Evaluating Polynomials

Description

Function to evaluate polynomials in a numerical robust way using the Horner scheme

Usage

evalPol(x, beta)

Arguments

Value

The result of evaluating the polynomial at the values in x, returned in the same dimension as x has.

Author(s)

Berwin A Turlach

Examples

beta <- c(1,2,1)

x <- 0:10
evalPol(x, beta)
str(evalPol(x, beta))

x <- cbind(0:10, 10:0)
evalPol(x, beta)
str(evalPol(x, beta))


x <- data.frame(x=0:10, y=10:0)
evalPol(x, beta)
str(evalPol(x, beta))

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(MonoPoly)
Loading required package: quadprog
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MonoPoly/evalPol.Rd_%03d_medium.png", width=480, height=480)
> ### Name: evalPol
> ### Title: Evaluating Polynomials
> ### Aliases: evalPol
> ### Keywords: utilities regression
> 
> ### ** Examples
> 
> beta <- c(1,2,1)
> 
> x <- 0:10
> evalPol(x, beta)
 [1]   1   4   9  16  25  36  49  64  81 100 121
> str(evalPol(x, beta))
 num [1:11] 1 4 9 16 25 36 49 64 81 100 ...
> 
> x <- cbind(0:10, 10:0)
> evalPol(x, beta)
      [,1] [,2]
 [1,]    1  121
 [2,]    4  100
 [3,]    9   81
 [4,]   16   64
 [5,]   25   49
 [6,]   36   36
 [7,]   49   25
 [8,]   64   16
 [9,]   81    9
[10,]  100    4
[11,]  121    1
> str(evalPol(x, beta))
 num [1:11, 1:2] 1 4 9 16 25 36 49 64 81 100 ...
> 
> 
> x <- data.frame(x=0:10, y=10:0)
> evalPol(x, beta)
     x   y
1    1 121
2    4 100
3    9  81
4   16  64
5   25  49
6   36  36
7   49  25
8   64  16
9   81   9
10 100   4
11 121   1
> str(evalPol(x, beta))
'data.frame':	11 obs. of  2 variables:
 $ x: num  1 4 9 16 25 36 49 64 81 100 ...
 $ y: num  121 100 81 64 49 36 25 16 9 4 ...
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>