DFFITs plot should return the threshold value

[aravindhebbali]

ols_dffits_plot() should return the threshold value used to classify observations as outliers.

> library(caret)
> data("Sacramento")
> lm_fit2 <- lm(price  ~ beds + baths + log(sqft), data = Sacramento)
> k <- ols_dffits_plot(lm_fit2)

> k$Observation
 [1]  19  48  69  74 109 121 153 154 155 156 157 158 173 278 292 294 313 315
[19] 321 322 327 329 331 332 333 334 366 382 443 457 511 543 544 547 548 549
[37] 550 551 552 553 555 599 612 705 761 781 784 801 803 805 806 807 808 809
[55] 810 811 812 813 814

> k$DFFITs
 [1]  0.1404691 -0.1609518 -0.1907771 -0.1536721 -0.1682443 -0.1427501
 [7]  0.1534405  0.1756946  0.1354857  0.1338164  0.2673870  0.5407350
[13] -0.1773137 -0.1657323  0.4548061  0.1467572  0.2090031  0.1698078
[19]  0.1797304  0.1376387  0.1711760  0.1863567  0.1720062  0.5773783
[25]  0.4298794  1.0215338 -0.2745943 -0.1857416  0.1330492 -0.1402640
[31]  0.1399046  0.1350228  0.1904313  0.1944542  0.1731152  0.1984881
[37]  0.2556528  0.2517874  0.2380658  0.3101655 -0.2244189 -0.2461501
[43] -0.1551047 -0.3362356 -0.1379339  0.1765531  0.1756202  0.2879229
[49]  0.3315288  0.1425844  0.2055529  0.1610110  0.1603154  0.1559717
[55]  0.1618058  0.1786605  0.2265931  0.4370566  0.4710879

[aravindhebbali]

ols_dffits_plot() returns the threshold value used to classify observations as outliers.

> library(olsrr)
> library(caret)
> data("Sacramento")
> lm_fit2 <- lm(price  ~ beds + baths + log(sqft), data = Sacramento)
> k <- ols_dffits_plot(lm_fit2)

> k$outliers
# A tibble: 59 × 2
   Observation     DFFITs
         <int>      <dbl>
1           19  0.1404691
2           48 -0.1609518
3           69 -0.1907771
4           74 -0.1536721
5          109 -0.1682443
6          121 -0.1427501
7          153  0.1534405
8          154  0.1756946
9          155  0.1354857
10         156  0.1338164
# ... with 49 more rows
Warning message:
In dontCheck(fnname) : reached elapsed time limit

> k$threshold
[1] 0.13