Análise Fatorial Confirmatória e Modelagem com Equações Estruturais usando o R lavan
Ricardo Primi
22 de novembro de 2016
O Pacote lavan (latent variable analysis)
- Desenvolvido por Yves Rosseel na Universidade de Gent, Bélgica
- Procura implementar os algoritmos do MPLUS
- Produz saídas similares ao MPLUS / AMOS / LISREL
- Informações no site http://lavaan.ugent.be
- Ref: Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. URL http://www.jstatsoft.org/v48/i02/
- Há um tutorial aqui: http://lavaan.ugent.be/tutorial/index.html
- Nesse exemplo trabalharemos com o Estudo 2 que precedeu a criação do SENNA. Aplicamos 209 itens de oito testes: Big Five Inventory (BFI; John, Naumann, & Soto, 2008), Big Five for Children (BFC; Barbaranelli, Caprara, Rabasca, & Pastorelli, 2003), Strengths and Difficulties Questionnaire (SDQ; Goodman, 1997), Norwick-Strickland Locus of Control Scale (Norwicki & Strickland, 1973), Self-Efficacy Questionnaire for Children (Muris, 2001), Grit Scale (Duckworth & Quinn, 2009), Core Self Evaluations (Judge, Erez, Bono, & Thoresen, 2003) and Rosenberg’s Self-Esteem Scale (Rosenberg, 1979).
- Foi usado um esquema de Blocos Balanceados Incompletos. Cdernos de testes com dois ou três testes no máximo. N=4640 alunos. Emgeral 900 alunos respondiam a um teste
- Criamos HICS (Homogeneous Internal Composits Scales)
setwd("~/Dropbox/R Stat")
load("sem.RData")
# install.packages("lavaan")
library(lavaan)## This is lavaan 0.5-20## lavaan is BETA software! Please report any bugs. library(psych)
library(semPlot)
names(bd)## [1] "id" "CD_ALUNO" "NM_ALUNO" "NM_ALUNO3"
## [5] "age_i" "age_c" "age_i2" "Sexo"
## [9] "Sexo_G" "serie" "correct_grade2" "CD_ESCOL"
## [13] "CD_TURNO" "NU_SEQUE" "CD_ETAPA" "ORDEM_CA"
## [17] "CD_CADER" "NU_FCA" "NU_LISTA" "NM_ESCOL"
## [21] "CD_REDE_" "DC_REDE_" "CD_ENSIN" "DC_ENSIN"
## [25] "DC_TURNO" "NM_ALUNO2" "DC_CADER" "T1_01A1"
## [29] "T1_02A1" "T1_03A1" "T1_04A1" "T1_05A1"
## [33] "T1_06A1" "T1_07A1" "T1_08A1" "T1_09A1"
## [37] "T1_10A1" "T1_11A1" "T1_12A1" "T1_13A1"
## [41] "T1_14N1" "T1_15N1" "T1_16N1" "T1_17N1"
## [45] "T1_18N1" "T1_19N1" "T1_20N1" "T1_21N1"
## [49] "T1_22N1" "T1_23N1" "T1_24N1" "T1_25N1"
## [53] "T1_26O1" "T1_27O1" "T1_28O1" "T1_29O1"
## [57] "T1_30O1" "T1_31O1" "T1_32O1" "T1_33O1"
## [61] "T1_34O1" "T1_35O1" "T1_36O1" "T1_37O1"
## [65] "T1_38O1" "T1_39E1" "T1_40E1" "T1_41E1"
## [69] "T1_42E1" "T1_43E1" "T1_44E1" "T1_45E1"
## [73] "T1_46E1" "T1_47E1" "T1_48E1" "T1_49E1"
## [77] "T1_50E1" "T1_51E1" "T1_52E1" "T1_53C0"
## [81] "T1_54C1" "T1_55C1" "T1_56C1" "T1_57C1"
## [85] "T1_58C1" "T1_59C1" "T1_60C0" "T1_61C1"
## [89] "T1_62C1" "T1_63C0" "T1_64C1" "T1_65C1"
## [93] "T5_01Ac1" "T5_02Ac1" "T5_03Ac1" "T5_04Ac1"
## [97] "T5_05Ac1" "T5_06Ac1" "T5_07Ac1" "T5_08Ac1"
## [101] "T5_09Sc1" "T5_10Sc1" "T5_11Sc1" "T5_12Sc1"
## [105] "T5_13Sc1" "T5_14Sc1" "T5_15Sc1" "T5_16Sc1"
## [109] "T5_17Em1" "T5_18Em1" "T5_19Em1" "T5_20Em1"
## [113] "T5_21Em1" "T5_22Em1" "T5_23Em1" "T5_24Em1"
## [117] "T6_01SE1" "T6_02SE1" "T6_03SE0" "T6_04SE1"
## [121] "T6_05SE0" "T6_06SE1" "T6_07SE1" "T6_08SE0"
## [125] "T6_09SE0" "T6_10SE0" "T8_01Gr0" "T8_02Gr1"
## [129] "T8_03Gr0" "T8_04Gr1" "T8_05Gr0" "T8_06Gr0"
## [133] "T8_07Gr1" "T8_08Gr1" "T2_01E1" "T2_02A0"
## [137] "T2_03C1" "T2_04N0" "T2_05O1" "T2_06E0"
## [141] "T2_07A1" "T2_08C0" "T2_09N1" "T2_10O1"
## [145] "T2_11E1" "T2_12A0" "T2_13C1" "T2_14N0"
## [149] "T2_15O1" "T2_16E1" "T2_17A1" "T2_18C0"
## [153] "T2_19N0" "T2_20O1" "T2_21E0" "T2_22A1"
## [157] "T2_23C0" "T2_24N1" "T2_25O1" "T2_26E1"
## [161] "T2_27A0" "T2_28C1" "T2_29N0" "T2_30O1"
## [165] "T2_31E0" "T2_32A1" "T2_33C1" "T2_34N1"
## [169] "T2_35O0" "T2_36E1" "T2_37A0" "T2_38C1"
## [173] "T2_39N0" "T2_40O1" "T2_41O0" "T2_42A1"
## [177] "T2_43C0" "T2_44O1" "T7_01SE1" "T7_02SE0"
## [181] "T7_03SE1" "T7_04SE0" "T7_05SE1" "T7_06SE0"
## [185] "T7_07SE1" "T7_08SE0" "T7_09SE1" "T7_10SE0"
## [189] "T7_11SE1" "T7_12SE0" "T3_01PS1" "T3_02HA1"
## [193] "T3_03ES1" "T3_04PS1" "T3_05CP1" "T3_06PP1"
## [197] "T3_07CP0" "T3_08ES1" "T3_09PS1" "T3_10HA1"
## [201] "T3_11PP0" "T3_12CP1" "T3_13ES1" "T3_14PP0"
## [205] "T3_15HA1" "T3_16ES1" "T3_17PS1" "T3_18CP1"
## [209] "T3_19PP1" "T3_20PS1" "T3_21HA0" "T3_22CP1"
## [213] "T3_23PP1" "T3_24ES1" "T3_25HA0" "T4_01LC1"
## [217] "T4_02LC1" "T4_03LC1" "T4_04LC0" "T4_05LC1"
## [221] "T4_06LC1" "T4_07LC1" "T4_08LC1" "T4_09LC1"
## [225] "T4_10LC1" "T4_11LC1" "T4_12LC1" "T4_13LC0"
## [229] "T4_14LC1" "T4_15LC1" "T4_16LC1" "T4_17LC1"
## [233] "T4_18LC1" "T4_19LC1" "T4_20LC1" "T4_21LC1"
## [237] "filter__" "grupo" "t1_mis" "t5_mis"
## [241] "t6_mis" "t8_mis" "t2_mis" "t7_mis"
## [245] "t3_mis" "t4_mis" "missings" "t1_bfc"
## [249] "t2_BFI" "t3_sdq" "t4_locus" "t5_auto"
## [253] "t6_rosemb" "t7_core" "t8_grit" "testes"
## [257] "N_BREAK" "Agree1" "Consc1" "Extra1"
## [261] "Neuro1" "Open1" "Agree2" "Consc2"
## [265] "Extra2" "Neuro2" "Open2" "CndProb"
## [269] "EmoSym" "HypAc" "PeerProb" "ProSoc"
## [273] "Locus" "SlfAcd" "SlfEmo" "SlfSoc"
## [277] "SE_rosem" "SE_core" "Grit" "SE"
## [281] "CD_ESCOLA" "CD_TURMA" "NM_TURMA" "NM_ESCOLA"
## [285] "DC_TURNO_TURMA" "RF_000_001" "final_filter" "miss_T2_A"
## [289] "miss_T2_C" "miss_T2_E" "miss_T2_N" "miss_T2_O"
