==================
Fatorial - AHL-C
==================
library(RCurl)
library(lpSolve)
library(irr)
library(moments)
library(psy)##
## Attaching package: 'psy'## The following object is masked from 'package:irr':
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## icclibrary(gdata)## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.## ## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.##
## Attaching package: 'gdata'## The following object is masked from 'package:stats':
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## nobs## The following object is masked from 'package:utils':
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## object.size## The following object is masked from 'package:base':
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## startsWithlibrary(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:gdata':
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## combine, first, last## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(devtools)## Loading required package: usethislibrary(mice)##
## Attaching package: 'mice'## The following object is masked from 'package:RCurl':
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## complete## The following object is masked from 'package:stats':
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## filter## The following objects are masked from 'package:base':
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## cbind, rbindlibrary(psych)##
## Attaching package: 'psych'## The following object is masked from 'package:psy':
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## wkappalibrary(remotes)##
## Attaching package: 'remotes'## The following objects are masked from 'package:devtools':
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## dev_package_deps, install_bioc, install_bitbucket, install_cran,
## install_deps, install_dev, install_git, install_github,
## install_gitlab, install_local, install_svn, install_url,
## install_version, update_packages## The following object is masked from 'package:usethis':
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## git_credentialslibrary(Hmisc)## Loading required package: lattice## Loading required package: survival## Loading required package: Formula## Loading required package: ggplot2##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
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## %+%, alpha##
## Attaching package: 'Hmisc'## The following object is masked from 'package:psych':
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## describe## The following objects are masked from 'package:dplyr':
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## src, summarize## The following objects are masked from 'package:base':
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## format.pval, unitslibrary(lavaan)## This is lavaan 0.6-8
## lavaan is FREE software! Please report any bugs.##
## Attaching package: 'lavaan'## The following object is masked from 'package:psych':
##
## cor2covdata<-read.csv("/Users/leopestillo/Google Drive/Analysis/Marcelo_PPGPS/dados_cancer.csv",sep=',')
describe(data)## data
##
## 51 Variables 112 Observations
## --------------------------------------------------------------------------------
## L_benigno
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.176 105 0.9375 0.1182
##
## --------------------------------------------------------------------------------
## L_biopsia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.328 98 0.875 0.2207
##
## --------------------------------------------------------------------------------
## L_coloutero
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.078 109 0.9732 0.05261
##
## --------------------------------------------------------------------------------
## L_ginecologia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.078 109 0.9732 0.05261
##
## --------------------------------------------------------------------------------
## L_papilomavirus
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.44 92 0.8214 0.296
##
## --------------------------------------------------------------------------------
## L_histerectomia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.423 93 0.8304 0.2843
##
## --------------------------------------------------------------------------------
## L_maligno
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.199 104 0.9286 0.1338
##
## --------------------------------------------------------------------------------
## L_mastectomia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.386 95 0.8482 0.2598
##
## --------------------------------------------------------------------------------
## L_metastase
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.405 94 0.8393 0.2722
##
## --------------------------------------------------------------------------------
## L_pelvico
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.222 103 0.9196 0.1491
##
## --------------------------------------------------------------------------------
## L_utero
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.078 109 0.9732 0.05261
##
## --------------------------------------------------------------------------------
## L_vagina
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.078 109 0.9732 0.05261
##
## --------------------------------------------------------------------------------
## F_benigno
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.053 110 0.9821 0.03539
##
## --------------------------------------------------------------------------------
## F_biopsia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.244 102 0.9107 0.1641
##
## --------------------------------------------------------------------------------
## F_coloutero
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.027 111 0.9911 0.01786
##
## --------------------------------------------------------------------------------
## F_ginecologia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.053 110 0.9821 0.03539
##
## --------------------------------------------------------------------------------
## F_papilomavirus
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.746 60 0.5357 0.5019
##
## --------------------------------------------------------------------------------
## F_histerectomia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.749 54 0.4821 0.5039
##
## --------------------------------------------------------------------------------
## F_maligno
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.053 110 0.9821 0.03539
