -   -0.179 winter - fall      +1 misty - clear
    -  -55.485 winter - fall      +1 rain - clear 
    +   -38.49 spring - fall      +1 misty - clear
    +   -81.69 spring - fall      +1 rain - clear 
    +   -14.95 summer - fall      +1 misty - clear
    +   -64.69 summer - fall      +1 rain - clear 
    +    -4.33 winter - fall      +1 misty - clear
    +   -58.45 winter - fall      +1 rain - clear 
     
     Columns: term, contrast_season, contrast_temp, contrast_weather, estimate 
     Type:  response 

    -

    -20 tidymodels

    -

    marginaleffects also supports the tidymodels machine learning framework. When the underlying engine used by tidymodels to train the model is itself supported as a standalone package by marginaleffects, we can obtain estimates of uncertainty estimates:

    -
    -
    suppressPackageStartupMessages(library(tidymodels))
    -mod <- linear_reg(mode = "regression") |>
    -    set_engine("lm") |>
    -    fit(count ~ ., data = bikes)
    -avg_comparisons(mod, newdata = bikes, type = "response")
    -
    -
    
    -       Term      Contrast Estimate Std. Error       z Pr(>|z|)     S   2.5 %  97.5 %
    - holiday    False - True   202.251     16.322  12.391  < 0.001 114.7  170.26 234.242
    - humidity   +1             -13.488     19.974  -0.675  0.49948   1.0  -52.64  25.659
    - month      +1              -1.363      1.398  -0.974  0.32987   1.6   -4.10   1.378
    - season     spring - fall  -67.924     14.119  -4.811  < 0.001  19.3  -95.60 -40.251
    - season     summer - fall   -9.242      9.583  -0.964  0.33485   1.6  -28.02   9.540
    - season     winter - fall   31.704     11.471   2.764  0.00571   7.5    9.22  54.187
    - temp       +1               5.010      0.630   7.954  < 0.001  49.0    3.78   6.244
    - weather    misty - clear  -20.147      6.215  -3.241  0.00119   9.7  -32.33  -7.965
    - weather    rain - clear  -112.211      9.881 -11.356  < 0.001  96.9 -131.58 -92.844
    - weekday    Fri - Sun      224.628      9.744  23.054  < 0.001 388.2  205.53 243.725
    - weekday    Mon - Sun      244.680     10.012  24.438  < 0.001 435.7  225.06 264.304
    - weekday    Sat - Sun       15.072      9.714   1.552  0.12075   3.0   -3.97  34.111
    - weekday    Thu - Sun      273.735      9.789  27.963  < 0.001 569.2  254.55 292.921
    - weekday    Tue - Sun      267.743      9.822  27.258  < 0.001 541.1  248.49 286.995
    - weekday    Wed - Sun      275.465      9.755  28.237  < 0.001 580.3  256.35 294.585
    - windspeed  +1              -0.542      0.398  -1.362  0.17310   2.5   -1.32   0.238
    - workingday False - True     0.000         NA      NA       NA    NA      NA      NA
    - year       1 - 0          108.398      5.226  20.741  < 0.001 315.0   98.16 118.641
    -
    -Columns: term, contrast, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high 
    -Type:  response 
    -
    -
    -

    When the underlying engine that tidymodels uses to fit the model is not supported by marginaleffects as a standalone model, we can also obtain correct results, but no uncertainy estimates. Here is a random forest model:

    -
    -
    forest_tidy <- rand_forest(mode = "regression") |>
    -    set_engine("ranger") |>
    -    fit(count ~ ., data = bikes)
    -avg_comparisons(forest_tidy, newdata = bikes, type = "numeric")
    -
    -
    
    -       Term      Contrast Estimate
    - count      +1               0.000
    - holiday    False - True    13.487
    - humidity   +1             -24.291
    - month      +1               4.076
    - season     spring - fall  -29.015
    - season     summer - fall   -6.781
    - season     winter - fall    4.958
    - temp       +1               3.399
    - weather    misty - clear   -7.555
    - weather    rain - clear   -59.817
    - weekday    Fri - Sun       70.596
    - weekday    Mon - Sun       78.772
    - weekday    Sat - Sun       22.198
    - weekday    Thu - Sun       86.375
    - weekday    Tue - Sun       84.493
    - weekday    Wed - Sun       86.895
    - windspeed  +1               0.141
    - workingday False - True  -192.057
    - year       1 - 0           99.677
    -
    -Columns: term, contrast, estimate 
    -Type:  numeric 
    -
    -
    -

    -21 Plot

    +

    +19.3 Plots

    We can plot the results using the standard marginaleffects helpers. For example, to plot predictions, we can do:

    -
    plot_predictions(forest, condition = "temp", newdata = bikes)
    +
    library(mlr3verse)
    +data("bikes", package = "fmeffects")
    +task <- as_task_regr(x = bikes, id = "bikes", target = "count")
    +forest <- lrn("regr.ranger")$train(task)
    +
    +plot_predictions(forest, condition = "temp", newdata = bikes)
    -

    +

    As documented in ?plot_predictions, using condition="temp" is equivalent to creating an equally-spaced grid of temp values, and holding all other predictors at their means or modes. In other words, it is equivalent to:

    -
    d <- datagrid(temp = seq(min(bikes$temp), max(bikes$temp), length.out = 100), newdata = bikes)
    +
    d <- datagrid(temp = seq(min(bikes$temp), max(bikes$temp), length.out = 100), newdata = bikes)
     p <- predict(forest, newdata = d)
     plot(d$temp, p, type = "l")

    Alternatively, we could plot “marginal” predictions, where replicate the full dataset once for every value of temp, and then average the predicted values over each value of the x-axis:

    -
    d <- datagridcf(newdata = bikes, temp = unique)
    -plot_predictions(forest, by = "temp", newdata = d)
    +
    plot_predictions(forest, by = "temp", newdata = bikes)
    -

    +

    Of course, we can customize the plot using all the standard ggplot2 functions:

    -
    plot_predictions(forest, by = "temp", newdata = d) +
    +
    plot_predictions(forest, by = "temp", newdata = d) +
         geom_point(data = bikes, aes(x = temp, y = count), alpha = 0.1) +
         geom_smooth(data = bikes, aes(x = temp, y = count), se = FALSE, color = "orange") +
         labs(x = "Temperature (Celcius)", y = "Predicted number of bikes rented per hour",
    @@ -725,7 +1025,7 @@ 