## [293] "bfiave" "bfistd" "zT2_01E1" "zT2_02A0"
## [297] "zT2_03C1" "zT2_04N0" "zT2_05O1" "zT2_06E0"
## [301] "zT2_07A1" "zT2_08C0" "zT2_09N1" "zT2_10O1"
## [305] "zT2_11E1" "zT2_12A0" "zT2_13C1" "zT2_14N0"
## [309] "zT2_15O1" "zT2_16E1" "zT2_17A1" "zT2_18C0"
## [313] "zT2_19N0" "zT2_20O1" "zT2_21E0" "zT2_22A1"
## [317] "zT2_23C0" "zT2_24N1" "zT2_25O1" "zT2_26E1"
## [321] "zT2_27A0" "zT2_28C1" "zT2_29N0" "zT2_30O1"
## [325] "zT2_31E0" "zT2_32A1" "zT2_33C1" "zT2_34N1"
## [329] "zT2_35O0" "zT2_36E1" "zT2_37A0" "zT2_38C1"
## [333] "zT2_39N0" "zT2_40O1" "zT2_41O0" "zT2_42A1"
## [337] "zT2_43C0" "zT2_44O1" "zBFI_A" "zBFI_C"
## [341] "zBFI_E" "zBFI_N" "zBFI_O" "zBFI_Ntest"
## [345] "BFI_A" "BFI_C" "BFI_E" "BFI_N"
## [349] "BFI_O" "T1.A.1" "T1.A.2" "T1.A.3"
## [353] "T1.C.1" "T1.C.2" "T1.C.3" "T1.E.1"
## [357] "T1.E.2" "T1.E.3" "T1.N.1" "T1.N.2"
## [361] "T1.N.3" "T1.O.1" "T1.O.2" "T1.O.3"
## [365] "T2.A.1" "T2.A.2" "T2.A.3" "T2.C.1"
## [369] "T2.C.2" "T2.C.3" "T2.E.1" "T2.E.2"
## [373] "T2.E.3" "T2.N.1" "T2.N.2" "T2.N.3"
## [377] "T2.O.1" "T2.O.2" "T2.O.3" "T3.CP.1"
## [381] "T3.CP.2" "T3.ES.1" "T3.ES.2" "T3.HA.1"
## [385] "T3.HA.2" "T3.PP.1" "T3.PS.1" "T3.PS.2"
## [389] "T4.LC.1" "T4.LC.2" "T4.LC.3" "T5.Ac.1"
## [393] "T5.Ac.2" "T5.Ac.3" "T5.Em.1" "T5.Em.2"
## [397] "T5.Em.3" "T5.Sc.1" "T5.Sc.2" "T5.Sc.3"
## [401] "T7.SE_gsc.1" "T7.SE_lc.2" "T7.SE_N.3" "T7.SE_se.4"
## [405] "T8.Gr.1" "T8.Gr.2" "T8.Gr.3" # BFI
describe(bd[ , 57:115])## vars n mean sd median trimmed mad min max range skew
## T1_30O1 1 914 3.30 1.07 3 3.32 1.48 1 5 4 -0.15
## T1_31O1 2 914 3.01 0.93 3 2.96 1.48 1 5 4 0.26
## T1_32O1 3 909 3.65 1.06 4 3.72 1.48 1 5 4 -0.44
## T1_33O1 4 908 3.49 1.02 3 3.52 1.48 1 5 4 -0.22
## T1_34O1 5 909 3.20 1.02 3 3.18 1.48 1 5 4 -0.02
## T1_35O1 6 910 3.05 1.24 3 3.07 1.48 1 5 4 -0.01
## T1_36O1 7 899 2.86 1.14 3 2.83 1.48 1 5 4 0.32
## T1_37O1 8 912 2.94 1.02 3 2.91 1.48 1 5 4 0.17
## T1_38O1 9 909 3.54 1.06 4 3.58 1.48 1 5 4 -0.24
## T1_39E1 10 905 3.70 1.17 4 3.81 1.48 1 5 4 -0.58
## T1_40E1 11 902 2.79 1.38 3 2.74 1.48 1 5 4 0.18
## T1_41E1 12 911 3.44 1.27 3 3.52 1.48 1 5 4 -0.26
## T1_42E1 13 909 3.83 1.16 4 3.97 1.48 1 5 4 -0.73
## T1_43E1 14 912 3.13 1.26 3 3.17 1.48 1 5 4 -0.05
## T1_44E1 15 902 3.58 1.07 4 3.63 1.48 1 5 4 -0.29
## T1_45E1 16 917 3.37 1.22 3 3.43 1.48 1 5 4 -0.25
## T1_46E1 17 911 4.09 1.02 4 4.25 1.48 1 5 4 -0.97
## T1_47E1 18 912 3.04 1.06 3 3.02 1.48 1 5 4 0.07
## T1_48E1 19 900 2.85 1.34 3 2.82 1.48 1 5 4 0.19
## T1_49E1 20 915 3.74 1.23 4 3.87 1.48 1 5 4 -0.63
## T1_50E1 21 909 3.85 1.15 4 3.98 1.48 1 5 4 -0.68
## T1_51E1 22 917 4.11 1.18 5 4.32 0.00 1 5 4 -1.19
## T1_52E1 23 910 4.07 1.03 4 4.22 1.48 1 5 4 -0.94
## T1_53C0 24 897 3.20 1.25 3 3.25 1.48 1 5 4 -0.12
## T1_54C1 25 906 3.12 1.35 3 3.16 1.48 1 5 4 0.00
## T1_55C1 26 907 2.68 1.26 3 2.60 1.48 1 5 4 0.35
## T1_56C1 27 905 3.59 1.13 4 3.65 1.48 1 5 4 -0.35
## T1_57C1 28 912 2.84 1.34 3 2.80 1.48 1 5 4 0.17
## T1_58C1 29 909 3.20 1.29 3 3.25 1.48 1 5 4 -0.14
## T1_59C1 30 909 3.37 1.29 3 3.46 1.48 1 5 4 -0.30
## T1_60C0 31 905 3.31 1.23 3 3.36 1.48 1 5 4 -0.12
## T1_61C1 32 912 3.45 1.24 3 3.54 1.48 1 5 4 -0.33
## T1_62C1 33 912 3.62 1.17 4 3.70 1.48 1 5 4 -0.40
## T1_63C0 34 900 2.69 1.03 3 2.66 1.48 1 5 4 0.24
## T1_64C1 35 910 4.18 0.93 4 4.31 1.48 1 5 4 -1.04
## T1_65C1 36 917 3.21 1.35 3 3.26 1.48 1 5 4 -0.11
## T5_01Ac1 37 1008 2.96 0.99 3 2.91 1.48 1 5 4 0.27
## T5_02Ac1 38 1011 3.04 1.02 3 3.03 1.48 1 5 4 0.00
## T5_03Ac1 39 1008 3.50 1.03 4 3.53 1.48 1 5 4 -0.36
## T5_04Ac1 40 1005 3.68 1.04 4 3.76 1.48 1 5 4 -0.39
## T5_05Ac1 41 996 3.86 0.90 4 3.92 1.48 1 5 4 -0.52
## T5_06Ac1 42 998 3.92 1.16 4 4.08 1.48 1 5 4 -0.85
## T5_07Ac1 43 1010 3.76 1.03 4 3.86 1.48 1 5 4 -0.62
## T5_08Ac1 44 1004 3.46 0.81 3 3.46 1.48 1 5 4 -0.14
## T5_09Sc1 45 1004 3.33 1.15 3 3.36 1.48 1 5 4 -0.18
## T5_10Sc1 46 1006 4.03 1.03 4 4.17 1.48 1 5 4 -0.91
## T5_11Sc1 47 1002 2.92 1.30 3 2.90 1.48 1 5 4 0.06
## T5_12Sc1 48 1007 3.78 1.04 4 3.88 1.48 1 5 4 -0.70
## T5_13Sc1 49 1012 3.47 1.24 4 3.55 1.48 1 5 4 -0.33
## T5_14Sc1 50 1003 3.67 1.18 4 3.78 1.48 1 5 4 -0.59
## T5_15Sc1 51 1000 4.05 0.98 4 4.19 1.48 1 5 4 -0.98
## T5_16Sc1 52 989 3.33 1.27 3 3.40 1.48 1 5 4 -0.27
## T5_17Em1 53 996 3.23 1.16 3 3.25 1.48 1 5 4 -0.09
## T5_18Em1 54 1001 3.20 1.07 3 3.20 1.48 1 5 4 -0.10
## T5_19Em1 55 1001 3.09 1.25 3 3.11 1.48 1 5 4 -0.08
## T5_20Em1 56 1000 3.23 1.25 3 3.29 1.48 1 5 4 -0.21
## T5_21Em1 57 1004 3.35 1.24 3 3.42 1.48 1 5 4 -0.25
## T5_22Em1 58 990 3.46 1.22 4 3.54 1.48 1 5 4 -0.35
## T5_23Em1 59 1004 3.18 1.23 3 3.23 1.48 1 5 4 -0.12
## kurtosis se
## T1_30O1 -0.44 0.04
## T1_31O1 0.03 0.03
## T1_32O1 -0.44 0.04
## T1_33O1 -0.47 0.03
## T1_34O1 -0.39 0.03
## T1_35O1 -0.95 0.04
## T1_36O1 -0.66 0.04
## T1_37O1 -0.21 0.03
## T1_38O1 -0.65 0.04
## T1_39E1 -0.60 0.04
## T1_40E1 -1.20 0.05
## T1_41E1 -1.05 0.04
## T1_42E1 -0.41 0.04
## T1_43E1 -0.96 0.04
## T1_44E1 -0.58 0.04
## T1_45E1 -0.85 0.04
## T1_46E1 0.20 0.03
## T1_47E1 -0.42 0.03
## T1_48E1 -1.07 0.04
## T1_49E1 -0.66 0.04
## T1_50E1 -0.52 0.04
## T1_51E1 0.35 0.04
## T1_52E1 0.12 0.03
## T1_53C0 -0.90 0.04
## T1_54C1 -1.20 0.04
## T1_55C1 -0.86 0.04
## T1_56C1 -0.78 0.04
## T1_57C1 -1.13 0.04