##
## --------------------------------------------------------------------------------
## F_mastectomia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.645 77 0.6875 0.4336
##
## --------------------------------------------------------------------------------
## F_metastase
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.563 84 0.75 0.3784
##
## --------------------------------------------------------------------------------
## F_pelvico
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.367 96 0.8571 0.2471
##
## --------------------------------------------------------------------------------
## F_utero
## n missing distinct Info Mean Gmd
## 112 0 1 0 1 0
##
## Value 1
## Frequency 112
## Proportion 1
## --------------------------------------------------------------------------------
## F_vagina
## n missing distinct Info Mean Gmd
## 112 0 1 0 1 0
##
## Value 1
## Frequency 112
## Proportion 1
## --------------------------------------------------------------------------------
## C_benigno
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.386 95 0.8482 0.2598
##
## --------------------------------------------------------------------------------
## C_biopsia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.474 90 0.8036 0.3185
##
## --------------------------------------------------------------------------------
## C_coloutero
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.405 94 0.8393 0.2722
##
## --------------------------------------------------------------------------------
## C_ginecologia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.222 103 0.9196 0.1491
##
## --------------------------------------------------------------------------------
## C_papilomavirus
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.681 39 0.3482 0.458
##
## --------------------------------------------------------------------------------
## C_histerectomia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.474 22 0.1964 0.3185
##
## --------------------------------------------------------------------------------
## C_maligno
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.308 99 0.8839 0.207
##
## --------------------------------------------------------------------------------
## C_mastectomia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.696 41 0.3661 0.4683
##
## --------------------------------------------------------------------------------
## C_metastase
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.746 52 0.4643 0.5019
##
## --------------------------------------------------------------------------------
## C_pelvico
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.735 64 0.5714 0.4942
##
## --------------------------------------------------------------------------------
## C_utero
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.027 111 0.9911 0.01786
##
## --------------------------------------------------------------------------------
## C_vagina
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.027 111 0.9911 0.01786
##
## --------------------------------------------------------------------------------
## D_1
## n missing distinct Info Mean Gmd
## 112 0 1 0 1 0
##
## Value 1
## Frequency 112
## Proportion 1
## --------------------------------------------------------------------------------
## D_2
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.222 103 0.9196 0.1491
##
## --------------------------------------------------------------------------------
## D_3
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.287 100 0.8929 0.1931
##
## --------------------------------------------------------------------------------
## D_4
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.423 93 0.8304 0.2843
##
## --------------------------------------------------------------------------------
## D_5
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.601 31 0.2768 0.404
##
## --------------------------------------------------------------------------------
## D_6
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.664 75 0.6696 0.4464
##
## --------------------------------------------------------------------------------
## D_7
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.664 75 0.6696 0.4464
##
## --------------------------------------------------------------------------------
## D_8
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.199 8 0.07143 0.1338
##
## --------------------------------------------------------------------------------
## D_9
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.588 30 0.2679 0.3958
##
## --------------------------------------------------------------------------------
## D_10
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.681 39 0.3482 0.458
##
## --------------------------------------------------------------------------------
## D_11
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.735 48 0.4286 0.4942
##
## --------------------------------------------------------------------------------
## M_1
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.689 72 0.6429 0.4633
##
## --------------------------------------------------------------------------------
## M_2
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.367 16 0.1429 0.2471
##
## --------------------------------------------------------------------------------
## M_3
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.721 45 0.4018 0.485
##
## --------------------------------------------------------------------------------
## M_4
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.726 46 0.4107 0.4884
##
## --------------------------------------------------------------------------------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))
summary(data_leitura)## L_benigno L_biopsia L_coloutero L_ginecologia
## Min. :0.0000 Min. :0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:1.000 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.0000 Median :1.000 Median :1.0000 Median :1.0000
## Mean :0.9375 Mean :0.875 Mean :0.9732 Mean :0.9732
## 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.000 Max. :1.0000 Max. :1.0000
## L_papilomavirus L_histerectomia L_maligno L_mastectomia
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.0000 Median :1.0000 Median :1.0000 Median :1.0000
## Mean :0.8214 Mean :0.8304 Mean :0.9286 Mean :0.8482
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## L_metastase L_pelvico L_utero L_vagina
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.0000 Median :1.0000 Median :1.0000 Median :1.0000
