leverage: Leverage
In sur: Companion to "Statistics Using R: An Integrative Approach"

Description Usage Arguments Value See Also Examples

Returns the leverage values for a linear regression model.

1	leverage(x)

`x`	linear regression model given as an `lm` object.

A numeric vector of leverage values.

lm, rstudent(), cooks.distance()

1 2	mod = lm(Framingham$SYSBP1 ~ Framingham$TOTCHOL1 + Framingham$AGE1) leverage(mod)

  [1] 0.003432357 0.006321887 0.006528248 0.008003329 0.008769666 0.008249407
  [7] 0.008717182 0.012557810 0.003888039 0.002744246 0.005101102 0.005039041
 [13] 0.005884883 0.004386046 0.009688211 0.004545636 0.003144131 0.005011837
 [19] 0.017563915 0.014852897 0.009688886 0.005939201 0.004176067 0.005580435
 [25] 0.002781072 0.017807004 0.004365759 0.019184733 0.010033597 0.002931297
 [31] 0.004709756 0.010675654 0.018225196 0.015636894 0.007790978 0.006121543
 [37] 0.008659515 0.005478192 0.004018614 0.014046357 0.008107794 0.004635770
 [43] 0.008910713 0.010411487 0.008557857 0.003876799 0.006199027 0.002778619
 [49] 0.010337499 0.003601001 0.006549643 0.007462204 0.010655241 0.010764032
 [55] 0.012755774 0.003111134 0.017600839 0.004734052 0.006281413 0.008169059
 [61] 0.010215318 0.009977459 0.003290239 0.002709296 0.016191506 0.002736117
 [67] 0.004632502 0.003494245 0.004769840 0.005316681 0.007226841 0.003967315
 [73] 0.013465019 0.007900068 0.006230788 0.003532849 0.004589015 0.003312610
 [79] 0.005949633 0.006664920 0.004063049 0.006945834 0.009403944 0.007849767
 [85] 0.010663425 0.002614459 0.006522871 0.016422407 0.003260639 0.006387841
 [91] 0.002647450 0.003000345 0.007865752 0.009163748 0.008125152 0.006959153
 [97] 0.003439586 0.004150792 0.011094976 0.007864571 0.009622472 0.011806402
[103] 0.003099196 0.004923390 0.004316885 0.014895121 0.006294821 0.007294613
[109] 0.006811895 0.008977003 0.007816584 0.005859293 0.009935073 0.012274065
[115] 0.002717469 0.009809627 0.010745585 0.013849012 0.012432750 0.005513986
[121] 0.007966544 0.005552116 0.007058374 0.004031861 0.005496429 0.014657272
[127] 0.003838872 0.003473422 0.006253909 0.005212060 0.006865678 0.009108137
[133] 0.003448034 0.007313832 0.005292183 0.002921714 0.003176994 0.015502510
[139] 0.004367054 0.011046501 0.010125896 0.010835711 0.015679580 0.005014744
[145] 0.005340065 0.004632502 0.003864231 0.003432357 0.004439535 0.007966544
[151] 0.004597256 0.013050906 0.019134542 0.005529190 0.009192521 0.014925659
[157] 0.005189518 0.005778775 0.006790718 0.006475673 0.011327859 0.020972485
[163] 0.007093301 0.003485879 0.019764807 0.005163365 0.003598420 0.004635681
[169] 0.012219124 0.004976899 0.006549643 0.004483928 0.002894735 0.003176994
[175] 0.013155970 0.006059870 0.003615845 0.003103376 0.003892141 0.006169741
[181] 0.003439586 0.007093488 0.003572828 0.005437186 0.002936193 0.003417291
[187] 0.017854463 0.003868235 0.004021525 0.008881689 0.013707297 0.006945834
[193] 0.020995364 0.003064104 0.005682634 0.010174204 0.016655030 0.002691541
[199] 0.005247008 0.010994165 0.006592164 0.008893663 0.002933888 0.015077367
[205] 0.088275744 0.014492676 0.005019355 0.004771212 0.004222598 0.007041470
[211] 0.010126723 0.010996151 0.005641816 0.009021958 0.003273908 0.003662386
[217] 0.010865163 0.014767195 0.012908798 0.013868182 0.006336232 0.014432236
[223] 0.008346815 0.004256072 0.009968889 0.003890801 0.004016515 0.005892889
[229] 0.002718743 0.009774004 0.014003535 0.004986295 0.008819386 0.017478103
[235] 0.002616710 0.004442248 0.011053806 0.008718889 0.002623452 0.010213511
[241] 0.011498142 0.006494525 0.004741240 0.003947008 0.015874087 0.004958794
[247] 0.007596079 0.013388923 0.002717132 0.006283054 0.002552853 0.003351126
[253] 0.012376945 0.003331762 0.004334652 0.008504673 0.005173865 0.006998424
[259] 0.006720842 0.005595784 0.005540682 0.002763977 0.014626002 0.006048664
[265] 0.012355321 0.006660101 0.004506106 0.015605398 0.005184057 0.002863801
[271] 0.002670240 0.002893309 0.014631258 0.007751310 0.006930068 0.013041326
[277] 0.013890658 0.002997204 0.012258353 0.007287745 0.007777610 0.015647581
[283] 0.010149351 0.004191115 0.004515474 0.004601668 0.007404194 0.010004727
[289] 0.003955396 0.019734778 0.004670082 0.004536784 0.008881689 0.009546622
[295] 0.007379373 0.007865055 0.003049903 0.003467705 0.013076423 0.002702708
[301] 0.010061500 0.002997204 0.007171754 0.005956544 0.006301000 0.009861488
[307] 0.006615411 0.003699228 0.007462204 0.007404194 0.015957159 0.008986570
[313] 0.004892163 0.002806959 0.003598420 0.006964201 0.009860116 0.005064697
[319] 0.003474835 0.022448514 0.005027958 0.003273908 0.004615586 0.005271865
[325] 0.003768596 0.007937242 0.004418340 0.004222598 0.005999682 0.006615411
[331] 0.002917317 0.006184196 0.008636806 0.013295969 0.003768596 0.003686207
[337] 0.005894265 0.009991764 0.003041137 0.005451460 0.006398301 0.005253913
[343] 0.004535347 0.017976609 0.004418910 0.006567953 0.008630744 0.013693817
[349] 0.015717463 0.006369564 0.002603455 0.010138242 0.008999273 0.009736764
[355] 0.019715883 0.003593622 0.004441554 0.006941248 0.002623452 0.003898286
[361] 0.004909130 0.004338867 0.006775557 0.005495150 0.002557650 0.004881608
[367] 0.006618039 0.006246837 0.006917939 0.009283876 0.012992442 0.005459312
[373] 0.011000042 0.004293369 0.004218411 0.006600910 0.005007288 0.005640103
[379] 0.009436310 0.007776163 0.004776676 0.007937242 0.006985640 0.009955933
[385] 0.004176067 0.005693367 0.003890801 0.005189724 0.010715673 0.006122621
[391] 0.002816900 0.007294613