## T1_58C1 -1.06 0.04
## T1_59C1 -0.96 0.04
## T1_60C0 -0.91 0.04
## T1_61C1 -0.86 0.04
## T1_62C1 -0.82 0.04
## T1_63C0 -0.21 0.03
## T1_64C1 0.60 0.03
## T1_65C1 -1.13 0.04
## T5_01Ac1 -0.17 0.03
## T5_02Ac1 -0.49 0.03
## T5_03Ac1 -0.49 0.03
## T5_04Ac1 -0.57 0.03
## T5_05Ac1 -0.03 0.03
## T5_06Ac1 -0.20 0.04
## T5_07Ac1 -0.16 0.03
## T5_08Ac1 0.16 0.03
## T5_09Sc1 -0.85 0.04
## T5_10Sc1 0.13 0.03
## T5_11Sc1 -1.08 0.04
## T5_12Sc1 -0.03 0.03
## T5_13Sc1 -0.94 0.04
## T5_14Sc1 -0.58 0.04
## T5_15Sc1 0.45 0.03
## T5_16Sc1 -1.00 0.04
## T5_17Em1 -0.83 0.04
## T5_18Em1 -0.56 0.03
## T5_19Em1 -1.03 0.04
## T5_20Em1 -0.96 0.04
## T5_21Em1 -0.94 0.04
## T5_22Em1 -0.92 0.04
## T5_23Em1 -0.97 0.04 table(bd$serie)##
## 1 2 3 4 5
## 1080 1080 1080 700 700Especifique o modelo
m <- 'A =~ T2.A.1+T2.A.2+T2.A.3
C =~ T2.C.1+T2.C.2+T2.C.3
E =~ T2.E.1+T2.E.2+T2.E.3
N =~ T2.N.1+T2.N.2+T2.N.3
O =~ T2.O.1+T2.O.2+T2.O.3'Rode as análises com as funções cfa ou sem
fit <- cfa(m, data = bd )## Found more than one class "Model" in cache; using the first, from namespace 'lavaan' # desenha o modelo
semPaths(fit)
Examine os resultados
summary(fit, standardized = TRUE, fit.measures = TRUE)## lavaan (0.5-20) converged normally after 44 iterations
##
## Used Total
## Number of observations 930 4640
##
## Estimator ML
## Minimum Function Test Statistic 498.661
## Degrees of freedom 80
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 3323.969
## Degrees of freedom 105
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.870
## Tucker-Lewis Index (TLI) 0.829
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -15640.706
## Loglikelihood unrestricted model (H1) -15391.376
##
## Number of free parameters 40
## Akaike (AIC) 31361.412
## Bayesian (BIC) 31554.819
## Sample-size adjusted Bayesian (BIC) 31427.783
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.075
## 90 Percent Confidence Interval 0.069 0.081
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.074
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## A =~
## T2.A.1 1.000 0.409 0.611
## T2.A.2 1.147 0.093 12.311 0.000 0.469 0.626
## T2.A.3 1.166 0.101 11.583 0.000 0.477 0.553
## C =~
## T2.C.1 1.000 0.582 0.767
## T2.C.2 0.956 0.056 16.929 0.000 0.556 0.644
## T2.C.3 1.001 0.054 18.606 0.000 0.582 0.755
## E =~
## T2.E.1 1.000 0.531 0.623
## T2.E.2 1.206 0.113 10.663 0.000 0.640 0.750
## T2.E.3 0.818 0.081 10.054 0.000 0.435 0.452
## N =~
## T2.N.1 1.000 0.700 0.816
## T2.N.2 0.576 0.052 11.028 0.000 0.403 0.516
## T2.N.3 0.570 0.054 10.563 0.000 0.399 0.478
## O =~
## T2.O.1 1.000 0.586 0.662
## T2.O.2 0.736 0.056 13.051 0.000 0.432 0.530
## T2.O.3 1.167 0.076 15.360 0.000 0.685 0.809
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## A ~~
## C 0.127 0.014 9.225 0.000 0.535 0.535
## E 0.036 0.011 3.196 0.001 0.166 0.166
## N 0.162 0.017 9.538 0.000 0.566 0.566
## O 0.083 0.013 6.384 0.000 0.344 0.344
## C ~~
## E 0.027 0.014 1.912 0.056 0.087 0.087
## N 0.136 0.019 7.172 0.000 0.333 0.333
## O 0.164 0.018 9.183 0.000 0.482 0.482
## E ~~
## N -0.019 0.017 -1.098 0.272 -0.051 -0.051
## O 0.120 0.017 6.928 0.000 0.387 0.387
## N ~~
## O -0.007 0.018 -0.396 0.692 -0.018 -0.018
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## T2.A.1 0.281 0.018 15.828 0.000 0.281 0.627
## T2.A.2 0.343 0.022 15.365 0.000 0.343 0.609
## T2.A.3 0.518 0.030 17.382 0.000 0.518 0.695
## T2.C.1 0.236 0.018 13.034 0.000 0.236 0.411
## T2.C.2 0.436 0.025 17.479 0.000 0.436 0.585
## T2.C.3 0.256 0.019 13.620 0.000 0.256 0.430
## T2.E.1 0.445 0.032 13.826 0.000 0.445 0.612
## T2.E.2 0.320 0.038 8.366 0.000 0.320 0.438
## T2.E.3 0.736 0.039 18.930 0.000 0.736 0.796
## T2.N.1 0.246 0.038 6.493 0.000 0.246 0.334
## T2.N.2 0.449 0.025 18.162 0.000 0.449 0.734
## T2.N.3 0.537 0.028 18.912 0.000 0.537 0.772
## T2.O.1 0.441 0.028 15.733 0.000 0.441 0.562
## T2.O.2 0.477 0.025 18.924 0.000 0.477 0.719
## T2.O.3 0.248 0.027 9.056 0.000 0.248 0.346
## A 0.168 0.020 8.290 0.000 1.000 1.000
## C 0.338 0.028 11.975 0.000 1.000 1.000
## E 0.282 0.036 7.754 0.000 1.000 1.000
## N 0.490 0.048 10.130 0.000 1.000 1.000
## O 0.344 0.036 9.628 0.000 1.000 1.000Examine índices de modificação
mi <- modindices(fit)
str(mi)## Classes 'lavaan.data.frame' and 'data.frame': 165 obs. of 8 variables:
## $ lhs : chr "A" "A" "A" "A" ...
## $ op : chr "=~" "=~" "=~" "=~" ...
## $ rhs : chr "T2.C.1" "T2.C.2" "T2.C.3" "T2.E.1" ...
## $ mi : num 1.799 5.112 0.394 71.459 13.131 ...
## $ epc : num -0.1192 0.2211 -0.0563 -0.6668 0.32 ...
## $ sepc.lv : num -0.0488 0.0905 -0.023 -0.2729 0.131 ...
## $ sepc.all: num -0.0644 0.1049 -0.0299 -0.32 0.1533 ...
## $ sepc.nox: num -0.0644 0.1049 -0.0299 -0.32 0.1533 ... class(mi)## [1] "lavaan.data.frame" "data.frame" head(mi)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 46 A =~ T2.C.1 1.799 -0.119 -0.049 -0.064 -0.064
## 47 A =~ T2.C.2 5.112 0.221 0.091 0.105 0.105
## 48 A =~ T2.C.3 0.394 -0.056 -0.023 -0.030 -0.030
## 49 A =~ T2.E.1 71.459 -0.667 -0.273 -0.320 -0.320
## 50 A =~ T2.E.2 13.131 0.320 0.131 0.153 0.153
## 51 A =~ T2.E.3 40.000 0.568 0.232 0.242 0.242 mi[mi$mi>20 , ]## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 49 A =~ T2.E.1 71.459 -0.667 -0.273 -0.320 -0.320
