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R version 2.12.0 Under development (unstable) (2010-08-30 r52829)
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R version 2.12.0 Under development (unstable) (2010-08-30 r52829)
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Copyright (C) 2010 The R Foundation for Statistical Computing
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Copyright (C) 2010 The R Foundation for Statistical Computing
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ISBN 3-900051-07-0
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ISBN 3-900051-07-0
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Platform: i386-apple-darwin9.8.0/i386 (32-bit)
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Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)
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 6 
 
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R is free software and comes with ABSOLUTELY NO WARRANTY.
 7 
R is free software and comes with ABSOLUTELY NO WARRANTY.
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You are welcome to redistribute it under certain conditions.
 8 
You are welcome to redistribute it under certain conditions.
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Type 'license()' or 'licence()' for distribution details.
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Type 'license()' or 'licence()' for distribution details.
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Line 9864... Line 9864...
9864 
> ### ** Examples
 9864 
> ### ** Examples
9865 
>
9865 
>
9866 
> cars.lo <- loess(dist ~ speed, cars)
 9866 
> cars.lo <- loess(dist ~ speed, cars)
9867 
> predict(cars.lo, data.frame(speed = seq(5, 30, 1)), se = TRUE)
 9867 
> predict(cars.lo, data.frame(speed = seq(5, 30, 1)), se = TRUE)
9868 
$fit
 9868 
$fit
- 
 
 9869 
        1         2         3         4         5         6         7         8
9869 
 [1]  7.797353 10.002308 12.499786 15.281082 18.446568 21.865315 25.517015
 9870 
 7.797353 10.002308 12.499786 15.281082 18.446568 21.865315 25.517015 29.350386
- 
 
 9871 
        9        10        11        12        13        14        15        16
9870 
 [8] 29.350386 33.230660 37.167935 41.205226 45.055736 48.355889 49.824812
 9872 
33.230660 37.167935 41.205226 45.055736 48.355889 49.824812 51.986702 56.461318
9871 
[15] 51.986702 56.461318 61.959729 68.569313 76.316068 85.212121 95.324047
 9873 
       17        18        19        20        21        22        23        24
9872 
[22]        NA        NA        NA        NA        NA
 9874 
61.959729 68.569313 76.316068 85.212121 95.324047        NA        NA        NA
- 
 
 9875 
       25        26
- 
 
 9876 
       NA        NA
9873 
 
 9877 
 
9874 
$se.fit
 9878 
$se.fit
- 
 
 9879 
       1        2        3        4        5        6        7        8
9875 
 [1] 7.568120 5.945831 4.990827 4.545284 4.308639 4.115049 3.789542 3.716231
 9880 
7.568120 5.945831 4.990827 4.545284 4.308639 4.115049 3.789542 3.716231
- 
 
 9881 
       9       10       11       12       13       14       15       16
9876 
 [9] 3.776947 4.091747 4.709568 4.245427 4.035929 3.753410 4.004705 4.043190
 9882 
3.776947 4.091747 4.709568 4.245427 4.035929 3.753410 4.004705 4.043190
- 
 
 9883 
      17       18       19       20       21       22       23       24
9877 
[17] 4.026105 4.074664 4.570818 5.954217 8.302014       NA       NA       NA
 9884 
4.026105 4.074664 4.570818 5.954217 8.302014       NA       NA       NA
- 
 
 9885 
      25       26
9878 
[25]       NA       NA
 9886 
      NA       NA
9879 
 
 9887 
 
9880 
$residual.scale
 9888 
$residual.scale
9881 
[1] 15.29496
 9889 
[1] 15.29496
9882 
 
 9890 
 
9883 
$df
 9891 
$df
Line 9886... Line 9894...
9886 
> # to allow extrapolation
 9894 
> # to allow extrapolation
9887 
> cars.lo2 <- loess(dist ~ speed, cars,
 9895 
> cars.lo2 <- loess(dist ~ speed, cars,
9888 
+   control = loess.control(surface = "direct"))
 9896 
+   control = loess.control(surface = "direct"))
9889 
> predict(cars.lo2, data.frame(speed = seq(5, 30, 1)), se = TRUE)
 9897 
> predict(cars.lo2, data.frame(speed = seq(5, 30, 1)), se = TRUE)
9890 
$fit
 9898 
$fit
- 
 