## Mean :0.8393 Mean :0.9196 Mean :0.9732 Mean :0.9732
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000describe(data_leitura)## data_leitura
##
## 12 Variables 112 Observations
## --------------------------------------------------------------------------------
## L_benigno
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.176 105 0.9375 0.1182
##
## --------------------------------------------------------------------------------
## L_biopsia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.328 98 0.875 0.2207
##
## --------------------------------------------------------------------------------
## L_coloutero
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.078 109 0.9732 0.05261
##
## --------------------------------------------------------------------------------
## L_ginecologia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.078 109 0.9732 0.05261
##
## --------------------------------------------------------------------------------
## L_papilomavirus
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.44 92 0.8214 0.296
##
## --------------------------------------------------------------------------------
## L_histerectomia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.423 93 0.8304 0.2843
##
## --------------------------------------------------------------------------------
## L_maligno
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.199 104 0.9286 0.1338
##
## --------------------------------------------------------------------------------
## L_mastectomia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.386 95 0.8482 0.2598
##
## --------------------------------------------------------------------------------
## L_metastase
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.405 94 0.8393 0.2722
##
## --------------------------------------------------------------------------------
## L_pelvico
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.222 103 0.9196 0.1491
##
## --------------------------------------------------------------------------------
## L_utero
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.078 109 0.9732 0.05261
##
## --------------------------------------------------------------------------------
## L_vagina
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.078 109 0.9732 0.05261
##
## --------------------------------------------------------------------------------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))
summary(data_dialogo)## D_2 D_3 D_4 D_5
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000 Median :1.0000 Median :0.0000
## Mean :0.9196 Mean :0.8929 Mean :0.8304 Mean :0.2768
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## D_6 D_7 D_8 D_9
## Min. :0.0000 Min. :0.0000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000 Median :0.00000 Median :0.0000
## Mean :0.6696 Mean :0.6696 Mean :0.07143 Mean :0.2679
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.00000 Max. :1.0000
## D_10 D_11
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.3482 Mean :0.4286
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000data_matematica <- with(data, data.frame(M_1,
M_2,
M_3,
M_4))
summary(data_matematica)## M_1 M_2 M_3 M_4
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.6429 Mean :0.1429 Mean :0.4018 Mean :0.4107
## 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000data_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
))
summary(data_compreensao)## C_benigno C_biopsia C_coloutero C_ginecologia
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.0000 Median :1.0000 Median :1.0000 Median :1.0000
## Mean :0.8482 Mean :0.8036 Mean :0.8393 Mean :0.9196
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## C_papilomavirus C_histerectomia C_maligno C_mastectomia
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :1.0000 Median :0.0000
## Mean :0.3482 Mean :0.1964 Mean :0.8839 Mean :0.3661
## 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## C_metastase C_pelvico
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :1.0000
## Mean :0.4643 Mean :0.5714
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000describe(data_compreensao)## data_compreensao
##
## 10 Variables 112 Observations
## --------------------------------------------------------------------------------
## C_benigno
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.386 95 0.8482 0.2598
##
## --------------------------------------------------------------------------------
## C_biopsia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.474 90 0.8036 0.3185
##
## --------------------------------------------------------------------------------
## C_coloutero
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.405 94 0.8393 0.2722
##
## --------------------------------------------------------------------------------
## C_ginecologia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.222 103 0.9196 0.1491
##
## --------------------------------------------------------------------------------
## C_papilomavirus
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.681 39 0.3482 0.458
##
## --------------------------------------------------------------------------------
## C_histerectomia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.474 22 0.1964 0.3185
##
## --------------------------------------------------------------------------------
## C_maligno
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.308 99 0.8839 0.207
##
## --------------------------------------------------------------------------------
## C_mastectomia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.696 41 0.3661 0.4683
##
## --------------------------------------------------------------------------------
## C_metastase
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.746 52 0.4643 0.5019
##
## --------------------------------------------------------------------------------
## C_pelvico
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.735 64 0.5714 0.4942
##
## --------------------------------------------------------------------------------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
))
summary(data_familiaridade)## F_biopsia F_papilomavirus F_histerectomia F_mastectomia
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000 Median :0.0000 Median :1.0000
## Mean :0.9107 Mean :0.5357 Mean :0.4821 Mean :0.6875
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## F_metastase F_pelvico
