In order to evaluate a trend in data the user might take advantage of the smooth.spline function.  How can they get the curve fit equation for the output?

 

Find the Curve Fit Equation for the smooth.spline function

Product: TIBCO Spotfire S+ Version: All supported versions OS: All supported operating systems --------------------

Unfortunately there is not a way to get the smoothing equation out of the smooth.spline() function.  It is possible to extract a list containing the details of the fit.  This list contains the knot locations, coefficients, etc.  Please see the following example:

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> attach(air)
test <- smooth.spline(ozone, temperature)

#Extract the 'fit':
> test$fit
$knot:
[1] 0.0 0.0 0.0 0.0 0.1 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.5 0.5
[22] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.7
[43] 0.7 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.8 0.8 0.9 0.9 0.9 1.0 1.0 1.0 1.0

$nk:
[1]  55

$min:
[1] 1

$range:
[1] 5

$coef:
[1]  59 156  57  48  80  63  83  60  83  58  85  65  61  82  74  74  67  81  82  64  92
[22]  50  85  76  70  97  53  88  76  86  82  85  91  73  84  87  75  93  87 100  89  90
[43]  84  89  96  86  96  94  86  98  73  99  55 127  81

attr(, "class"):
[1] "smooth.spline.fit"
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For additional information on the attributes of the smooth.spline object please type at the command prompt:

> attributes(test)
$names:
[1] "x"        "y"        "w"        "yin"      "lev"      "cv.crit"  "pen.crit"
[8] "df"       "spar"     "fit"      "call"    

$class:
[1] "smooth.spline"


You can extract any of these attributes by using the '$':

> test$spar
[1] 7e-009