2.4 Ajuste do modelo com três parâmetros
> manha.tpm<-tpm(manha,control=list(optimizer="nlminb"))
Os valores dos parâmetros estimados foram:
Call:
tpm(data = manha, control = list(optimizer = "nlminb"))
Coefficients:
Gussng Dffclt Dscrmn
i1 0.243 1.979 0.632
i2 0.191 0.254 1.896
i3 0.156 1.114 1.717
i4 0.140 0.246 1.078
i5 0.024 -2.761 0.935
i6 0.025 -0.463 0.562
i7 0.098 0.786 0.812
i8 0.000 1.344 0.519
i9 0.175 1.043 1.520
i10 0.308 1.292 1.463
i11 0.005 -0.659 0.600
i12 0.323 0.972 0.771
i13 0.040 -0.104 1.169
i14 0.244 0.236 1.230
i15 0.265 2.298 1.741
i16 0.158 1.709 1.605
i17 0.493 0.798 2.322
i18 0.732 0.003 3.155
i19 0.076 0.434 0.956
i20 0.591 0.532 1.322
i21 0.000 0.975 0.581
i22 0.172 2.199 2.803
i23 0.215 2.352 2.245
i24 0.400 2.588 0.787
i25 0.002 0.996 0.959
i26 0.193 -0.244 1.362
i27 0.578 0.604 1.985
i28 0.067 0.361 1.518
i29 0.000 -1.089 1.219
i30 0.427 -0.616 1.365
Log.Lik: -17784.91
Os valores de theta podem ser obtidos com a função factor.scores() aplicada sobre
o objeto da classe tpm que contém as estimativas dos parâmetros do modelo.
> manha.prof<-factor.scores(manha.tpm)
Os valores de theta estimados são:
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10 i11 i12 i13 i14 i15 i16 i17 i18
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
2 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 1
3 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0
4 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1
5 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1
6 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 1
i19 i20 i21 i22 i23 i24 i25 i26 i27 i28 i29 i30 Obs Exp
1 0 1 0 0 0 1 0 0 1 1 1 1 1 0
2 0 1 0 0 0 1 0 0 1 0 0 1 1 0
3 0 0 0 1 1 0 0 0 1 1 0 1 1 0
4 0 0 1 0 1 0 0 1 1 0 0 1 1 0
5 0 0 0 0 1 0 1 0 0 0 1 1 1 0
6 0 1 0 0 0 0 0 0 0 1 1 0 1 0
z1 se.z1
1 -1.254365 0.6110829
2 -1.800145 0.6841814
3 -1.840739 0.6910352
4 -1.526407 0.6573003
5 -0.976459 0.5065874
6 -1.330498 0.5644371