    `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

    -

    +

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    print(cmp)
    shape: (96, 9)
    -┌──────┬──────────┬──────────┬───────────┬───┬─────────┬───────┬───────┬───────┐
    -│ Term ┆ Contrast ┆ Estimate ┆ Std.Error ┆ … ┆ P(>|z|) ┆ S     ┆ 2.5%  ┆ 97.5% │
    -│ ---  ┆ ---      ┆ ---      ┆ ---       ┆   ┆ ---     ┆ ---   ┆ ---   ┆ ---   │
    -│ str  ┆ str      ┆ str      ┆ str       ┆   ┆ str     ┆ str   ┆ str   ┆ str   │
    -╞══════╪══════════╪══════════╪═══════════╪═══╪═════════╪═══════╪═══════╪═══════╡
    -│ am   ┆ 1 - 0    ┆ 0.325    ┆ 1.68      ┆ … ┆ 0.848   ┆ 0.237 ┆ -3.15 ┆ 3.8   │
    -│ am   ┆ 1 - 0    ┆ -0.544   ┆ 1.57      ┆ … ┆ 0.732   ┆ 0.451 ┆ -3.78 ┆ 2.69  │
    -│ am   ┆ 1 - 0    ┆ 1.2      ┆ 2.35      ┆ … ┆ 0.614   ┆ 0.704 ┆ -3.64 ┆ 6.05  │
    -│ am   ┆ 1 - 0    ┆ -1.7     ┆ 1.87      ┆ … ┆ 0.371   ┆ 1.43  ┆ -5.56 ┆ 2.15  │
    -│ …    ┆ …        ┆ …        ┆ …         ┆ … ┆ …       ┆ …     ┆ …     ┆ …     │
    -│ wt   ┆ +1       ┆ -1.65    ┆ 3.74      ┆ … ┆ 0.663   ┆ 0.593 ┆ -9.38 ┆ 6.07  │
    -│ wt   ┆ +1       ┆ -4.52    ┆ 2.47      ┆ … ┆ 0.0797  ┆ 3.65  ┆ -9.62 ┆ 0.577 │
    -│ wt   ┆ +1       ┆ 0.635    ┆ 4.89      ┆ … ┆ 0.898   ┆ 0.156 ┆ -9.45 ┆ 10.7  │
    -│ wt   ┆ +1       ┆ -6.65    ┆ 1.86      ┆ … ┆ 0.00154 ┆ 9.34  ┆ -10.5 ┆ -2.81 │
    -└──────┴──────────┴──────────┴───────────┴───┴─────────┴───────┴───────┴───────┘
    +┌──────┬──────────┬──────────┬───────────┬───┬─────────┬───────┬─────────┬─────────┐
    +│ Term ┆ Contrast ┆ Estimate ┆ Std.Error ┆ … ┆ P(>|z|) ┆ S     ┆ 2.5%    ┆ 97.5%   │
    +│ ---  ┆ ---      ┆ ---      ┆ ---       ┆   ┆ ---     ┆ ---   ┆ ---     ┆ ---     │
    +│ str  ┆ str      ┆ str      ┆ str       ┆   ┆ str     ┆ str   ┆ str     ┆ str     │
    +╞══════╪══════════╪══════════╪═══════════╪═══╪═════════╪═══════╪═════════╪═════════╡
    +│ hp   ┆ +1       ┆ -0.0369  ┆ 0.0185    ┆ … ┆ 0.0575  ┆ 4.12  ┆ -0.0751 ┆ 0.00128 │
    +│ hp   ┆ +1       ┆ -0.0287  ┆ 0.0156    ┆ … ┆ 0.0788  ┆ 3.67  ┆ -0.0609 ┆ 0.00357 │
    +│ hp   ┆ +1       ┆ -0.0466  ┆ 0.0226    ┆ … ┆ 0.0502  ┆ 4.32  ┆ -0.0932 ┆ 4.6e-05 │
    +│ hp   ┆ +1       ┆ -0.0423  ┆ 0.0133    ┆ … ┆ 0.00401 ┆ 7.96  ┆ -0.0697 ┆ -0.0149 │
    +│ …    ┆ …        ┆ …        ┆ …         ┆ … ┆ …       ┆ …     ┆ …       ┆ …       │
    +│ am   ┆ 1 - 0    ┆ 2.11     ┆ 2.29      ┆ … ┆ 0.367   ┆ 1.45  ┆ -2.62   ┆ 6.83    │
    +│ am   ┆ 1 - 0    ┆ 0.895    ┆ 1.64      ┆ … ┆ 0.591   ┆ 0.758 ┆ -2.5    ┆ 4.29    │
    +│ am   ┆ 1 - 0    ┆ 4.03     ┆ 3.24      ┆ … ┆ 0.226   ┆ 2.15  ┆ -2.66   ┆ 10.7    │
    +│ am   ┆ 1 - 0    ┆ -0.237   ┆ 1.59      ┆ … ┆ 0.883   ┆ 0.18  ┆ -3.51   ┆ 3.04    │
    +└──────┴──────────┴──────────┴───────────┴───┴─────────┴───────┴─────────┴─────────┘
     
     Columns: rowid, term, contrast, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high, predicted, predicted_lo, predicted_hi, rownames, mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb
    @@ -786,7 +786,7 @@