## 51 A =~ T2.E.3 40.000 0.568 0.232 0.242 0.242
## 54 A =~ T2.N.3 34.880 0.658 0.269 0.323 0.323
## 61 C =~ T2.E.1 69.167 -0.425 -0.247 -0.290 -0.290
## 63 C =~ T2.E.3 23.748 0.284 0.165 0.172 0.172
## 66 C =~ T2.N.3 27.513 0.292 0.170 0.203 0.203
## 75 E =~ T2.C.3 29.461 0.261 0.139 0.180 0.180
## 76 E =~ T2.N.1 58.553 -0.564 -0.300 -0.349 -0.349
## 78 E =~ T2.N.3 45.069 0.387 0.205 0.246 0.246
## 83 N =~ T2.A.2 28.802 -0.321 -0.225 -0.300 -0.300
## 86 N =~ T2.C.2 50.385 0.315 0.220 0.255 0.255
## 88 N =~ T2.E.1 29.354 -0.233 -0.163 -0.191 -0.191
## 90 N =~ T2.E.3 34.764 0.292 0.204 0.213 0.213
## 98 O =~ T2.C.2 59.108 -0.450 -0.264 -0.306 -0.306
## 99 O =~ T2.C.3 30.155 0.289 0.169 0.220 0.220
## 105 O =~ T2.N.3 35.584 0.295 0.173 0.207 0.207
## 111 T2.A.1 ~~ T2.E.1 22.237 -0.068 -0.068 -0.119 -0.119
## 138 T2.A.3 ~~ T2.E.3 29.945 0.124 0.124 0.150 0.150
## 159 T2.C.2 ~~ T2.E.3 24.321 0.103 0.103 0.124 0.124SEM quanto BIG 5 explica Grit?
# Especifica o modelo
m <- 'A =~ T2.A.1+T2.A.2+T2.A.3
C =~ T2.C.1+T2.C.2+T2.C.3
E =~ T2.E.1+T2.E.2+T2.E.3
N =~ T2.N.1+T2.N.2+T2.N.3
O =~ T2.O.1+T2.O.2+T2.O.3
grit =~ T8.Gr.1 + T8.Gr.2 + T8.Gr.3
grit ~ A + C + E + N + O '
# Roda as análises
fit <- cfa(m, data = bd)
summary(fit, standardized = TRUE, fit.measures = TRUE)## lavaan (0.5-20) converged normally after 60 iterations
##
## Used Total
## Number of observations 227 4640
##
## Estimator ML
## Minimum Function Test Statistic 294.339
## Degrees of freedom 120
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 1213.569
## Degrees of freedom 153
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.836
## Tucker-Lewis Index (TLI) 0.790
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4527.009
## Loglikelihood unrestricted model (H1) -4379.839
##
## Number of free parameters 51
## Akaike (AIC) 9156.018
## Bayesian (BIC) 9330.690
## Sample-size adjusted Bayesian (BIC) 9169.057
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.080
## 90 Percent Confidence Interval 0.068 0.092
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.094
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## A =~
## T2.A.1 1.000 0.343 0.560
## T2.A.2 1.110 0.236 4.710 0.000 0.381 0.494
## T2.A.3 1.445 0.287 5.031 0.000 0.496 0.574
## C =~
## T2.C.1 1.000 0.598 0.759
## T2.C.2 0.986 0.105 9.385 0.000 0.589 0.664
## T2.C.3 1.003 0.093 10.787 0.000 0.600 0.768
## E =~
## T2.E.1 1.000 0.708 0.879
## T2.E.2 0.568 0.140 4.069 0.000 0.402 0.488
## T2.E.3 0.340 0.117 2.903 0.004 0.241 0.253
## N =~
## T2.N.1 1.000 0.724 0.814
## T2.N.2 0.662 0.102 6.506 0.000 0.479 0.599
## T2.N.3 0.574 0.101 5.693 0.000 0.416 0.482
## O =~
## T2.O.1 1.000 0.683 0.745
## T2.O.2 0.606 0.088 6.876 0.000 0.414 0.526
## T2.O.3 1.043 0.117 8.929 0.000 0.713 0.795
## grit =~
## T8.Gr.1 1.000 0.553 0.718
## T8.Gr.2 1.171 0.131 8.958 0.000 0.647 0.710
## T8.Gr.3 0.569 0.098 5.818 0.000 0.314 0.439
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## grit ~
## A 0.099 0.227 0.436 0.663 0.061 0.061
## C 0.932 0.133 7.033 0.000 1.008 1.008
## E 0.022 0.074 0.300 0.764 0.029 0.029
## N -0.010 0.097 -0.099 0.921 -0.013 -0.013
## O -0.266 0.107 -2.494 0.013 -0.329 -0.329
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## A ~~
## C 0.086 0.024 3.602 0.000 0.419 0.419
## E -0.037 0.025 -1.480 0.139 -0.151 -0.151
## N 0.145 0.032 4.460 0.000 0.583 0.583
## O 0.038 0.024 1.551 0.121 0.161 0.161
## C ~~
## E -0.071 0.036 -1.950 0.051 -0.167 -0.167
## N 0.100 0.039 2.573 0.010 0.232 0.232
## O 0.180 0.040 4.511 0.000 0.441 0.441
## E ~~
## N -0.101 0.045 -2.227 0.026 -0.197 -0.197
## O 0.176 0.045 3.956 0.000 0.364 0.364
## N ~~
## O -0.101 0.045 -2.260 0.024 -0.204 -0.204
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## T2.A.1 0.257 0.033 7.862 0.000 0.257 0.686
## T2.A.2 0.450 0.052 8.713 0.000 0.450 0.756
## T2.A.3 0.500 0.065 7.650 0.000 0.500 0.671
## T2.C.1 0.263 0.033 7.893 0.000 0.263 0.424
## T2.C.2 0.439 0.048 9.087 0.000 0.439 0.559
## T2.C.3 0.250 0.032 7.717 0.000 0.250 0.410
## T2.E.1 0.147 0.111 1.330 0.183 0.147 0.227
## T2.E.2 0.516 0.060 8.541 0.000 0.516 0.762
## T2.E.3 0.847 0.081 10.403 0.000 0.847 0.936
## T2.N.1 0.267 0.071 3.754 0.000 0.267 0.337
## T2.N.2 0.411 0.050 8.288 0.000 0.411 0.641
## T2.N.3 0.571 0.060 9.511 0.000 0.571 0.768
## T2.O.1 0.375 0.056 6.743 0.000 0.375 0.445
## T2.O.2 0.450 0.047 9.575 0.000 0.450 0.724
## T2.O.3 0.296 0.054 5.508 0.000 0.296 0.368
## T8.Gr.1 0.286 0.038 7.632 0.000 0.286 0.484
## T8.Gr.2 0.412 0.053 7.795 0.000 0.412 0.496
## T8.Gr.3 0.414 0.041 10.037 0.000 0.414 0.808
## A 0.118 0.034 3.482 0.000 1.000 1.000
## C 0.357 0.057 6.234 0.000 1.000 1.000
## E 0.501 0.125 4.014 0.000 1.000 1.000
## N 0.525 0.097 5.436 0.000 1.000 1.000
## O 0.467 0.083 5.638 0.000 1.000 1.000
## grit 0.044 0.029 1.515 0.130 0.144 0.144# R2 (quanto cada variável endógena é explicada)
lavInspect(fit, "rsquare") ## T2.A.1 T2.A.2 T2.A.3 T2.C.1 T2.C.2 T2.C.3 T2.E.1 T2.E.2 T2.E.3
## 0.314 0.244 0.329 0.576 0.441 0.590 0.773 0.238 0.064
## T2.N.1 T2.N.2 T2.N.3 T2.O.1 T2.O.2 T2.O.3 T8.Gr.1 T8.Gr.2 T8.Gr.3
## 0.663 0.359 0.232 0.555 0.276 0.632 0.516 0.504 0.192
## grit
## 0.856# desenha o modelo
semPaths(fit)
SEM quanto BIG 5 explica do CORE Self Esteem?