## Min. :0.00 Min. :0.0000
## 1st Qu.:0.75 1st Qu.:1.0000
## Median :1.00 Median :1.0000
## Mean :0.75 Mean :0.8571
## 3rd Qu.:1.00 3rd Qu.:1.0000
## Max. :1.00 Max. :1.0000describe(data_familiaridade)## data_familiaridade
##
## 6 Variables 112 Observations
## --------------------------------------------------------------------------------
## F_biopsia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.244 102 0.9107 0.1641
##
## --------------------------------------------------------------------------------
## F_papilomavirus
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.746 60 0.5357 0.5019
##
## --------------------------------------------------------------------------------
## F_histerectomia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.749 54 0.4821 0.5039
##
## --------------------------------------------------------------------------------
## F_mastectomia
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.645 77 0.6875 0.4336
##
## --------------------------------------------------------------------------------
## F_metastase
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.563 84 0.75 0.3784
##
## --------------------------------------------------------------------------------
## F_pelvico
## n missing distinct Info Sum Mean Gmd
## 112 0 2 0.367 96 0.8571 0.2471
##
## --------------------------------------------------------------------------------cfa_model <- '
leitura =~ L_benigno+
L_biopsia+
L_coloutero+
L_ginecologia+
L_papilomavirus+
L_histerectomia+
L_maligno+
L_mastectomia+
L_metastase+
L_pelvico+
L_utero+
L_vagina
'
fit <- lavaan::cfa(cfa_model,
data = data_leitura,
estimator="WLSMV",
ordered=colnames(data_leitura)
)## Warning in lav_bvord_cor_twostep_fit(fit.y1 = UNI[[j]], fit.y2 = UNI[[i]], :
## lavaan WARNING: two empty cells in 2x2 table## Warning in lav_samplestats_step2(UNI = FIT, wt = wt, ov.names = ov.names, :
## lavaan WARNING: correlation between variables L_utero and L_ginecologia is
## (nearly) 1.0## Warning in lav_bvord_cor_twostep_fit(fit.y1 = UNI[[j]], fit.y2 = UNI[[i]], :
## lavaan WARNING: two empty cells in 2x2 table## Warning in lav_samplestats_step2(UNI = FIT, wt = wt, ov.names = ov.names, :
## lavaan WARNING: correlation between variables L_vagina and L_ginecologia is
## (nearly) 1.0## Warning in lav_bvord_cor_twostep_fit(fit.y1 = UNI[[j]], fit.y2 = UNI[[i]], :
## lavaan WARNING: two empty cells in 2x2 table## Warning in lav_samplestats_step2(UNI = FIT, wt = wt, ov.names = ov.names, :
## lavaan WARNING: correlation between variables L_vagina and L_utero is (nearly)
## 1.0## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= -1.689504e-16) is smaller than zero. This may be a symptom that
## the model is not identified.summary(fit, fit.measures=TRUE)## lavaan 0.6-8 ended normally after 34 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 24
##
## Number of observations 112
##
## Model Test User Model:
## Standard Robust
## Test Statistic 68.190 73.323
## Degrees of freedom 54 54
## P-value (Chi-square) 0.093 0.041
## Scaling correction factor 1.821
## Shift parameter 35.869
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 379529.899 104454.653
## Degrees of freedom 66 66
## P-value 0.000 0.000
## Scaling correction factor 3.635
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.049 0.057
## 90 Percent confidence interval - lower 0.000 0.012
## 90 Percent confidence interval - upper 0.081 0.087
## P-value RMSEA <= 0.05 0.500 0.349
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.087 0.087
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## leitura =~
## L_benigno 1.000
## L_biopsia 0.957 0.041 23.264 0.000
## L_coloutero 0.944 0.050 18.797 0.000
## L_ginecologia 1.047 0.038 27.677 0.000
## L_papilomavirs 0.877 0.047 18.774 0.000
## L_histerectomi 0.901 0.041 21.853 0.000
## L_maligno 0.977 0.034 28.972 0.000
## L_mastectomia 0.929 0.041 22.869 0.000
## L_metastase 0.904 0.047 19.237 0.000
## L_pelvico 0.956 0.076 12.509 0.000
## L_utero 1.047 0.038 27.677 0.000
## L_vagina 1.047 0.038 27.677 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .L_benigno 0.000
## .L_biopsia 0.000
## .L_coloutero 0.000
## .L_ginecologia 0.000
## .L_papilomavirs 0.000
## .L_histerectomi 0.000
## .L_maligno 0.000
## .L_mastectomia 0.000
## .L_metastase 0.000
## .L_pelvico 0.000
## .L_utero 0.000
## .L_vagina 0.000
## leitura 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## L_benigno|t1 -1.534 0.187 -8.212 0.000
## L_biopsia|t1 -1.150 0.152 -7.544 0.000
## L_coloutero|t1 -1.930 0.248 -7.799 0.000
## L_ginecolog|t1 -1.930 0.248 -7.799 0.000
## L_papilmvrs|t1 -0.921 0.139 -6.614 0.000
## L_histerctm|t1 -0.956 0.141 -6.779 0.000
## L_maligno|t1 -1.465 0.179 -8.174 0.000
## L_mastectom|t1 -1.029 0.145 -7.099 0.000
## L_metastase|t1 -0.992 0.143 -6.941 0.000
## L_pelvico|t1 -1.403 0.173 -8.109 0.000
## L_utero|t1 -1.930 0.248 -7.799 0.000
## L_vagina|t1 -1.930 0.248 -7.799 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .L_benigno 0.087
## .L_biopsia 0.164
## .L_coloutero 0.187
## .L_ginecologia 0.000
## .L_papilomavirs 0.298
## .L_histerectomi 0.259
## .L_maligno 0.128
## .L_mastectomia 0.212
## .L_metastase 0.254
## .L_pelvico 0.165
## .L_utero 0.000
## .L_vagina 0.000
## leitura 0.913 0.067 13.584 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## L_benigno 1.000
## L_biopsia 1.000
## L_coloutero 1.000
## L_ginecologia 1.000
## L_papilomavirs 1.000
## L_histerectomi 1.000
## L_maligno 1.000
## L_mastectomia 1.000
## L_metastase 1.000
## L_pelvico 1.000
## L_utero 1.000
## L_vagina 1.000lavaan::fitMeasures(fit, fit.measures = c("rmsea.scaled",
"rmsea.ci.lower.scaled",
"rmsea.ci.upper.scaled",
"cfi.scaled",
"tli.scaled",
"nnfi.scaled",
"chisq.scaled",
"pvalue.scaled"
))## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.057 0.012 0.087
## cfi.scaled tli.scaled nnfi.scaled
## 1.000 1.000 1.000
## chisq.scaled pvalue.scaled
## 73.323 0.041# AIC(fit)
Est <- lavaan::parameterEstimates(fit, ci = TRUE, standardized = TRUE)
subset(Est, op == "=~")## lhs op rhs est se z pvalue ci.lower ci.upper
