Group based trajectory models in Stata – some graphs and fit statistics

For my advanced research design course this semester I have been providing code snippets in Stata and R. This is the first time I’ve really sat down and programmed extensively in Stata, and this is a followup to produce some of the same plots and model fit statistics for group based trajectory statistics as this post in R. The code and the simulated data I made to reproduce this analysis can be downloaded here.

First, for my own notes my version of Stata is on a server here at UT Dallas. So I cannot simply go

net from
net install traj, force

to install the group based trajectory code. First I have this set of code in the header of all my do files now

*let stata know to search for a new location for stata plug ins
adopath + "C:\Users\axw161530\Documents\Stata_PlugIns"
*to install on your own in the lab it would be
net set ado "C:\Users\axw161530\Documents\Stata_PlugIns"

So after running that then I can install the traj command, and Stata will know where to look for it!

Once that is taken care of after setting the working directory, I can simply load in the csv file. Here my first variable was read in as ïid instead of id (I’m thinking because of the encoding in the csv file). So I rename that variable to id.

*Load in csv file
import delimited GroupTraj_Sim.csv
*BOM mark makes the id variable weird
rename ïid id

Second, the traj model expects the data in wide format (which this data set already is), and has counts in count_1, count_2count_10. The traj command also wants you to input a time variable though to, which I do not have in this file. So I create a set of t_ variables to mimic the counts, going from 1 to 10.

*Need to generate a set of time variables to pass to traj, just label 1 to 10
forval i = 1/10 { 
  generate t_`i' = `i'

Now we can estimate our group based models, and we get a pretty nice default plot.

traj, var(count_*) indep(t_*) model(zip) order(2 2 2) iorder(0)

Now for absolute model fit statistics, there are the average posterior probabilities, the odds of correct classification, and the observed classification proportion versus the expected classification proportion. Here I made a program that I will surely be ashamed of later (I should not brutalize the data and do all the calculations in matrix), but it works and produces an ugly table to get us these stats.

*I made a function to print out summary stats
program summary_table_procTraj
    *now lets look at the average posterior probability
    gen Mp = 0
    foreach i of varlist _traj_ProbG* {
        replace Mp = `i' if `i' > Mp 
    sort _traj_Group
    *and the odds of correct classification
    by _traj_Group: gen countG = _N
    by _traj_Group: egen groupAPP = mean(Mp)
    by _traj_Group: gen counter = _n
    gen n = groupAPP/(1 - groupAPP)
    gen p = countG/ _N
    gen d = p/(1-p)
    gen occ = n/d
    *Estimated proportion for each group
    scalar c = 0
    gen TotProb = 0
    foreach i of varlist _traj_ProbG* {
       scalar c = c + 1
       quietly summarize `i'
       replace TotProb = r(sum)/ _N if _traj_Group == c 
    *This displays the group number, the count per group, the average posterior probability for each group,
    *the odds of correct classification, and the observed probability of groups versus the probability 
    *based on the posterior probabilities
    list _traj_Group countG groupAPP occ p TotProb if counter == 1

This should work after any model as long as the naming conventions for the assigned groups are _traj_Group and the posterior probabilities are in the variables _traj_ProbG*. So when you run


You get this ugly table:

     | _traj_~p   countG   groupAPP        occ       p    TotProb |
  1. |        1      103    .937933   43.57431   .2575   .2573651 |
104. |        2      161   .9935606   229.0434   .4025   .4064893 |
265. |        3      136   .9607248   47.48378     .34   .3361456 |

The groupAPP are the average posterior probabilities – here you can see they are all quite high. occ is the odds of correct classification, and again they are all quite high. p is the proportion in each group based on the assignments for the maximum posterior probability, and the TotProb are the expected number based on the sums of the posterior probabilities. TotProb should be the same as in the Group Membership part at the bottom of the traj model. They are close (to 5 decimals), but not exactly the same (and I do not know why that is the case).

Next, to generate a plot of the individual trajectories, I want to reshape the data to long format. I use preserve in case I want to go back to wide format later and estimate more models. If you need to look to see how the reshape command works, type help reshape at the Stata prompt. (Ditto for help on all Stata commands.)

reshape long count_ t_, i(id)

To get the behavior I want in the plot, I use a scatter plot but have them connected via c(L). Then I create small multiples for each trajectory group using the by() option. Before that I slightly jitter the count data, so the lines are not always perfectly overlapped. I make the lines thin and grey — I would also use transparency but Stata graphs do not support this.

gen count_jit = count_ + ( 0.2*runiform()-0.1 )
graph twoway scatter count_jit t_, c(L) by(_traj_Group) msize(tiny) mcolor(gray) lwidth(vthin) lcolor(gray)

I’m too lazy at the moment to clean up the axis titles and such, but I think this plot of the individual trajectories should always be done. See Breaking Bad: Two Decades of Life-Course Data Analysis in Criminology, Developmental Psychology, and Beyond (Erosheva et al., 2014).

While this fit looks good, this is not the correct number of groups given how I simulated the data. I will give those trying to find the right answer a few hints; none of the groups have a higher polynomial than 2, and there is a constant zero inflation for the entire sample, so iorder(0) will be the correct specification for the zero inflation part. If you take a stab at it let me know, I will fill you in on how I generated the simulation.

Leave a comment

1 Comment

  1. Paper – Replicating Group Based Trajectory Models of Crime at Micro-Places in Albany, NY published | Andrew Wheeler

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: