data-cps71: Canadian High School Graduate Earnings

Description Usage Format Source References Examples

Description

Canadian cross-section wage data consisting of a random sample taken from the 1971 Canadian Census Public Use Tapes for male individuals having common education (grade 13). There are 205 observations in total.

Usage

1
data("cps71")

Format

A data frame with 2 columns, and 205 rows.

logwage

the first column, of type numeric

age

the second column, of type integer

Source

Aman Ullah

References

Pagan, A. and A. Ullah (1999), Nonparametric Econometrics, Cambridge University Press.

Examples

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## Example - we compare the nonparametric local linear kernel regression
## method with the regression spline for the cps71 data. Note that there
## are no categorical predictors in this dataset so we are merely
## comparing and contrasting the two nonparametric estimates.

data(cps71)
attach(cps71)
require(np)

model.crs <- crs(logwage~age,complexity="degree-knots")
model.np <- npreg(logwage~age,regtype="ll")

plot(age,logwage,cex=0.25,col="grey",
     sub=paste("crs-CV = ", formatC(model.crs$cv.score,format="f",digits=3),
       ", npreg-CV = ", formatC(model.np$bws$fval,format="f",digits=3),sep=""))
lines(age,fitted(model.crs),lty=1,col=1)
lines(age,fitted(model.np),lty=2,col=2)

crs.txt <- paste("crs (R-squared = ",formatC(model.crs$r.squared,format="f",digits=3),")",sep="")
np.txt <- paste("ll-npreg (R-squared = ",formatC(model.np$R2,format="f",digits=3),")",sep="")

legend(22.5,15,c(crs.txt,np.txt),lty=c(1,2),col=c(1,2),bty="n")

summary(model.crs)
summary(model.np)
detach("package:np")