## 1 leitura =~ L_benigno 1.000 0.000 NA NA 1.000 1.000
## 2 leitura =~ L_biopsia 0.957 0.041 23.264 0 0.876 1.038
## 3 leitura =~ L_coloutero 0.944 0.050 18.797 0 0.846 1.043
## 4 leitura =~ L_ginecologia 1.047 0.038 27.677 0 0.973 1.121
## 5 leitura =~ L_papilomavirus 0.877 0.047 18.774 0 0.786 0.969
## 6 leitura =~ L_histerectomia 0.901 0.041 21.853 0 0.820 0.982
## 7 leitura =~ L_maligno 0.977 0.034 28.972 0 0.911 1.043
## 8 leitura =~ L_mastectomia 0.929 0.041 22.869 0 0.850 1.009
## 9 leitura =~ L_metastase 0.904 0.047 19.237 0 0.812 0.996
## 10 leitura =~ L_pelvico 0.956 0.076 12.509 0 0.807 1.106
## 11 leitura =~ L_utero 1.047 0.038 27.677 0 0.973 1.121
## 12 leitura =~ L_vagina 1.047 0.038 27.677 0 0.973 1.121
## std.lv std.all std.nox
## 1 0.955 0.955 0.955
## 2 0.914 0.914 0.914
## 3 0.902 0.902 0.902
## 4 1.000 1.000 1.000
## 5 0.838 0.838 0.838
## 6 0.861 0.861 0.861
## 7 0.934 0.934 0.934
## 8 0.888 0.888 0.888
## 9 0.864 0.864 0.864
## 10 0.914 0.914 0.914
## 11 1.000 1.000 1.000
## 12 1.000 1.000 1.000subset(Est, op == "~~")## lhs op rhs est se z pvalue ci.lower
## 25 L_benigno ~~ L_benigno 0.087 0.000 NA NA 0.087
## 26 L_biopsia ~~ L_biopsia 0.164 0.000 NA NA 0.164
## 27 L_coloutero ~~ L_coloutero 0.187 0.000 NA NA 0.187
## 28 L_ginecologia ~~ L_ginecologia 0.000 0.000 NA NA 0.000
## 29 L_papilomavirus ~~ L_papilomavirus 0.298 0.000 NA NA 0.298
## 30 L_histerectomia ~~ L_histerectomia 0.259 0.000 NA NA 0.259
## 31 L_maligno ~~ L_maligno 0.128 0.000 NA NA 0.128
## 32 L_mastectomia ~~ L_mastectomia 0.212 0.000 NA NA 0.212
## 33 L_metastase ~~ L_metastase 0.254 0.000 NA NA 0.254
## 34 L_pelvico ~~ L_pelvico 0.165 0.000 NA NA 0.165
## 35 L_utero ~~ L_utero 0.000 0.000 NA NA 0.000
## 36 L_vagina ~~ L_vagina 0.000 0.000 NA NA 0.000
## 37 leitura ~~ leitura 0.913 0.067 13.584 0 0.781
## ci.upper std.lv std.all std.nox
## 25 0.087 0.087 0.087 0.087
## 26 0.164 0.164 0.164 0.164
## 27 0.187 0.187 0.187 0.187
## 28 0.000 0.000 0.000 0.000
## 29 0.298 0.298 0.298 0.298
## 30 0.259 0.259 0.259 0.259
## 31 0.128 0.128 0.128 0.128
## 32 0.212 0.212 0.212 0.212
## 33 0.254 0.254 0.254 0.254
## 34 0.165 0.165 0.165 0.165
## 35 0.000 0.000 0.000 0.000
## 36 0.000 0.000 0.000 0.000
## 37 1.044 1.000 1.000 1.000Mod <- lavaan::modificationIndices(fit)
subset(Mod, mi > 10)## [1] lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## <0 rows> (or 0-length row.names)sum(Est$std.all[1:12])^2/(sum(Est$std.all[1:12])^2+sum(Est$std.all[25:36]))## [1] 0.9858648sum(Est$std.all[1:12]^2)/length(Est$std.all[1:12])## [1] 0.8536349by(Est$std.all[1:12],Est$lhs[1:12],mean)## Est$lhs[1:12]: leitura
## [1] 0.9223293leitura_scores<-lavaan::predict(fit)
write.csv(leitura_scores,"/Users/leopestillo/Google Drive/Analysis/Marcelo_PPGPS//leitura_scores.csv")cfa_model <- '
dialogo =~
# D_1+
D_2+
D_3+
D_4+
D_5+
D_6+
D_7+
D_8+
D_9+
D_10+
D_11
'
fit <- lavaan::cfa(cfa_model,
data = data_dialogo,
estimator="WLSMV",
ordered=colnames(data_dialogo)
)
summary(fit, fit.measures=TRUE)## lavaan 0.6-8 ended normally after 45 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 20
##
## Number of observations 112
##
## Model Test User Model:
## Standard Robust
## Test Statistic 33.134 40.081
## Degrees of freedom 35 35
## P-value (Chi-square) 0.558 0.255
## Scaling correction factor 1.100
## Shift parameter 9.953
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 267.097 193.188
## Degrees of freedom 45 45
## P-value 0.000 0.000
## Scaling correction factor 1.499
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 0.966
## Tucker-Lewis Index (TLI) 1.011 0.956
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.036
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.063 0.080
## P-value RMSEA <= 0.05 0.873 0.649
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.134 0.134
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## dialogo =~
## D_2 1.000
## D_3 -0.116 0.391 -0.296 0.767
## D_4 1.658 0.945 1.754 0.079
## D_5 1.898 1.178 1.611 0.107
## D_6 1.704 1.028 1.658 0.097
## D_7 1.282 0.813 1.577 0.115
## D_8 2.489 1.448 1.719 0.086
## D_9 1.509 0.981 1.539 0.124
## D_10 2.258 1.366 1.652 0.098
## D_11 2.261 1.309 1.727 0.084
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .D_2 0.000
## .D_3 0.000
## .D_4 0.000
## .D_5 0.000
## .D_6 0.000
## .D_7 0.000
## .D_8 0.000
## .D_9 0.000
## .D_10 0.000
## .D_11 0.000
## dialogo 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## D_2|t1 -1.403 0.173 -8.109 0.000
## D_3|t1 -1.242 0.159 -7.805 0.000
## D_4|t1 -0.956 0.141 -6.779 0.000
## D_5|t1 0.592 0.127 4.670 0.000
## D_6|t1 -0.439 0.123 -3.562 0.000
## D_7|t1 -0.439 0.123 -3.562 0.000
## D_8|t1 1.465 0.179 8.174 0.000
## D_9|t1 0.619 0.128 4.852 0.000
## D_10|t1 0.390 0.122 3.190 0.001
## D_11|t1 0.180 0.120 1.504 0.132
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .D_2 0.883
## .D_3 0.998
## .D_4 0.678
## .D_5 0.578
## .D_6 0.660
## .D_7 0.807
## .D_8 0.274
## .D_9 0.733
## .D_10 0.403
## .D_11 0.401
## dialogo 0.117 0.136 0.862 0.389
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## D_2 1.000
## D_3 1.000
## D_4 1.000
## D_5 1.000
## D_6 1.000
## D_7 1.000
## D_8 1.000
## D_9 1.000
## D_10 1.000
## D_11 1.000lavaan::fitMeasures(fit, fit.measures = c("rmsea.scaled",
"rmsea.ci.lower.scaled",
"rmsea.ci.upper.scaled",
"cfi.scaled",
"tli.scaled",
"nnfi.scaled",
"chisq.scaled",
"pvalue.scaled"
))## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.036 0.000 0.080
## cfi.scaled tli.scaled nnfi.scaled
## 0.966 0.956 0.956
## chisq.scaled pvalue.scaled
## 40.081 0.255# AIC(fit)
Est <- lavaan::parameterEstimates(fit, ci = TRUE, standardized = TRUE)
subset(Est, op == "=~")## lhs op rhs est se z pvalue ci.lower ci.upper std.lv std.all
## 1 dialogo =~ D_2 1.000 0.000 NA NA 1.000 1.000 0.342 0.342
## 2 dialogo =~ D_3 -0.116 0.391 -0.296 0.767 -0.881 0.650 -0.040 -0.040
## 3 dialogo =~ D_4 1.658 0.945 1.754 0.079 -0.195 3.510 0.567 0.567
## 4 dialogo =~ D_5 1.898 1.178 1.611 0.107 -0.411 4.207 0.650 0.650
## 5 dialogo =~ D_6 1.704 1.028 1.658 0.097 -0.310 3.718 0.583 0.583
## 6 dialogo =~ D_7 1.282 0.813 1.577 0.115 -0.311 2.876 0.439 0.439
## 7 dialogo =~ D_8 2.489 1.448 1.719 0.086 -0.349 5.327 0.852 0.852
## 8 dialogo =~ D_9 1.509 0.981 1.539 0.124 -0.413 3.431 0.516 0.516
## 9 dialogo =~ D_10 2.258 1.366 1.652 0.098 -0.420 4.936 0.773 0.773
## 10 dialogo =~ D_11 2.261 1.309 1.727 0.084 -0.305 4.827 0.774 0.774
## std.nox
## 1 0.342
## 2 -0.040
## 3 0.567
## 4 0.650
## 5 0.583
## 6 0.439
## 7 0.852
## 8 0.516
## 9 0.773
## 10 0.774subset(Est, op == "~~")## lhs op rhs est se z pvalue ci.lower ci.upper std.lv std.all
## 21 D_2 ~~ D_2 0.883 0.000 NA NA 0.883 0.883 0.883 0.883
## 22 D_3 ~~ D_3 0.998 0.000 NA NA 0.998 0.998 0.998 0.998
## 23 D_4 ~~ D_4 0.678 0.000 NA NA 0.678 0.678 0.678 0.678
## 24 D_5 ~~ D_5 0.578 0.000 NA NA 0.578 0.578 0.578 0.578
## 25 D_6 ~~ D_6 0.660 0.000 NA NA 0.660 0.660 0.660 0.660
## 26 D_7 ~~ D_7 0.807 0.000 NA NA 0.807 0.807 0.807 0.807
## 27 D_8 ~~ D_8 0.274 0.000 NA NA 0.274 0.274 0.274 0.274
## 28 D_9 ~~ D_9 0.733 0.000 NA NA 0.733 0.733 0.733 0.733
## 29 D_10 ~~ D_10 0.403 0.000 NA NA 0.403 0.403 0.403 0.403
## 30 D_11 ~~ D_11 0.401 0.000 NA NA 0.401 0.401 0.401 0.401
## 31 dialogo ~~ dialogo 0.117 0.136 0.862 0.389 -0.149 0.383 1.000 1.000
## std.nox
## 21 0.883
## 22 0.998
## 23 0.678
## 24 0.578
## 25 0.660
## 26 0.807
## 27 0.274
## 28 0.733
## 29 0.403
## 30 0.401
## 31 1.000Mod <- lavaan::modificationIndices(fit)
subset(Mod, mi > 10)## [1] lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## <0 rows> (or 0-length row.names)sum(Est$std.all[1:10])^2/(sum(Est$std.all[1:10])^2+sum(Est$std.all[21:30]))## [1] 0.822675sum(Est$std.all[1:10]^2)/length(Est$std.all[1:10])## [1] 0.3583144by(Est$std.all[1:10],Est$lhs[1:10],mean)## Est$lhs[1:10]: dialogo
