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based on internal repository 0a462b6 2017-11-22 14:41:39 +0100

pmsco/loess/predict.m

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+NAME
+	predict, pointwise, pred_free_mem, pw_free_mem
+
+SYNOPSIS
+	#include "loess.h"
+
+	double  *eval, coverage;
+	long    m, se;
+	struct  loess_struct    *lo;
+	struct  predict_struct  *pre;
+	struct  ci_struct 	*ci;
+
+	void	predict(eval, m, lo, pre, se)
+	
+	void	pointwise(pre, m, coverage, ci)
+
+	void	pred_free_mem(pre)
+
+	void	pw_free_mem(ci)
+
+PARAMETERS
+
+	eval	a vector of length m * p specifying the values of the
+		predictors at which the evaluation is to be carried out.
+		The j-th coordinate of the i-th point is in eval[i+m*j],
+		where 0<=j<p, 0<=i<m.
+
+	m	number of evaluations.
+
+	lo	k-d tree and coefficients.
+
+	pre	predicted values; output by predict(), input to pointwise().
+
+	se	logical flag for computing standard errors at eval.
+
+	ci	pointwise confidence limits.
+
+	coverage  (input) confidence level of the limits as a fraction.
+
+DESCRIPTION
+
+	predict() takes all the loess structures from earlier calls to
+	loess_setup() and loess(), does an evaluation based on 
+	eval and m, and stores the results in the pre structure.
+	if se is TRUE, then pre.se_fit are computed along with the 
+	surface values, pre.fit.  These returned vectors 
+	are vectors of the same structural arrangement as that of eval.
+
+	pointwise() computes the pointwise confidence limits from the
+	result of predict().
+	
+	pred_free_mem() and pw_free_mem() frees up the allocated memory 
+	used by the pre and ci structures respectively.
+
+	loess_struct, pred_struct, and ci_struct are defined in loess.h 
+	and documented in struct.m. 
+
+NOTES
+
+	The computations of predict() that produce the component se_fit
+	are much more costly than those that producing the fit,
+	so the number of points at which standard errors are
+	computed should be modest compared to those at which we do
+	evaluations.  Often this means calling predict() twice,
+	once at a large number of points, with se = FALSE,
+	to get a thorough description of the surface; and once 
+	at a small number of points, with se = TRUE,
+	to get standard-error information.
+
+	Suppose the computation method for loess surfaces is
+	interpolate, the default for the argument surface. Then the
+	evaluation values of a numeric predictor must lie within
+	the range of the values of the predictor used in the fit.
+
+SEE ALSO
+
+	loess_setup, loess, loess_summary, loess_free_mem