Example output

Registered S3 method overwritten by 'crs':
  method         from
  predict.gsl.bs np  
Categorical Regression Splines (version 0.15-31)
[vignette("crs_faq") provides answers to frequently asked questions]
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl.init' failed, running with 'rgl.useNULL = TRUE'. 
Loading required package: np
Nonparametric Kernel Methods for Mixed Datatypes (version 0.60-10)
[vignette("np_faq",package="np") provides answers to frequently asked questions]
[vignette("np",package="np") an overview]
[vignette("entropy_np",package="np") an overview of entropy-based methods]
Working...          
1/101, d[1]=0, s[1]=1, cv=0.406892                                   
2/101, 0.01/0.00m, d[1]=1, s[1]=1, cv=0.39111                                              
3/101, 0.01/0.00m, d[1]=2, s[1]=1, cv=0.322175                                               
4/101, 0.01/0.00m, d[1]=3, s[1]=1, cv=0.319665                                               
5/101, 0.01/0.00m, d[1]=4, s[1]=1, cv=0.294984                                               
6/101, 0.01/0.00m, d[1]=5, s[1]=1, cv=0.295883                                               
7/101, 0.01/0.00m, d[1]=6, s[1]=1, cv=0.301604                                               
8/101, 0.01/0.00m, d[1]=7, s[1]=1, cv=0.320802                                               
9/101, 0.01/0.00m, d[1]=8, s[1]=1, cv=0.319564                                               
10/101, 0.01/0.00m, d[1]=9, s[1]=1, cv=0.320517                                                
11/101, 0.00/0.00m, d[1]=10, s[1]=1, cv=0.327647                                                 
12/101, 0.00/0.00m, d[1]=1, s[1]=2, cv=0.332637                                                
13/101, 0.00/0.00m, d[1]=2, s[1]=2, cv=0.312969                                                
14/101, 0.00/0.00m, d[1]=3, s[1]=2, cv=0.292779                                                
15/101, 0.00/0.00m, d[1]=4, s[1]=2, cv=0.294617                                                
16/101, 0.00/0.00m, d[1]=5, s[1]=2, cv=0.299124                                                
17/101, 0.00/0.00m, d[1]=6, s[1]=2, cv=0.311768                                                
18/101, 0.00/0.00m, d[1]=7, s[1]=2, cv=0.321582                                                
19/101, 0.00/0.00m, d[1]=8, s[1]=2, cv=0.322449                                                
20/101, 0.00/0.00m, d[1]=9, s[1]=2, cv=0.324965                                                
21/101, 0.00/0.00m, d[1]=10, s[1]=2, cv=0.364333                                                 
22/101, 0.00/0.00m, d[1]=1, s[1]=3, cv=0.316037                                                
23/101, 0.00/0.00m, d[1]=2, s[1]=3, cv=0.293462                                                
24/101, 0.00/0.00m, d[1]=3, s[1]=3, cv=0.292497                                                
25/101, 0.00/0.00m, d[1]=4, s[1]=3, cv=0.296949                                                
26/101, 0.00/0.00m, d[1]=5, s[1]=3, cv=0.305706                                                
27/101, 0.00/0.00m, d[1]=6, s[1]=3, cv=0.322314                                                
28/101, 0.00/0.00m, d[1]=7, s[1]=3, cv=0.328449                                                
29/101, 0.00/0.00m, d[1]=8, s[1]=3, cv=0.325644                                                
30/101, 0.00/0.00m, d[1]=9, s[1]=3, cv=0.362927                                                
31/101, 0.00/0.00m, d[1]=10, s[1]=3, cv=0.346823                                                 
32/101, 0.00/0.00m, d[1]=1, s[1]=4, cv=0.29707                                               
33/101, 0.00/0.00m, d[1]=2, s[1]=4, cv=0.289811                                                
34/101, 0.00/0.00m, d[1]=3, s[1]=4, cv=0.29569                                               
35/101, 0.00/0.00m, d[1]=4, s[1]=4, cv=0.301927                                                
36/101, 0.00/0.00m, d[1]=5, s[1]=4, cv=0.320594                                                
37/101, 0.00/0.00m, d[1]=6, s[1]=4, cv=0.340682                                                
38/101, 0.00/0.00m, d[1]=7, s[1]=4, cv=0.332416                                                
39/101, 0.00/0.00m, d[1]=8, s[1]=4, cv=0.367082                                                
40/101, 0.00/0.00m, d[1]=9, s[1]=4, cv=0.353865                                                
41/101, 0.00/0.00m, d[1]=10, s[1]=4, cv=0.428145                                                 
42/101, 0.00/0.00m, d[1]=1, s[1]=5, cv=0.292869                                                
43/101, 0.00/0.00m, d[1]=2, s[1]=5, cv=0.293627                                                
44/101, 0.00/0.00m, d[1]=3, s[1]=5, cv=0.299169                                                
45/101, 0.00/0.00m, d[1]=4, s[1]=5, cv=0.314637                                                
46/101, 0.00/0.00m, d[1]=5, s[1]=5, cv=0.339987                                                
47/101, 0.00/0.00m, d[1]=6, s[1]=5, cv=0.332409                                                
48/101, 0.00/0.00m, d[1]=7, s[1]=5, cv=0.35851                                               
49/101, 0.00/0.00m, d[1]=8, s[1]=5, cv=0.355263                                                
50/101, 0.00/0.00m, d[1]=9, s[1]=5, cv=0.430177                                                
51/101, 0.00/0.00m, d[1]=10, s[1]=5, cv=0.332386                                                 
52/101, 0.00/0.00m, d[1]=1, s[1]=6, cv=0.2901                                              
53/101, 0.00/0.00m, d[1]=2, s[1]=6, cv=0.297312                                                
54/101, 0.00/0.00m, d[1]=3, s[1]=6, cv=0.305058                                                
55/101, 0.00/0.00m, d[1]=4, s[1]=6, cv=0.329438                                                
56/101, 0.00/0.00m, d[1]=5, s[1]=6, cv=0.333395                                                
57/101, 0.00/0.00m, d[1]=6, s[1]=6, cv=0.355433                                                
58/101, 0.00/0.00m, d[1]=7, s[1]=6, cv=0.357398                                                
59/101, 0.00/0.00m, d[1]=8, s[1]=6, cv=0.435383                                                
60/101, 0.00/0.00m, d[1]=9, s[1]=6, cv=0.343636                                                
61/101, 0.00/0.00m, d[1]=10, s[1]=6, cv=2.91447                                                
62/101, 0.00/0.00m, d[1]=1, s[1]=7, cv=0.294547                                                
63/101, 0.00/0.00m, d[1]=2, s[1]=7, cv=0.298514                                                
64/101, 0.00/0.00m, d[1]=3, s[1]=7, cv=0.31613                                               
65/101, 0.00/0.00m, d[1]=4, s[1]=7, cv=0.332805                                                
66/101, 0.00/0.00m, d[1]=5, s[1]=7, cv=0.35454                                               
67/101, 0.00/0.00m, d[1]=6, s[1]=7, cv=0.357559                                                
68/101, 0.00/0.00m, d[1]=7, s[1]=7, cv=0.456365                                                
69/101, 0.00/0.00m, d[1]=8, s[1]=7, cv=0.367922                                                
70/101, 0.00/0.00m, d[1]=9, s[1]=7, cv=10.7819                                               
71/101, 0.00/0.00m, d[1]=10, s[1]=7, cv=21.904                                               
72/101, 0.00/0.00m, d[1]=1, s[1]=8, cv=0.297298                                                
73/101, 0.00/0.00m, d[1]=2, s[1]=8, cv=0.303663                                                
74/101, 0.00/0.00m, d[1]=3, s[1]=8, cv=0.321312                                                
75/101, 0.00/0.00m, d[1]=4, s[1]=8, cv=0.337897                                                
76/101, 0.00/0.00m, d[1]=5, s[1]=8, cv=0.344366                                                
77/101, 0.00/0.00m, d[1]=6, s[1]=8, cv=0.399728                                                
78/101, 0.00/0.00m, d[1]=7, s[1]=8, cv=0.445125                                                
79/101, 0.00/0.00m, d[1]=8, s[1]=8, cv=6.78408                                               
80/101, 0.00/0.00m, d[1]=9, s[1]=8, cv=123.77                                              
81/101, 0.00/0.00m, d[1]=10, s[1]=8, cv=1552.27                                                
82/101, 0.00/0.00m, d[1]=1, s[1]=9, cv=0.297094                                                
83/101, 0.00/0.00m, d[1]=2, s[1]=9, cv=0.307864                                                
84/101, 0.00/0.00m, d[1]=3, s[1]=9, cv=0.322057                                                
85/101, 0.00/0.00m, d[1]=4, s[1]=9, cv=0.332904                                                
86/101, 0.00/0.00m, d[1]=5, s[1]=9, cv=0.36399                                               
87/101, 0.00/0.00m, d[1]=6, s[1]=9, cv=0.466078                                                
88/101, 0.00/0.00m, d[1]=7, s[1]=9, cv=1.37943                                               
89/101, 0.00/0.00m, d[1]=8, s[1]=9, cv=118.251                                               
90/101, 0.00/0.00m, d[1]=9, s[1]=9, cv=1102.96                                               
91/101, 0.00/0.00m, d[1]=10, s[1]=9, cv=9057.6                                               
92/101, 0.00/0.00m, d[1]=1, s[1]=10, cv=0.304438                                                 
93/101, 0.00/0.00m, d[1]=2, s[1]=10, cv=0.309745                                                 
94/101, 0.00/0.00m, d[1]=3, s[1]=10, cv=0.324339                                                 
95/101, 0.00/0.00m, d[1]=4, s[1]=10, cv=0.34466                                                
96/101, 0.00/0.00m, d[1]=5, s[1]=10, cv=0.403782                                                 
97/101, 0.00/0.00m, d[1]=6, s[1]=10, cv=0.487324                                                 
98/101, 0.00/0.00m, d[1]=7, s[1]=10, cv=9.91548                                                
99/101, 0.00/0.00m, d[1]=8, s[1]=10, cv=121.055                                                
100/101, 0.00/0.00m, d[1]=9, s[1]=10, cv=1536.35                                                 
101/101, 0.00/0.00m, d[1]=10, s[1]=10, cv=22174.9                                                  Working...          Warning message:
In crs.formula(logwage ~ age, complexity = "degree-knots") :
   Dynamically changing search from nomad to exhaustive (if unwanted set cv.threshold to 0)