    ╞══════╪═══════════╪══════════╪═══════════╪═══╪═════════╪═══════╪═══════════╪═══════╡ │ hp ┆ 100 - 120 ┆ 0.738 ┆ 0.37 ┆ … ┆ 0.0576 ┆ 4.12 ┆ -0.0256 ┆ 1.5 │ │ hp ┆ 100 - 120 ┆ 0.574 ┆ 0.313 ┆ … ┆ 0.0788 ┆ 3.67 ┆ -0.0713 ┆ 1.22 │ -│ hp ┆ 100 - 120 ┆ 0.931 ┆ 0.452 ┆ … ┆ 0.0502 ┆ 4.32 ┆ -0.000919 ┆ 1.86 │ +│ hp ┆ 100 - 120 ┆ 0.931 ┆ 0.452 ┆ … ┆ 0.0502 ┆ 4.32 ┆ -0.000918 ┆ 1.86 │ │ hp ┆ 100 - 120 ┆ 0.845 ┆ 0.266 ┆ … ┆ 0.00401 ┆ 7.96 ┆ 0.297 ┆ 1.39 │ │ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │ │ hp ┆ 100 - 120 ┆ 0.384 ┆ 0.27 ┆ … ┆ 0.168 ┆ 2.57 ┆ -0.173 ┆ 0.941 │ @@ -930,11 +930,11 @@

    am 1 - 0 -1.703 1.87 -0.912 0.3618 1.5 -5.36 1.957 am 1 - 0 -0.615 1.68 -0.366 0.7146 0.5 -3.91 2.680 --- 86 rows omitted. See ?avg_slopes and ?print.marginaleffects --- - wt dY/dX -6.518 1.88 -3.462 <0.001 10.9 -10.21 -2.828 - wt dY/dX -1.653 3.74 -0.442 0.6588 0.6 -8.99 5.682 - wt dY/dX -4.520 2.47 -1.830 0.0672 3.9 -9.36 0.321 - wt dY/dX 0.635 4.89 0.130 0.8966 0.2 -8.95 10.216 - wt dY/dX -6.647 1.86 -3.572 <0.001 11.5 -10.29 -2.999 + wt dY/dX -6.518 1.88 -3.462 <0.001 10.9 -10.21 -2.827 + wt dY/dX -1.653 3.74 -0.442 0.6588 0.6 -8.99 5.683 + wt dY/dX -4.520 2.47 -1.830 0.0673 3.9 -9.36 0.321 + wt dY/dX 0.635 4.89 0.130 0.8966 0.2 -8.95 10.215 + wt dY/dX -6.647 1.86 -3.571 <0.001 11.5 -10.29 -2.999 Columns: rowid, term, contrast, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, predicted_lo, predicted_hi, predicted, mpg, hp, wt, am Type: response

    @@ -951,21 +951,21 @@

    print(mfx)
    shape: (96, 9)
    -┌──────┬──────────┬──────────┬───────────┬───┬─────────┬───────┬─────────┬──────────┐
    -│ Term ┆ Contrast ┆ Estimate ┆ Std.Error ┆ … ┆ P(>|z|) ┆ S     ┆ 2.5%    ┆ 97.5%    │
    -│ ---  ┆ ---      ┆ ---      ┆ ---       ┆   ┆ ---     ┆ ---   ┆ ---     ┆ ---      │
    -│ str  ┆ str      ┆ str      ┆ str       ┆   ┆ str     ┆ str   ┆ str     ┆ str      │
    -╞══════╪══════════╪══════════╪═══════════╪═══╪═════════╪═══════╪═════════╪══════════╡
    -│ hp   ┆ dY/dX    ┆ -0.0369  ┆ 0.019     ┆ … ┆ 0.0639  ┆ 3.97  ┆ -0.0761 ┆ 0.00231  │
    -│ hp   ┆ dY/dX    ┆ -0.0287  ┆ 0.018     ┆ … ┆ 0.124   ┆ 3.01  ┆ -0.0659 ┆ 0.00851  │
    -│ hp   ┆ dY/dX    ┆ -0.0466  ┆ 0.0208    ┆ … ┆ 0.0343  ┆ 4.86  ┆ -0.0894 ┆ -0.00374 │
    -│ hp   ┆ dY/dX    ┆ -0.0423  ┆ 0.0134    ┆ … ┆ 0.00432 ┆ 7.86  ┆ -0.07   ┆ -0.0146  │
    -│ …    ┆ …        ┆ …        ┆ …         ┆ … ┆ …       ┆ …     ┆ …       ┆ …        │
    -│ wt   ┆ dY/dX    ┆ -1.65    ┆ 3.74      ┆ … ┆ 0.662   ┆ 0.594 ┆ -9.37   ┆ 6.06     │
    -│ wt   ┆ dY/dX    ┆ -4.52    ┆ 2.47      ┆ … ┆ 0.0795  ┆ 3.65  ┆ -9.61   ┆ 0.574    │
    -│ wt   ┆ dY/dX    ┆ 0.635    ┆ 4.89      ┆ … ┆ 0.898   ┆ 0.156 ┆ -9.45   ┆ 10.7     │
    -│ wt   ┆ dY/dX    ┆ -6.65    ┆ 1.86      ┆ … ┆ 0.00155 ┆ 9.33  ┆ -10.5   ┆ -2.8     │
    -└──────┴──────────┴──────────┴───────────┴───┴─────────┴───────┴─────────┴──────────┘
    +┌──────┬──────────┬───────────┬───────────┬───┬──────────┬───────┬───────────┬──────────┐
    +│ Term ┆ Contrast ┆ Estimate  ┆ Std.Error ┆ … ┆ P(>|z|)  ┆ S     ┆ 2.5%      ┆ 97.5%    │
    +│ ---  ┆ ---      ┆ ---       ┆ ---       ┆   ┆ ---      ┆ ---   ┆ ---       ┆ ---      │
    +│ str  ┆ str      ┆ str       ┆ str       ┆   ┆ str      ┆ str   ┆ str       ┆ str      │
    +╞══════╪══════════╪═══════════╪═══════════╪═══╪══════════╪═══════╪═══════════╪══════════╡
    +│ wt   ┆ dY/dX    ┆ -6.61     ┆ 1.87      ┆ … ┆ 0.00164  ┆ 9.25  ┆ -10.5     ┆ -2.76    │
    +│ wt   ┆ dY/dX    ┆ -6.61     ┆ 1.87      ┆ … ┆ 0.00165  ┆ 9.25  ┆ -10.5     ┆ -2.76    │
    +│ wt   ┆ dY/dX    ┆ -7.16     ┆ 1.8       ┆ … ┆ 0.000558 ┆ 10.8  ┆ -10.9     ┆ -3.45    │
    +│ wt   ┆ dY/dX    ┆ -3.21     ┆ 2.01      ┆ … ┆ 0.123    ┆ 3.02  ┆ -7.35     ┆ 0.939    │
    +│ …    ┆ …        ┆ …         ┆ …         ┆ … ┆ …        ┆ …     ┆ …         ┆ …        │
    +│ am   ┆ dY/dX    ┆ 2.11e+04  ┆ 2.29e+04  ┆ … ┆ 0.367    ┆ 1.45  ┆ -2.62e+04 ┆ 6.83e+04 │
    +│ am   ┆ dY/dX    ┆ 8.95e+03  ┆ 1.64e+04  ┆ … ┆ 0.591    ┆ 0.758 ┆ -2.5e+04  ┆ 4.29e+04 │
    +│ am   ┆ dY/dX    ┆ 4.03e+04  ┆ 3.24e+04  ┆ … ┆ 0.226    ┆ 2.15  ┆ -2.66e+04 ┆ 1.07e+05 │
    +│ am   ┆ dY/dX    ┆ -2.37e+03 ┆ 1.59e+04  ┆ … ┆ 0.883    ┆ 0.18  ┆ -3.51e+04 ┆ 3.04e+04 │
    +└──────┴──────────┴───────────┴───────────┴───┴──────────┴───────┴───────────┴──────────┘
     
     Columns: rowid, term, contrast, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high, predicted, predicted_lo, predicted_hi, rownames, mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb
    @@ -1110,8 +1110,8 @@

    
      Term am Estimate Std. Error     z Pr(>|z|)   S 2.5 % 97.5 %
    -   wt  0    -2.68       1.42 -1.89   0.0593 4.1 -5.46  0.105
    -   wt  1    -5.43       2.15 -2.52   0.0116 6.4 -9.65 -1.213
    +   wt  0    -2.68       1.42 -1.89   0.0593 4.1 -5.46  0.106
    +   wt  1    -5.43       2.15 -2.52   0.0116 6.4 -9.65 -1.214
     
     Columns: rowid, term, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, am, predicted_lo, predicted_hi, predicted, mpg, hp, wt 
     Type:  response 
    @@ -1133,7 +1133,7 @@

    │ str ┆ str ┆ str ┆ str ┆ ┆ str ┆ str ┆ str ┆ str │ ╞══════╪══════════╪══════════╪═══════════╪═══╪═════════╪══════╪═══════╪════════╡ │ wt ┆ dY/dX ┆ -2.68 ┆ 1.42 ┆ … ┆ 0.072 ┆ 3.8 ┆ -5.61 ┆ 0.258 │ -│ wt ┆ dY/dX ┆ -5.43 ┆ 2.15 ┆ … ┆ 0.0187 ┆ 5.74 ┆ -9.88 ┆ -0.986 │ +│ wt ┆ dY/dX ┆ -5.43 ┆ 2.15 ┆ … ┆ 0.0186 ┆ 5.75 ┆ -9.87 ┆ -0.993 │ └──────┴──────────┴──────────┴───────────┴───┴─────────┴──────┴───────┴────────┘ Columns: rowid, term, contrast, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high, predicted, predicted_lo, predicted_hi, am, rownames, mpg, cyl, disp, hp, drat, wt, qsec, vs, gear, carb @@ -1229,7 +1229,7 @@

    np.mean(mod.predict())
    -
    20.090625000000014
    +
    20.090624999999992

    @@ -1251,7 +1251,7 @@

    am mean(1) - mean(0) 0 -1.3830 2.5250 -0.548 0.58388 0.8 -6.3319 3.56589 am mean(1) - mean(0) 1 1.9029 2.3086 0.824 0.40980 1.3 -2.6219 6.42773 hp mean(+1) 0 -0.0343 0.0159 -2.160 0.03079 5.0 -0.0654 -0.00317 - hp mean(+1) 1 -0.0436 0.0213 -2.050 0.04038 4.6 -0.0854 -0.00191 + hp mean(+1) 1 -0.0436 0.0213 -2.050 0.04039 4.6 -0.0854 -0.00191 wt mean(+1) 0 -2.4799 1.2316 -2.014 0.04406 4.5 -4.8939 -0.06595 wt mean(+1) 1 -6.0718 1.9762 -3.072 0.00212 8.9 -9.9451 -2.19846 @@ -1271,12 +1271,12 @@

    │ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ str ┆ str ┆ str ┆ ┆ str ┆ str ┆ str ┆ str │ ╞═════╪══════╪═══════════════════╪══════════╪═══╪═════════╪═══════╪═════════╪══════════╡ +│ 1 ┆ hp ┆ +1 ┆ -0.0436 ┆ … ┆ 0.0515 ┆ 4.28 ┆ -0.0876 ┆ 0.000301 │ +│ 0 ┆ hp ┆ +1 ┆ -0.0343 ┆ … ┆ 0.041 ┆ 4.61 ┆ -0.067 ┆ -0.00152 │ │ 1 ┆ wt ┆ +1 ┆ -6.07 ┆ … ┆ 0.00522 ┆ 7.58 ┆ -10.2 ┆ -1.99 │ │ 0 ┆ wt ┆ +1 ┆ -2.48 ┆ … ┆ 0.0554 ┆ 4.17 ┆ -5.02 ┆ 0.0621 │ │ 1 ┆ am ┆ mean(1) - mean(0) ┆ 1.9 ┆ … ┆ 0.418 ┆ 1.26 ┆ -2.86 ┆ 6.67 │ │ 0 ┆ am ┆ mean(1) - mean(0) ┆ -1.38 ┆ … ┆ 0.589 ┆ 0.764 ┆ -6.59 ┆ 3.83 │ -│ 1 ┆ hp ┆ +1 ┆ -0.0436 ┆ … ┆ 0.0515 ┆ 4.28 ┆ -0.0876 ┆ 0.000301 │ -│ 0 ┆ hp ┆ +1 ┆ -0.0343 ┆ … ┆ 0.041 ┆ 4.61 ┆ -0.067 ┆ -0.00152 │ └─────┴──────┴───────────────────┴──────────┴───┴─────────┴───────┴─────────┴──────────┘ Columns: am, term, contrast, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high @@ -1474,8 +1474,8 @@

    
      Term qsec Estimate Std. Error    z Pr(>|z|)   S  2.5 % 97.5 %
    - drat 14.5     5.22       3.79 1.38   0.1682 2.6 -2.206   12.7
    - drat 22.9    10.24       5.16 1.98   0.0472 4.4  0.127   20.4
    + drat 14.5     5.22       3.80 1.38   0.1690 2.6 -2.221   12.7
    + drat 22.9    10.24       5.15 1.99   0.0469 4.4  0.142   20.3
     
