wages.RdData on the labor-market experience of male high school dropouts.
A data frame with 6402 observations on the following 15 variables.
respondent id - a factor with 888 levels.
natural log of wages expressed in 1990 dollars.
years of experience in the work force
equals 1 if respondent has obtained a GED as of the time of survey, 0 otherwise
labor force participation since obtaining a GED (in years) - before a GED is earned postexp = 0, and on the day a GED is earned postexp = 0
factor - equals 1 if subject is black, 0 otherwise
factor - equals 1 if subject is hispanic, 0 otherwise
highest grade completed - takes integers 6 through 12
hgc - 9, a centered version of hgc
local area unemployment rate for that year
These data are originally from the 1979 National Longitudinal Survey on Youth (NLSY79).
Singer and Willett (2003) used these data for examples in chapter (insert info. here) and the data sets used can be found on the UCLA Statistical Computing website: https://stats.oarc.ucla.edu/other/examples/alda/
Additionally the data were discussed by Cook and Swayne (2003) and the data can be found on the GGobi website: http://ggobi.org/book.html.
Singer, J. D. and Willett, J. B. (2003), Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence, New York: Oxford University Press.
Cook, D. and Swayne, D. F. (2007), Interactive and Dynamic Graphics for Data Analysis with R and GGobi, Springer.
str(wages)
#> 'data.frame': 6402 obs. of 15 variables:
#> $ id : Factor w/ 888 levels "31","36","53",..: 1 1 1 1 1 1 1 1 2 2 ...
#> $ lnw : num 1.49 1.43 1.47 1.75 1.93 ...
#> $ exper : num 0.015 0.715 1.734 2.773 3.927 ...
#> $ ged : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ postexp : num 0.015 0.715 1.734 2.773 3.927 ...
#> $ black : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
#> $ hispanic : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 1 1 ...
#> $ hgc : int 8 8 8 8 8 8 8 8 9 9 ...
#> $ hgc.9 : int -1 -1 -1 -1 -1 -1 -1 -1 0 0 ...
#> $ uerate : num 3.21 3.21 3.21 3.3 2.89 2.49 2.6 4.8 4.89 7.4 ...
#> $ ue.7 : num -3.79 -3.79 -3.79 -3.71 -4.11 ...
#> $ ue.centert1 : num 0 0 0 0.08 -0.32 ...
#> $ ue.mean : num 3.21 3.21 3.21 3.21 3.21 3.21 3.21 3.21 5.1 5.1 ...
#> $ ue.person.cen: num 0 0 0 0.08 -0.32 ...
#> $ ue1 : num 3.21 3.21 3.21 3.21 3.21 3.21 3.21 3.21 4.89 4.89 ...
summary(wages)
#> id lnw exper ged
#> 1204 : 13 Min. :0.708 Min. : 0.001 Min. :0.0000
#> 3440 : 13 1st Qu.:1.591 1st Qu.: 1.609 1st Qu.:0.0000
#> 7373 : 13 Median :1.842 Median : 3.451 Median :0.0000
#> 9968 : 13 Mean :1.897 Mean : 3.957 Mean :0.2719
#> 10392 : 13 3rd Qu.:2.140 3rd Qu.: 5.949 3rd Qu.:1.0000
#> 12043 : 13 Max. :4.304 Max. :12.700 Max. :1.0000
#> (Other):6324
#> postexp black hispanic hgc hgc.9
#> Min. : 0.0000 0:4783 0:4859 Min. : 6.000 Min. :-3.00000
#> 1st Qu.: 0.0000 1:1619 1:1543 1st Qu.: 8.000 1st Qu.:-1.00000
#> Median : 0.0000 Median : 9.000 Median : 0.00000
#> Mean : 0.9076 Mean : 8.948 Mean :-0.05248
#> 3rd Qu.: 0.1168 3rd Qu.:10.000 3rd Qu.: 1.00000
#> Max. :12.2600 Max. :12.000 Max. : 3.00000
#>
#> uerate ue.7 ue.centert1 ue.mean
#> Min. : 1.790 Min. :-5.2050 Min. :-15.310 Min. : 2.890
#> 1st Qu.: 5.390 1st Qu.:-1.6050 1st Qu.: -2.900 1st Qu.: 6.070
#> Median : 7.000 Median : 0.0000 Median : -0.500 Median : 7.150
#> Mean : 7.733 Mean : 0.7297 Mean : -1.015 Mean : 7.734
#> 3rd Qu.: 9.400 3rd Qu.: 2.3000 3rd Qu.: 0.600 3rd Qu.: 8.650
#> Max. :23.700 Max. :16.7050 Max. : 13.005 Max. :17.130
#> NA's :402 NA's :406
#> ue.person.cen ue1
#> Min. :-8.403000 Min. : 2.890
#> 1st Qu.:-1.350500 1st Qu.: 6.200
#> Median :-0.142000 Median : 8.300
#> Mean :-0.000011 Mean : 8.762
#> 3rd Qu.: 1.133000 3rd Qu.:10.200
#> Max. :13.421000 Max. :23.700
#>
if (FALSE) { # \dontrun{
library(lme4)
lmer(lnw ~ exper + (exper | id), data = wages)
} # }