wage1 | R Documentation |
Cross-section wage data consisting of a random sample taken from the U.S. Current Population Survey for the year 1976. There are 526 observations in total.
data("wage1")
A data frame with 24 columns, and 526 rows.
column 1, of type numeric
, average hourly earnings
column 2, of type numeric
, years of education
column 3, of type numeric
, years potential experience
column 4, of type numeric
, years with current employer
column 5, of type factor
, =“Nonwhite” if nonwhite, “White” otherwise
column 6, of type factor
, =“Female” if female, “Male” otherwise
column 7, of type factor
, =“Married” if Married, “Nonmarried” otherwise
column 8, of type numeric
, number of dependents
column 9, of type numeric
, =1 if live in SMSA
column 10, of type numeric
, =1 if live in north central U.S
column 11, of type numeric
, =1 if live in southern region
column 12, of type numeric
, =1 if live in western region
column 13, of type numeric
, =1 if work in construc. indus.
column 14, of type numeric
, =1 if in nondur. manuf. indus.
column 15, of type numeric
, =1 if in trans, commun, pub ut
column 16, of type numeric
, =1 if in wholesale or retail
column 17, of type numeric
, =1 if in services indus.
column 18, of type numeric
, =1 if in prof. serv. indus.
column 19, of type numeric
, =1 if in profess. occupation
column 20, of type numeric
, =1 if in clerical occupation
column 21, of type numeric
, =1 if in service occupation
column 22, of type numeric
, log(wage)
column 23, of type numeric
, exper**2
column 24, of type numeric
, tenure**2
Jeffrey M. Wooldridge
Wooldridge, J.M. (2000), Introductory Econometrics: A Modern Approach, South-Western College Publishing.
## Not run: data(wage1) ## Cross-validated model selection for spline degree and bandwidths Note ## - we override the default nmulti here to get a quick illustration ## (we don't advise doing this, in fact advise using more restarts in ## serious applications) model <- crs(lwage~married+ female+ nonwhite+ educ+ exper+ tenure, basis="additive", complexity="degree", data=wage1, segments=c(1,1,1), nmulti=1) summary(model) ## Residual plots plot(model) ## Partial mean plots (control for non axis predictors) plot(model,mean=TRUE) ## Partial first derivative plots (control for non axis predictors) plot(model,deriv=1) ## Partial second derivative plots (control for non axis predictors) plot(model,deriv=2) ## Compare with local linear kernel regression require(np) model <- npreg(lwage~married+ female+ nonwhite+ educ+ exper+ tenure, regtype="ll", bwmethod="cv.aic", data=wage1) summary(model) ## Partial mean plots (control for non axis predictors) plot(model,common.scale=FALSE) ## Partial first derivative plots (control for non axis predictors) plot(model,gradients=TRUE,common.scale=FALSE) detach("package:np") ## End(Not run)
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