## [1] 0.5456201dialogo_scores<-lavaan::predict(fit)
write.csv(dialogo_scores,"/Users/leopestillo/Google Drive/Analysis/Marcelo_PPGPS//dialogo_scores.csv")cfa_model <- '
matematica =~ M_1+
M_2+
M_3+
M_4
'
fit <- lavaan::cfa(cfa_model,
data = data_matematica,
estimator="WLSMV",
ordered=colnames(data_matematica)
)
summary(fit, fit.measures=TRUE)## lavaan 0.6-8 ended normally after 11 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 8
##
## Number of observations 112
##
## Model Test User Model:
## Standard Robust
## Test Statistic 1.645 2.985
## Degrees of freedom 2 2
## P-value (Chi-square) 0.439 0.225
## Scaling correction factor 0.552
## Shift parameter 0.004
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 345.418 304.438
## Degrees of freedom 6 6
## P-value 0.000 0.000
## Scaling correction factor 1.137
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 0.997
## Tucker-Lewis Index (TLI) 1.003 0.990
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.067
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.177 0.212
## P-value RMSEA <= 0.05 0.532 0.315
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.065 0.065
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## matematica =~
## M_1 1.000
## M_2 0.557 0.150 3.720 0.000
## M_3 0.855 0.105 8.126 0.000
## M_4 0.969 0.103 9.399 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .M_1 0.000
## .M_2 0.000
## .M_3 0.000
## .M_4 0.000
## matematica 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## M_1|t1 -0.366 0.122 -3.003 0.003
## M_2|t1 1.068 0.147 7.253 0.000
## M_3|t1 0.249 0.120 2.067 0.039
## M_4|t1 0.226 0.120 1.880 0.060
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .M_1 0.060
## .M_2 0.708
## .M_3 0.313
## .M_4 0.118
## matematica 0.940 0.128 7.371 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## M_1 1.000
## M_2 1.000
## M_3 1.000
## M_4 1.000lavaan::fitMeasures(fit, fit.measures = c("rmsea.scaled",
"rmsea.ci.lower.scaled",
"rmsea.ci.upper.scaled",
"cfi.scaled",
"tli.scaled",
"nnfi.scaled",
"chisq.scaled",
"pvalue.scaled"
))## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.067 0.000 0.212
## cfi.scaled tli.scaled nnfi.scaled
## 0.997 0.990 0.990
## chisq.scaled pvalue.scaled
## 2.985 0.225# AIC(fit)
Est <- lavaan::parameterEstimates(fit, ci = TRUE, standardized = TRUE)
subset(Est, op == "=~")## lhs op rhs est se z pvalue ci.lower ci.upper std.lv std.all
## 1 matematica =~ M_1 1.000 0.000 NA NA 1.000 1.000 0.970 0.970
## 2 matematica =~ M_2 0.557 0.150 3.720 0 0.263 0.850 0.540 0.540
## 3 matematica =~ M_3 0.855 0.105 8.126 0 0.648 1.061 0.829 0.829
## 4 matematica =~ M_4 0.969 0.103 9.399 0 0.767 1.171 0.939 0.939
## std.nox
## 1 0.970
## 2 0.540
## 3 0.829
## 4 0.939subset(Est, op == "~~")## lhs op rhs est se z pvalue ci.lower ci.upper std.lv
## 9 M_1 ~~ M_1 0.060 0.000 NA NA 0.060 0.060 0.060
## 10 M_2 ~~ M_2 0.708 0.000 NA NA 0.708 0.708 0.708
## 11 M_3 ~~ M_3 0.313 0.000 NA NA 0.313 0.313 0.313
## 12 M_4 ~~ M_4 0.118 0.000 NA NA 0.118 0.118 0.118
## 13 matematica ~~ matematica 0.940 0.128 7.371 0 0.690 1.190 1.000
## std.all std.nox
## 9 0.060 0.060
## 10 0.708 0.708
## 11 0.313 0.313
## 12 0.118 0.118
## 13 1.000 1.000Mod <- lavaan::modificationIndices(fit)
subset(Mod, mi > 10)## [1] lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## <0 rows> (or 0-length row.names)sum(Est$std.all[1:4])^2/(sum(Est$std.all[1:4])^2+sum(Est$std.all[9:12]))## [1] 0.8995431sum(Est$std.all[1:4]^2)/length(Est$std.all[1:4])## [1] 0.7001114by(Est$std.all[1:4],Est$lhs[1:4],mean)## Est$lhs[1:4]: matematica
## [1] 0.8193531matematica_scores<-lavaan::predict(fit)
write.csv(matematica_scores,"/Users/leopestillo/Google Drive/Analysis/Marcelo_PPGPS//matematica_scores.csv")cfa_model <- '
compreensao =~ C_benigno+
C_biopsia+
C_coloutero+
C_ginecologia+
C_papilomavirus+
C_histerectomia+
C_maligno+
C_mastectomia+
C_metastase+
C_pelvico
# C_utero+
# C_vagina
'
fit <- lavaan::cfa(cfa_model,
data = data_compreensao,
estimator="WLSMV",
ordered=colnames(data_compreensao)
)
summary(fit, fit.measures=TRUE)## lavaan 0.6-8 ended normally after 21 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 20
##
## Number of observations 112
##
## Model Test User Model:
## Standard Robust
## Test Statistic 20.338 30.450
## Degrees of freedom 35 35
## P-value (Chi-square) 0.977 0.687
## Scaling correction factor 1.269
## Shift parameter 14.426
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 1024.926 618.686
## Degrees of freedom 45 45
## P-value 0.000 0.000
## Scaling correction factor 1.708
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.019 1.010
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.055
## P-value RMSEA <= 0.05 0.998 0.928
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.107 0.107
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## compreensao =~
## C_benigno 1.000
## C_biopsia 0.983 0.142 6.944 0.000
## C_coloutero 0.637 0.136 4.678 0.000
## C_ginecologia 0.668 0.169 3.946 0.000
## C_papilomavirs 1.114 0.135 8.242 0.000
## C_histerectomi 1.032 0.160 6.440 0.000
## C_maligno 0.962 0.123 7.851 0.000
## C_mastectomia 1.122 0.138 8.122 0.000
## C_metastase 1.041 0.129 8.078 0.000
## C_pelvico 1.160 0.136 8.511 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .C_benigno 0.000
## .C_biopsia 0.000
## .C_coloutero 0.000
## .C_ginecologia 0.000
## .C_papilomavirs 0.000
## .C_histerectomi 0.000
## .C_maligno 0.000
## .C_mastectomia 0.000
## .C_metastase 0.000
## .C_pelvico 0.000
## compreensao 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## C_benigno|t1 -1.029 0.145 -7.099 0.000
## C_biopsia|t1 -0.854 0.136 -6.275 0.000
## C_coloutero|t1 -0.992 0.143 -6.941 0.000
## C_ginecolog|t1 -1.403 0.173 -8.109 0.000
## C_papilmvrs|t1 0.390 0.122 3.190 0.001
## C_histerctm|t1 0.854 0.136 6.275 0.000
## C_maligno|t1 -1.195 0.156 -7.679 0.000
## C_mastectom|t1 0.342 0.122 2.816 0.005
## C_metastase|t1 0.090 0.119 0.752 0.452
## C_pelvico|t1 -0.180 0.120 -1.504 0.132
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .C_benigno 0.398