# Especifica o modelo
m <- 'A =~ T2.A.1+T2.A.2+T2.A.3
C =~ T2.C.1+T2.C.2+T2.C.3
E =~ T2.E.1+T2.E.2+T2.E.3
N =~ T2.N.1+T2.N.2+T2.N.3
O =~ T2.O.1+T2.O.2+T2.O.3
core =~ T7.SE_gsc.1 + T7.SE_lc.2+ T7.SE_N.3
core ~ A + C + E + N + O '
# Roda as análises
fit <- cfa(m, data = bd)
summary(fit, standardized = TRUE, fit.measures = TRUE)## lavaan (0.5-20) converged normally after 70 iterations
##
## Used Total
## Number of observations 135 4640
##
## Estimator ML
## Minimum Function Test Statistic 280.622
## Degrees of freedom 120
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 862.918
## Degrees of freedom 153
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.774
## Tucker-Lewis Index (TLI) 0.712
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2507.361
## Loglikelihood unrestricted model (H1) -2367.050
##
## Number of free parameters 51
## Akaike (AIC) 5116.722
## Bayesian (BIC) 5264.891
## Sample-size adjusted Bayesian (BIC) 5103.560
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.100
## 90 Percent Confidence Interval 0.084 0.115
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.099
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## A =~
## T2.A.1 1.000 0.375 0.610
## T2.A.2 1.180 0.241 4.895 0.000 0.442 0.636
## T2.A.3 1.693 0.340 4.985 0.000 0.634 0.677
## C =~
## T2.C.1 1.000 0.506 0.699
## T2.C.2 1.299 0.175 7.437 0.000 0.657 0.760
## T2.C.3 1.160 0.151 7.669 0.000 0.587 0.805
## E =~
## T2.E.1 1.000 0.729 0.832
## T2.E.2 0.847 0.133 6.365 0.000 0.618 0.709
## T2.E.3 0.714 0.122 5.848 0.000 0.520 0.601
## N =~
## T2.N.1 1.000 0.410 0.491
## T2.N.2 1.226 0.281 4.363 0.000 0.503 0.636
## T2.N.3 1.369 0.304 4.498 0.000 0.562 0.717
## O =~
## T2.O.1 1.000 0.511 0.657
## T2.O.2 0.617 0.133 4.632 0.000 0.315 0.472
## T2.O.3 1.217 0.240 5.076 0.000 0.622 0.866
## core =~
## T7.SE_gsc.1 1.000 0.399 0.625
## T7.SE_lc.2 0.787 0.171 4.600 0.000 0.314 0.458
## T7.SE_N.3 0.917 0.200 4.589 0.000 0.366 0.457
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## core ~
## A -0.205 0.192 -1.068 0.286 -0.192 -0.192
## C 0.578 0.123 4.717 0.000 0.732 0.732
## E 0.101 0.065 1.550 0.121 0.183 0.183
## N 0.624 0.202 3.093 0.002 0.641 0.641
## O -0.069 0.092 -0.751 0.453 -0.089 -0.089
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## A ~~
## C 0.078 0.026 2.985 0.003 0.410 0.410
## E 0.024 0.032 0.737 0.461 0.087 0.087
## N 0.086 0.028 3.080 0.002 0.561 0.561
## O 0.029 0.023 1.250 0.211 0.151 0.151
## C ~~
## E 0.076 0.041 1.854 0.064 0.206 0.206
## N 0.053 0.027 1.982 0.047 0.256 0.256
## O 0.074 0.031 2.363 0.018 0.286 0.286
## E ~~
## N 0.048 0.037 1.311 0.190 0.160 0.160
## O 0.115 0.045 2.538 0.011 0.310 0.310
## N ~~
## O 0.008 0.025 0.314 0.753 0.037 0.037
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## T2.A.1 0.237 0.038 6.200 0.000 0.237 0.628
## T2.A.2 0.288 0.049 5.896 0.000 0.288 0.596
## T2.A.3 0.474 0.089 5.320 0.000 0.474 0.541
## T2.C.1 0.267 0.041 6.556 0.000 0.267 0.511
## T2.C.2 0.315 0.055 5.785 0.000 0.315 0.422
## T2.C.3 0.186 0.037 4.975 0.000 0.186 0.351
## T2.E.1 0.236 0.075 3.157 0.002 0.236 0.308
## T2.E.2 0.376 0.068 5.515 0.000 0.376 0.497
## T2.E.3 0.478 0.069 6.887 0.000 0.478 0.638
## T2.N.1 0.529 0.073 7.251 0.000 0.529 0.759
## T2.N.2 0.373 0.061 6.080 0.000 0.373 0.596
## T2.N.3 0.299 0.061 4.897 0.000 0.299 0.487
## T2.O.1 0.344 0.062 5.562 0.000 0.344 0.568
## T2.O.2 0.346 0.046 7.482 0.000 0.346 0.777
## T2.O.3 0.128 0.068 1.891 0.059 0.128 0.249
## T7.SE_gsc.1 0.249 0.040 6.306 0.000 0.249 0.610
## T7.SE_lc.2 0.372 0.049 7.650 0.000 0.372 0.790
## T7.SE_N.3 0.507 0.066 7.655 0.000 0.507 0.791
## A 0.140 0.044 3.218 0.001 1.000 1.000
## C 0.256 0.060 4.262 0.000 1.000 1.000
## E 0.531 0.112 4.722 0.000 1.000 1.000
## N 0.168 0.065 2.586 0.010 1.000 1.000
## O 0.261 0.076 3.448 0.001 1.000 1.000
## core -0.008 0.027 -0.311 0.756 -0.052 -0.052 lavInspect(fit, "rsquare") ## T2.A.1 T2.A.2 T2.A.3 T2.C.1 T2.C.2 T2.C.3
## 0.372 0.404 0.459 0.489 0.578 0.649
## T2.E.1 T2.E.2 T2.E.3 T2.N.1 T2.N.2 T2.N.3
## 0.692 0.503 0.362 0.241 0.404 0.513
## T2.O.1 T2.O.2 T2.O.3 T7.SE_gsc.1 T7.SE_lc.2 T7.SE_N.3
## 0.432 0.223 0.751 0.390 0.210 0.209
## core
## NA # desenha o modelo
semPaths(fit)
SEM quanto cada componente do CORE Self Esteem explica BIG 5 ?
# Especifica o modelo
m <- 'A =~ T2.A.1+T2.A.2+T2.A.3
C =~ T2.C.1+T2.C.2+T2.C.3
E =~ T2.E.1+T2.E.2+T2.E.3
N =~ T2.N.1+T2.N.2+T2.N.3
O =~ T2.O.1+T2.O.2+T2.O.3
A ~ T7.SE_gsc.1 + T7.SE_lc.2+ T7.SE_N.3
C ~ T7.SE_gsc.1 + T7.SE_lc.2+ T7.SE_N.3
E ~ T7.SE_gsc.1 + T7.SE_lc.2+ T7.SE_N.3
N ~ T7.SE_gsc.1 + T7.SE_lc.2+ T7.SE_N.3
O ~ T7.SE_gsc.1 + T7.SE_lc.2+ T7.SE_N.3'
# Roda as análises
fit <- cfa(m, data = bd)
summary(fit, standardized = TRUE, fit.measures = TRUE)## lavaan (0.5-20) converged normally after 62 iterations
##
## Used Total
## Number of observations 135 4640
##
## Estimator ML
## Minimum Function Test Statistic 224.821
## Degrees of freedom 110
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 828.937
## Degrees of freedom 150
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.831
## Tucker-Lewis Index (TLI) 0.769
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2479.461
## Loglikelihood unrestricted model (H1) -2367.050
##
## Number of free parameters 55
## Akaike (AIC) 5068.921
## Bayesian (BIC) 5228.711
## Sample-size adjusted Bayesian (BIC) 5054.727
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.088
## 90 Percent Confidence Interval 0.071 0.104
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.086
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## A =~
## T2.A.1 1.000 0.377 0.613
## T2.A.2 1.161 0.237 4.907 0.000 0.438 0.629
## T2.A.3 1.691 0.336 5.028 0.000 0.637 0.680
## C =~
## T2.C.1 1.000 0.504 0.696
## T2.C.2 1.273 0.172 7.388 0.000 0.641 0.742
## T2.C.3 1.190 0.151 7.866 0.000 0.599 0.823
## E =~
## T2.E.1 1.000 0.732 0.835
## T2.E.2 0.841 0.131 6.444 0.000 0.615 0.707
## T2.E.3 0.709 0.121 5.886 0.000 0.519 0.600
## N =~
## T2.N.1 1.000 0.418 0.501
## T2.N.2 1.172 0.257 4.555 0.000 0.490 0.619
## T2.N.3 1.354 0.280 4.829 0.000 0.566 0.722
## O =~
## T2.O.1 1.000 0.568 0.731
## T2.O.2 0.572 0.122 4.692 0.000 0.325 0.487
## T2.O.3 0.985 0.171 5.754 0.000 0.560 0.780
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## A ~
