错误于UseMethod("npplot", bws) : 如何解决？-经管之家官网！

> #China production function noparametric estimate
> rt<-read.table("china.txt",header=TRUE); rt
year realgdpcapital labor
119794016.290 4493.1 41024
219804194.429 5152.7 42361
319814460.196 5835.5 43725
419824882.096 6700.4 45295
519835322.089 7794.1 46436
619846135.583 9123.9 48197
719856923.49410893.4 49873
819867522.19012877.9 51282
919878195.86114889.5 52783
10 19888645.28116957.5 54334
11 19898247.40418431.3 55329
12 19908940.75819783.6 56740
13 1991 10088.20421428.6 58360
14 1992 11577.15223813.8 59432
15 1993 13525.48527064.5 60220
16 1994 14813.39230770.9 61470
17 1995 15927.66434712.5 67947
18 1996 17251.36138966.8 68850
19 1997 18478.95543547.6 69600
20 1998 19738.04748916.7 69957
21 1999 21046.76154518.2 71394
22 2000 22753.22660400.8 72085
23 2001 24936.47667044.9 73025
24 2002 27762.46874964.9 73740
25 2003 31091.58984902.1 74432
26 2004 35164.63495584.3 75200
27 2005 40237.263 108940.2 75825
> y=log((rt$realgdp/rt$labor)*10000)
> x=log((rt$capital/rt$labor)*10000)
>
>
> #package np, nonparametric and semiparametric kernel methods
> library(np)
> h1=npregbw(formula=y~x); h1
Multistart 1 of 1 |
Multistart 1 of 1 |
Multistart 1 of 1 |
Multistart 1 of 1 /
Multistart 1 of 1 |
Multistart 1 of 1 |

Regression Data (27 observations, 1 variable(s)):
x
Bandwidth(s): 0.06893708
Regression Type: Local-Constant
Bandwidth Selection Method: Least Squares Cross-Validation
Formula: y ~ x
Bandwidth Type: Fixed
Objective Function Value: 0.001361645 (achieved on multistart 1)
Continuous Kernel Type: Second-Order Gaussian
No. Continuous Explanatory Vars.: 1
> fit1<-npreg(bws=h1,txdat=x,tydat=y)
> npplot(x,y)
错误于UseMethod("npplot", bws) :
"npplot"没有适用于"c('double', 'numeric')"目标对象的方法
> points(x,y)
错误于plot.xy(xy.coords(x, y), type = type, ...) : 还没有调用plot.new
>
> #package KernSmooth, local linear regression
> library(KernSmooth)
> h2=dpill(x,y); h2
[1] 0.8108148
> fit2<-locpoly(x,y,bandwidth=h2)
> plot(x,y)
> lines(fit2)
> #first order derivatives
> order1<-locpoly(x,y,bandwidth=h2,drv=1)
> plot(order1$x,order1$y,type='l')
>
>
> #semiparametric regression, package np
> y1=log(rt$realgdp*100000000)
> x1=log(rt$labor*10000)
> x2=log(rt$capital*100000000)
>
> h3<-npplregbw(y1~x1|x2); h3
Multistart 1 of 1 |
Multistart 1 of 1 |
Multistart 1 of 1 |
Multistart 1 of 1 /
Multistart 1 of 1 |
Multistart 1 of 1 |

Multistart 1 of 1 |
Multistart 1 of 1 |
Multistart 1 of 1 |
Multistart 1 of 1 /
Multistart 1 of 1 |
Multistart 1 of 1 |

Partially Linear Model
Regression data (27 observations,
1 linear parametric variable(s) 1 nonparametric variable(s):
y(z)
Bandwidth(s): 0.0753333
x(z)
Bandwidth(s): 0.08117952
Regression Type: Local-Constant
Bandwidth Selection Method: Least Squares Cross-Validation
Formula: y1 ~ x1 | x2
Bandwidth Type: Fixed
Continuous Kernel Type: Second-Order Gaussian
No. Continuous Explanatory Vars.: 1
> #h3<-npplregbw(xdat=x1,ydat=y1,zdat=x2); h3
> fit3<-npplreg(bws=h3)
> summary(fit3)
Partially Linear Model
Regression data: 27 training points, in 2 variable(s)
With 1 linear parametric regressor(s), 1 nonparametric regressor(s)
y(z)
Bandwidth(s): 0.0753333
x(z)
Bandwidth(s): 0.08117952
x1
Coefficient(s): 0.5709484
Kernel Regression Estimator: Local-Constant
Bandwidth Type: Fixed
Residual standard error: 0.0001465206
R-squared: 0.999693
Continuous Kernel Type: Second-Order Gaussian
No. Continuous Explanatory Vars.: 1
> coef(fit3)
x1
0.5709484
> npplot(bws=h3)
>