     Columns: rowid, term, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, qsec, predicted_lo, predicted_hi, predicted, mpg, drat 
     Type:  response 
    @@ -1496,8 +1496,8 @@

    │ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ str ┆ str ┆ str ┆ ┆ str ┆ str ┆ str ┆ str │ ╞══════╪══════════╪══════════╪═══════════╪═══╪═════════╪══════╪════════╪═══════╡ -│ drat ┆ dY/dX ┆ 5.22 ┆ 3.81 ┆ … ┆ 0.181 ┆ 2.47 ┆ -2.57 ┆ 13 │ -│ drat ┆ dY/dX ┆ 10.2 ┆ 5.16 ┆ … ┆ 0.057 ┆ 4.13 ┆ -0.328 ┆ 20.8 │ +│ drat ┆ dY/dX ┆ 5.22 ┆ 3.8 ┆ … ┆ 0.18 ┆ 2.47 ┆ -2.56 ┆ 13 │ +│ drat ┆ dY/dX ┆ 10.2 ┆ 5.16 ┆ … ┆ 0.0573 ┆ 4.13 ┆ -0.338 ┆ 20.8 │ └──────┴──────────┴──────────┴───────────┴───┴─────────┴──────┴────────┴───────┘ Columns: rowid, term, contrast, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high, predicted, predicted_lo, predicted_hi, qsec, rownames, mpg, cyl, disp, hp, drat, wt, vs, am, gear, carb @@ -1545,7 +1545,7 @@

    │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ str ┆ str ┆ str ┆ str ┆ str ┆ str ┆ str │ ╞═══════╪══════════╪═══════════╪════════╪═════════╪═══════╪═══════╪═══════╡ -│ b1=b2 ┆ -5.02 ┆ 8.53 ┆ -0.588 ┆ 0.561 ┆ 0.833 ┆ -22.5 ┆ 12.5 │ +│ b1=b2 ┆ -5.02 ┆ 8.53 ┆ -0.588 ┆ 0.561 ┆ 0.834 ┆ -22.5 ┆ 12.5 │ └───────┴──────────┴───────────┴────────┴─────────┴───────┴───────┴───────┘ Columns: term, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high @@ -1568,7 +1568,7 @@

    
      Term Estimate Std. Error    z Pr(>|z|)    S 2.5 % 97.5 %
      drat     7.22      1.365 5.29  < 0.001 23.0 4.549   9.90
    - qsec     1.12      0.433 2.59  0.00947  6.7 0.275   1.97
    + qsec     1.12      0.433 2.60  0.00942  6.7 0.276   1.97
     
     Columns: term, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high 
     Type:  response 
    @@ -1595,8 +1595,8 @@

    │ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ str ┆ str ┆ str ┆ ┆ str ┆ str ┆ str ┆ str │ ╞══════╪═════════════╪══════════╪═══════════╪═══╪══════════╪══════╪═══════╪═══════╡ +│ qsec ┆ mean(dY/dX) ┆ 1.12 ┆ 0.432 ┆ … ┆ 0.0147 ┆ 6.09 ┆ 0.239 ┆ 2.01 │ │ drat ┆ mean(dY/dX) ┆ 7.22 ┆ 1.37 ┆ … ┆ 1.25e-05 ┆ 16.3 ┆ 4.43 ┆ 10 │ -│ qsec ┆ mean(dY/dX) ┆ 1.12 ┆ 0.435 ┆ … ┆ 0.0152 ┆ 6.04 ┆ 0.234 ┆ 2.01 │ └──────┴─────────────┴──────────┴───────────┴───┴──────────┴──────┴───────┴───────┘ Columns: term, contrast, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high @@ -1633,7 +1633,7 @@

    
      Term Estimate Std. Error    z Pr(>|z|)    S 2.5 % 97.5 % p (NonSup) p (NonInf) p (Equiv)
      drat     7.22      1.365 5.29  < 0.001 23.0 4.549   9.90     0.9999     <0.001    0.9999
    - qsec     1.12      0.433 2.59  0.00947  6.7 0.275   1.97     0.0216     <0.001    0.0216
    + qsec     1.12      0.433 2.60  0.00942  6.7 0.276   1.97     0.0215     <0.001    0.0215
     
     Columns: term, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, statistic.noninf, statistic.nonsup, p.value.noninf, p.value.nonsup, p.value.equiv 
     Type:  response 
    @@ -1651,8 +1651,8 @@

    │ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ str ┆ str ┆ str ┆ ┆ str ┆ str ┆ str ┆ str │ ╞══════╪═════════════╪══════════╪═══════════╪═══╪══════════╪══════╪═══════╪═══════╡ -│ qsec ┆ mean(dY/dX) ┆ 1.12 ┆ 0.435 ┆ … ┆ 0.0152 ┆ 6.04 ┆ 0.234 ┆ 2.01 │ │ drat ┆ mean(dY/dX) ┆ 7.22 ┆ 1.37 ┆ … ┆ 1.25e-05 ┆ 16.3 ┆ 4.43 ┆ 10 │ +│ qsec ┆ mean(dY/dX) ┆ 1.12 ┆ 0.432 ┆ … ┆ 0.0147 ┆ 6.09 ┆ 0.239 ┆ 2.01 │ └──────┴─────────────┴──────────┴───────────┴───┴──────────┴──────┴───────┴───────┘ Columns: term, contrast, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high, statistic_noninf, statistic_nonsup, p_value_noninf, p_value_nonsup, p_value_equiv diff --git a/docs/articles/matching.html b/docs/articles/matching.html index ff2b0a930..1956bdcf4 100644 --- a/docs/articles/matching.html +++ b/docs/articles/matching.html @@ -580,7 +580,7 @@

    
       Term Contrast Estimate Std. Error     z Pr(>|z|)   S 2.5 % 97.5 %
    - treat    1 - 0      637       1014 0.628     0.53 0.9 -1350   2625
    + treat    1 - 0      661       1009 0.654    0.513 1.0 -1318   2639
     
     Columns: term, contrast, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high 
     Type:  response 
    diff --git a/docs/articles/mrp.html b/docs/articles/mrp.html index a07c1cfb3..f4048b56f 100644 --- a/docs/articles/mrp.html +++ b/docs/articles/mrp.html @@ -533,7 +533,7 @@