## .C_biopsia 0.419
## .C_coloutero 0.756
## .C_ginecologia 0.732
## .C_papilomavirs 0.254
## .C_histerectomi 0.359
## .C_maligno 0.443
## .C_mastectomia 0.243
## .C_metastase 0.349
## .C_pelvico 0.190
## compreensao 0.602 0.124 4.855 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## C_benigno 1.000
## C_biopsia 1.000
## C_coloutero 1.000
## C_ginecologia 1.000
## C_papilomavirs 1.000
## C_histerectomi 1.000
## C_maligno 1.000
## C_mastectomia 1.000
## C_metastase 1.000
## C_pelvico 1.000lavaan::fitMeasures(fit, fit.measures = c("rmsea.scaled",
"rmsea.ci.lower.scaled",
"rmsea.ci.upper.scaled",
"cfi.scaled",
"tli.scaled",
"nnfi.scaled",
"chisq.scaled",
"pvalue.scaled"
))## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.000 0.000 0.055
## cfi.scaled tli.scaled nnfi.scaled
## 1.000 1.010 1.010
## chisq.scaled pvalue.scaled
## 30.450 0.687# AIC(fit)
Est <- lavaan::parameterEstimates(fit, ci = TRUE, standardized = TRUE)
subset(Est, op == "=~")## lhs op rhs est se z pvalue ci.lower ci.upper
## 1 compreensao =~ C_benigno 1.000 0.000 NA NA 1.000 1.000
## 2 compreensao =~ C_biopsia 0.983 0.142 6.944 0 0.705 1.260
## 3 compreensao =~ C_coloutero 0.637 0.136 4.678 0 0.370 0.904
## 4 compreensao =~ C_ginecologia 0.668 0.169 3.946 0 0.336 0.999
## 5 compreensao =~ C_papilomavirus 1.114 0.135 8.242 0 0.849 1.379
## 6 compreensao =~ C_histerectomia 1.032 0.160 6.440 0 0.718 1.347
## 7 compreensao =~ C_maligno 0.962 0.123 7.851 0 0.722 1.202
## 8 compreensao =~ C_mastectomia 1.122 0.138 8.122 0 0.851 1.393
## 9 compreensao =~ C_metastase 1.041 0.129 8.078 0 0.788 1.293
## 10 compreensao =~ C_pelvico 1.160 0.136 8.511 0 0.893 1.427
## std.lv std.all std.nox
## 1 0.776 0.776 0.776
## 2 0.762 0.762 0.762
## 3 0.494 0.494 0.494
## 4 0.518 0.518 0.518
## 5 0.864 0.864 0.864
## 6 0.801 0.801 0.801
## 7 0.746 0.746 0.746
## 8 0.870 0.870 0.870
## 9 0.807 0.807 0.807
## 10 0.900 0.900 0.900subset(Est, op == "~~")## lhs op rhs est se z pvalue ci.lower
## 21 C_benigno ~~ C_benigno 0.398 0.000 NA NA 0.398
## 22 C_biopsia ~~ C_biopsia 0.419 0.000 NA NA 0.419
## 23 C_coloutero ~~ C_coloutero 0.756 0.000 NA NA 0.756
## 24 C_ginecologia ~~ C_ginecologia 0.732 0.000 NA NA 0.732
## 25 C_papilomavirus ~~ C_papilomavirus 0.254 0.000 NA NA 0.254
## 26 C_histerectomia ~~ C_histerectomia 0.359 0.000 NA NA 0.359
## 27 C_maligno ~~ C_maligno 0.443 0.000 NA NA 0.443
## 28 C_mastectomia ~~ C_mastectomia 0.243 0.000 NA NA 0.243
## 29 C_metastase ~~ C_metastase 0.349 0.000 NA NA 0.349
## 30 C_pelvico ~~ C_pelvico 0.190 0.000 NA NA 0.190
## 31 compreensao ~~ compreensao 0.602 0.124 4.855 0 0.359
## ci.upper std.lv std.all std.nox
## 21 0.398 0.398 0.398 0.398
## 22 0.419 0.419 0.419 0.419
## 23 0.756 0.756 0.756 0.756
## 24 0.732 0.732 0.732 0.732
## 25 0.254 0.254 0.254 0.254
## 26 0.359 0.359 0.359 0.359
## 27 0.443 0.443 0.443 0.443
## 28 0.243 0.243 0.243 0.243
## 29 0.349 0.349 0.349 0.349
## 30 0.190 0.190 0.190 0.190
## 31 0.844 1.000 1.000 1.000Mod <- lavaan::modificationIndices(fit)
subset(Mod, mi > 10)## [1] lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## <0 rows> (or 0-length row.names)sum(Est$std.all[1:10])^2/(sum(Est$std.all[1:10])^2+sum(Est$std.all[21:30]))## [1] 0.932035sum(Est$std.all[1:10]^2)/length(Est$std.all[1:10])## [1] 0.5857094by(Est$std.all[1:10],Est$lhs[1:10],mean)## Est$lhs[1:10]: compreensao
## [1] 0.7537475compreensao_scores<-lavaan::predict(fit)
write.csv(compreensao_scores,"/Users/leopestillo/Google Drive/Analysis/Marcelo_PPGPS//compreensao_scores.csv")cfa_model <- '
familiaridade =~
# F_benigno+
F_biopsia+
# F_coloutero+
# F_ginecologia+
F_papilomavirus+
F_histerectomia+
# F_maligno+
F_mastectomia+
F_metastase+
F_pelvico
# F_utero+
# F_vagina
'
fit <- lavaan::cfa(cfa_model,
data = data_familiaridade,
estimator="WLSMV",
ordered=colnames(data_familiaridade)
)
summary(fit, fit.measures=TRUE)## lavaan 0.6-8 ended normally after 21 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 12
##
## Number of observations 112
##
## Model Test User Model:
## Standard Robust
## Test Statistic 6.663 10.129
## Degrees of freedom 9 9
## P-value (Chi-square) 0.672 0.340
## Scaling correction factor 0.737
## Shift parameter 1.082
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 430.846 337.689
## Degrees of freedom 15 15
## P-value 0.000 0.000
## Scaling correction factor 1.289
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 0.997
## Tucker-Lewis Index (TLI) 1.009 0.994
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.034
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.085 0.115
## P-value RMSEA <= 0.05 0.822 0.547
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.098 0.098
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## familiaridade =~
## F_biopsia 1.000
## F_papilomavirs 1.135 0.228 4.987 0.000
## F_histerectomi 0.949 0.198 4.799 0.000
## F_mastectomia 1.231 0.193 6.389 0.000
## F_metastase 1.114 0.218 5.100 0.000
## F_pelvico 1.256 0.200 6.292 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .F_biopsia 0.000
## .F_papilomavirs 0.000
## .F_histerectomi 0.000
## .F_mastectomia 0.000
## .F_metastase 0.000
## .F_pelvico 0.000
## familiaridade 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## F_biopsia|t1 -1.345 0.168 -8.023 0.000
## F_papilmvrs|t1 -0.090 0.119 -0.752 0.452
## F_histerctm|t1 0.045 0.119 0.376 0.707
## F_mastectom|t1 -0.489 0.124 -3.933 0.000
## F_metastase|t1 -0.674 0.129 -5.215 0.000
## F_pelvico|t1 -1.068 0.147 -7.253 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .F_biopsia 0.492
## .F_papilomavirs 0.346
## .F_histerectomi 0.543
## .F_mastectomia 0.230
## .F_metastase 0.370
## .F_pelvico 0.199
## familiaridade 0.508 0.154 3.295 0.001
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## F_biopsia 1.000
## F_papilomavirs 1.000
## F_histerectomi 1.000
## F_mastectomia 1.000
## F_metastase 1.000
## F_pelvico 1.000lavaan::fitMeasures(fit, fit.measures = c("rmsea.scaled",
"rmsea.ci.lower.scaled",
"rmsea.ci.upper.scaled",
"cfi.scaled",
"tli.scaled",
"nnfi.scaled",
"chisq.scaled",
"pvalue.scaled"
))## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.034 0.000 0.115
## cfi.scaled tli.scaled nnfi.scaled
## 0.997 0.994 0.994
## chisq.scaled pvalue.scaled
## 10.129 0.340# AIC(fit)
Est <- lavaan::parameterEstimates(fit, ci = TRUE, standardized = TRUE)