## T7.SE_gsc.1 0.164 0.065 2.538 0.011 0.435 0.278
## T7.SE_lc.2 -0.023 0.061 -0.384 0.701 -0.062 -0.042
## T7.SE_N.3 0.113 0.053 2.151 0.032 0.300 0.240
## C ~
## T7.SE_gsc.1 0.442 0.074 5.970 0.000 0.879 0.562
## T7.SE_lc.2 0.215 0.065 3.324 0.001 0.427 0.293
## T7.SE_N.3 -0.051 0.052 -0.989 0.323 -0.102 -0.081
## E ~
## T7.SE_gsc.1 0.132 0.112 1.179 0.238 0.180 0.115
## T7.SE_lc.2 0.119 0.111 1.073 0.283 0.162 0.111
## T7.SE_N.3 0.172 0.093 1.859 0.063 0.236 0.189
## N ~
## T7.SE_gsc.1 0.184 0.067 2.771 0.006 0.441 0.282
## T7.SE_lc.2 -0.058 0.060 -0.971 0.332 -0.138 -0.095
## T7.SE_N.3 0.322 0.072 4.480 0.000 0.770 0.616
## O ~
## T7.SE_gsc.1 0.225 0.091 2.465 0.014 0.397 0.254
## T7.SE_lc.2 0.067 0.088 0.756 0.450 0.117 0.080
## T7.SE_N.3 -0.183 0.076 -2.419 0.016 -0.322 -0.258
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## A ~~
## C 0.040 0.018 2.245 0.025 0.315 0.315
## E -0.005 0.029 -0.173 0.863 -0.021 -0.021
## N 0.051 0.019 2.630 0.009 0.483 0.483
## O 0.031 0.024 1.280 0.201 0.165 0.165
## C ~~
## E 0.024 0.030 0.797 0.425 0.094 0.094
## N 0.008 0.016 0.477 0.634 0.069 0.069
## O 0.040 0.025 1.613 0.107 0.202 0.202
## E ~~
## N -0.009 0.029 -0.313 0.754 -0.042 -0.042
## O 0.124 0.046 2.673 0.008 0.331 0.331
## N ~~
## O 0.020 0.023 0.854 0.393 0.121 0.121
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## T2.A.1 0.235 0.038 6.185 0.000 0.235 0.624
## T2.A.2 0.292 0.049 6.003 0.000 0.292 0.604
## T2.A.3 0.471 0.089 5.308 0.000 0.471 0.537
## T2.C.1 0.270 0.040 6.689 0.000 0.270 0.515
## T2.C.2 0.336 0.054 6.194 0.000 0.336 0.450
## T2.C.3 0.171 0.036 4.791 0.000 0.171 0.323
## T2.E.1 0.232 0.074 3.149 0.002 0.232 0.302
## T2.E.2 0.379 0.068 5.610 0.000 0.379 0.500
## T2.E.3 0.479 0.069 6.921 0.000 0.479 0.640
## T2.N.1 0.523 0.071 7.360 0.000 0.523 0.749
## T2.N.2 0.386 0.059 6.579 0.000 0.386 0.616
## T2.N.3 0.294 0.056 5.225 0.000 0.294 0.478
## T2.O.1 0.282 0.060 4.702 0.000 0.282 0.466
## T2.O.2 0.340 0.046 7.405 0.000 0.340 0.763
## T2.O.3 0.202 0.053 3.790 0.000 0.202 0.392
## A 0.121 0.038 3.179 0.001 0.853 0.853
## C 0.135 0.034 3.976 0.000 0.533 0.533
## E 0.486 0.104 4.673 0.000 0.907 0.907
## N 0.092 0.037 2.496 0.013 0.526 0.526
## O 0.288 0.074 3.911 0.000 0.891 0.891 lavInspect(fit, "rsquare") ## T2.A.1 T2.A.2 T2.A.3 T2.C.1 T2.C.2 T2.C.3 T2.E.1 T2.E.2 T2.E.3 T2.N.1
## 0.376 0.396 0.463 0.485 0.550 0.677 0.698 0.500 0.360 0.251
## T2.N.2 T2.N.3 T2.O.1 T2.O.2 T2.O.3 A C E N O
## 0.384 0.522 0.534 0.237 0.608 0.147 0.467 0.093 0.474 0.109 # desenha o modelo
semPaths(fit)
Análise com variáveis categóricas
# Examina nomes das variáveis
colnames(bpr_df)## [1] "ahv_1" "ahv_2" "ahv_3" "ahv_4" "ahv_5" "ahv_6" "ahv_7" "ahv_8"
## [9] "ahv_9" "aha_1" "aha_2" "aha_3" "aha_4" "aha_5" "aha_6" "aha_7"
## [17] "aha_8" "aha_9" "ahe_1" "ahe_2" "ahe_3" "ahe_4" "ahe_5" "ahe_6"
## [25] "ahe_7" "ahe_8" "ahe_9" paste(colnames(bpr_df), collapse = "+")## [1] "ahv_1+ahv_2+ahv_3+ahv_4+ahv_5+ahv_6+ahv_7+ahv_8+ahv_9+aha_1+aha_2+aha_3+aha_4+aha_5+aha_6+aha_7+aha_8+aha_9+ahe_1+ahe_2+ahe_3+ahe_4+ahe_5+ahe_6+ahe_7+ahe_8+ahe_9"# Cria modelo usadno paste
m <- paste("g =~", paste(colnames(bpr_df), collapse = "+"), sep="")
# Executa a análise
fit <- cfa(m, data = bpr_df, ordered = colnames(bpr_df))
# Examina os resultados
summary(fit, standardized = TRUE, fit.measures = TRUE)## lavaan (0.5-20) converged normally after 30 iterations
##
## Used Total
## Number of observations 952 3578
##
## Estimator DWLS Robust
## Minimum Function Test Statistic 537.890 566.540
## Degrees of freedom 324 324
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.064
## Shift parameter 61.203
## for simple second-order correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 2606.156 2032.940
## Degrees of freedom 351 351
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.905 0.856
## Tucker-Lewis Index (TLI) 0.897 0.844
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.026 0.028
## 90 Percent Confidence Interval 0.022 0.030 0.024 0.032
## P-value RMSEA <= 0.05 1.000 1.000
##
## Weighted Root Mean Square Residual:
##
## WRMR 1.193 1.193
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Robust.sem
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## g =~
## ahv_1 1.000 0.481 0.481
## ahv_2 0.921 0.120 7.681 0.000 0.443 0.443
## ahv_3 1.085 0.134 8.072 0.000 0.521 0.521
## ahv_4 0.853 0.123 6.942 0.000 0.410 0.410
## ahv_5 0.621 0.118 5.247 0.000 0.298 0.298
## ahv_6 0.355 0.107 3.320 0.001 0.170 0.170
## ahv_7 0.060 0.151 0.398 0.691 0.029 0.029
## ahv_8 -0.051 0.150 -0.341 0.733 -0.025 -0.025
## ahv_9 0.218 0.104 2.105 0.035 0.105 0.105
## aha_1 1.229 0.144 8.523 0.000 0.590 0.590
## aha_2 1.374 0.164 8.375 0.000 0.660 0.660
## aha_3 1.294 0.162 7.969 0.000 0.622 0.622
## aha_4 0.749 0.121 6.214 0.000 0.360 0.360
## aha_5 0.566 0.112 5.038 0.000 0.272 0.272
## aha_6 0.696 0.122 5.718 0.000 0.334 0.334
## aha_7 1.375 0.157 8.778 0.000 0.661 0.661
## aha_8 0.477 0.138 3.465 0.001 0.229 0.229
## aha_9 0.071 0.140 0.511 0.610 0.034 0.034
## ahe_1 0.504 0.110 4.569 0.000 0.242 0.242
## ahe_2 0.343 0.101 3.406 0.001 0.165 0.165
## ahe_3 0.306 0.115 2.657 0.008 0.147 0.147
## ahe_4 0.671 0.115 5.837 0.000 0.322 0.322
## ahe_5 0.689 0.126 5.481 0.000 0.331 0.331
## ahe_6 0.530 0.118 4.493 0.000 0.255 0.255
## ahe_7 0.682 0.120 5.678 0.000 0.328 0.328
## ahe_8 0.364 0.130 2.800 0.005 0.175 0.175
## ahe_9 0.344 0.120 2.872 0.004 0.165 0.165
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## ahv_1 0.000 0.000 0.000