    The (simulated) data that we will use is stored in a R data frame called survey. We can use the nrow() function to confirm the sample size, and the datasummary_df() function from the modelsummary package to display the first few rows of data:

    library(marginaleffects)
    -library(modelsummary)
    +library(modelsummary)
     library(tidyverse)
     library(ggridges)
     library(brms)
    @@ -733,7 +733,7 @@ 

    library(marginaleffects)
     library(countrycode)
     library(tidyverse)
    -library(modelsummary)
    +library(modelsummary)
     library(gt)
     library(brms)
     set.seed(1024)
    diff --git a/docs/articles/multiple_imputation.html b/docs/articles/multiple_imputation.html
    index b17b56973..c3d623bd4 100644
    --- a/docs/articles/multiple_imputation.html
    +++ b/docs/articles/multiple_imputation.html
    @@ -549,12 +549,12 @@ 

    mfx_mice #> #> Term Contrast Species Estimate Std. Error t Pr(>|t|) S 2.5 % 97.5 % Df -#> Sepal.Length mean(dY/dX) setosa 0.0684 0.0560 1.222 0.22413 2.2 -0.0424 0.179 120.0 -#> Sepal.Length mean(dY/dX) versicolor 0.0540 0.0558 0.968 0.33557 1.6 -0.0568 0.165 93.6 -#> Sepal.Length mean(dY/dX) virginica 0.0582 0.0512 1.137 0.25822 2.0 -0.0434 0.160 101.3 -#> Sepal.Width mean(dY/dX) setosa 0.1890 0.0836 2.261 0.02432 5.4 0.0246 0.353 400.1 +#> Sepal.Length mean(dY/dX) setosa 0.0684 0.0560 1.222 0.22414 2.2 -0.0424 0.179 120.0 +#> Sepal.Length mean(dY/dX) versicolor 0.0540 0.0558 0.968 0.33550 1.6 -0.0567 0.165 93.6 +#> Sepal.Length mean(dY/dX) virginica 0.0582 0.0512 1.137 0.25818 2.0 -0.0434 0.160 101.2 +#> Sepal.Width mean(dY/dX) setosa 0.1890 0.0836 2.260 0.02436 5.4 0.0246 0.353 400.5 #> Sepal.Width mean(dY/dX) versicolor 0.2092 0.0772 2.710 0.00807 7.0 0.0558 0.363 89.0 -#> Sepal.Width mean(dY/dX) virginica 0.2242 0.1041 2.154 0.03511 4.8 0.0162 0.432 61.9 +#> Sepal.Width mean(dY/dX) virginica 0.2242 0.1041 2.155 0.03506 4.8 0.0162 0.432 61.8 #> Species mean(versicolor) - mean(setosa) setosa 1.1399 0.0977 11.668 < 0.001 68.1 0.9464 1.333 114.8 #> Species mean(versicolor) - mean(setosa) versicolor 1.1399 0.0977 11.668 < 0.001 68.1 0.9464 1.333 114.8 #> Species mean(versicolor) - mean(setosa) virginica 1.1399 0.0977 11.668 < 0.001 68.1 0.9464 1.333 114.8 @@ -585,11 +585,11 @@

    #> #> Term Contrast Species Estimate Std. Error t Pr(>|t|) S 2.5 % 97.5 % Df #> Sepal.Length mean(dY/dX) setosa 0.3878 0.0907 4.278 < 0.001 13.3 0.205 0.5705 43.9 -#> Sepal.Length mean(dY/dX) versicolor 0.3231 0.0802 4.030 < 0.001 12.5 0.162 0.4838 55.9 +#> Sepal.Length mean(dY/dX) versicolor 0.3231 0.0802 4.029 < 0.001 12.5 0.162 0.4838 56.0 #> Sepal.Length mean(dY/dX) virginica 0.3467 0.0799 4.340 < 0.001 13.6 0.186 0.5077 44.7 -#> Sepal.Width mean(dY/dX) setosa -0.2079 0.1491 -1.394 0.16879 2.6 -0.507 0.0909 55.0 -#> Sepal.Width mean(dY/dX) versicolor -0.1157 0.1168 -0.991 0.32648 1.6 -0.350 0.1187 51.8 -#> Sepal.Width mean(dY/dX) virginica -0.0452 0.1272 -0.355 0.72319 0.5 -0.298 0.2078 82.1 +#> Sepal.Width mean(dY/dX) setosa -0.2079 0.1491 -1.394 0.16878 2.6 -0.507 0.0909 55.0 +#> Sepal.Width mean(dY/dX) versicolor -0.1157 0.1168 -0.991 0.32647 1.6 -0.350 0.1187 51.8 +#> Sepal.Width mean(dY/dX) virginica -0.0452 0.1272 -0.355 0.72323 0.5 -0.298 0.2079 82.1 #> Species mean(versicolor) - mean(setosa) setosa 0.6127 0.1731 3.541 0.00111 9.8 0.262 0.9635 36.7 #> Species mean(versicolor) - mean(setosa) versicolor 0.6127 0.1731 3.541 0.00111 9.8 0.262 0.9635 36.7 #> Species mean(versicolor) - mean(setosa) virginica 0.6127 0.1731 3.541 0.00111 9.8 0.262 0.9635 36.7 @@ -626,12 +626,12 @@

