[TEMPLATE] TRI
Leonardo
6/9/2021
=====================================
Teoria de Resposta ao Item com R
===================================== #install.packages("psych") #caso ainda não tenha os pacotes instalados, tira a # da linha e roda
#install.packages("mirt")
#install.packages("readxl")
library(readxl)
library(psych)
library(mirt) # multidimensional## Loading required package: stats4## Loading required package: latticelibrary(eRm) # modelos da familia Rasch##
## Attaching package: 'eRm'## The following objects are masked from 'package:mirt':
##
## itemfit, personfit## The following object is masked from 'package:psych':
##
## sim.raschdata<-read.csv("/Users/leopestillo/Google Drive/Analysis/Marcelo_PPGPS/dados_cancer.csv",sep=',')
colnames(data) #verificar o nome das colunas## [1] "L_benigno" "L_biopsia" "L_coloutero" "L_ginecologia"
## [5] "L_papilomavirus" "L_histerectomia" "L_maligno" "L_mastectomia"
## [9] "L_metastase" "L_pelvico" "L_utero" "L_vagina"
## [13] "F_benigno" "F_biopsia" "F_coloutero" "F_ginecologia"
## [17] "F_papilomavirus" "F_histerectomia" "F_maligno" "F_mastectomia"
## [21] "F_metastase" "F_pelvico" "F_utero" "F_vagina"
## [25] "C_benigno" "C_biopsia" "C_coloutero" "C_ginecologia"
## [29] "C_papilomavirus" "C_histerectomia" "C_maligno" "C_mastectomia"
## [33] "C_metastase" "C_pelvico" "C_utero" "C_vagina"
## [37] "D_1" "D_2" "D_3" "D_4"
## [41] "D_5" "D_6" "D_7" "D_8"
## [45] "D_9" "D_10" "D_11" "M_1"
## [49] "M_2" "M_3" "M_4"View(data) #ver o banco de dados
#data_validation<-subset(data[data$redcap_event_name=="enrollment_arm_1",]) #para selecionar apenas o grupo desejado
#dassbin<-read.csv("https://raw.githubusercontent.com/wagnerLM/netusf/master/dassbin",sep = ";") modelo Wagner
#View(dassbin) #modelo professor Wagner#Nomeando os itens (se necessario)
# Nome resumido dos itens
#dasslabels<-scan("https://raw.githubusercontent.com/wagnerLM/netusf/master/dasslabels",what = "character", sep = "\n")
#dasslabels
# Itens completos
#dassnames<-scan("https://raw.githubusercontent.com/wagnerLM/netusf/master/dassnames",what = "character", sep = "\n")
#dassnamesSelecionando apenas os itens de LEITURA
data_leitura <- with(data, data.frame(L_benigno,
L_biopsia,
L_coloutero,
L_ginecologia,
L_papilomavirus,
L_histerectomia,
L_maligno,
L_mastectomia,
L_metastase,
L_pelvico,
L_utero,
L_vagina))
describe(data_leitura)## vars n mean sd median trimmed mad min max range skew
## L_benigno 1 112 0.94 0.24 1 1.00 0 0 1 1 -3.57
## L_biopsia 2 112 0.88 0.33 1 0.97 0 0 1 1 -2.24
## L_coloutero 3 112 0.97 0.16 1 1.00 0 0 1 1 -5.78
## L_ginecologia 4 112 0.97 0.16 1 1.00 0 0 1 1 -5.78
## L_papilomavirus 5 112 0.82 0.38 1 0.90 0 0 1 1 -1.66
## L_histerectomia 6 112 0.83 0.38 1 0.91 0 0 1 1 -1.74
## L_maligno 7 112 0.93 0.26 1 1.00 0 0 1 1 -3.28
## L_mastectomia 8 112 0.85 0.36 1 0.93 0 0 1 1 -1.91
## L_metastase 9 112 0.84 0.37 1 0.92 0 0 1 1 -1.82
## L_pelvico 10 112 0.92 0.27 1 1.00 0 0 1 1 -3.05
## L_utero 11 112 0.97 0.16 1 1.00 0 0 1 1 -5.78
## L_vagina 12 112 0.97 0.16 1 1.00 0 0 1 1 -5.78
## kurtosis se
## L_benigno 10.82 0.02
## L_biopsia 3.03 0.03
## L_coloutero 31.73 0.02
## L_ginecologia 31.73 0.02
## L_papilomavirus 0.75 0.04
## L_histerectomia 1.03 0.04
## L_maligno 8.86 0.02
## L_mastectomia 1.68 0.03
## L_metastase 1.34 0.03
## L_pelvico 7.34 0.03
## L_utero 31.73 0.02
## L_vagina 31.73 0.02Selecionando apenas os itens de DIÁLOGO
data_dialogo <- with(data, data.frame(#D_1,
D_2,
D_3,
D_4,
D_5,
D_6,
D_7,
D_8,
D_9,
D_10,
D_11))
describe(data_dialogo)## vars n mean sd median trimmed mad min max range skew kurtosis se
## D_2 1 112 0.92 0.27 1 1.00 0 0 1 1 -3.05 7.34 0.03
## D_3 2 112 0.89 0.31 1 0.99 0 0 1 1 -2.51 4.32 0.03
## D_4 3 112 0.83 0.38 1 0.91 0 0 1 1 -1.74 1.03 0.04
## D_5 4 112 0.28 0.45 0 0.22 0 0 1 1 0.98 -1.04 0.04
## D_6 5 112 0.67 0.47 1 0.71 0 0 1 1 -0.71 -1.51 0.04
## D_7 6 112 0.67 0.47 1 0.71 0 0 1 1 -0.71 -1.51 0.04
## D_8 7 112 0.07 0.26 0 0.00 0 0 1 1 3.28 8.86 0.02
## D_9 8 112 0.27 0.44 0 0.21 0 0 1 1 1.03 -0.94 0.04
## D_10 9 112 0.35 0.48 0 0.31 0 0 1 1 0.63 -1.62 0.05
## D_11 10 112 0.43 0.50 0 0.41 0 0 1 1 0.28 -1.94 0.05Selecionando apenas os itens de MATEMÁTICA
data_matematica <- with(data, data.frame(M_1,
M_2,
M_3,
M_4))
describe(data_matematica)## vars n mean sd median trimmed mad min max range skew kurtosis se
## M_1 1 112 0.64 0.48 1 0.68 0 0 1 1 -0.59 -1.67 0.05
## M_2 2 112 0.14 0.35 0 0.06 0 0 1 1 2.01 2.07 0.03
## M_3 3 112 0.40 0.49 0 0.38 0 0 1 1 0.40 -1.86 0.05
## M_4 4 112 0.41 0.49 0 0.39 0 0 1 1 0.36 -1.89 0.05Selecionando apenas os itens de COMPREENSÃO
data_compreensao <- with (data,data.frame(C_benigno,
C_biopsia,
C_coloutero,
C_ginecologia,
C_papilomavirus,
C_histerectomia,
C_maligno,
C_mastectomia,
C_metastase,
C_pelvico
# C_utero,
# C_vagina
))
describe(data_compreensao)## vars n mean sd median trimmed mad min max range skew
## C_benigno 1 112 0.85 0.36 1 0.93 0 0 1 1 -1.91
## C_biopsia 2 112 0.80 0.40 1 0.88 0 0 1 1 -1.51
## C_coloutero 3 112 0.84 0.37 1 0.92 0 0 1 1 -1.82
## C_ginecologia 4 112 0.92 0.27 1 1.00 0 0 1 1 -3.05
## C_papilomavirus 5 112 0.35 0.48 0 0.31 0 0 1 1 0.63
## C_histerectomia 6 112 0.20 0.40 0 0.12 0 0 1 1 1.51
## C_maligno 7 112 0.88 0.32 1 0.98 0 0 1 1 -2.37
## C_mastectomia 8 112 0.37 0.48 0 0.33 0 0 1 1 0.55
## C_metastase 9 112 0.46 0.50 0 0.46 0 0 1 1 0.14
## C_pelvico 10 112 0.57 0.50 1 0.59 0 0 1 1 -0.28
## kurtosis se
## C_benigno 1.68 0.03
## C_biopsia 0.28 0.04
## C_coloutero 1.34 0.03
## C_ginecologia 7.34 0.03
## C_papilomavirus -1.62 0.05
## C_histerectomia 0.28 0.04
## C_maligno 3.63 0.03
## C_mastectomia -1.71 0.05
## C_metastase -2.00 0.05
## C_pelvico -1.94 0.05Selecionando apenas os itens de FAMILIARIDADE
data_familiaridade <- with (data, data.frame(
# F_benigno,
F_biopsia,
# F_coloutero,
# F_ginecologia,
F_papilomavirus,
F_histerectomia,
# F_maligno,
F_mastectomia,
F_metastase,
F_pelvico
# F_utero,
# F_vagina
))
describe(data_familiaridade)## vars n mean sd median trimmed mad min max range skew
## F_biopsia 1 112 0.91 0.29 1 1.00 0 0 1 1 -2.84
## F_papilomavirus 2 112 0.54 0.50 1 0.54 0 0 1 1 -0.14
## F_histerectomia 3 112 0.48 0.50 0 0.48 0 0 1 1 0.07
## F_mastectomia 4 112 0.69 0.47 1 0.73 0 0 1 1 -0.80
## F_metastase 5 112 0.75 0.43 1 0.81 0 0 1 1 -1.14
## F_pelvico 6 112 0.86 0.35 1 0.94 0 0 1 1 -2.01
## kurtosis se
## F_biopsia 6.13 0.03
## F_papilomavirus -2.00 0.05
## F_histerectomia -2.01 0.05
## F_mastectomia -1.37 0.04
## F_metastase -0.71 0.04
## F_pelvico 2.07 0.03Criar somas dos testes
#data_leitura$total_leitura<-rowSums(data_leitura[,1:12])
#data_dialogo$total_dialogo<-rowSums(data_dialogo[,1:11])
#data_matematica$total_matematica<-rowSums(data_matematica[,1:4])
#data_compreensao$total_compreensao<-rowSums(data_compreensao[,1:12])
#data_familiaridade$total_familiaridade<-rowSums(data_familiaridade[,1:12])#Correlação e análise descritiva dos dados
#cor.plot(dassbin_sub)
#fa(dassbin_sub[,-8],1)Modelo de 2 parâmetros (2PL)
# itemtype vai variar de acordo com a estrutura de resposta
# Likert = itemtype = "graded", "rsm"
#mod1<-mirt(dassbin_sub[,-8],1,itemtype = "2PL")
#coef(mod1)
#M2(mod1)#plot(mod1)
#plot(mod1, type = 'trace')
#plot(mod1, type = 'info')
#itemplot(mod1,7)Modelo de 1 parâmetro (Rasch no mirt) LEITURA
mod_leitura<-mirt(data_leitura,1, itemtype = "Rasch")##
Iteration: 1, Log-Lik: -283.809, Max-Change: 0.58560
Iteration: 2, Log-Lik: -267.158, Max-Change: 0.72523
Iteration: 3, Log-Lik: -255.924, Max-Change: 0.80259
Iteration: 4, Log-Lik: -248.915, Max-Change: 0.76669
Iteration: 5, Log-Lik: -244.902, Max-Change: 0.64096
Iteration: 6, Log-Lik: -242.670, Max-Change: 0.47800
Iteration: 7, Log-Lik: -240.842, Max-Change: 0.41576
Iteration: 8, Log-Lik: -240.222, Max-Change: 0.22893
Iteration: 9, Log-Lik: -239.936, Max-Change: 0.13287
Iteration: 10, Log-Lik: -239.761, Max-Change: 0.08704
Iteration: 11, Log-Lik: -239.668, Max-Change: 0.05053
Iteration: 12, Log-Lik: -239.615, Max-Change: 0.03124
Iteration: 13, Log-Lik: -239.576, Max-Change: 0.02695
Iteration: 14, Log-Lik: -239.552, Max-Change: 0.01537
Iteration: 15, Log-Lik: -239.538, Max-Change: 0.00947
Iteration: 16, Log-Lik: -239.529, Max-Change: 0.00865
Iteration: 17, Log-Lik: -239.521, Max-Change: 0.00490
Iteration: 18, Log-Lik: -239.517, Max-Change: 0.00299
Iteration: 19, Log-Lik: -239.514, Max-Change: 0.00277
Iteration: 20, Log-Lik: -239.512, Max-Change: 0.00157
Iteration: 21, Log-Lik: -239.510, Max-Change: 0.00095
Iteration: 22, Log-Lik: -239.510, Max-Change: 0.00090
Iteration: 23, Log-Lik: -239.509, Max-Change: 0.00041
Iteration: 24, Log-Lik: -239.509, Max-Change: 0.00030
Iteration: 25, Log-Lik: -239.508, Max-Change: 0.00037
Iteration: 26, Log-Lik: -239.508, Max-Change: 0.00013
Iteration: 27, Log-Lik: -239.508, Max-Change: 0.00010coef(mod_leitura)## $L_benigno
## a1 d g u
## par 1 4.614 0 1
##
## $L_biopsia
## a1 d g u
## par 1 3.278 0 1
##
## $L_coloutero
## a1 d g u
## par 1 5.943 0 1
##
## $L_ginecologia
## a1 d g u
## par 1 5.943 0 1
##
## $L_papilomavirus
## a1 d g u
## par 1 2.503 0 1
##
## $L_histerectomia
## a1 d g u
## par 1 2.618 0 1
##
## $L_maligno
## a1 d g u
## par 1 4.374 0 1
##
## $L_mastectomia
## a1 d g u
## par 1 2.864 0 1
##
## $L_metastase
## a1 d g u
## par 1 2.739 0 1
##
## $L_pelvico
## a1 d g u
## par 1 4.155 0 1
##
## $L_utero
## a1 d g u
## par 1 5.943 0 1
##
## $L_vagina
## a1 d g u
## par 1 5.943 0 1
##
## $GroupPars
## MEAN_1 COV_11
## par 0 5.981summary(mod_leitura)## F1 h2
## L_benigno 1.24 1.53
## L_biopsia 1.24 1.53
## L_coloutero 1.24 1.53
## L_ginecologia 1.24 1.53
## L_papilomavirus 1.24 1.53
## L_histerectomia 1.24 1.53
## L_maligno 1.24 1.53
## L_mastectomia 1.24 1.53
## L_metastase 1.24 1.53
## L_pelvico 1.24 1.53
## L_utero 1.24 1.53
## L_vagina 1.24 1.53
##
## SS loadings: 18.419
## Proportion Var: 1.535
##
## Factor correlations:
##
## F1
## F1 1M2(mod_leitura)## M2 df p RMSEA RMSEA_5 RMSEA_95 SRMSR
## stats 86.22512 66 0.04807164 0.05254267 0.005199753 0.08087511 0.309244
## TLI CFI
## stats 0.9902173 0.9902173plot(mod_leitura)
plot(mod_leitura, type = 'trace')
plot(mod_leitura, type = 'info')
Modelo de 1 parâmetro (Rasch no mirt) DIÁLOGO
mod_dialogo<-mirt(data_dialogo,1, itemtype = "Rasch")##
Iteration: 1, Log-Lik: -536.192, Max-Change: 0.13886
Iteration: 2, Log-Lik: -535.714, Max-Change: 0.07471
Iteration: 3, Log-Lik: -535.459, Max-Change: 0.06100
Iteration: 4, Log-Lik: -535.353, Max-Change: 0.06793
Iteration: 5, Log-Lik: -535.192, Max-Change: 0.03358
Iteration: 6, Log-Lik: -535.155, Max-Change: 0.02549
Iteration: 7, Log-Lik: -535.135, Max-Change: 0.02486
Iteration: 8, Log-Lik: -535.120, Max-Change: 0.01301
Iteration: 9, Log-Lik: -535.115, Max-Change: 0.00965
Iteration: 10, Log-Lik: -535.112, Max-Change: 0.00912
Iteration: 11, Log-Lik: -535.110, Max-Change: 0.00476
Iteration: 12, Log-Lik: -535.109, Max-Change: 0.00351
Iteration: 13, Log-Lik: -535.109, Max-Change: 0.00325
Iteration: 14, Log-Lik: -535.109, Max-Change: 0.00174
Iteration: 15, Log-Lik: -535.109, Max-Change: 0.00126
Iteration: 16, Log-Lik: -535.109, Max-Change: 0.00113
Iteration: 17, Log-Lik: -535.109, Max-Change: 0.00062
Iteration: 18, Log-Lik: -535.109, Max-Change: 0.00046
Iteration: 19, Log-Lik: -535.109, Max-Change: 0.00042
Iteration: 20, Log-Lik: -535.109, Max-Change: 0.00023
Iteration: 21, Log-Lik: -535.109, Max-Change: 0.00017
Iteration: 22, Log-Lik: -535.109, Max-Change: 0.00015
Iteration: 23, Log-Lik: -535.109, Max-Change: 0.00010coef(mod_dialogo)## $D_2
## a1 d g u
## par 1 2.951 0 1
##
## $D_3
## a1 d g u
## par 1 2.596 0 1
##
## $D_4
## a1 d g u
## par 1 1.984 0 1
##
## $D_5
## a1 d g u
## par 1 -1.209 0 1
##
## $D_6
## a1 d g u
## par 1 0.916 0 1
##
## $D_7
## a1 d g u
## par 1 0.916 0 1
##
## $D_8
## a1 d g u
## par 1 -3.145 0 1
##
## $D_9
## a1 d g u
## par 1 -1.266 0 1
##
## $D_10
## a1 d g u
## par 1 -0.784 0 1
##
## $D_11
## a1 d g u
## par 1 -0.349 0 1
##
## $GroupPars
## MEAN_1 COV_11
## par 0 1.42summary(mod_dialogo)## F1 h2
## D_2 0.604 0.364
## D_3 0.604 0.364
## D_4 0.604 0.364
## D_5 0.604 0.364
## D_6 0.604 0.364
## D_7 0.604 0.364
## D_8 0.604 0.364
## D_9 0.604 0.364
## D_10 0.604 0.364
## D_11 0.604 0.364
##
## SS loadings: 3.645
## Proportion Var: 0.364
##
## Factor correlations:
##
## F1
## F1 1M2(mod_dialogo)## M2 df p RMSEA RMSEA_5 RMSEA_95 SRMSR TLI
## stats 58.50763 45 0.08521805 0.05200221 0 0.08612902 0.1146573 0.9017296
## CFI
## stats 0.9017296plot(mod_dialogo)
plot(mod_dialogo, type = 'trace')
plot(mod_dialogo, type = 'info')
Modelo de 1 parâmetro (Rasch no mirt) MATEMÁTICA
mod_matematica<-mirt(data_matematica,1, itemtype = "Rasch")##
Iteration: 1, Log-Lik: -241.632, Max-Change: 0.24458
Iteration: 2, Log-Lik: -238.568, Max-Change: 0.29686
Iteration: 3, Log-Lik: -235.667, Max-Change: 0.34787
Iteration: 4, Log-Lik: -233.727, Max-Change: 0.50208
Iteration: 5, Log-Lik: -230.265, Max-Change: 0.43535
Iteration: 6, Log-Lik: -228.534, Max-Change: 0.44014
Iteration: 7, Log-Lik: -227.899, Max-Change: 0.63573
Iteration: 8, Log-Lik: -225.773, Max-Change: 0.36747
Iteration: 9, Log-Lik: -225.179, Max-Change: 0.30070
Iteration: 10, Log-Lik: -224.868, Max-Change: 0.36701
Iteration: 11, Log-Lik: -224.345, Max-Change: 0.16060
Iteration: 12, Log-Lik: -224.191, Max-Change: 0.11383
Iteration: 13, Log-Lik: -224.087, Max-Change: 0.10422
Iteration: 14, Log-Lik: -224.001, Max-Change: 0.05445
Iteration: 15, Log-Lik: -223.957, Max-Change: 0.03806
Iteration: 16, Log-Lik: -223.927, Max-Change: 0.03419
Iteration: 17, Log-Lik: -223.901, Max-Change: 0.01787
Iteration: 18, Log-Lik: -223.887, Max-Change: 0.01240
Iteration: 19, Log-Lik: -223.878, Max-Change: 0.01129
Iteration: 20, Log-Lik: -223.870, Max-Change: 0.00587
Iteration: 21, Log-Lik: -223.866, Max-Change: 0.00401
Iteration: 22, Log-Lik: -223.863, Max-Change: 0.00365
Iteration: 23, Log-Lik: -223.860, Max-Change: 0.00189
Iteration: 24, Log-Lik: -223.859, Max-Change: 0.00135
Iteration: 25, Log-Lik: -223.858, Max-Change: 0.00125
Iteration: 26, Log-Lik: -223.857, Max-Change: 0.00062
Iteration: 27, Log-Lik: -223.856, Max-Change: 0.00040
Iteration: 28, Log-Lik: -223.856, Max-Change: 0.00047
Iteration: 29, Log-Lik: -223.856, Max-Change: 0.00020
Iteration: 30, Log-Lik: -223.855, Max-Change: 0.00008coef(mod_matematica)## $M_1
## a1 d g u
## par 1 1.08 0 1
##
## $M_2
## a1 d g u
## par 1 -3.18 0 1
##
## $M_3
## a1 d g u
## par 1 -0.794 0 1
##
## $M_4
## a1 d g u
## par 1 -0.725 0 1
##
## $GroupPars
## MEAN_1 COV_11
## par 0 5.505summary(mod_matematica)## F1 h2
## M_1 1.19 1.41
## M_2 1.19 1.41
## M_3 1.19 1.41
## M_4 1.19 1.41
##
## SS loadings: 5.65
## Proportion Var: 1.413
##
## Factor correlations:
##
## F1
## F1 1M2(mod_matematica)## M2 df p RMSEA RMSEA_5 RMSEA_95 SRMSR TLI
## stats 17.57112 6 0.007398331 0.1318107 0.06242334 0.2045778 0.1317779 0.905146
## CFI
## stats 0.905146plot(mod_matematica)
plot(mod_matematica, type = 'trace')
plot(mod_matematica, type = 'info')
Modelo de 1 parâmetro (Rasch no mirt) COMPREENSÃO
mod_compreensao<-mirt(data_compreensao,1, itemtype = "Rasch")##
Iteration: 1, Log-Lik: -492.860, Max-Change: 0.47128
Iteration: 2, Log-Lik: -482.185, Max-Change: 0.55258
Iteration: 3, Log-Lik: -475.039, Max-Change: 0.56187
Iteration: 4, Log-Lik: -470.726, Max-Change: 0.51114
Iteration: 5, Log-Lik: -468.284, Max-Change: 0.42470
Iteration: 6, Log-Lik: -466.945, Max-Change: 0.32915
Iteration: 7, Log-Lik: -466.207, Max-Change: 0.45200
Iteration: 8, Log-Lik: -465.504, Max-Change: 0.12553
Iteration: 9, Log-Lik: -465.386, Max-Change: 0.07716
Iteration: 10, Log-Lik: -465.307, Max-Change: 0.07068
Iteration: 11, Log-Lik: -465.258, Max-Change: 0.03276
Iteration: 12, Log-Lik: -465.234, Max-Change: 0.02221
Iteration: 13, Log-Lik: -465.213, Max-Change: 0.02752
Iteration: 14, Log-Lik: -465.197, Max-Change: 0.00944
Iteration: 15, Log-Lik: -465.191, Max-Change: 0.00649
Iteration: 16, Log-Lik: -465.186, Max-Change: 0.00983
Iteration: 17, Log-Lik: -465.181, Max-Change: 0.00303
Iteration: 18, Log-Lik: -465.179, Max-Change: 0.00213
Iteration: 19, Log-Lik: -465.178, Max-Change: 0.00356
Iteration: 20, Log-Lik: -465.176, Max-Change: 0.00098
Iteration: 21, Log-Lik: -465.175, Max-Change: 0.00070
Iteration: 22, Log-Lik: -465.175, Max-Change: 0.00109
Iteration: 23, Log-Lik: -465.175, Max-Change: 0.00033
Iteration: 24, Log-Lik: -465.174, Max-Change: 0.00022
Iteration: 25, Log-Lik: -465.174, Max-Change: 0.00032
Iteration: 26, Log-Lik: -465.174, Max-Change: 0.00012
Iteration: 27, Log-Lik: -465.174, Max-Change: 0.00009coef(mod_compreensao)## $C_benigno
## a1 d g u
## par 1 2.893 0 1
##
## $C_biopsia
## a1 d g u
## par 1 2.429 0 1
##
## $C_coloutero
## a1 d g u
## par 1 2.794 0 1
##
## $C_ginecologia
## a1 d g u
## par 1 3.885 0 1
##
## $C_papilomavirus
## a1 d g u
## par 1 -1.04 0 1
##
## $C_histerectomia
## a1 d g u
## par 1 -2.41 0 1
##
## $C_maligno
## a1 d g u
## par 1 3.333 0 1
##
## $C_mastectomia
## a1 d g u
## par 1 -0.899 0 1
##
## $C_metastase
## a1 d g u
## par 1 -0.167 0 1
##
## $C_pelvico
## a1 d g u
## par 1 0.597 0 1
##
## $GroupPars
## MEAN_1 COV_11
## par 0 4.697summary(mod_compreensao)## F1 h2
## C_benigno 1.1 1.21
## C_biopsia 1.1 1.21
## C_coloutero 1.1 1.21
## C_ginecologia 1.1 1.21
## C_papilomavirus 1.1 1.21
## C_histerectomia 1.1 1.21
## C_maligno 1.1 1.21
## C_mastectomia 1.1 1.21
## C_metastase 1.1 1.21
## C_pelvico 1.1 1.21
##
## SS loadings: 12.053
## Proportion Var: 1.205
##
## Factor correlations:
##
## F1
## F1 1M2(mod_compreensao)## M2 df p RMSEA RMSEA_5 RMSEA_95 SRMSR TLI
## stats 56.74873 45 0.1124681 0.04849842 0 0.08352123 0.0955044 0.9743571
## CFI
## stats 0.9743571plot(mod_compreensao)
plot(mod_compreensao, type = 'trace')
plot(mod_compreensao, type = 'info')
Modelo de 1 parâmetro (Rasch no mirt) FAMILIARIDADE
mod_familiaridade<-mirt(data_familiaridade,1, itemtype = "Rasch")##
Iteration: 1, Log-Lik: -322.177, Max-Change: 0.31256
Iteration: 2, Log-Lik: -317.289, Max-Change: 0.37342
Iteration: 3, Log-Lik: -313.293, Max-Change: 0.41296
Iteration: 4, Log-Lik: -311.219, Max-Change: 0.61195
Iteration: 5, Log-Lik: -307.147, Max-Change: 0.43018
Iteration: 6, Log-Lik: -305.866, Max-Change: 0.36940
Iteration: 7, Log-Lik: -305.514, Max-Change: 0.48950
Iteration: 8, Log-Lik: -304.259, Max-Change: 0.22232
Iteration: 9, Log-Lik: -304.024, Max-Change: 0.15462
Iteration: 10, Log-Lik: -303.867, Max-Change: 0.13627
Iteration: 11, Log-Lik: -303.760, Max-Change: 0.07811
Iteration: 12, Log-Lik: -303.702, Max-Change: 0.05452
Iteration: 13, Log-Lik: -303.659, Max-Change: 0.05154
Iteration: 14, Log-Lik: -303.626, Max-Change: 0.02858
Iteration: 15, Log-Lik: -303.609, Max-Change: 0.01995
Iteration: 16, Log-Lik: -303.595, Max-Change: 0.01993
Iteration: 17, Log-Lik: -303.584, Max-Change: 0.01097
Iteration: 18, Log-Lik: -303.577, Max-Change: 0.00761
Iteration: 19, Log-Lik: -303.573, Max-Change: 0.00782
Iteration: 20, Log-Lik: -303.569, Max-Change: 0.00438
Iteration: 21, Log-Lik: -303.566, Max-Change: 0.00302
Iteration: 22, Log-Lik: -303.564, Max-Change: 0.00304
Iteration: 23, Log-Lik: -303.563, Max-Change: 0.00177
Iteration: 24, Log-Lik: -303.562, Max-Change: 0.00118
Iteration: 25, Log-Lik: -303.561, Max-Change: 0.00118
Iteration: 26, Log-Lik: -303.560, Max-Change: 0.00066
Iteration: 27, Log-Lik: -303.560, Max-Change: 0.00051
Iteration: 28, Log-Lik: -303.560, Max-Change: 0.00069
Iteration: 29, Log-Lik: -303.559, Max-Change: 0.00031
Iteration: 30, Log-Lik: -303.559, Max-Change: 0.00014
Iteration: 31, Log-Lik: -303.559, Max-Change: 0.00019
Iteration: 32, Log-Lik: -303.559, Max-Change: 0.00007coef(mod_familiaridade)## $F_biopsia
## a1 d g u
## par 1 3.799 0 1
##
## $F_papilomavirus
## a1 d g u
## par 1 0.288 0 1
##
## $F_histerectomia
## a1 d g u
## par 1 -0.098 0 1
##
## $F_mastectomia
## a1 d g u
## par 1 1.422 0 1
##
## $F_metastase
## a1 d g u
## par 1 1.943 0 1
##
## $F_pelvico
## a1 d g u
## par 1 3.033 0 1
##
## $GroupPars
## MEAN_1 COV_11
## par 0 4.809summary(mod_familiaridade)## F1 h2
## F_biopsia 1.11 1.23
## F_papilomavirus 1.11 1.23
## F_histerectomia 1.11 1.23
## F_mastectomia 1.11 1.23
## F_metastase 1.11 1.23
## F_pelvico 1.11 1.23
##
## SS loadings: 7.405
## Proportion Var: 1.234
##
## Factor correlations:
##
## F1
## F1 1M2(mod_familiaridade)## M2 df p RMSEA RMSEA_5 RMSEA_95 SRMSR TLI
## stats 19.19227 15 0.2051529 0.05017849 0 0.1081294 0.089017 0.9805341
## CFI
## stats 0.9805341plot(mod_familiaridade)
plot(mod_familiaridade, type = 'trace')
plot(mod_familiaridade, type = 'info')
Comparando modelos em termos de resíduos
#anova(mod2,mod1)Modelo de 1 parâmetro (Rasch no eRm)
Fit do modelo rasch
Leitura
mod_leitura<-RM(data_leitura)
summary(mod_leitura)##
## Results of RM estimation:
##
## Call: RM(X = data_leitura)
##
## Conditional log-likelihood: -83.85195
## Number of iterations: 14
## Number of parameters: 11
##
## Item (Category) Difficulty Parameters (eta): with 0.95 CI:
## Estimate Std. Error lower CI upper CI
## L_biopsia 1.731 0.543 0.667 2.794
## L_coloutero -3.096 1.203 -5.453 -0.739
## L_ginecologia -3.096 1.203 -5.453 -0.739
## L_papilomavirus 2.690 0.520 1.671 3.708
## L_histerectomia 2.548 0.522 1.526 3.570
## L_maligno 0.273 0.628 -0.957 1.503
## L_mastectomia 2.246 0.527 1.213 3.280
## L_metastase 2.400 0.524 1.373 3.427
## L_pelvico 0.576 0.602 -0.605 1.757
## L_utero -3.096 1.203 -5.453 -0.739
## L_vagina -3.096 1.203 -5.453 -0.739
##
## Item Easiness Parameters (beta) with 0.95 CI:
## Estimate Std. Error lower CI upper CI
## beta L_benigno 0.080 0.664 -1.222 1.382
## beta L_biopsia -1.731 0.543 -2.794 -0.667
## beta L_coloutero 3.096 1.203 0.739 5.453
## beta L_ginecologia 3.096 1.203 0.739 5.453
## beta L_papilomavirus -2.690 0.520 -3.708 -1.671
## beta L_histerectomia -2.548 0.522 -3.570 -1.526
## beta L_maligno -0.273 0.628 -1.503 0.957
## beta L_mastectomia -2.246 0.527 -3.280 -1.213
## beta L_metastase -2.400 0.524 -3.427 -1.373
## beta L_pelvico -0.576 0.602 -1.757 0.605
## beta L_utero 3.096 1.203 0.739 5.453
## beta L_vagina 3.096 1.203 0.739 5.453par(mfrow=c(1,1))
plotINFO(mod_leitura,type="item")
#dasslabels[data_leitura]#medidas de ajuste
pres <- person.parameter(mod_leitura)
IC(pres)##
## Information Criteria:
## value npar AIC BIC cAIC
## joint log-lik -118.00650 20 276.0130 305.9432 325.9432
## marginal log-lik -220.46239 11 462.9248 492.8283 503.8283
## conditional log-lik -83.85195 11 189.7039 219.6074 230.6074itemfit(pres)## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0##
## Itemfit Statistics:
## Chisq df p-value Outfit MSQ Infit MSQ Outfit t Infit t Discrim
## L_benigno 17.290 32 0.984 0.524 0.881 -0.402 -0.185 0.715
## L_biopsia 27.090 32 0.714 0.821 0.753 -0.190 -1.033 0.626
## L_coloutero 3.552 32 1.000 0.108 1.048 0.894 0.300 0.394
## L_ginecologia 1.628 32 1.000 0.049 0.460 0.810 -0.747 0.591
## L_papilomavirus 31.744 32 0.480 0.962 1.119 0.193 0.707 0.332
## L_histerectomia 30.137 32 0.561 0.913 0.911 0.100 -0.432 0.403
## L_maligno 20.377 32 0.944 0.617 0.916 -0.383 -0.125 0.646
## L_mastectomia 25.578 32 0.782 0.775 0.871 -0.200 -0.589 0.501
## L_metastase 33.481 32 0.395 1.015 1.116 0.238 0.639 0.404
## L_pelvico 70.512 32 0.000 2.137 1.376 1.587 1.131 0.314
## L_utero 1.628 32 1.000 0.049 0.460 0.810 -0.747 0.591
## L_vagina 1.628 32 1.000 0.049 0.460 0.810 -0.747 0.591personfit(pres)##
## Personfit Statistics:
## Chisq df p-value Outfit MSQ Infit MSQ Outfit t Infit t
## P8 5.273 11 0.917 0.439 0.976 1.69 0.21
## P9 4.591 11 0.949 0.383 0.725 -0.50 -0.50
## P11 6.019 11 0.872 0.502 1.031 1.71 0.29
## P14 4.649 11 0.947 0.387 0.918 1.67 0.13
## P16 5.273 11 0.917 0.439 0.976 1.69 0.21
## P28 4.119 11 0.966 0.343 0.858 1.65 0.04
## P37 2.928 11 0.992 0.244 0.479 -0.89 -0.91
## P47 5.664 11 0.895 0.472 0.958 0.94 0.01
## P54 9.470 11 0.579 0.789 1.177 1.78 0.47
## P61 6.019 11 0.872 0.502 1.031 1.71 0.29
## P62 12.202 11 0.349 1.017 1.258 0.81 0.88
## P63 12.202 11 0.349 1.017 1.258 0.81 0.88
## P67 4.350 11 0.959 0.362 0.746 0.89 -0.61
## P69 4.278 11 0.961 0.357 0.631 0.44 -1.28
## P72 4.119 11 0.966 0.343 0.858 1.65 0.04
## P76 48.019 11 0.000 4.002 1.719 1.61 2.15
## P78 9.470 11 0.579 0.789 1.177 1.78 0.47
## P79 5.338 11 0.914 0.445 0.905 0.93 -0.13
## P81 3.834 11 0.975 0.319 0.704 0.19 -0.50
## P83 7.696 11 0.740 0.641 0.904 0.11 -0.10
## P85 2.961 11 0.991 0.247 0.377 -0.44 -1.87
## P89 5.273 11 0.917 0.439 0.976 1.69 0.21
## P90 4.119 11 0.966 0.343 0.858 1.65 0.04
## P92 4.350 11 0.959 0.362 0.746 0.89 -0.61
## P96 16.946 11 0.109 1.412 1.559 0.96 1.65
## P98 14.571 11 0.203 1.214 1.336 1.20 0.93
## P99 10.291 11 0.504 0.858 1.217 0.74 0.76
## P100 4.350 11 0.959 0.362 0.746 0.89 -0.61
## P101 14.056 11 0.230 1.171 1.317 0.49 0.78
## P104 3.253 11 0.987 0.271 0.491 -0.74 -1.21
## P108 10.250 11 0.508 0.854 1.283 0.50 0.84
## P110 4.119 11 0.966 0.343 0.858 1.65 0.04
## P111 4.588 11 0.949 0.382 0.681 0.46 -1.07summary(pres)##
## Estimation of Ability Parameters
##
## Collapsed log-likelihood: -36.42696
## Number of iterations: 8
## Number of parameters: 9
##
## ML estimated ability parameters (without spline interpolated values):
## Estimate Std. Err. 2.5 % 97.5 %
## theta P8 3.8548635 1.1040315 1.69100144 6.018726
## theta P9 0.2935890 0.8494611 -1.37132416 1.958502
## theta P11 3.8548635 1.1040315 1.69100144 6.018726
## theta P14 3.8548635 1.1040315 1.69100144 6.018726
## theta P16 3.8548635 1.1040315 1.69100144 6.018726
## theta P28 3.8548635 1.1040315 1.69100144 6.018726
## theta P37 -0.4859159 0.9199362 -2.28895776 1.317126
## theta P47 2.9242651 0.8706829 1.21775797 4.630772
## theta P54 3.8548635 1.1040315 1.69100144 6.018726
## theta P61 3.8548635 1.1040315 1.69100144 6.018726
## theta P62 2.2364716 0.8012694 0.66601247 3.806931
## theta P63 2.2364716 0.8012694 0.66601247 3.806931
## theta P67 2.9242651 0.8706829 1.21775797 4.630772
## theta P69 2.2364716 0.8012694 0.66601247 3.806931
## theta P72 3.8548635 1.1040315 1.69100144 6.018726
## theta P76 -3.2108005 0.9499422 -5.07265292 -1.348948
## theta P78 3.8548635 1.1040315 1.69100144 6.018726
## theta P79 2.9242651 0.8706829 1.21775797 4.630772
## theta P81 -2.3271260 0.9468980 -4.18301205 -0.471240
## theta P83 0.9759831 0.8069021 -0.60551597 2.557482
## theta P85 0.9759831 0.8069021 -0.60551597 2.557482
## theta P89 3.8548635 1.1040315 1.69100144 6.018726
## theta P90 3.8548635 1.1040315 1.69100144 6.018726
## theta P92 2.9242651 0.8706829 1.21775797 4.630772
## theta P96 2.2364716 0.8012694 0.66601247 3.806931
## theta P98 2.9242651 0.8706829 1.21775797 4.630772
## theta P99 2.2364716 0.8012694 0.66601247 3.806931
## theta P100 2.9242651 0.8706829 1.21775797 4.630772
## theta P101 0.2935890 0.8494611 -1.37132416 1.958502
## theta P104 0.2935890 0.8494611 -1.37132416 1.958502
## theta P108 1.6094084 0.7889033 0.06318638 3.155630
## theta P110 3.8548635 1.1040315 1.69100144 6.018726
## theta P111 2.2364716 0.8012694 0.66601247 3.806931gof.res <- gofIRT(pres)
summary(gof.res)##
## Goodness-of-Fit Tests
## value df p-value
## Collapsed Deviance 66.178 108 0.999
## Hosmer-Lemeshow 3.918 8 0.864
## Rost Deviance 88.291 4084 1.000
## Casewise Deviance 236.013 376 1.000
##
## R-Squared Measures
## Pearson R2: 0.494
## Sum-of-Squares R2: 0.494
## McFadden R2: 0.896
##
## Classifier Results - Confusion Matrix (relative frequencies)
## observed
## predicted 0 1
## 0 0.164 0.051
## 1 0.088 0.697
##
## Accuracy: 0.861
## Sensitivity: 0.932
## Specificity: 0.65
## Area under ROC: 0.922
## Gini coefficient: 0.844gof.res##
## Goodness-of-Fit Results:
## Collapsed Deviance = 66.178 (df = 108, p-value = 0.999)
## Pearson R2: 0.494
## Area Under ROC: 0.922#curvas
plotICC(mod_leitura)
plotICC(mod_leitura, empICC=list("raw"))
par(mfrow=c(1,1))
plotjointICC(mod_leitura, legpos = "left")
plotPImap(mod_leitura,sorted=T)
#dasslabelsDiálogo
mod_dialogo<-RM(data_dialogo)
summary(mod_dialogo)##
## Results of RM estimation:
##
## Call: RM(X = data_dialogo)
##
## Conditional log-likelihood: -298.7652
## Number of iterations: 14
## Number of parameters: 9
##
## Item (Category) Difficulty Parameters (eta): with 0.95 CI:
## Estimate Std. Error lower CI upper CI
## D_3 -2.291 0.305 -2.890 -1.693
## D_4 -1.710 0.260 -2.218 -1.201
## D_5 1.430 0.244 0.952 1.908
## D_6 -0.709 0.219 -1.138 -0.281
## D_7 -0.709 0.219 -1.138 -0.281
## D_8 3.622 0.438 2.763 4.481
## D_9 1.491 0.247 1.008 1.974
## D_10 0.978 0.228 0.531 1.424
## D_11 0.528 0.218 0.102 0.955
##
## Item Easiness Parameters (beta) with 0.95 CI:
## Estimate Std. Error lower CI upper CI
## beta D_2 2.629 0.341 1.960 3.297
## beta D_3 2.291 0.305 1.693 2.890
## beta D_4 1.710 0.260 1.201 2.218
## beta D_5 -1.430 0.244 -1.908 -0.952
## beta D_6 0.709 0.219 0.281 1.138
## beta D_7 0.709 0.219 0.281 1.138
## beta D_8 -3.622 0.438 -4.481 -2.763
## beta D_9 -1.491 0.247 -1.974 -1.008
## beta D_10 -0.978 0.228 -1.424 -0.531
## beta D_11 -0.528 0.218 -0.955 -0.102par(mfrow=c(1,1))
plotINFO(mod_dialogo,type="item")
#dasslabels[data_leitura]#medidas de ajuste
pres01 <- person.parameter(mod_dialogo)
IC(pres01)##
## Information Criteria:
## value npar AIC BIC cAIC
## joint log-lik -415.0313 18 866.0625 914.5068 932.5068
## marginal log-lik -530.1634 9 1078.3268 1102.7933 1111.7933
## conditional log-lik -298.7652 9 615.5305 639.9970 648.9970itemfit(pres01)##
## Itemfit Statistics:
## Chisq df p-value Outfit MSQ Infit MSQ Outfit t Infit t Discrim
## D_2 168.932 108 0.000 1.550 0.764 0.943 -1.023 0.199
## D_3 144.586 108 0.011 1.326 1.165 0.725 0.864 -0.058
## D_4 59.769 108 1.000 0.548 0.780 -1.226 -1.599 0.406
## D_5 82.898 108 0.965 0.761 0.887 -0.919 -0.832 0.443
## D_6 88.896 108 0.910 0.816 0.939 -0.759 -0.611 0.357
## D_7 97.630 108 0.753 0.896 0.960 -0.381 -0.391 0.365
## D_8 30.889 108 1.000 0.283 0.643 -0.968 -1.313 0.322
## D_9 134.341 108 0.044 1.232 1.108 0.895 0.804 0.263
## D_10 82.630 108 0.967 0.758 0.841 -1.199 -1.404 0.540
## D_11 73.172 108 0.996 0.671 0.787 -2.021 -2.220 0.618personfit(pres01)##
## Personfit Statistics:
## Chisq df p-value Outfit MSQ Infit MSQ Outfit t Infit t
## P1 4.288 9 0.891 0.429 0.730 -0.28 -0.60
## P2 4.944 9 0.839 0.494 0.774 0.04 -0.43
## P3 5.431 9 0.795 0.543 0.767 -0.40 -0.44
## P4 4.412 9 0.882 0.441 0.753 -0.26 -0.53
## P5 4.288 9 0.891 0.429 0.730 -0.28 -0.60
## P6 8.638 9 0.471 0.864 1.726 1.01 1.02
## P7 12.933 9 0.166 1.293 1.546 0.60 1.22
## P8 6.314 9 0.708 0.631 0.971 0.18 0.08
## P9 2.793 9 0.972 0.279 0.363 -0.99 -1.83
## P10 6.646 9 0.674 0.665 0.897 -0.19 -0.10
## P11 3.438 9 0.944 0.344 0.493 -0.54 -1.29
## P12 2.804 9 0.972 0.280 0.419 -0.24 -1.61
## P13 3.245 9 0.954 0.324 0.468 -0.82 -1.52
## P14 5.225 9 0.814 0.523 0.765 -0.40 -0.50
## P15 6.314 9 0.708 0.631 0.971 0.18 0.08
## P16 3.344 9 0.949 0.334 0.639 0.04 -0.56
## P17 7.504 9 0.585 0.750 0.989 -0.04 0.11
## P18 3.438 9 0.944 0.344 0.493 -0.54 -1.29
## P19 7.504 9 0.585 0.750 0.989 -0.04 0.11
## P20 8.962 9 0.441 0.896 1.190 0.23 0.55
## P21 3.245 9 0.954 0.324 0.468 -0.82 -1.52
## P22 13.328 9 0.148 1.333 1.420 0.64 0.99
## P23 10.000 9 0.351 1.000 1.010 0.34 0.17
## P24 3.500 9 0.941 0.350 0.664 0.05 -0.50
## P25 12.699 9 0.177 1.270 1.855 1.13 1.13
## P26 3.438 9 0.944 0.344 0.493 -0.54 -1.29
## P27 51.905 9 0.000 5.190 2.083 2.32 2.17
## P28 2.798 9 0.972 0.280 0.520 0.23 -1.17
## P29 3.584 9 0.937 0.358 0.904 0.97 0.08
## P30 3.500 9 0.941 0.350 0.664 0.05 -0.50
## P31 6.881 9 0.650 0.688 0.970 0.37 0.13
## P32 1.272 9 0.999 0.127 0.368 0.58 -0.80
## P33 13.328 9 0.148 1.333 1.420 0.64 0.99
## P34 23.363 9 0.005 2.336 1.899 1.30 1.77
## P35 11.319 9 0.254 1.132 1.199 0.49 0.59
## P37 2.804 9 0.972 0.280 0.419 -0.24 -1.61
## P38 17.161 9 0.046 1.716 1.632 0.99 1.37
## P39 3.367 9 0.948 0.337 0.560 -0.44 -1.15
## P40 3.344 9 0.949 0.334 0.639 0.04 -0.56
## P41 3.344 9 0.949 0.334 0.639 0.04 -0.56
## P42 1.272 9 0.999 0.127 0.368 0.58 -0.80
## P43 124.457 9 0.000 12.446 1.262 2.88 0.62
## P44 5.431 9 0.795 0.543 0.767 -0.40 -0.44
## P45 8.962 9 0.441 0.896 1.190 0.23 0.55
## P46 26.611 9 0.002 2.661 1.827 1.28 1.70
## P47 3.438 9 0.944 0.344 0.493 -0.54 -1.29
## P48 7.504 9 0.585 0.750 0.989 -0.04 0.11
## P49 2.798 9 0.972 0.280 0.520 0.23 -1.17
## P50 3.344 9 0.949 0.334 0.639 0.04 -0.56
## P51 14.522 9 0.105 1.452 1.227 0.76 0.63
## P52 26.391 9 0.002 2.639 2.517 1.67 2.62
## P53 2.793 9 0.972 0.279 0.363 -0.99 -1.83
## P54 8.375 9 0.497 0.838 1.002 0.06 0.15
## P55 3.438 9 0.944 0.344 0.493 -0.54 -1.29
## P56 3.344 9 0.949 0.334 0.639 0.04 -0.56
## P57 23.409 9 0.005 2.341 1.375 1.16 0.92
## P58 9.653 9 0.379 0.965 1.303 0.24 0.82
## P59 5.225 9 0.814 0.523 0.765 -0.40 -0.50
## P60 5.225 9 0.814 0.523 0.765 -0.40 -0.50
## P61 11.319 9 0.254 1.132 1.199 0.49 0.59
## P62 8.030 9 0.531 0.803 0.950 0.13 0.03
## P63 5.766 9 0.763 0.577 0.813 -0.15 -0.31
## P64 3.438 9 0.944 0.344 0.493 -0.54 -1.29
## P65 2.804 9 0.972 0.280 0.419 -0.24 -1.61
## P67 28.641 9 0.001 2.864 2.397 1.49 2.62
## P68 3.367 9 0.948 0.337 0.560 -0.44 -1.15
## P69 12.315 9 0.196 1.232 1.259 0.54 0.69
## P70 8.667 9 0.469 0.867 1.014 0.10 0.18
## P71 3.438 9 0.944 0.344 0.493 -0.54 -1.29
## P72 12.515 9 0.186 1.251 1.040 0.57 0.24
## P73 2.793 9 0.972 0.279 0.363 -0.99 -1.83
## P74 8.740 9 0.462 0.874 1.150 0.13 0.49
## P75 5.431 9 0.795 0.543 0.767 -0.40 -0.44
## P76 9.485 9 0.394 0.949 1.285 0.22 0.78
## P77 3.367 9 0.948 0.337 0.560 -0.44 -1.15
## P78 5.766 9 0.763 0.577 0.813 -0.15 -0.31
## P79 4.944 9 0.839 0.494 0.774 0.04 -0.43
## P80 17.107 9 0.047 1.711 1.133 0.99 0.44
## P81 3.245 9 0.954 0.324 0.468 -0.82 -1.52
## P82 2.793 9 0.972 0.279 0.363 -0.99 -1.83
## P83 7.962 9 0.538 0.796 1.241 0.62 0.65
## P84 3.438 9 0.944 0.344 0.493 -0.54 -1.29
## P85 2.804 9 0.972 0.280 0.419 -0.24 -1.61
## P87 9.653 9 0.379 0.965 1.303 0.24 0.82
## P88 6.634 9 0.675 0.663 0.870 -0.04 -0.16
## P89 10.000 9 0.351 1.000 1.010 0.34 0.17
## P90 7.376 9 0.598 0.738 1.068 0.28 0.30
## P91 6.401 9 0.699 0.640 1.077 0.01 0.32
## P92 11.958 9 0.216 1.196 1.032 0.52 0.22
## P93 4.158 9 0.901 0.416 0.622 -0.61 -0.95
## P94 25.645 9 0.002 2.565 1.662 1.58 1.50
## P95 1.272 9 0.999 0.127 0.368 0.58 -0.80
## P96 3.245 9 0.954 0.324 0.468 -0.82 -1.52
## P97 2.793 9 0.972 0.279 0.363 -0.99 -1.83
## P98 8.667 9 0.469 0.867 1.014 0.10 0.18
## P99 8.210 9 0.513 0.821 1.704 0.99 1.00
## P100 14.843 9 0.095 1.484 1.360 0.75 0.88
## P101 4.944 9 0.839 0.494 0.774 0.04 -0.43
## P102 5.225 9 0.814 0.523 0.765 -0.40 -0.50
## P103 3.584 9 0.937 0.358 0.904 0.97 0.08
## P104 5.431 9 0.795 0.543 0.767 -0.40 -0.44
## P105 23.496 9 0.005 2.350 1.348 1.44 0.91
## P106 6.646 9 0.674 0.665 0.897 -0.19 -0.10
## P107 3.438 9 0.944 0.344 0.493 -0.54 -1.29
## P108 2.804 9 0.972 0.280 0.419 -0.24 -1.61
## P109 5.225 9 0.814 0.523 0.765 -0.40 -0.50
## P110 4.944 9 0.839 0.494 0.774 0.04 -0.43
## P111 2.793 9 0.972 0.279 0.363 -0.99 -1.83
## P112 3.344 9 0.949 0.334 0.639 0.04 -0.56summary(pres01)##
## Estimation of Ability Parameters
##
## Collapsed log-likelihood: -25.04474
## Number of iterations: 9
## Number of parameters: 9
##
## ML estimated ability parameters (without spline interpolated values):
## Estimate Std. Err. 2.5 % 97.5 %
## theta P1 1.34237132 0.8687144 -0.3602777 3.0450203
## theta P2 -1.43962335 0.8572193 -3.1197423 0.2404956
## theta P3 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P4 1.34237132 0.8687144 -0.3602777 3.0450203
## theta P5 1.34237132 0.8687144 -0.3602777 3.0450203
## theta P6 3.37776635 1.2479377 0.9318534 5.8236793
## theta P7 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P8 -1.43962335 0.8572193 -3.1197423 0.2404956
## theta P9 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P10 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P11 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P12 -1.43962335 0.8572193 -3.1197423 0.2404956
## theta P13 0.62781932 0.8293772 -0.9977301 2.2533687
## theta P14 0.62781932 0.8293772 -0.9977301 2.2533687
## theta P15 -1.43962335 0.8572193 -3.1197423 0.2404956
## theta P16 2.18044393 0.9776950 0.2641969 4.0966910
## theta P17 0.62781932 0.8293772 -0.9977301 2.2533687
## theta P18 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P19 0.62781932 0.8293772 -0.9977301 2.2533687
## theta P20 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P21 0.62781932 0.8293772 -0.9977301 2.2533687
## theta P22 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P23 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P24 2.18044393 0.9776950 0.2641969 4.0966910
## theta P25 3.37776635 1.2479377 0.9318534 5.8236793
## theta P26 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P27 1.34237132 0.8687144 -0.3602777 3.0450203
## theta P28 -2.22594152 0.9270793 -4.0429836 -0.4088994
## theta P29 -3.25631902 1.1466365 -5.5036853 -1.0089528
## theta P30 2.18044393 0.9776950 0.2641969 4.0966910
## theta P31 2.18044393 0.9776950 0.2641969 4.0966910
## theta P32 3.37776635 1.2479377 0.9318534 5.8236793
## theta P33 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P34 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P35 1.34237132 0.8687144 -0.3602777 3.0450203
## theta P37 -1.43962335 0.8572193 -3.1197423 0.2404956
## theta P38 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P39 1.34237132 0.8687144 -0.3602777 3.0450203
## theta P40 2.18044393 0.9776950 0.2641969 4.0966910
## theta P41 2.18044393 0.9776950 0.2641969 4.0966910
## theta P42 3.37776635 1.2479377 0.9318534 5.8236793
## theta P43 2.18044393 0.9776950 0.2641969 4.0966910
## theta P44 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P45 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P46 -1.43962335 0.8572193 -3.1197423 0.2404956
## theta P47 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P48 0.62781932 0.8293772 -0.9977301 2.2533687
## theta P49 -2.22594152 0.9270793 -4.0429836 -0.4088994
## theta P50 2.18044393 0.9776950 0.2641969 4.0966910
## theta P51 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P52 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P53 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P54 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P55 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P56 2.18044393 0.9776950 0.2641969 4.0966910
## theta P57 -1.43962335 0.8572193 -3.1197423 0.2404956
## theta P58 0.62781932 0.8293772 -0.9977301 2.2533687
## theta P59 0.62781932 0.8293772 -0.9977301 2.2533687
## theta P60 0.62781932 0.8293772 -0.9977301 2.2533687
## theta P61 1.34237132 0.8687144 -0.3602777 3.0450203
## theta P62 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P63 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P64 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P65 -1.43962335 0.8572193 -3.1197423 0.2404956
## theta P67 1.34237132 0.8687144 -0.3602777 3.0450203
## theta P68 1.34237132 0.8687144 -0.3602777 3.0450203
## theta P69 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P70 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P71 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P72 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P73 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P74 0.62781932 0.8293772 -0.9977301 2.2533687
## theta P75 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P76 0.62781932 0.8293772 -0.9977301 2.2533687
## theta P77 1.34237132 0.8687144 -0.3602777 3.0450203
## theta P78 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P79 -1.43962335 0.8572193 -3.1197423 0.2404956
## theta P80 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P81 0.62781932 0.8293772 -0.9977301 2.2533687
## theta P82 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P83 -2.22594152 0.9270793 -4.0429836 -0.4088994
## theta P84 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P85 -1.43962335 0.8572193 -3.1197423 0.2404956
## theta P87 0.62781932 0.8293772 -0.9977301 2.2533687
## theta P88 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P89 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P90 -1.43962335 0.8572193 -3.1197423 0.2404956
## theta P91 1.34237132 0.8687144 -0.3602777 3.0450203
## theta P92 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P93 0.62781932 0.8293772 -0.9977301 2.2533687
## theta P94 0.62781932 0.8293772 -0.9977301 2.2533687
## theta P95 3.37776635 1.2479377 0.9318534 5.8236793
## theta P96 0.62781932 0.8293772 -0.9977301 2.2533687
## theta P97 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P98 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P99 3.37776635 1.2479377 0.9318534 5.8236793
## theta P100 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P101 -1.43962335 0.8572193 -3.1197423 0.2404956
## theta P102 0.62781932 0.8293772 -0.9977301 2.2533687
## theta P103 -3.25631902 1.1466365 -5.5036853 -1.0089528
## theta P104 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P105 0.62781932 0.8293772 -0.9977301 2.2533687
## theta P106 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P107 -0.73123870 0.8302229 -2.3584456 0.8959682
## theta P108 -1.43962335 0.8572193 -3.1197423 0.2404956
## theta P109 0.62781932 0.8293772 -0.9977301 2.2533687
## theta P110 -1.43962335 0.8572193 -3.1197423 0.2404956
## theta P111 -0.05092566 0.8217494 -1.6615248 1.5596735
## theta P112 2.18044393 0.9776950 0.2641969 4.0966910gof.res <- gofIRT(pres01)
summary(gof.res)##
## Goodness-of-Fit Tests
## value df p-value
## Collapsed Deviance 82.338 90 0.705
## Hosmer-Lemeshow 8.498 8 0.386
## Rost Deviance 181.894 1014 1.000
## Casewise Deviance 830.063 1072 1.000
##
## R-Squared Measures
## Pearson R2: 0.521
## Sum-of-Squares R2: 0.519
## McFadden R2: 0.817
##
## Classifier Results - Confusion Matrix (relative frequencies)
## observed
## predicted 0 1
## 0 0.398 0.091
## 1 0.077 0.434
##
## Accuracy: 0.832
## Sensitivity: 0.827
## Specificity: 0.838
## Area under ROC: 0.91
## Gini coefficient: 0.82gof.res##
## Goodness-of-Fit Results:
## Collapsed Deviance = 82.338 (df = 90, p-value = 0.705)
## Pearson R2: 0.521
## Area Under ROC: 0.91#curvas
plotICC(mod_dialogo)
plotICC(mod_dialogo, empICC=list("raw"))
par(mfrow=c(1,1))
plotjointICC(mod_dialogo, legpos = "left")
plotPImap(mod_dialogo,sorted=T)
#dasslabelsMatemática
mod_matematica<-RM(data_matematica)
summary(mod_matematica)##
## Results of RM estimation:
##
## Call: RM(X = data_matematica)
##
## Conditional log-likelihood: -48.86672
## Number of iterations: 11
## Number of parameters: 3
##
## Item (Category) Difficulty Parameters (eta): with 0.95 CI:
## Estimate Std. Error lower CI upper CI
## M_2 2.229 0.328 1.585 2.872
## M_3 0.132 0.268 -0.393 0.658
## M_4 0.051 0.269 -0.475 0.577
##
## Item Easiness Parameters (beta) with 0.95 CI:
## Estimate Std. Error lower CI upper CI
## beta M_1 2.412 0.405 1.618 3.206
## beta M_2 -2.229 0.328 -2.872 -1.585
## beta M_3 -0.132 0.268 -0.658 0.393
## beta M_4 -0.051 0.269 -0.577 0.475par(mfrow=c(1,1))
plotINFO(mod_matematica,type="item")
#dasslabels[data_leitura]#medidas de ajuste
pres02 <- person.parameter(mod_matematica)
IC(pres02)##
## Information Criteria:
## value npar AIC BIC cAIC
## joint log-lik -92.32606 6 196.6521 209.8803 215.8803
## marginal log-lik -218.96397 3 443.9279 452.0834 455.0834
## conditional log-lik -48.86672 3 103.7334 111.8889 114.8889itemfit(pres02)##
## Itemfit Statistics:
## Chisq df p-value Outfit MSQ Infit MSQ Outfit t Infit t Discrim
## M_1 18.071 66 1.000 0.270 0.588 -1.449 -2.062 0.226
## M_2 89.551 66 0.028 1.337 0.876 0.788 -0.764 -0.514
## M_3 48.763 66 0.945 0.728 0.754 -1.511 -1.683 0.341
## M_4 41.866 66 0.991 0.625 0.699 -2.198 -2.104 0.715personfit(pres02)##
## Personfit Statistics:
## Chisq df p-value Outfit MSQ Infit MSQ Outfit t Infit t
## P1 6.593 3 0.086 1.648 2.085 0.94 1.37
## P2 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P3 2.283 3 0.516 0.571 0.810 -0.16 -0.24
## P4 2.121 3 0.548 0.530 0.749 -0.22 -0.39
## P5 0.838 3 0.840 0.210 0.298 -0.04 -0.90
## P6 0.838 3 0.840 0.210 0.298 -0.04 -0.90
## P7 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P12 0.838 3 0.840 0.210 0.298 -0.04 -0.90
## P13 0.838 3 0.840 0.210 0.298 -0.04 -0.90
## P14 6.999 3 0.072 1.750 2.129 0.97 1.41
## P15 0.838 3 0.840 0.210 0.298 -0.04 -0.90
## P17 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P18 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P19 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P20 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P21 0.838 3 0.840 0.210 0.298 -0.04 -0.90
## P22 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P23 2.283 3 0.516 0.571 0.810 -0.16 -0.24
## P24 2.121 3 0.548 0.530 0.749 -0.22 -0.39
## P25 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P26 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P27 6.999 3 0.072 1.750 2.129 0.97 1.41
## P29 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P30 2.121 3 0.548 0.530 0.749 -0.22 -0.39
## P31 2.121 3 0.548 0.530 0.749 -0.22 -0.39
## P32 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P33 0.838 3 0.840 0.210 0.298 -0.04 -0.90
## P35 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P36 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P39 2.121 3 0.548 0.530 0.749 -0.22 -0.39
## P41 0.838 3 0.840 0.210 0.298 -0.04 -0.90
## P42 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P44 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P46 2.283 3 0.516 0.571 0.810 -0.16 -0.24
## P50 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P51 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P53 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P54 2.121 3 0.548 0.530 0.749 -0.22 -0.39
## P55 8.291 3 0.040 2.073 2.490 1.06 1.54
## P56 10.865 3 0.012 2.716 1.948 1.50 1.66
## P58 8.291 3 0.040 2.073 2.490 1.06 1.54
## P59 2.121 3 0.548 0.530 0.749 -0.22 -0.39
## P60 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P61 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P67 10.865 3 0.012 2.716 1.948 1.50 1.66
## P68 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P73 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P75 2.121 3 0.548 0.530 0.749 -0.22 -0.39
## P77 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P79 0.838 3 0.840 0.210 0.298 -0.04 -0.90
## P80 0.838 3 0.840 0.210 0.298 -0.04 -0.90
## P82 50.889 3 0.000 12.722 2.995 2.78 1.85
## P83 8.291 3 0.040 2.073 2.490 1.06 1.54
## P86 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P87 2.283 3 0.516 0.571 0.810 -0.16 -0.24
## P90 1.000 3 0.801 0.250 0.358 0.10 -0.93
## P91 2.121 3 0.548 0.530 0.749 -0.22 -0.39
## P93 0.838 3 0.840 0.210 0.298 -0.04 -0.90
## P94 0.838 3 0.840 0.210 0.298 -0.04 -0.90
## P97 0.838 3 0.840 0.210 0.298 -0.04 -0.90
## P102 0.838 3 0.840 0.210 0.298 -0.04 -0.90
## P103 0.838 3 0.840 0.210 0.298 -0.04 -0.90
## P104 2.121 3 0.548 0.530 0.749 -0.22 -0.39
## P106 2.283 3 0.516 0.571 0.810 -0.16 -0.24
## P109 2.121 3 0.548 0.530 0.749 -0.22 -0.39
## P110 6.999 3 0.072 1.750 2.129 0.97 1.41
## P112 0.838 3 0.840 0.210 0.298 -0.04 -0.90summary(pres02)##
## Estimation of Ability Parameters
##
## Collapsed log-likelihood: -5.28443
## Number of iterations: 6
## Number of parameters: 3
##
## ML estimated ability parameters (without spline interpolated values):
## Estimate Std. Err. 2.5 % 97.5 %
## theta P1 1.66047298 1.370169 -1.025008 4.345954
## theta P2 1.66047298 1.370169 -1.025008 4.345954
## theta P3 0.04648387 1.227896 -2.360148 2.453115
## theta P4 0.04648387 1.227896 -2.360148 2.453115
## theta P5 -1.65077686 1.427776 -4.449167 1.147613
## theta P6 -1.65077686 1.427776 -4.449167 1.147613
## theta P7 1.66047298 1.370169 -1.025008 4.345954
## theta P12 -1.65077686 1.427776 -4.449167 1.147613
## theta P13 -1.65077686 1.427776 -4.449167 1.147613
## theta P14 1.66047298 1.370169 -1.025008 4.345954
## theta P15 -1.65077686 1.427776 -4.449167 1.147613
## theta P17 1.66047298 1.370169 -1.025008 4.345954
## theta P18 1.66047298 1.370169 -1.025008 4.345954
## theta P19 1.66047298 1.370169 -1.025008 4.345954
## theta P20 1.66047298 1.370169 -1.025008 4.345954
## theta P21 -1.65077686 1.427776 -4.449167 1.147613
## theta P22 1.66047298 1.370169 -1.025008 4.345954
## theta P23 0.04648387 1.227896 -2.360148 2.453115
## theta P24 0.04648387 1.227896 -2.360148 2.453115
## theta P25 1.66047298 1.370169 -1.025008 4.345954
## theta P26 1.66047298 1.370169 -1.025008 4.345954
## theta P27 1.66047298 1.370169 -1.025008 4.345954
## theta P29 1.66047298 1.370169 -1.025008 4.345954
## theta P30 0.04648387 1.227896 -2.360148 2.453115
## theta P31 0.04648387 1.227896 -2.360148 2.453115
## theta P32 1.66047298 1.370169 -1.025008 4.345954
## theta P33 -1.65077686 1.427776 -4.449167 1.147613
## theta P35 1.66047298 1.370169 -1.025008 4.345954
## theta P36 1.66047298 1.370169 -1.025008 4.345954
## theta P39 0.04648387 1.227896 -2.360148 2.453115
## theta P41 -1.65077686 1.427776 -4.449167 1.147613
## theta P42 1.66047298 1.370169 -1.025008 4.345954
## theta P44 1.66047298 1.370169 -1.025008 4.345954
## theta P46 0.04648387 1.227896 -2.360148 2.453115
## theta P50 1.66047298 1.370169 -1.025008 4.345954
## theta P51 1.66047298 1.370169 -1.025008 4.345954
## theta P53 1.66047298 1.370169 -1.025008 4.345954
## theta P54 0.04648387 1.227896 -2.360148 2.453115
## theta P55 -1.65077686 1.427776 -4.449167 1.147613
## theta P56 0.04648387 1.227896 -2.360148 2.453115
## theta P58 -1.65077686 1.427776 -4.449167 1.147613
## theta P59 0.04648387 1.227896 -2.360148 2.453115
## theta P60 1.66047298 1.370169 -1.025008 4.345954
## theta P61 1.66047298 1.370169 -1.025008 4.345954
## theta P67 0.04648387 1.227896 -2.360148 2.453115
## theta P68 1.66047298 1.370169 -1.025008 4.345954
## theta P73 1.66047298 1.370169 -1.025008 4.345954
## theta P75 0.04648387 1.227896 -2.360148 2.453115
## theta P77 1.66047298 1.370169 -1.025008 4.345954
## theta P79 -1.65077686 1.427776 -4.449167 1.147613
## theta P80 -1.65077686 1.427776 -4.449167 1.147613
## theta P82 -1.65077686 1.427776 -4.449167 1.147613
## theta P83 -1.65077686 1.427776 -4.449167 1.147613
## theta P86 1.66047298 1.370169 -1.025008 4.345954
## theta P87 0.04648387 1.227896 -2.360148 2.453115
## theta P90 1.66047298 1.370169 -1.025008 4.345954
## theta P91 0.04648387 1.227896 -2.360148 2.453115
## theta P93 -1.65077686 1.427776 -4.449167 1.147613
## theta P94 -1.65077686 1.427776 -4.449167 1.147613
## theta P97 -1.65077686 1.427776 -4.449167 1.147613
## theta P102 -1.65077686 1.427776 -4.449167 1.147613
## theta P103 -1.65077686 1.427776 -4.449167 1.147613
## theta P104 0.04648387 1.227896 -2.360148 2.453115
## theta P106 0.04648387 1.227896 -2.360148 2.453115
## theta P109 0.04648387 1.227896 -2.360148 2.453115
## theta P110 1.66047298 1.370169 -1.025008 4.345954
## theta P112 -1.65077686 1.427776 -4.449167 1.147613#gof.res <- gofIRT(pres02)
#gof.res <- gofIRT(pres02)
#Error in cut.default(pi.hat, cutpoints, include.lowest = TRUE, labels = 1:groups.hl) :
# 'breaks' are not unique
summary(gof.res)##
## Goodness-of-Fit Tests
## value df p-value
## Collapsed Deviance 82.338 90 0.705
## Hosmer-Lemeshow 8.498 8 0.386
## Rost Deviance 181.894 1014 1.000
## Casewise Deviance 830.063 1072 1.000
##
## R-Squared Measures
## Pearson R2: 0.521
## Sum-of-Squares R2: 0.519
## McFadden R2: 0.817
##
## Classifier Results - Confusion Matrix (relative frequencies)
## observed
## predicted 0 1
## 0 0.398 0.091
## 1 0.077 0.434
##
## Accuracy: 0.832
## Sensitivity: 0.827
## Specificity: 0.838
## Area under ROC: 0.91
## Gini coefficient: 0.82gof.res##
## Goodness-of-Fit Results:
## Collapsed Deviance = 82.338 (df = 90, p-value = 0.705)
## Pearson R2: 0.521
## Area Under ROC: 0.91#curvas
plotICC(mod_matematica)
plotICC(mod_matematica, empICC=list("raw"))## Warning in plotICC.Rm(mod_matematica, empICC = list("raw")): No empirical ICCs for less the 4 different person parameters!
par(mfrow=c(1,1))
plotjointICC(mod_matematica, legpos = "left")
plotPImap(mod_matematica,sorted=T)
#dasslabelsCompreensão
mod_compreensao<-RM(data_compreensao)
summary(mod_compreensao)##
## Results of RM estimation:
##
## Call: RM(X = data_compreensao)
##
## Conditional log-likelihood: -211.9245
## Number of iterations: 17
## Number of parameters: 9
##
## Item (Category) Difficulty Parameters (eta): with 0.95 CI:
## Estimate Std. Error lower CI upper CI
## C_biopsia -1.332 0.283 -1.886 -0.778
## C_coloutero -1.673 0.300 -2.260 -1.086
## C_ginecologia -2.681 0.374 -3.414 -1.949
## C_papilomavirus 2.197 0.297 1.615 2.779
## C_histerectomia 3.760 0.385 3.006 4.515
## C_maligno -2.178 0.332 -2.829 -1.528
## C_mastectomia 2.036 0.291 1.466 2.606
## C_metastase 1.224 0.267 0.701 1.748
## C_pelvico 0.413 0.254 -0.086 0.912
##
## Item Easiness Parameters (beta) with 0.95 CI:
## Estimate Std. Error lower CI upper CI
## beta C_benigno 1.766 0.305 1.168 2.364
## beta C_biopsia 1.332 0.283 0.778 1.886
## beta C_coloutero 1.673 0.300 1.086 2.260
## beta C_ginecologia 2.681 0.374 1.949 3.414
## beta C_papilomavirus -2.197 0.297 -2.779 -1.615
## beta C_histerectomia -3.760 0.385 -4.515 -3.006
## beta C_maligno 2.178 0.332 1.528 2.829
## beta C_mastectomia -2.036 0.291 -2.606 -1.466
## beta C_metastase -1.224 0.267 -1.748 -0.701
## beta C_pelvico -0.413 0.254 -0.912 0.086par(mfrow=c(1,1))
plotINFO(mod_compreensao,type="item")
#dasslabels[data_leitura]#medidas de ajuste
pres03 <- person.parameter(mod_compreensao)
IC(pres03)##
## Information Criteria:
## value npar AIC BIC cAIC
## joint log-lik -307.2549 18 650.5098 697.0393 715.0393
## marginal log-lik -459.4772 9 936.9543 961.4208 970.4208
## conditional log-lik -211.9245 9 441.8490 466.3155 475.3155itemfit(pres03)##
## Itemfit Statistics:
## Chisq df p-value Outfit MSQ Infit MSQ Outfit t Infit t
## C_benigno 51.302 97 1.000 0.523 0.709 -0.625 -1.810
## C_biopsia 60.680 97 0.999 0.619 0.860 -0.628 -0.926
## C_coloutero 105.787 97 0.255 1.079 1.168 0.336 1.001
## C_ginecologia 72.668 97 0.969 0.742 1.058 0.101 0.319
## C_papilomavirus 67.006 97 0.991 0.684 0.935 -0.544 -0.355
## C_histerectomia 179.443 97 0.000 1.831 0.809 0.973 -0.951
## C_maligno 40.102 97 1.000 0.409 0.780 -0.636 -1.121
## C_mastectomia 66.311 97 0.993 0.677 0.820 -0.637 -1.161
## C_metastase 79.608 97 0.900 0.812 0.921 -0.518 -0.523
## C_pelvico 72.500 97 0.970 0.740 0.701 -0.932 -2.566
## Discrim
## C_benigno 0.577
## C_biopsia 0.519
## C_coloutero 0.302
## C_ginecologia 0.280
## C_papilomavirus 0.496
## C_histerectomia 0.196
## C_maligno 0.513
## C_mastectomia 0.554
## C_metastase 0.551
## C_pelvico 0.682personfit(pres03)##
## Personfit Statistics:
## Chisq df p-value Outfit MSQ Infit MSQ Outfit t Infit t
## P1 1.489 9 0.997 0.149 0.451 0.96 -0.74
## P2 2.727 9 0.974 0.273 0.594 0.22 -0.79
## P3 18.130 9 0.034 1.813 1.632 1.01 1.16
## P4 1.985 9 0.992 0.198 0.266 -1.00 -1.68
## P5 2.462 9 0.982 0.246 0.453 -0.38 -1.33
## P6 5.841 9 0.756 0.584 1.461 1.19 0.80
## P7 30.646 9 0.000 3.065 1.892 1.46 1.67
## P8 3.339 9 0.949 0.334 0.517 -0.29 -1.12
## P9 3.339 9 0.949 0.334 0.517 -0.29 -1.12
## P10 3.071 9 0.961 0.307 0.673 0.26 -0.58
## P11 3.339 9 0.949 0.334 0.517 -0.29 -1.12
## P12 2.276 9 0.986 0.228 0.368 -0.90 -1.38
## P13 4.655 9 0.863 0.465 0.845 0.65 -0.34
## P14 4.135 9 0.902 0.414 0.805 -0.12 -0.29
## P15 1.985 9 0.992 0.198 0.266 -1.00 -1.68
## P16 1.985 9 0.992 0.198 0.266 -1.00 -1.68
## P17 3.071 9 0.961 0.307 0.673 0.26 -0.58
## P18 3.957 9 0.914 0.396 0.709 -0.51 -0.41
## P19 2.462 9 0.982 0.246 0.453 -0.38 -1.33
## P20 4.135 9 0.902 0.414 0.805 -0.12 -0.29
## P21 1.985 9 0.992 0.198 0.266 -1.00 -1.68
## P22 13.269 9 0.151 1.327 1.524 0.63 1.01
## P23 5.694 9 0.770 0.569 0.787 0.02 -0.34
## P24 1.489 9 0.997 0.149 0.451 0.96 -0.74
## P25 2.727 9 0.974 0.273 0.594 0.22 -0.79
## P26 13.913 9 0.125 1.391 1.299 0.68 0.68
## P27 18.149 9 0.033 1.815 1.456 1.01 0.89
## P28 6.121 9 0.728 0.612 0.927 -0.16 0.06
## P29 3.339 9 0.949 0.334 0.517 -0.29 -1.12
## P30 1.489 9 0.997 0.149 0.451 0.96 -0.74
## P33 4.510 9 0.875 0.451 0.870 -0.07 -0.13
## P34 14.955 9 0.092 1.496 1.311 0.77 0.70
## P35 3.071 9 0.961 0.307 0.673 0.26 -0.58
## P37 2.922 9 0.967 0.292 0.724 1.26 -0.21
## P38 6.648 9 0.674 0.665 1.530 1.22 0.88
## P39 4.135 9 0.902 0.414 0.805 -0.12 -0.29
## P40 1.489 9 0.997 0.149 0.451 0.96 -0.74
## P45 16.726 9 0.053 1.673 1.470 0.91 0.91
## P46 1.985 9 0.992 0.198 0.266 -1.00 -1.68
## P47 2.276 9 0.986 0.228 0.368 -0.90 -1.38
## P48 4.510 9 0.875 0.451 0.870 -0.07 -0.13
## P49 3.339 9 0.949 0.334 0.517 -0.29 -1.12
## P50 1.489 9 0.997 0.149 0.451 0.96 -0.74
## P51 3.071 9 0.961 0.307 0.673 0.26 -0.58
## P52 5.841 9 0.756 0.584 1.461 1.19 0.80
## P53 1.985 9 0.992 0.198 0.266 -1.00 -1.68
## P54 8.797 9 0.456 0.880 1.257 0.33 0.67
## P55 37.312 9 0.000 3.731 2.629 1.72 2.61
## P57 32.433 9 0.000 3.243 1.389 1.82 0.82
## P58 4.135 9 0.902 0.414 0.805 -0.12 -0.29
## P59 3.071 9 0.961 0.307 0.673 0.26 -0.58
## P60 4.510 9 0.875 0.451 0.870 -0.07 -0.13
## P61 1.489 9 0.997 0.149 0.451 0.96 -0.74
## P62 5.694 9 0.770 0.569 0.787 0.02 -0.34
## P63 4.136 9 0.902 0.414 0.636 -0.17 -0.75
## P65 1.489 9 0.997 0.149 0.451 0.96 -0.74
## P67 1.985 9 0.992 0.198 0.266 -1.00 -1.68
## P68 1.489 9 0.997 0.149 0.451 0.96 -0.74
## P69 17.088 9 0.047 1.709 1.620 0.93 1.14
## P70 2.922 9 0.967 0.292 0.724 1.26 -0.21
## P71 3.957 9 0.914 0.396 0.709 -0.51 -0.41
## P72 1.985 9 0.992 0.198 0.266 -1.00 -1.68
## P73 6.272 9 0.712 0.627 1.115 0.13 0.39
## P74 2.462 9 0.982 0.246 0.453 -0.38 -1.33
## P75 13.262 9 0.151 1.326 1.779 1.39 1.13
## P76 3.819 9 0.923 0.382 0.643 0.16 -1.13
## P77 5.385 9 0.800 0.538 1.014 0.45 0.19
## P78 3.339 9 0.949 0.334 0.517 -0.29 -1.12
## P79 5.755 9 0.764 0.575 0.981 0.34 0.05
## P80 2.922 9 0.967 0.292 0.724 1.26 -0.21
## P81 4.382 9 0.885 0.438 0.666 -0.14 -0.66
## P82 27.950 9 0.001 2.795 1.906 1.63 1.25
## P83 5.671 9 0.772 0.567 1.050 0.71 0.26
## P84 7.842 9 0.550 0.784 1.357 0.82 1.01
## P85 4.005 9 0.911 0.401 0.677 0.18 -0.99
## P87 126.521 9 0.000 12.652 2.769 3.55 2.76
## P88 56.375 9 0.000 5.637 1.581 2.78 1.05
## P89 9.297 9 0.410 0.930 1.263 1.47 0.58
## P90 1.985 9 0.992 0.198 0.266 -1.00 -1.68
## P91 5.841 9 0.756 0.584 1.461 1.19 0.80
## P92 2.462 9 0.982 0.246 0.453 -0.38 -1.33
## P94 2.462 9 0.982 0.246 0.453 -0.38 -1.33
## P95 6.421 9 0.697 0.642 0.987 -0.11 0.16
## P96 7.939 9 0.540 0.794 1.287 0.49 0.90
## P97 7.533 9 0.582 0.753 1.018 0.03 0.22
## P98 1.985 9 0.992 0.198 0.266 -1.00 -1.68
## P99 25.617 9 0.002 2.562 2.498 1.47 2.15
## P100 1.985 9 0.992 0.198 0.266 -1.00 -1.68
## P101 3.496 9 0.941 0.350 0.651 0.58 -0.99
## P102 2.727 9 0.974 0.273 0.594 0.22 -0.79
## P103 10.051 9 0.346 1.005 1.282 0.32 0.64
## P104 6.189 9 0.721 0.619 0.983 0.37 0.06
## P105 2.727 9 0.974 0.273 0.594 0.22 -0.79
## P107 3.496 9 0.941 0.350 0.651 0.58 -0.99
## P108 6.189 9 0.721 0.619 0.983 0.37 0.06
## P109 10.626 9 0.302 1.063 1.122 0.38 0.40
## P110 1.985 9 0.992 0.198 0.266 -1.00 -1.68
## P111 13.784 9 0.130 1.378 1.412 0.69 0.94summary(pres03)##
## Estimation of Ability Parameters
##
## Collapsed log-likelihood: -23.158
## Number of iterations: 8
## Number of parameters: 9
##
## ML estimated ability parameters (without spline interpolated values):
## Estimate Std. Err. 2.5 % 97.5 %
## theta P1 3.6843056 1.2282130 1.2770524 6.091558873
## theta P2 2.4833319 1.0050131 0.5135425 4.453121290
## theta P3 0.6885124 0.9242350 -1.1229550 2.499979797
## theta P4 -0.1527157 0.9059859 -1.9284156 1.622984101
## theta P5 1.5525624 0.9384364 -0.2867391 3.391863857
## theta P6 3.6843056 1.2282130 1.2770524 6.091558873
## theta P7 1.5525624 0.9384364 -0.2867391 3.391863857
## theta P8 -0.9409998 0.8692401 -2.6446790 0.762679426
## theta P9 -0.9409998 0.8692401 -2.6446790 0.762679426
## theta P10 2.4833319 1.0050131 0.5135425 4.453121290
## theta P11 -0.9409998 0.8692401 -2.6446790 0.762679426
## theta P12 0.6885124 0.9242350 -1.1229550 2.499979797
## theta P13 -2.4402067 0.9062963 -4.2165147 -0.663898621
## theta P14 1.5525624 0.9384364 -0.2867391 3.391863857
## theta P15 -0.1527157 0.9059859 -1.9284156 1.622984101
## theta P16 -0.1527157 0.9059859 -1.9284156 1.622984101
## theta P17 2.4833319 1.0050131 0.5135425 4.453121290
## theta P18 0.6885124 0.9242350 -1.1229550 2.499979797
## theta P19 1.5525624 0.9384364 -0.2867391 3.391863857
## theta P20 1.5525624 0.9384364 -0.2867391 3.391863857
## theta P21 -0.1527157 0.9059859 -1.9284156 1.622984101
## theta P22 0.6885124 0.9242350 -1.1229550 2.499979797
## theta P23 -0.9409998 0.8692401 -2.6446790 0.762679426
## theta P24 3.6843056 1.2282130 1.2770524 6.091558873
## theta P25 2.4833319 1.0050131 0.5135425 4.453121290
## theta P26 0.6885124 0.9242350 -1.1229550 2.499979797
## theta P27 -0.1527157 0.9059859 -1.9284156 1.622984101
## theta P28 -0.1527157 0.9059859 -1.9284156 1.622984101
## theta P29 -0.9409998 0.8692401 -2.6446790 0.762679426
## theta P30 3.6843056 1.2282130 1.2770524 6.091558873
## theta P33 1.5525624 0.9384364 -0.2867391 3.391863857
## theta P34 0.6885124 0.9242350 -1.1229550 2.499979797
## theta P35 2.4833319 1.0050131 0.5135425 4.453121290
## theta P37 -3.4275842 1.1270756 -5.6366118 -1.218556572
## theta P38 3.6843056 1.2282130 1.2770524 6.091558873
## theta P39 1.5525624 0.9384364 -0.2867391 3.391863857
## theta P40 3.6843056 1.2282130 1.2770524 6.091558873
## theta P45 -0.1527157 0.9059859 -1.9284156 1.622984101
## theta P46 -0.1527157 0.9059859 -1.9284156 1.622984101
## theta P47 0.6885124 0.9242350 -1.1229550 2.499979797
## theta P48 1.5525624 0.9384364 -0.2867391 3.391863857
## theta P49 -0.9409998 0.8692401 -2.6446790 0.762679426
## theta P50 3.6843056 1.2282130 1.2770524 6.091558873
## theta P51 2.4833319 1.0050131 0.5135425 4.453121290
## theta P52 3.6843056 1.2282130 1.2770524 6.091558873
## theta P53 -0.1527157 0.9059859 -1.9284156 1.622984101
## theta P54 -0.9409998 0.8692401 -2.6446790 0.762679426
## theta P55 -0.9409998 0.8692401 -2.6446790 0.762679426
## theta P57 0.6885124 0.9242350 -1.1229550 2.499979797
## theta P58 1.5525624 0.9384364 -0.2867391 3.391863857
## theta P59 2.4833319 1.0050131 0.5135425 4.453121290
## theta P60 1.5525624 0.9384364 -0.2867391 3.391863857
## theta P61 3.6843056 1.2282130 1.2770524 6.091558873
## theta P62 -0.9409998 0.8692401 -2.6446790 0.762679426
## theta P63 -0.9409998 0.8692401 -2.6446790 0.762679426
## theta P65 3.6843056 1.2282130 1.2770524 6.091558873
## theta P67 -0.1527157 0.9059859 -1.9284156 1.622984101
## theta P68 3.6843056 1.2282130 1.2770524 6.091558873
## theta P69 0.6885124 0.9242350 -1.1229550 2.499979797
## theta P70 -3.4275842 1.1270756 -5.6366118 -1.218556572
## theta P71 0.6885124 0.9242350 -1.1229550 2.499979797
## theta P72 -0.1527157 0.9059859 -1.9284156 1.622984101
## theta P73 1.5525624 0.9384364 -0.2867391 3.391863857
## theta P74 1.5525624 0.9384364 -0.2867391 3.391863857
## theta P75 3.6843056 1.2282130 1.2770524 6.091558873
## theta P76 -1.6777944 0.8549258 -3.3534181 -0.002170755
## theta P77 2.4833319 1.0050131 0.5135425 4.453121290
## theta P78 -0.9409998 0.8692401 -2.6446790 0.762679426
## theta P79 -1.6777944 0.8549258 -3.3534181 -0.002170755
## theta P80 -3.4275842 1.1270756 -5.6366118 -1.218556572
## theta P81 -0.9409998 0.8692401 -2.6446790 0.762679426
## theta P82 3.6843056 1.2282130 1.2770524 6.091558873
## theta P83 -2.4402067 0.9062963 -4.2165147 -0.663898621
## theta P84 -2.4402067 0.9062963 -4.2165147 -0.663898621
## theta P85 -1.6777944 0.8549258 -3.3534181 -0.002170755
## theta P87 -0.9409998 0.8692401 -2.6446790 0.762679426
## theta P88 -0.1527157 0.9059859 -1.9284156 1.622984101
## theta P89 -3.4275842 1.1270756 -5.6366118 -1.218556572
## theta P90 -0.1527157 0.9059859 -1.9284156 1.622984101
## theta P91 3.6843056 1.2282130 1.2770524 6.091558873
## theta P92 1.5525624 0.9384364 -0.2867391 3.391863857
## theta P94 1.5525624 0.9384364 -0.2867391 3.391863857
## theta P95 0.6885124 0.9242350 -1.1229550 2.499979797
## theta P96 -1.6777944 0.8549258 -3.3534181 -0.002170755
## theta P97 -0.1527157 0.9059859 -1.9284156 1.622984101
## theta P98 -0.1527157 0.9059859 -1.9284156 1.622984101
## theta P99 0.6885124 0.9242350 -1.1229550 2.499979797
## theta P100 -0.1527157 0.9059859 -1.9284156 1.622984101
## theta P101 -2.4402067 0.9062963 -4.2165147 -0.663898621
## theta P102 2.4833319 1.0050131 0.5135425 4.453121290
## theta P103 -0.1527157 0.9059859 -1.9284156 1.622984101
## theta P104 -1.6777944 0.8549258 -3.3534181 -0.002170755
## theta P105 2.4833319 1.0050131 0.5135425 4.453121290
## theta P107 -2.4402067 0.9062963 -4.2165147 -0.663898621
## theta P108 -1.6777944 0.8549258 -3.3534181 -0.002170755
## theta P109 -0.1527157 0.9059859 -1.9284156 1.622984101
## theta P110 -0.1527157 0.9059859 -1.9284156 1.622984101
## theta P111 -0.9409998 0.8692401 -2.6446790 0.762679426gof.res <- gofIRT(pres03)
summary(gof.res)##
## Goodness-of-Fit Tests
## value df p-value
## Collapsed Deviance 88.869 90 0.514
## Hosmer-Lemeshow 11.536 8 0.173
## Rost Deviance 138.322 1014 1.000
## Casewise Deviance 614.510 962 1.000
##
## R-Squared Measures
## Pearson R2: 0.61
## Sum-of-Squares R2: 0.608
## McFadden R2: 1.179
##
## Classifier Results - Confusion Matrix (relative frequencies)
## observed
## predicted 0 1
## 0 0.355 0.059
## 1 0.074 0.511
##
## Accuracy: 0.866
## Sensitivity: 0.896
## Specificity: 0.827
## Area under ROC: 0.939
## Gini coefficient: 0.878gof.res##
## Goodness-of-Fit Results:
## Collapsed Deviance = 88.869 (df = 90, p-value = 0.514)
## Pearson R2: 0.61
## Area Under ROC: 0.939#curvas
plotICC(mod_compreensao)
plotICC(mod_compreensao, empICC=list("raw"))
par(mfrow=c(1,1))
plotjointICC(mod_compreensao, legpos = "left")
plotPImap(mod_compreensao,sorted=T)
#dasslabelsFamiliaridade
mod_familiaridade<-RM(data_familiaridade)
summary(mod_familiaridade)##
## Results of RM estimation:
##
## Call: RM(X = data_familiaridade)
##
## Conditional log-likelihood: -108.8602
## Number of iterations: 15
## Number of parameters: 5
##
## Item (Category) Difficulty Parameters (eta): with 0.95 CI:
## Estimate Std. Error lower CI upper CI
## F_papilomavirus 1.454 0.272 0.922 1.987
## F_histerectomia 1.913 0.290 1.344 2.481
## F_mastectomia 0.242 0.254 -0.257 0.741
## F_metastase -0.262 0.261 -0.774 0.251
## F_pelvico -1.327 0.304 -1.923 -0.730
##
## Item Easiness Parameters (beta) with 0.95 CI:
## Estimate Std. Error lower CI upper CI
## beta F_biopsia 2.020 0.354 1.327 2.713
## beta F_papilomavirus -1.454 0.272 -1.987 -0.922
## beta F_histerectomia -1.913 0.290 -2.481 -1.344
## beta F_mastectomia -0.242 0.254 -0.741 0.257
## beta F_metastase 0.262 0.261 -0.251 0.774
## beta F_pelvico 1.327 0.304 0.730 1.923par(mfrow=c(1,1))
plotINFO(mod_familiaridade,type="item")
#dasslabels[data_leitura]#medidas de ajuste
pres04 <- person.parameter(mod_familiaridade)
IC(pres04)##
## Information Criteria:
## value npar AIC BIC cAIC
## joint log-lik -170.5663 10 361.1327 383.6176 393.6176
## marginal log-lik -300.3701 5 610.7402 624.3327 629.3327
## conditional log-lik -108.8602 5 227.7203 241.3128 246.3128itemfit(pres04)##
## Itemfit Statistics:
## Chisq df p-value Outfit MSQ Infit MSQ Outfit t Infit t
## F_biopsia 38.037 69 0.999 0.543 0.790 -0.801 -0.939
## F_papilomavirus 93.031 69 0.029 1.329 0.955 1.061 -0.288
## F_histerectomia 100.530 69 0.008 1.436 1.207 1.094 1.342
## F_mastectomia 44.136 69 0.991 0.631 0.764 -2.048 -1.948
## F_metastase 60.547 69 0.756 0.865 0.906 -0.557 -0.651
## F_pelvico 26.187 69 1.000 0.374 0.573 -2.056 -2.698
## Discrim
## F_biopsia 0.355
## F_papilomavirus 0.190
## F_histerectomia -0.085
## F_mastectomia 0.564
## F_metastase 0.406
## F_pelvico 0.676personfit(pres04)##
## Personfit Statistics:
## Chisq df p-value Outfit MSQ Infit MSQ Outfit t Infit t
## P2 1.935 5 0.858 0.322 0.411 -0.72 -1.39
## P3 4.540 5 0.475 0.757 1.023 0.02 0.21
## P4 14.145 5 0.015 2.357 1.484 1.40 1.02
## P5 2.074 5 0.839 0.346 0.657 0.09 -0.42
## P8 3.352 5 0.646 0.559 0.724 -0.60 -0.47
## P9 39.969 5 0.000 6.662 1.862 2.09 1.30
## P10 1.935 5 0.858 0.322 0.411 -0.72 -1.39
## P11 8.303 5 0.140 1.384 1.565 0.78 0.97
## P13 1.886 5 0.865 0.314 0.596 0.04 -0.53
## P14 3.352 5 0.646 0.559 0.724 -0.60 -0.47
## P15 1.935 5 0.858 0.322 0.411 -0.72 -1.39
## P16 4.540 5 0.475 0.757 1.023 0.02 0.21
## P17 2.074 5 0.839 0.346 0.657 0.09 -0.42
## P19 2.334 5 0.801 0.389 0.480 -1.03 -1.19
## P21 6.124 5 0.294 1.021 1.229 0.33 0.60
## P22 2.074 5 0.839 0.346 0.657 0.09 -0.42
## P23 3.122 5 0.681 0.520 0.968 0.26 0.16
## P24 2.074 5 0.839 0.346 0.657 0.09 -0.42
## P26 2.334 5 0.801 0.389 0.480 -1.03 -1.19
## P27 3.122 5 0.681 0.520 0.968 0.26 0.16
## P28 1.988 5 0.851 0.331 0.429 -0.72 -1.33
## P29 1.935 5 0.858 0.322 0.411 -0.72 -1.39
## P33 2.074 5 0.839 0.346 0.657 0.09 -0.42
## P34 1.935 5 0.858 0.322 0.411 -0.72 -1.39
## P39 2.074 5 0.839 0.346 0.657 0.09 -0.42
## P46 9.468 5 0.092 1.578 1.331 0.97 0.78
## P47 3.352 5 0.646 0.559 0.724 -0.60 -0.47
## P48 2.074 5 0.839 0.346 0.657 0.09 -0.42
## P49 9.468 5 0.092 1.578 1.331 0.97 0.78
## P50 2.074 5 0.839 0.346 0.657 0.09 -0.42
## P54 10.251 5 0.068 1.708 1.342 1.12 0.80
## P55 3.122 5 0.681 0.520 0.968 0.26 0.16
## P57 4.540 5 0.475 0.757 1.023 0.02 0.21
## P59 2.074 5 0.839 0.346 0.657 0.09 -0.42
## P60 2.074 5 0.839 0.346 0.657 0.09 -0.42
## P62 1.988 5 0.851 0.331 0.429 -0.72 -1.33
## P63 9.468 5 0.092 1.578 1.331 0.97 0.78
## P67 1.988 5 0.851 0.331 0.429 -0.72 -1.33
## P69 6.124 5 0.294 1.021 1.229 0.33 0.60
## P70 1.886 5 0.865 0.314 0.596 0.04 -0.53
## P71 2.334 5 0.801 0.389 0.480 -1.03 -1.19
## P72 1.935 5 0.858 0.322 0.411 -0.72 -1.39
## P73 3.122 5 0.681 0.520 0.968 0.26 0.16
## P76 13.800 5 0.017 2.300 1.830 1.69 1.55
## P78 10.251 5 0.068 1.708 1.342 1.12 0.80
## P79 2.334 5 0.801 0.389 0.480 -1.03 -1.19
## P80 1.886 5 0.865 0.314 0.596 0.04 -0.53
## P81 12.450 5 0.029 2.075 1.769 1.49 1.46
## P82 3.122 5 0.681 0.520 0.968 0.26 0.16
## P83 8.303 5 0.140 1.384 1.565 0.78 0.97
## P84 9.468 5 0.092 1.578 1.331 0.97 0.78
## P85 20.903 5 0.001 3.484 1.542 2.01 1.11
## P87 2.334 5 0.801 0.389 0.480 -1.03 -1.19
## P89 20.903 5 0.001 3.484 1.542 2.01 1.11
## P90 3.122 5 0.681 0.520 0.968 0.26 0.16
## P92 1.935 5 0.858 0.322 0.411 -0.72 -1.39
## P94 1.935 5 0.858 0.322 0.411 -0.72 -1.39
## P95 7.273 5 0.201 1.212 1.442 0.52 0.95
## P96 5.817 5 0.324 0.969 1.178 0.27 0.51
## P98 5.817 5 0.324 0.969 1.178 0.27 0.51
## P99 2.074 5 0.839 0.346 0.657 0.09 -0.42
## P100 3.352 5 0.646 0.559 0.724 -0.60 -0.47
## P102 1.935 5 0.858 0.322 0.411 -0.72 -1.39
## P103 4.540 5 0.475 0.757 1.023 0.02 0.21
## P104 8.303 5 0.140 1.384 1.565 0.78 0.97
## P107 1.886 5 0.865 0.314 0.596 0.04 -0.53
## P108 3.498 5 0.624 0.583 1.068 0.30 0.32
## P109 1.988 5 0.851 0.331 0.429 -0.72 -1.33
## P110 2.334 5 0.801 0.389 0.480 -1.03 -1.19
## P111 2.334 5 0.801 0.389 0.480 -1.03 -1.19summary(pres04)##
## Estimation of Ability Parameters
##
## Collapsed log-likelihood: -14.73959
## Number of iterations: 6
## Number of parameters: 5
##
## ML estimated ability parameters (without spline interpolated values):
## Estimate Std. Err. 2.5 % 97.5 %
## theta P2 1.000364696 1.0224898 -1.0036785 3.0044079
## theta P3 1.000364696 1.0224898 -1.0036785 3.0044079
## theta P4 -0.993849540 1.0240520 -3.0009546 1.0132555
## theta P5 2.193698062 1.2058450 -0.1697146 4.5571107
## theta P8 0.003761652 0.9868206 -1.9303712 1.9378945
## theta P9 -2.195514074 1.2116151 -4.5702360 0.1792078
## theta P10 1.000364696 1.0224898 -1.0036785 3.0044079
## theta P11 -2.195514074 1.2116151 -4.5702360 0.1792078
## theta P13 -2.195514074 1.2116151 -4.5702360 0.1792078
## theta P14 0.003761652 0.9868206 -1.9303712 1.9378945
## theta P15 1.000364696 1.0224898 -1.0036785 3.0044079
## theta P16 1.000364696 1.0224898 -1.0036785 3.0044079
## theta P17 2.193698062 1.2058450 -0.1697146 4.5571107
## theta P19 0.003761652 0.9868206 -1.9303712 1.9378945
## theta P21 1.000364696 1.0224898 -1.0036785 3.0044079
## theta P22 2.193698062 1.2058450 -0.1697146 4.5571107
## theta P23 2.193698062 1.2058450 -0.1697146 4.5571107
## theta P24 2.193698062 1.2058450 -0.1697146 4.5571107
## theta P26 0.003761652 0.9868206 -1.9303712 1.9378945
## theta P27 2.193698062 1.2058450 -0.1697146 4.5571107
## theta P28 -0.993849540 1.0240520 -3.0009546 1.0132555
## theta P29 1.000364696 1.0224898 -1.0036785 3.0044079
## theta P33 2.193698062 1.2058450 -0.1697146 4.5571107
## theta P34 1.000364696 1.0224898 -1.0036785 3.0044079
## theta P39 2.193698062 1.2058450 -0.1697146 4.5571107
## theta P46 0.003761652 0.9868206 -1.9303712 1.9378945
## theta P47 0.003761652 0.9868206 -1.9303712 1.9378945
## theta P48 2.193698062 1.2058450 -0.1697146 4.5571107
## theta P49 0.003761652 0.9868206 -1.9303712 1.9378945
## theta P50 2.193698062 1.2058450 -0.1697146 4.5571107
## theta P54 0.003761652 0.9868206 -1.9303712 1.9378945
## theta P55 2.193698062 1.2058450 -0.1697146 4.5571107
## theta P57 1.000364696 1.0224898 -1.0036785 3.0044079
## theta P59 2.193698062 1.2058450 -0.1697146 4.5571107
## theta P60 2.193698062 1.2058450 -0.1697146 4.5571107
## theta P62 -0.993849540 1.0240520 -3.0009546 1.0132555
## theta P63 0.003761652 0.9868206 -1.9303712 1.9378945
## theta P67 -0.993849540 1.0240520 -3.0009546 1.0132555
## theta P69 1.000364696 1.0224898 -1.0036785 3.0044079
## theta P70 -2.195514074 1.2116151 -4.5702360 0.1792078
## theta P71 0.003761652 0.9868206 -1.9303712 1.9378945
## theta P72 1.000364696 1.0224898 -1.0036785 3.0044079
## theta P73 2.193698062 1.2058450 -0.1697146 4.5571107
## theta P76 0.003761652 0.9868206 -1.9303712 1.9378945
## theta P78 0.003761652 0.9868206 -1.9303712 1.9378945
## theta P79 0.003761652 0.9868206 -1.9303712 1.9378945
## theta P80 -2.195514074 1.2116151 -4.5702360 0.1792078
## theta P81 0.003761652 0.9868206 -1.9303712 1.9378945
## theta P82 2.193698062 1.2058450 -0.1697146 4.5571107
## theta P83 -2.195514074 1.2116151 -4.5702360 0.1792078
## theta P84 0.003761652 0.9868206 -1.9303712 1.9378945
## theta P85 -0.993849540 1.0240520 -3.0009546 1.0132555
## theta P87 0.003761652 0.9868206 -1.9303712 1.9378945
## theta P89 -0.993849540 1.0240520 -3.0009546 1.0132555
## theta P90 2.193698062 1.2058450 -0.1697146 4.5571107
## theta P92 1.000364696 1.0224898 -1.0036785 3.0044079
## theta P94 1.000364696 1.0224898 -1.0036785 3.0044079
## theta P95 1.000364696 1.0224898 -1.0036785 3.0044079
## theta P96 -0.993849540 1.0240520 -3.0009546 1.0132555
## theta P98 -0.993849540 1.0240520 -3.0009546 1.0132555
## theta P99 2.193698062 1.2058450 -0.1697146 4.5571107
## theta P100 0.003761652 0.9868206 -1.9303712 1.9378945
## theta P102 1.000364696 1.0224898 -1.0036785 3.0044079
## theta P103 1.000364696 1.0224898 -1.0036785 3.0044079
## theta P104 -2.195514074 1.2116151 -4.5702360 0.1792078
## theta P107 -2.195514074 1.2116151 -4.5702360 0.1792078
## theta P108 -2.195514074 1.2116151 -4.5702360 0.1792078
## theta P109 -0.993849540 1.0240520 -3.0009546 1.0132555
## theta P110 0.003761652 0.9868206 -1.9303712 1.9378945
## theta P111 0.003761652 0.9868206 -1.9303712 1.9378945gof.res <- gofIRT(pres04)
summary(gof.res)##
## Goodness-of-Fit Tests
## value df p-value
## Collapsed Deviance 57.093 30 0.002
## Hosmer-Lemeshow 19.581 8 0.012
## Rost Deviance 55.861 58 0.555
## Casewise Deviance 341.133 410 0.994
##
## R-Squared Measures
## Pearson R2: 0.487
## Sum-of-Squares R2: 0.484
## McFadden R2: 0.692
##
## Classifier Results - Confusion Matrix (relative frequencies)
## observed
## predicted 0 1
## 0 0.348 0.079
## 1 0.098 0.476
##
## Accuracy: 0.824
## Sensitivity: 0.858
## Specificity: 0.781
## Area under ROC: 0.901
## Gini coefficient: 0.802gof.res##
## Goodness-of-Fit Results:
## Collapsed Deviance = 57.093 (df = 30, p-value = 0.002)
## Pearson R2: 0.487
## Area Under ROC: 0.901#curvas
plotICC(mod_familiaridade)
plotICC(mod_familiaridade, empICC=list("raw"))
par(mfrow=c(1,1))
plotjointICC(mod_familiaridade, legpos = "left")
plotPImap(mod_familiaridade,sorted=T)
#dasslabelsnão rodei
difficulty estimates
#b <- coef(mod3, simplify = TRUE)
#b_mean<-(rowMeans(b$items[,-1]))*-1
#par(mfrow=c(2,1))#theta <- as.vector(fscores(mod3))
#hist(theta,xlim = c(-5,5))
#hist(b_mean,xlim = c(-5,5))