## ahv_2 0.000 0.000 0.000
## ahv_3 0.000 0.000 0.000
## ahv_4 0.000 0.000 0.000
## ahv_5 0.000 0.000 0.000
## ahv_6 0.000 0.000 0.000
## ahv_7 0.000 0.000 0.000
## ahv_8 0.000 0.000 0.000
## ahv_9 0.000 0.000 0.000
## aha_1 0.000 0.000 0.000
## aha_2 0.000 0.000 0.000
## aha_3 0.000 0.000 0.000
## aha_4 0.000 0.000 0.000
## aha_5 0.000 0.000 0.000
## aha_6 0.000 0.000 0.000
## aha_7 0.000 0.000 0.000
## aha_8 0.000 0.000 0.000
## aha_9 0.000 0.000 0.000
## ahe_1 0.000 0.000 0.000
## ahe_2 0.000 0.000 0.000
## ahe_3 0.000 0.000 0.000
## ahe_4 0.000 0.000 0.000
## ahe_5 0.000 0.000 0.000
## ahe_6 0.000 0.000 0.000
## ahe_7 0.000 0.000 0.000
## ahe_8 0.000 0.000 0.000
## ahe_9 0.000 0.000 0.000
## g 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## ahv_1|t1 -0.732 0.045 -16.315 0.000 -0.732 -0.732
## ahv_2|t1 -0.220 0.041 -5.373 0.000 -0.220 -0.220
## ahv_3|t1 0.008 0.041 0.194 0.846 0.008 0.008
## ahv_4|t1 0.264 0.041 6.407 0.000 0.264 0.264
## ahv_5|t1 0.127 0.041 3.109 0.002 0.127 0.127
## ahv_6|t1 0.403 0.042 9.626 0.000 0.403 0.403
## ahv_7|t1 1.392 0.059 23.695 0.000 1.392 1.392
## ahv_8|t1 1.428 0.060 23.822 0.000 1.428 1.428
## ahv_9|t1 0.210 0.041 5.115 0.000 0.210 0.210
## aha_1|t1 -1.036 0.050 -20.860 0.000 -1.036 -1.036
## aha_2|t1 -0.316 0.041 -7.632 0.000 -0.316 -0.316
## aha_3|t1 0.458 0.042 10.842 0.000 0.458 0.458
## aha_4|t1 0.264 0.041 6.407 0.000 0.264 0.264
## aha_5|t1 0.286 0.041 6.923 0.000 0.286 0.286
## aha_6|t1 0.417 0.042 9.946 0.000 0.417 0.417
## aha_7|t1 -0.137 0.041 -3.368 0.001 -0.137 -0.137
## aha_8|t1 1.101 0.051 21.589 0.000 1.101 1.101
## aha_9|t1 1.077 0.050 21.333 0.000 1.077 1.077
## ahe_1|t1 -0.069 0.041 -1.684 0.092 -0.069 -0.069
## ahe_2|t1 0.286 0.041 6.923 0.000 0.286 0.286
## ahe_3|t1 0.832 0.046 18.011 0.000 0.832 0.832
## ahe_4|t1 0.420 0.042 10.010 0.000 0.420 0.420
## ahe_5|t1 0.544 0.043 12.689 0.000 0.544 0.544
## ahe_6|t1 0.344 0.042 8.276 0.000 0.344 0.344
## ahe_7|t1 0.409 0.042 9.754 0.000 0.409 0.409
## ahe_8|t1 0.987 0.049 20.261 0.000 0.987 0.987
## ahe_9|t1 0.781 0.045 17.169 0.000 0.781 0.781
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## ahv_1 0.769 0.769 0.769
## ahv_2 0.804 0.804 0.804
## ahv_3 0.728 0.728 0.728
## ahv_4 0.832 0.832 0.832
## ahv_5 0.911 0.911 0.911
## ahv_6 0.971 0.971 0.971
## ahv_7 0.999 0.999 0.999
## ahv_8 0.999 0.999 0.999
## ahv_9 0.989 0.989 0.989
## aha_1 0.651 0.651 0.651
## aha_2 0.564 0.564 0.564
## aha_3 0.613 0.613 0.613
## aha_4 0.870 0.870 0.870
## aha_5 0.926 0.926 0.926
## aha_6 0.888 0.888 0.888
## aha_7 0.564 0.564 0.564
## aha_8 0.948 0.948 0.948
## aha_9 0.999 0.999 0.999
## ahe_1 0.941 0.941 0.941
## ahe_2 0.973 0.973 0.973
## ahe_3 0.978 0.978 0.978
## ahe_4 0.896 0.896 0.896
## ahe_5 0.890 0.890 0.890
## ahe_6 0.935 0.935 0.935
## ahe_7 0.893 0.893 0.893
## ahe_8 0.969 0.969 0.969
## ahe_9 0.973 0.973 0.973
## g 0.231 0.046 5.019 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## ahv_1 1.000 1.000 1.000
## ahv_2 1.000 1.000 1.000
## ahv_3 1.000 1.000 1.000
## ahv_4 1.000 1.000 1.000
## ahv_5 1.000 1.000 1.000
## ahv_6 1.000 1.000 1.000
## ahv_7 1.000 1.000 1.000
## ahv_8 1.000 1.000 1.000
## ahv_9 1.000 1.000 1.000
## aha_1 1.000 1.000 1.000
## aha_2 1.000 1.000 1.000
## aha_3 1.000 1.000 1.000
## aha_4 1.000 1.000 1.000
## aha_5 1.000 1.000 1.000
## aha_6 1.000 1.000 1.000
## aha_7 1.000 1.000 1.000
## aha_8 1.000 1.000 1.000
## aha_9 1.000 1.000 1.000
## ahe_1 1.000 1.000 1.000
## ahe_2 1.000 1.000 1.000
## ahe_3 1.000 1.000 1.000
## ahe_4 1.000 1.000 1.000
## ahe_5 1.000 1.000 1.000
## ahe_6 1.000 1.000 1.000
## ahe_7 1.000 1.000 1.000
## ahe_8 1.000 1.000 1.000
## ahe_9 1.000 1.000 1.000 lavInspect(fit, "rsquare") ## ahv_1 ahv_2 ahv_3 ahv_4 ahv_5 ahv_6 ahv_7 ahv_8 ahv_9 aha_1 aha_2 aha_3
## 0.231 0.196 0.272 0.168 0.089 0.029 0.001 0.001 0.011 0.349 0.436 0.387
## aha_4 aha_5 aha_6 aha_7 aha_8 aha_9 ahe_1 ahe_2 ahe_3 ahe_4 ahe_5 ahe_6
## 0.130 0.074 0.112 0.436 0.052 0.001 0.059 0.027 0.022 0.104 0.110 0.065
## ahe_7 ahe_8 ahe_9
## 0.107 0.031 0.027 # desenha o modelo
semPaths(fit) 
Análise Bi-Fatorial Confirmatória
# Cria modelo usadno paste
m <-
'g =~ahv_1+ahv_2+ahv_3+ahv_4+ahv_5+ahv_6+ahv_7+ahv_8+ahv_9+
aha_1+aha_2+aha_3+aha_4+aha_5+aha_6+aha_7+aha_8+aha_9 +
ahe_1+ahe_2+ahe_3+ahe_4+ahe_5+ahe_6+ahe_7+ahe_8+ahe_9
v =~ahv_1+ahv_2+ahv_3+ahv_4+ahv_5+ahv_6+ahv_7+ahv_8+ahv_9
a =~aha_1+aha_2+aha_3+aha_4+aha_5+aha_6+aha_7+aha_8+aha_9
e =~ahe_1+ahe_2+ahe_3+ahe_4+ahe_5+ahe_6+ahe_7+ahe_8+ahe_9
g ~~ 0*a
g ~~ 0*v
g ~~ 0*e
v ~~ 0*a
v ~~ 0*e
a ~~ 0*e'
# Executa a análise
fit <- cfa(m, data = bpr_df, ordered = colnames(bpr_df))
# Examina os resultados
summary(fit, standardized = TRUE, fit.measures = TRUE)## lavaan (0.5-20) converged normally after 197 iterations
##
## Used Total
## Number of observations 952 3578
##
## Estimator DWLS Robust
## Minimum Function Test Statistic 352.243 396.203
## Degrees of freedom 297 297
## P-value (Chi-square) 0.015 0.000
## Scaling correction factor 1.025
## Shift parameter 52.717
## for simple second-order correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 2606.156 2032.940
## Degrees of freedom 351 351
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.976 0.941
## Tucker-Lewis Index (TLI) 0.971 0.930
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.014 0.019
## 90 Percent Confidence Interval 0.007 0.019 0.013 0.023
## P-value RMSEA <= 0.05 1.000 1.000
##
## Weighted Root Mean Square Residual:
##
## WRMR 0.965 0.965
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Robust.sem
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## g =~
