Can I see coefficients of difficulty and discrimination in summary() function?

[Seongho Bae]

Hi, Pill.

I write again in this forum.

I was encountered one trouble.

Can I see coefficients of difficulty and discrimination in summary () function?

I can not see any coefficients of difficulty and discrimination in summary() function without coef().

Like this:

> summary(GAT.stage2.result)

Factor loadings metric: 

            F1       h2

언어1   0.2296 0.052715

언어2   0.1335 0.017832

언어3  -0.0195 0.000381

언어4   0.2846 0.081009

언어5   0.3418 0.116849

언어6   0.2252 0.050724

언어7   0.9796 0.959566

언어8   0.1128 0.012717

언어9  -0.3541 0.125385

언어10  0.7331 0.537432

언어11  0.1619 0.026206

언어12 -0.1264 0.015974

언어13 -0.0717 0.005145

언어14  0.4049 0.163958

언어15  0.3617 0.130832

언어16  0.8687 0.754685

언어17  0.3283 0.107776

언어18  0.1184 0.014027

언어19  0.5138 0.263964

언어20  0.2753 0.075810

언어21  0.3500 0.122486

언어22  0.5244 0.274947

언어23 -0.4178 0.174549

언어24  0.4786 0.229085

언어25  0.2289 0.052384

SS loadings:  4.366 

Factor covariance: 

   F1

F1  1

So, I want to get your advice.

Thanks a lot every times.

--

Seongho

[Phil Chalmers]

No, summary() only provides standardized loadings for most models. That being said, you could always modify the outputs yourself to get what you are looking for (this is R, after all). Perhaps the as.data.frame() function is what you are looking for?

dat <- expand.table(LSAT7)

mod <- mirt(dat, 1, SE = T)

t(as.data.frame(coef(mod)))

Phil

--
You received this message because you are subscribed to the Google Groups "mirt-package" group.
To unsubscribe from this group and stop receiving emails from it, send an email to mirt-package...@googlegroups.com.
For more options, visit https://groups.google.com/groups/opt_out.

[Seongho Bae]

I was just want to save difficulty and discrimination coefficient .csv file. So, I tried to use "as.data. frame()" function to save them. ;)

2014년 1월 28일 화요일 오후 10시 47분 45초 UTC+9, Phil Chalmers 님의 말:

[Phil Chalmers]

You also could have used the more verbose mod2values() function since it returns a lot more information (including which parameters were freely estimated, which is nice for simulations). I tend to use that approach in my simulations. Cheers.

Phil

For more options, visit https://groups.google.com/d/optout.