subset(Est, op == "=~")## lhs op rhs est se z pvalue ci.lower ci.upper
## 1 familiaridade =~ F_biopsia 1.000 0.000 NA NA 1.000 1.000
## 2 familiaridade =~ F_papilomavirus 1.135 0.228 4.987 0 0.689 1.581
## 3 familiaridade =~ F_histerectomia 0.949 0.198 4.799 0 0.562 1.337
## 4 familiaridade =~ F_mastectomia 1.231 0.193 6.389 0 0.854 1.609
## 5 familiaridade =~ F_metastase 1.114 0.218 5.100 0 0.686 1.543
## 6 familiaridade =~ F_pelvico 1.256 0.200 6.292 0 0.865 1.647
## std.lv std.all std.nox
## 1 0.713 0.713 0.713
## 2 0.809 0.809 0.809
## 3 0.676 0.676 0.676
## 4 0.877 0.877 0.877
## 5 0.794 0.794 0.794
## 6 0.895 0.895 0.895subset(Est, op == "~~")## lhs op rhs est se z pvalue ci.lower
## 13 F_biopsia ~~ F_biopsia 0.492 0.000 NA NA 0.492
## 14 F_papilomavirus ~~ F_papilomavirus 0.346 0.000 NA NA 0.346
## 15 F_histerectomia ~~ F_histerectomia 0.543 0.000 NA NA 0.543
## 16 F_mastectomia ~~ F_mastectomia 0.230 0.000 NA NA 0.230
## 17 F_metastase ~~ F_metastase 0.370 0.000 NA NA 0.370
## 18 F_pelvico ~~ F_pelvico 0.199 0.000 NA NA 0.199
## 19 familiaridade ~~ familiaridade 0.508 0.154 3.295 0.001 0.206
## ci.upper std.lv std.all std.nox
## 13 0.492 0.492 0.492 0.492
## 14 0.346 0.346 0.346 0.346
## 15 0.543 0.543 0.543 0.543
## 16 0.230 0.230 0.230 0.230
## 17 0.370 0.370 0.370 0.370
## 18 0.199 0.199 0.199 0.199
## 19 0.810 1.000 1.000 1.000Mod <- lavaan::modificationIndices(fit)
subset(Mod, mi > 10)## [1] lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## <0 rows> (or 0-length row.names)sum(Est$std.all[1:6])^2/(sum(Est$std.all[1:6])^2+sum(Est$std.all[13:19]))## [1] 0.8771049sum(Est$std.all[1:6]^2)/length(Est$std.all[1:6])## [1] 0.6366993by(Est$std.all[1:6],Est$lhs[1:6],mean)## Est$lhs[1:6]: familiaridade
## [1] 0.7939762familiaridade_scores<-lavaan::predict(fit)
write.csv(familiaridade_scores,"/Users/leopestillo/Google Drive/Analysis/Marcelo_PPGPS//familiaridade_scores.csv")L_scores<-read.csv("/Users/leopestillo/Google Drive/Analysis/Marcelo_PPGPS/leitura_scores.csv",sep=',')
D_Scores<-read.csv("/Users/leopestillo/Google Drive/Analysis/Marcelo_PPGPS/dialogo_scores.csv",sep=',')
M_scores<-read.csv("/Users/leopestillo/Google Drive/Analysis/Marcelo_PPGPS/matematica_scores.csv",sep=',')
C_scores<-read.csv("/Users/leopestillo/Google Drive/Analysis/Marcelo_PPGPS/compreensao_scores.csv",sep=',')
F_scores<-read.csv("/Users/leopestillo/Google Drive/Analysis/Marcelo_PPGPS/familiaridade_scores.csv",sep=',')
dados_finais <- (L_scores %>% full_join (D_Scores) %>% full_join (M_scores) %>% full_join (C_scores) %>% full_join (F_scores))## Joining, by = "X"
## Joining, by = "X"
## Joining, by = "X"
## Joining, by = "X"dados_finais$X <- NULL
describe(dados_finais)## dados_finais
##
## 5 Variables 112 Observations
## --------------------------------------------------------------------------------
## leitura
## n missing distinct Info Mean Gmd .05 .10
## 112 0 25 0.675 -0.184 0.589 -1.5359 -1.1904
## .25 .50 .75 .90 .95
## -0.5500 0.2025 0.2025 0.2025 0.2025
##
## lowest : -2.1819854 -1.8883508 -1.8021437 -1.6210504 -1.5616960
## highest: -0.6021580 -0.5500235 -0.5395380 -0.4987015 0.2025133
## --------------------------------------------------------------------------------
## dialogo
## n missing distinct Info Mean Gmd .05 .10
## 112 0 63 0.999 0.01043 0.3133 -0.37688 -0.32991
## .25 .50 .75 .90 .95
## -0.19122 0.01249 0.19370 0.36132 0.45010
##
## lowest : -0.5971150 -0.4907210 -0.4714414 -0.3768838 -0.3300695
## highest: 0.4326218 0.4714630 0.5333095 0.5695673 0.6565559
## --------------------------------------------------------------------------------
## matematica
## n missing distinct Info Mean Gmd .05 .10
## 112 0 11 0.951 0.02468 0.7396 -0.7449 -0.7449
## .25 .50 .75 .90 .95
## -0.7449 -0.0889 0.7495 0.7495 1.0920
##
## lowest : -0.74492321 -0.55410966 -0.42911678 -0.14751153 -0.03028742
## highest: 0.22822985 0.33098806 0.49968789 0.74946454 1.09196308
##
## -0.74492320974229 (36, 0.321), -0.554109661899536 (1, 0.009),
## -0.429116784325155 (3, 0.027), -0.147511533612 (16, 0.143), -0.0302874169970451
## (2, 0.018), 0.0908370404668369 (5, 0.045), 0.228229852939543 (3, 0.027),
## 0.330988063670241 (11, 0.098), 0.499687886828032 (1, 0.009), 0.749464539738382
## (25, 0.223), 1.09196307769414 (9, 0.080)
## --------------------------------------------------------------------------------
## compreensao
## n missing distinct Info Mean Gmd .05 .10
## 112 0 51 0.996 -0.0048 0.7556 -1.090368 -0.782845
## .25 .50 .75 .90 .95
## -0.390413 0.002136 0.434991 1.050943 1.050943
##
## lowest : -1.4348968 -1.2432912 -1.2391944 -1.1005977 -1.0819988
## highest: 0.5955824 0.6098572 0.6557837 0.6979306 1.0509430
## --------------------------------------------------------------------------------
## familiaridade
## n missing distinct Info Mean Gmd .05 .10
## 112 0 22 0.952 -0.03581 0.6207 -0.99192 -0.82944
## .25 .50 .75 .90 .95
## -0.46316 0.07195 0.55921 0.55921 0.55921
##
## lowest : -1.27353124 -1.02747873 -0.96283000 -0.88582154 -0.85554981
## highest: -0.14485060 -0.13369611 0.07194999 0.20319039 0.55920552
## --------------------------------------------------------------------------------summary(dados_finais)## leitura dialogo matematica compreensao
## Min. :-2.1820 Min. :-0.59711 Min. :-0.74492 Min. :-1.434897
## 1st Qu.:-0.5500 1st Qu.:-0.19122 1st Qu.:-0.74492 1st Qu.:-0.390413
## Median : 0.2025 Median : 0.01249 Median :-0.08890 Median : 0.002135
## Mean :-0.1840 Mean : 0.01043 Mean : 0.02468 Mean :-0.004799
## 3rd Qu.: 0.2025 3rd Qu.: 0.19370 3rd Qu.: 0.74946 3rd Qu.: 0.434991
## Max. : 0.2025 Max. : 0.65656 Max. : 1.09196 Max. : 1.050943
## familiaridade
## Min. :-1.27353
## 1st Qu.:-0.46316
## Median : 0.07195
## Mean :-0.03581
## 3rd Qu.: 0.55921
## Max. : 0.55921lavCor(dados_finais)## leitur dialog matmtc cmprns fmlrdd
## leitura 1.000
## dialogo 0.293 1.000
## matematica 0.472 0.419 1.000
## compreensao 0.609 0.595 0.513 1.000
## familiaridade 0.671 0.501 0.457 0.839 1.000#extract thresholds polycor_data<-polychoric(na.omit(dados_finais),correct=.01) # crossvalidate cor<-polycor_data\(rho #extract correlation matrix tau<-rowMeans(polycor_data\)tau) #extract thresholds
#Community analysis - Walking Trap #Generate glasso network network_glasso<-qgraph( cor_data, layout=“spring”, #vsize=tau, # esize=20, graph=“glasso”, sampleSize=nrow(dados_finais), legend.cex = 0.5, GLratio=1.5, minimum=0.1, cut=0, border.width=1.5, shape=“square” )
```