## ahv_1 1.000 0.514 0.514
## ahv_2 0.870 0.123 7.076 0.000 0.448 0.448
## ahv_3 1.065 0.140 7.585 0.000 0.548 0.548
## ahv_4 0.859 0.129 6.669 0.000 0.442 0.442
## ahv_5 0.535 0.118 4.518 0.000 0.275 0.275
## ahv_6 0.329 0.108 3.035 0.002 0.169 0.169
## ahv_7 0.169 0.160 1.056 0.291 0.087 0.087
## ahv_8 0.051 0.160 0.319 0.750 0.026 0.026
## ahv_9 0.099 0.108 0.911 0.362 0.051 0.051
## aha_1 0.909 0.152 5.968 0.000 0.468 0.468
## aha_2 0.941 0.157 5.980 0.000 0.484 0.484
## aha_3 1.061 0.163 6.525 0.000 0.546 0.546
## aha_4 0.709 0.132 5.362 0.000 0.364 0.364
## aha_5 0.521 0.122 4.275 0.000 0.268 0.268
## aha_6 0.492 0.130 3.791 0.000 0.253 0.253
## aha_7 1.152 0.162 7.108 0.000 0.593 0.593
## aha_8 0.317 0.142 2.228 0.026 0.163 0.163
## aha_9 0.184 0.147 1.252 0.210 0.094 0.094
## ahe_1 0.370 0.111 3.340 0.001 0.190 0.190
## ahe_2 0.243 0.102 2.375 0.018 0.125 0.125
## ahe_3 0.301 0.118 2.539 0.011 0.155 0.155
## ahe_4 0.618 0.118 5.234 0.000 0.318 0.318
## ahe_5 0.682 0.129 5.283 0.000 0.351 0.351
## ahe_6 0.482 0.121 3.983 0.000 0.248 0.248
## ahe_7 0.691 0.127 5.458 0.000 0.355 0.355
## ahe_8 0.403 0.134 3.000 0.003 0.207 0.207
## ahe_9 0.291 0.123 2.373 0.018 0.150 0.150
## v =~
## ahv_1 1.000 0.100 0.100
## ahv_2 2.917 2.484 1.174 0.240 0.292 0.292
## ahv_3 1.835 1.655 1.109 0.268 0.184 0.184
## ahv_4 0.742 0.966 0.769 0.442 0.074 0.074
## ahv_5 3.747 3.377 1.110 0.267 0.375 0.375
## ahv_6 0.731 1.023 0.715 0.475 0.073 0.073
## ahv_7 -3.913 3.716 -1.053 0.292 -0.391 -0.391
## ahv_8 -3.702 3.740 -0.990 0.322 -0.370 -0.370
## ahv_9 4.043 3.742 1.080 0.280 0.404 0.404
## a =~
## aha_1 1.000 0.426 0.426
## aha_2 1.655 0.450 3.680 0.000 0.705 0.705
## aha_3 0.751 0.198 3.792 0.000 0.320 0.320
## aha_4 0.168 0.166 1.016 0.310 0.072 0.072
## aha_5 0.175 0.169 1.039 0.299 0.075 0.075
## aha_6 0.661 0.194 3.412 0.001 0.282 0.282
## aha_7 0.715 0.187 3.831 0.000 0.304 0.304
## aha_8 0.514 0.218 2.359 0.018 0.219 0.219
## aha_9 -0.348 0.210 -1.656 0.098 -0.148 -0.148
## e =~
## ahe_1 1.000 0.640 0.640
## ahe_2 0.752 0.242 3.107 0.002 0.482 0.482
## ahe_3 0.037 0.123 0.299 0.765 0.024 0.024
## ahe_4 0.418 0.140 2.995 0.003 0.268 0.268
## ahe_5 0.146 0.111 1.312 0.190 0.093 0.093
## ahe_6 0.315 0.125 2.530 0.011 0.202 0.202
## ahe_7 0.076 0.109 0.701 0.483 0.049 0.049
## ahe_8 -0.133 0.125 -1.069 0.285 -0.085 -0.085
## ahe_9 0.367 0.144 2.558 0.011 0.235 0.235
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## g ~~
## a 0.000 0.000 0.000
## v 0.000 0.000 0.000
## e 0.000 0.000 0.000
## v ~~
## a 0.000 0.000 0.000
## e 0.000 0.000 0.000
## a ~~
## e 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## ahv_1 0.000 0.000 0.000
## ahv_2 0.000 0.000 0.000
## ahv_3 0.000 0.000 0.000
## ahv_4 0.000 0.000 0.000
## ahv_5 0.000 0.000 0.000
## ahv_6 0.000 0.000 0.000
## ahv_7 0.000 0.000 0.000
## ahv_8 0.000 0.000 0.000
## ahv_9 0.000 0.000 0.000
## aha_1 0.000 0.000 0.000
## aha_2 0.000 0.000 0.000
## aha_3 0.000 0.000 0.000
## aha_4 0.000 0.000 0.000
## aha_5 0.000 0.000 0.000
## aha_6 0.000 0.000 0.000
## aha_7 0.000 0.000 0.000
## aha_8 0.000 0.000 0.000
## aha_9 0.000 0.000 0.000
## ahe_1 0.000 0.000 0.000
## ahe_2 0.000 0.000 0.000
## ahe_3 0.000 0.000 0.000
## ahe_4 0.000 0.000 0.000
## ahe_5 0.000 0.000 0.000
## ahe_6 0.000 0.000 0.000
## ahe_7 0.000 0.000 0.000
## ahe_8 0.000 0.000 0.000
## ahe_9 0.000 0.000 0.000
## g 0.000 0.000 0.000
## v 0.000 0.000 0.000
## a 0.000 0.000 0.000
## e 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## ahv_1|t1 -0.732 0.045 -16.315 0.000 -0.732 -0.732
## ahv_2|t1 -0.220 0.041 -5.373 0.000 -0.220 -0.220
## ahv_3|t1 0.008 0.041 0.194 0.846 0.008 0.008
## ahv_4|t1 0.264 0.041 6.407 0.000 0.264 0.264
## ahv_5|t1 0.127 0.041 3.109 0.002 0.127 0.127
## ahv_6|t1 0.403 0.042 9.626 0.000 0.403 0.403
## ahv_7|t1 1.392 0.059 23.695 0.000 1.392 1.392
## ahv_8|t1 1.428 0.060 23.822 0.000 1.428 1.428
## ahv_9|t1 0.210 0.041 5.115 0.000 0.210 0.210
## aha_1|t1 -1.036 0.050 -20.860 0.000 -1.036 -1.036
## aha_2|t1 -0.316 0.041 -7.632 0.000 -0.316 -0.316
## aha_3|t1 0.458 0.042 10.842 0.000 0.458 0.458
## aha_4|t1 0.264 0.041 6.407 0.000 0.264 0.264
## aha_5|t1 0.286 0.041 6.923 0.000 0.286 0.286
## aha_6|t1 0.417 0.042 9.946 0.000 0.417 0.417
## aha_7|t1 -0.137 0.041 -3.368 0.001 -0.137 -0.137
## aha_8|t1 1.101 0.051 21.589 0.000 1.101 1.101
## aha_9|t1 1.077 0.050 21.333 0.000 1.077 1.077
## ahe_1|t1 -0.069 0.041 -1.684 0.092 -0.069 -0.069
## ahe_2|t1 0.286 0.041 6.923 0.000 0.286 0.286
## ahe_3|t1 0.832 0.046 18.011 0.000 0.832 0.832
## ahe_4|t1 0.420 0.042 10.010 0.000 0.420 0.420
## ahe_5|t1 0.544 0.043 12.689 0.000 0.544 0.544
## ahe_6|t1 0.344 0.042 8.276 0.000 0.344 0.344
## ahe_7|t1 0.409 0.042 9.754 0.000 0.409 0.409
## ahe_8|t1 0.987 0.049 20.261 0.000 0.987 0.987
## ahe_9|t1 0.781 0.045 17.169 0.000 0.781 0.781
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## ahv_1 0.726 0.726 0.726
## ahv_2 0.715 0.715 0.715
## ahv_3 0.666 0.666 0.666
## ahv_4 0.799 0.799 0.799
## ahv_5 0.784 0.784 0.784
## ahv_6 0.966 0.966 0.966
## ahv_7 0.839 0.839 0.839
## ahv_8 0.862 0.862 0.862
## ahv_9 0.834 0.834 0.834
## aha_1 0.600 0.600 0.600
## aha_2 0.269 0.269 0.269
## aha_3 0.600 0.600 0.600
## aha_4 0.862 0.862 0.862
## aha_5 0.923 0.923 0.923
## aha_6 0.857 0.857 0.857
## aha_7 0.556 0.556 0.556
## aha_8 0.926 0.926 0.926
## aha_9 0.969 0.969 0.969
## ahe_1 0.554 0.554 0.554
## ahe_2 0.752 0.752 0.752
## ahe_3 0.976 0.976 0.976
## ahe_4 0.827 0.827 0.827
## ahe_5 0.868 0.868 0.868
## ahe_6 0.898 0.898 0.898
## ahe_7 0.871 0.871 0.871
## ahe_8 0.950 0.950 0.950
## ahe_9 0.922 0.922 0.922
## g 0.264 0.057 4.651 0.000 1.000 1.000
## v 0.010 0.018 0.565 0.572 1.000 1.000
## a 0.181 0.070 2.603 0.009 1.000 1.000
## e 0.410 0.139 2.961 0.003 1.000 1.000
##
## Scales y*:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## ahv_1 1.000 1.000 1.000
## ahv_2 1.000 1.000 1.000
## ahv_3 1.000 1.000 1.000
## ahv_4 1.000 1.000 1.000
## ahv_5 1.000 1.000 1.000
## ahv_6 1.000 1.000 1.000
## ahv_7 1.000 1.000 1.000
## ahv_8 1.000 1.000 1.000
## ahv_9 1.000 1.000 1.000
## aha_1 1.000 1.000 1.000
## aha_2 1.000 1.000 1.000
## aha_3 1.000 1.000 1.000
## aha_4 1.000 1.000 1.000
## aha_5 1.000 1.000 1.000
## aha_6 1.000 1.000 1.000
## aha_7 1.000 1.000 1.000
## aha_8 1.000 1.000 1.000
## aha_9 1.000 1.000 1.000
## ahe_1 1.000 1.000 1.000
## ahe_2 1.000 1.000 1.000
## ahe_3 1.000 1.000 1.000
## ahe_4 1.000 1.000 1.000
## ahe_5 1.000 1.000 1.000
## ahe_6 1.000 1.000 1.000
## ahe_7 1.000 1.000 1.000
## ahe_8 1.000 1.000 1.000
## ahe_9 1.000 1.000 1.000 lavInspect(fit, "rsquare") ## ahv_1 ahv_2 ahv_3 ahv_4 ahv_5 ahv_6 ahv_7 ahv_8 ahv_9 aha_1 aha_2 aha_3
## 0.274 0.285 0.334 0.201 0.216 0.034 0.161 0.138 0.166 0.400 0.731 0.400
## aha_4 aha_5 aha_6 aha_7 aha_8 aha_9 ahe_1 ahe_2 ahe_3 ahe_4 ahe_5 ahe_6
## 0.138 0.077 0.143 0.444 0.074 0.031 0.446 0.248 0.024 0.173 0.132 0.102
## ahe_7 ahe_8 ahe_9
## 0.129 0.050 0.078# Desenha o modelo
semPaths(fit)