    mfx_missRanger #> #> Term Contrast Species Estimate Std. Error t Pr(>|t|) S 2.5 % 97.5 % Df -#> Sepal.Length mean(dY/dX) setosa 0.0586 0.0414 1.42 0.15671 2.7 -0.02248 0.140 2780500 -#> Sepal.Length mean(dY/dX) versicolor 0.0675 0.0392 1.72 0.08514 3.6 -0.00934 0.144 721295 -#> Sepal.Length mean(dY/dX) virginica 0.0643 0.0367 1.75 0.07987 3.6 -0.00766 0.136 1020654 -#> Sepal.Width mean(dY/dX) setosa 0.2314 0.0690 3.35 < 0.001 10.3 0.09612 0.367 1911457 -#> Sepal.Width mean(dY/dX) versicolor 0.2186 0.0551 3.97 < 0.001 13.8 0.11066 0.327 246693 -#> Sepal.Width mean(dY/dX) virginica 0.2089 0.0687 3.04 0.00237 8.7 0.07422 0.344 194780 +#> Sepal.Length mean(dY/dX) setosa 0.0586 0.0414 1.42 0.15672 2.7 -0.02249 0.140 2780689 +#> Sepal.Length mean(dY/dX) versicolor 0.0675 0.0392 1.72 0.08514 3.6 -0.00934 0.144 721339 +#> Sepal.Length mean(dY/dX) virginica 0.0643 0.0367 1.75 0.07986 3.6 -0.00766 0.136 1020604 +#> Sepal.Width mean(dY/dX) setosa 0.2314 0.0690 3.35 < 0.001 10.3 0.09612 0.367 1911621 +#> Sepal.Width mean(dY/dX) versicolor 0.2186 0.0551 3.97 < 0.001 13.8 0.11066 0.327 246676 +#> Sepal.Width mean(dY/dX) virginica 0.2089 0.0687 3.04 0.00237 8.7 0.07420 0.344 194885 #> Species mean(versicolor) - mean(setosa) setosa 1.1589 0.0704 16.46 < 0.001 199.8 1.02091 1.297 1115135 #> Species mean(versicolor) - mean(setosa) versicolor 1.1589 0.0704 16.46 < 0.001 199.8 1.02091 1.297 1115135 #> Species mean(versicolor) - mean(setosa) virginica 1.1589 0.0704 16.46 < 0.001 199.8 1.02091 1.297 1115135 @@ -646,7 +646,7 @@

    21.4 Comparing results with different imputation software

    We can use the modelsummary package to compare the results with listwise deletion to the results using different imputations software:

    -
    library(modelsummary)
    +
    library(modelsummary)
     
     ## listwise deletion slopes
     mod_lwd <- lm(Petal.Width ~ Sepal.Length * Sepal.Width + Species, data = dat)
    diff --git a/docs/articles/plot.html b/docs/articles/plot.html
    index 667975b90..c73d3c344 100644
    --- a/docs/articles/plot.html
    +++ b/docs/articles/plot.html
    @@ -661,10 +661,10 @@ 

    #> #> Term Contrast flipper_length_mm species Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 % bill_length_mm island #> flipper_length_mm +1 172 Adelie 15.3 9.25 1.66 0.0976 3.4 -2.81 33.5 43.9 Biscoe -#> flipper_length_mm +1 172 Chinstrap 15.9 11.37 1.40 0.1609 2.6 -6.34 38.2 43.9 Biscoe +#> flipper_length_mm +1 172 Chinstrap 15.9 11.37 1.40 0.1610 2.6 -6.34 38.2 43.9 Biscoe #> flipper_length_mm +1 172 Gentoo 51.7 8.70 5.95 <0.001 28.5 34.68 68.8 43.9 Biscoe #> flipper_length_mm +1 231 Adelie 15.3 9.25 1.66 0.0976 3.4 -2.81 33.5 43.9 Biscoe -#> flipper_length_mm +1 231 Chinstrap 15.9 11.37 1.40 0.1609 2.6 -6.34 38.2 43.9 Biscoe +#> flipper_length_mm +1 231 Chinstrap 15.9 11.37 1.40 0.1610 2.6 -6.34 38.2 43.9 Biscoe #> flipper_length_mm +1 231 Gentoo 51.7 8.70 5.95 <0.001 28.5 34.68 68.8 43.9 Biscoe #> #> Columns: rowid, term, contrast, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, flipper_length_mm, species, predicted_lo, predicted_hi, predicted, body_mass_g, bill_length_mm, island @@ -829,12 +829,12 @@

    p <- plot_predictions(mod, condition = c("Age", "PClass"), draw = FALSE)
     head(p)
     #>   rowid  estimate      p.value   s.value  conf.low conf.high  Survived   SexCode     Age PClass
    -#> 1     1 0.8679723 0.0013307149  9.553583 0.6754794 0.9540527 0.4140212 0.3809524 0.17000    1st
    +#> 1     1 0.8679723 0.0013307148  9.553583 0.6754794 0.9540527 0.4140212 0.3809524 0.17000    1st
     #> 2     2 0.8956789 0.0001333343 12.872665 0.7401973 0.9627887 0.4140212 0.3809524 0.17000    2nd
     #> 3     3 0.4044513 0.2667759176  1.906300 0.2554245 0.5734603 0.4140212 0.3809524 0.17000    3rd
    -#> 4     4 0.8631027 0.0011563594  9.756194 0.6749549 0.9503544 0.4140212 0.3809524 1.61551    1st
    -#> 5     5 0.8813224 0.0001728862 12.497890 0.7228529 0.9548415 0.4140212 0.3809524 1.61551    2nd
    -#> 6     6 0.3934924 0.1899483149  2.396321 0.2535791 0.5533716 0.4140212 0.3809524 1.61551    3rd
    +#> 4 4 0.8631027 0.0011563592 9.756195 0.6749549 0.9503543 0.4140212 0.3809524 1.61551 1st +#> 5 5 0.8813224 0.0001728858 12.497893 0.7228530 0.9548415 0.4140212 0.3809524 1.61551 2nd +#> 6 6 0.3934924 0.1899483119 2.396321 0.2535791 0.5533716 0.4140212 0.3809524 1.61551 3rd

    This allows us to feed the data easily to other functions, such as those in the useful ggdist and distributional packages:

    @@ -930,7 +930,7 @@

    #> [1] 26.66364 19.74286 15.10000 p$std.error -#> [1] 0.6951236 0.8713836 0.6161612 +#> [1] 0.6951236 0.8713835 0.6161612 p$conf.low #> [1] 25.30122 18.03498 13.89235

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    Arguments

    - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
    -model +model

    @@ -606,9 +606,9 @@

    comparisons

    -newdata +newdata

    @@ -687,9 +687,9 @@

    comparisons

    -variables +variables

    @@ -844,9 +844,9 @@

    comparisons

    -comparison +comparison

    @@ -885,9 +885,9 @@

    comparisons

    -type +type

    @@ -895,9 +895,9 @@

    comparisons

    -vcov +vcov

    @@ -959,9 +959,9 @@

    comparisons

    -by +by

    @@ -1001,9 +1001,9 @@

    comparisons

    -conf_level +conf_level

    @@ -1011,9 +1011,9 @@

    comparisons

    -transform +transform

    @@ -1021,9 +1021,9 @@

    comparisons

    -cross +cross
      @@ -1040,9 +1040,9 @@

      comparisons

    -wts +wts

    @@ -1062,9 +1062,9 @@

    comparisons

    -hypothesis +hypothesis

    @@ -1155,9 +1155,9 @@

    comparisons

    -equivalence +equivalence

    @@ -1165,9 +1165,9 @@

    comparisons

    -p_adjust +p_adjust

    @@ -1175,9 +1175,9 @@

    comparisons

    -df +df

    @@ -1185,9 +1185,9 @@

    comparisons

    -eps +eps

    @@ -1195,9 +1195,9 @@

    comparisons

    -numderiv +numderiv

    @@ -1227,9 +1227,9 @@

    comparisons

    - +

    @@ -1785,7 +1785,7 @@

    comparisons

    Some of the most common type values are:

    -response, link, E, Ep, average, class, conditional, count, cum.prob, cumprob, density, disp, expected, expvalue, fitted, invlink(link), latent, linear.predictor, linpred, location, lp, mean, p, pr, precision, prediction, prob, probability, probs, quantile, risk, scale, survival, unconditional, utility, variance, xb, zero, zlink, zprob +response, link, E, Ep, average, class, conditional, count, cum.prob, cumprob, density, disp, expected, expvalue, fitted, invlink(link), latent, linear.predictor, linpred, location, lp, mean, numeric, p, pr, precision, prediction, prob, probability, probs, quantile, risk, scale, survival, unconditional, utility, variance, xb, zero, zlink, zprob

    References

      @@ -2154,7 +2154,7 @@

      comparisons

      hp mean(+1) 0 0 -0.0422 0.0248 -1.70 0.08879 3.5 -0.0907 0.00639 hp mean(+1) 0 1 -0.0368 0.0124 -2.97 0.00297 8.4 -0.0612 -0.01254 hp mean(+1) 1 0 -0.0994 0.0534 -1.86 0.06289 4.0 -0.2042 0.00534 - hp mean(+1) 1 1 -0.1112 0.0463 -2.40 0.01645 5.9 -0.2020 -0.02033 + hp mean(+1) 1 1 -0.1112 0.0463 -2.40 0.01645 5.9 -0.2020 -0.02034 Columns: term, contrast, vs, am, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, predicted_lo, predicted_hi, predicted Type: response diff --git a/docs/articles/reference/datagrid.html b/docs/articles/reference/datagrid.html index b68eb82a9..886df1e85 100644 --- a/docs/articles/reference/datagrid.html +++ b/docs/articles/reference/datagrid.html @@ -546,9 +546,9 @@

      datagrid

    Arguments

    - + - + - + - + - + - + - + - + - + - + - +
    - +

    @@ -568,9 +568,9 @@

    datagrid

    -model +model

    @@ -578,9 +578,9 @@

    datagrid

    -newdata +newdata

    @@ -588,9 +588,9 @@

    datagrid

    -by +by

    @@ -598,9 +598,9 @@

    datagrid

    -FUN_character +FUN_character

    @@ -608,9 +608,9 @@

    datagrid

    -FUN_factor +FUN_factor

    @@ -618,9 +618,9 @@

    datagrid

    -FUN_logical +FUN_logical

    @@ -628,9 +628,9 @@

    datagrid

    -FUN_numeric +FUN_numeric

    @@ -638,9 +638,9 @@

    datagrid

    -FUN_integer +FUN_integer

    @@ -648,9 +648,9 @@

    datagrid

    -FUN_other +FUN_other

    @@ -658,9 +658,9 @@

    datagrid

    -grid_type +grid_type

    diff --git a/docs/articles/reference/hypotheses.html b/docs/articles/reference/hypotheses.html index 59e622adb..2c5695b39 100644 --- a/docs/articles/reference/hypotheses.html +++ b/docs/articles/reference/hypotheses.html @@ -556,9 +556,9 @@

    hypotheses

    Arguments

    - + - + - + - + - + - + - + - + - + - + - +
    -model +model

    @@ -566,9 +566,9 @@

    hypotheses

    -hypothesis +hypothesis

    @@ -659,9 +659,9 @@

    hypotheses

    -vcov +vcov

    @@ -723,9 +723,9 @@

    hypotheses

    -conf_level +conf_level

    @@ -733,9 +733,9 @@

    hypotheses

    -df +df

    @@ -743,9 +743,9 @@

    hypotheses

    -equivalence +equivalence

    @@ -753,9 +753,9 @@

    hypotheses

    -joint +joint

    @@ -790,9 +790,9 @@

    hypotheses

    -joint_test +joint_test

    @@ -800,9 +800,9 @@

    hypotheses

    -FUN +FUN

    @@ -822,9 +822,9 @@

    hypotheses

    -numderiv +numderiv

    @@ -854,9 +854,9 @@

    hypotheses

    - +

    diff --git a/docs/articles/reference/inferences.html b/docs/articles/reference/inferences.html index 1235d272d..3c9a45d27 100644 --- a/docs/articles/reference/inferences.html +++ b/docs/articles/reference/inferences.html @@ -533,9 +533,9 @@

    inferences

    Arguments

    - + - + - + - + - + - + - + - +
    -x +x

    @@ -543,9 +543,9 @@

    inferences

    -method +method

    @@ -595,9 +595,9 @@

    inferences

    -R +R

    @@ -605,9 +605,9 @@

    inferences

    -conf_type +conf_type

    @@ -637,9 +637,9 @@

    inferences

    -conformal_test +conformal_test

    @@ -647,9 +647,9 @@

    inferences

    -conformal_calibration +conformal_calibration

    @@ -657,9 +657,9 @@

    inferences

    -conformal_score +conformal_score

    @@ -679,9 +679,9 @@

    inferences

    - +

    bootstrap
                               estimate std.error conf.low conf.high
    -1 Treatment - No Treatment 3.517374  0.493746  2.54965  4.485099
    +1 Treatment - No Treatment 3.517374 0.4765446 2.583364 4.451385

    The results are close to those that we obtained with comparisons(), but the confidence interval differs slightly because of the difference between bootstrapping and the delta method.

    diff --git a/docs/articles/hypothesis.html b/docs/articles/hypothesis.html index 97bbd81e9..ef9cffeae 100644 --- a/docs/articles/hypothesis.html +++ b/docs/articles/hypothesis.html @@ -744,8 +744,8 @@