Multistart 1 of 1 |
Multistart 1 of 1 |
Multistart 1 of 1 |
Multistart 1 of 1 /
Multistart 1 of 1 |
Multistart 1 of 1 |
                   
Call:
crs.formula(formula = logwage ~ age, complexity = "degree-knots")

Indicator Bases/B-spline Bases Regression Spline

There is 1 continuous predictor
Spline degree/number of segments for age: 2/4
Model complexity proxy: degree-knots
Knot type: quantiles
Pruning of final model: FALSE
Training observations: 205
Rank of model frame: 6
Trace of smoother matrix: 6

Residual standard error: 0.5261 on 199 degrees of freedom
Multiple R-squared: 0.3332,   Adjusted R-squared: 0.3165
F-statistic: 19.89 on 5 and 199 DF, p-value: 4.624e-16

Cross-validation score: 0.28981112
Number of multistarts: 5
Estimation time: 0.3 seconds


Regression Data: 205 training points, in 1 variable(s)
                   age
Bandwidth(s): 3.268425

Kernel Regression Estimator: Local-Linear
Bandwidth Type: Fixed
Residual standard error: 0.5245445
R-squared: 0.3175747

Continuous Kernel Type: Second-Order Gaussian
No. Continuous Explanatory Vars.: 1

crs documentation built on Feb. 2, 2021, 5:13 p.m.