B. Data set
This tutorial follows the example in Estabrook (2015) and makes use of data from the National Longitudinal Survey of Youth. The NLSY 1997 sample (NLSY97) has a 5-item depression scale that was administered at three occasions. The five items are all on a 4-point Likert scale. Participants were asked how often they felt “like a nervous person”, “calm and peaceful”, “down or blue”, “like a happy person”, and “depressed” in the last month. These example data are included in the mxmmod package:
data(nlsy97depression)
summary(nlsy97depression)
#> pid sex birth_m birth_y occasion
#> 1 : 3 M:13797 Min. : 1.000 Min. :1980 Min. :0
#> 2 : 3 F:13155 1st Qu.: 3.000 1st Qu.:1981 1st Qu.:0
#> 3 : 3 Median : 7.000 Median :1982 Median :1
#> 4 : 3 Mean : 6.556 Mean :1982 Mean :1
#> 5 : 3 3rd Qu.:10.000 3rd Qu.:1983 3rd Qu.:2
#> 6 : 3 Max. :12.000 Max. :1984 Max. :2
#> (Other):26934
#> nervous calm down happy depressed
#> Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000
#> 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:3.00 1st Qu.:2.000 1st Qu.:3.000
#> Median :3.000 Median :2.000 Median :3.00 Median :2.000 Median :4.000
#> Mean :3.187 Mean :2.386 Mean :3.15 Mean :2.205 Mean :3.572
#> 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.00 3rd Qu.:3.000 3rd Qu.:4.000
#> Max. :4.000 Max. :4.000 Max. :4.00 Max. :4.000 Max. :4.000
#> NA's :3678 NA's :3646 NA's :3716 NA's :3631 NA's :3730Before building any models, we first plot a few example trajectories and mean trajectories for the five items assessed:
set.seed(1000)
subset <- sample(unique(nlsy97depression$pid), 9)
nlsy97depression %>%
filter(pid %in% subset) %>%
gather(measure, val, -pid, -occasion) %>%
ggplot(aes(x=occasion, group=measure, color=measure, y=val)) +
geom_line(position=position_jitter(w=0.1, h=0.1)) +
facet_wrap(~pid)
#> Warning: attributes are not identical across measure variables;
#> they will be dropped
nlsy97depression %>%
gather(measure, val, -occasion, -pid) %>%
na.omit() %>%
ggplot(aes(x=occasion, color=measure, y=val)) +
stat_summary(fun.y = mean, geom='line') +
stat_summary(fun.y = mean, geom='point') +
stat_summary(fun.data = mean_se, geom='errorbar', width=0.2)
#> Warning: attributes are not identical across measure variables;
#> they will be dropped
#> Warning: `fun.y` is deprecated. Use `fun` instead.
#> Warning: `fun.y` is deprecated. Use `fun` instead.
#> geom_path: Each group consists of only one observation. Do you need to adjust
#> the group aesthetic?