# Change in output depending on mirt version

I have a matrix which I'm trying to run through the mirt function of the mirt package:

resp.freq <- data.frame(matrix(c(11, 46, 12, 31, 13, 8, 21, 20, 22, 68, 23, 12,
31,  1, 32, 12, 33, 11), nrow = 9, ncol = 2,
byrow = T))
dados3 <- matrix(rep(resp.freq$X1, resp.freq$X2), ncol = 1)

require(mirt)
m1 <- mirt(dados3, 1, D = 1, SE = TRUE)
coef(m1)


I used to run this through mirt 0.5.0 and would get the following end results:

$X1 a1 d1 d2 pars 1.913 0.633 -3.152 SE 0.228 0.181 0.322$X2
a1    d1     d2
pars 1.659 1.121 -2.488
SE   0.200 0.190  0.261


However, my workstation has been updated and on mirt 1.2.1 and now I get the following output upon running m1 <- mirt(...:

Iteration: 1000, Log-Lik: -387.670, Max-Change: 0.00007
EM iterations terminated after 1000 iterations.

Calculating information matrix...
Warning message:
In loadESTIMATEinfo(info = info, ESTIMATE = ESTIMATE, constrain = constrain) :
Negative SEs set to NaN.


And coef(m1) gives me:

$X1 a1 d1 d2 par 11.515 2.732 -13.98 CI_2.5 NaN 0.685 NaN CI_97.5 NaN 4.778 NaN$X2
a1    d1     d2
par     1.099 0.903 -2.139
CI_2.5  0.712 0.561 -2.611
CI_97.5 1.485 1.245 -1.668

\$GroupPars
MEAN_1 COV_11
par          0      1
CI_2.5      NA     NA
CI_97.5     NA     NA


I've read the changelog, but couldn't find a reason for the behavior change. I've tried desperately changing the parameters of mirt(), but couldn't reach even a similar level of convergence. What gives? The data doesn't look like it would be inappropriate for this kind of prodecude, am I missing anything?

There's an option now to print the original standard error format by passing coef(mod, printSE=TRUE), and that should work on the 1.2.1 version. If not, check out the dev version on Github, which certainly includes this feature.
Onto the other part of your model, with only two items it is not even uniquely identified, so I wouldn't expect the results to agree across versions or even operating systems; hence why the first a1 parameter has essentially an infinite standard error. You need at least 3 items per factor in order to identify the metric properly.