 9899 
         1          2          3          4          5          6          7
9891 
 [1]   7.741006   9.926596  12.442424  15.281082  18.425712  21.865315
 9900 
  7.741006   9.926596  12.442424  15.281082  18.425712  21.865315  25.713413
- 
 
 9901 
         8          9         10         11         12         13         14
9892 
 [7]  25.713413  29.350386  33.230660  37.167935  41.205226  45.781544
 9902 
 29.350386  33.230660  37.167935  41.205226  45.781544  48.355889  50.067148
9893 
[13]  48.355889  50.067148  51.986702  56.445263  62.025404  68.569313
 9903 
        15         16         17         18         19         20         21
9894 
[19]  76.193111  85.053364  95.300523 106.974661 120.092581 134.665851
 9904 
 51.986702  56.445263  62.025404  68.569313  76.193111  85.053364  95.300523
- 
 
 9905 
        22         23         24         25         26
9895 
[25] 150.698545 168.190283
 9906 
106.974661 120.092581 134.665851 150.698545 168.190283
9896 
 
 9907 
 
9897 
$se.fit
 9908 
$se.fit
- 
 
 9909 
        1         2         3         4         5         6         7         8
9898 
 [1]  7.565991  5.959097  5.012013  4.550013  4.321596  4.119331  3.939804
 9910 
 7.565991  5.959097  5.012013  4.550013  4.321596  4.119331  3.939804  3.720098
- 
 
 9911 
        9        10        11        12        13        14        15        16
9899 
 [8]  3.720098  3.780877  4.096004  4.714469  4.398936  4.040129  4.184257
 9912 
 3.780877  4.096004  4.714469  4.398936  4.040129  4.184257  4.008873  4.061865
- 
 
 9913 
       17        18        19        20        21        22        23        24
9900 
[15]  4.008873  4.061865  4.033998  4.078904  4.584606  5.952480  8.306901
 9914 
 4.033998  4.078904  4.584606  5.952480  8.306901 11.601911 15.792480 20.864660
- 
 
 9915 
       25        26
9901 
[22] 11.601911 15.792480 20.864660 26.823827 33.683999
 9916 
26.823827 33.683999
9902 
 
 9917 
 
9903 
$residual.scale
 9918 
$residual.scale
9904 
[1] 15.31087
 9919 
[1] 15.31087
9905 
 
 9920 
 
9906 
$df
 9921 
$df
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14002 
> ### ** Examples
 14017 
> ### ** Examples
14003 
>
14018 
>
14004 
> cars.lo <- loess(dist ~ speed, cars)
 14019 
> cars.lo <- loess(dist ~ speed, cars)
14005 
> predict(cars.lo, data.frame(speed=seq(5, 30, 1)), se=TRUE)
 14020 
> predict(cars.lo, data.frame(speed=seq(5, 30, 1)), se=TRUE)
14006 
$fit
 14021 
$fit
- 
 
 14022 
        1         2         3         4         5         6         7         8
14007 
 [1]  7.797353 10.002308 12.499786 15.281082 18.446568 21.865315 25.517015
 14023 
 7.797353 10.002308 12.499786 15.281082 18.446568 21.865315 25.517015 29.350386
- 
 
 14024 
        9        10        11        12        13        14        15        16
14008 
 [8] 29.350386 33.230660 37.167935 41.205226 45.055736 48.355889 49.824812
 14025 
33.230660 37.167935 41.205226 45.055736 48.355889 49.824812 51.986702 56.461318
14009 
[15] 51.986702 56.461318 61.959729 68.569313 76.316068 85.212121 95.324047
 14026 
       17        18        19        20        21        22        23        24
14010 
[22]        NA        NA        NA        NA        NA
 14027 
61.959729 68.569313 76.316068 85.212121 95.324047        NA        NA        NA
- 
 
 14028 
       25        26
- 
 
 14029 
       NA        NA
14011 
 
 14030 
 
14012 
$se.fit
 14031 
$se.fit
- 
 
 14032 
       1        2        3        4        5        6        7        8
14013 
 [1] 7.568120 5.945831 4.990827 4.545284 4.308639 4.115049 3.789542 3.716231
 14033 
7.568120 5.945831 4.990827 4.545284 4.308639 4.115049 3.789542 3.716231
- 
 
 14034 
       9       10       11       12       13       14       15       16
14014 
 [9] 3.776947 4.091747 4.709568 4.245427 4.035929 3.753410 4.004705 4.043190
 14035 
3.776947 4.091747 4.709568 4.245427 4.035929 3.753410 4.004705 4.043190
- 
 
 14036 
      17       18       19       20       21       22       23       24
14015 
[17] 4.026105 4.074664 4.570818 5.954217 8.302014       NA       NA       NA
 14037 
4.026105 4.074664 4.570818 5.954217 8.302014       NA       NA       NA
- 
 
 14038 
      25       26
14016 
[25]       NA       NA
 14039 
      NA       NA
14017 
 
 14040 
 
14018 
$residual.scale
 14041 
$residual.scale
14019 
[1] 15.29496
 14042 
[1] 15.29496
14020 
 
 14043 
 
14021 
$df
 14044 
$df
Line 14024... Line 14047...
14024 
> # to get extrapolation
 14047 
> # to get extrapolation
14025 
> cars.lo2 <- loess(dist ~ speed, cars,
 14048 
> cars.lo2 <- loess(dist ~ speed, cars,
14026 
+   control=loess.control(surface="direct"))
 14049 
+   control=loess.control(surface="direct"))
14027 
> predict(cars.lo2, data.frame(speed=seq(5, 30, 1)), se=TRUE)
 14050 
> predict(cars.lo2, data.frame(speed=seq(5, 30, 1)), se=TRUE)
14028 
$fit
 14051 
$fit
- 
 
 14052 
         1          2          3          4          5          6          7
14029 
 [1]   7.741006   9.926596  12.442424  15.281082  18.425712  21.865315
 14053 
  7.741006   9.926596  12.442424  15.281082  18.425712  21.865315  25.713413
- 
 
 14054 
         8          9         10         11         12         13         14
14030 
 [7]  25.713413  29.350386  33.230660  37.167935  41.205226  45.781544
 14055 
 29.350386  33.230660  37.167935  41.205226  45.781544  48.355889  50.067148
14031 
[13]  48.355889  50.067148  51.986702  56.445263  62.025404  68.569313
 14056 
        15         16         17         18         19         20         21
14032 
[19]  76.193111  85.053364  95.300523 106.974661 120.092581 134.665851
 14057 
 51.986702  56.445263  62.025404  68.569313  76.193111  85.053364  95.300523
- 
 
 14058 
        22         23         24         25         26
14033 
[25] 150.698545 168.190283
 14059 
106.974661 120.092581 134.665851 150.698545 168.190283
14034 
 
 14060 
 
14035 
$se.fit
 14061 
$se.fit
- 
 
 14062 
        1         2         3         4         5         6         7         8
14036 
 [1]  7.565991  5.959097  5.012013  4.550013  4.321596  4.119331  3.939804
 14063 
 7.565991  5.959097  5.012013  4.550013  4.321596  4.119331  3.939804  3.720098
- 
 
 14064 
        9        10        11        12        13        14        15        16
14037 
 [8]  3.720098  3.780877  4.096004  4.714469  4.398936  4.040129  4.184257
 14065 
 3.780877  4.096004  4.714469  4.398936  4.040129  4.184257  4.008873  4.061865
- 
 
 14066 
       17        18        19        20        21        22        23        24
14038 
[15]  4.008873  4.061865  4.033998  4.078904  4.584606  5.952480  8.306901
 14067 
 4.033998  4.078904  4.584606  5.952480  8.306901 11.601911 15.792480 20.864660
- 
 
 14068 
       25        26
14039 
[22] 11.601911 15.792480 20.864660 26.823827 33.683999
 14069 
26.823827 33.683999
14040 
 
 14070 
 
14041 
$residual.scale
 14071 
$residual.scale
14042 
[1] 15.31087
 14072 
[1] 15.31087
14043 
 
 14073 
 
14044 
$df
 14074 
$df