SEM_BAYES_CMAMA
Mauricio Hernandez Carrillo
17/5/2022
Paquetes utilizados
Se hará uso de la base de datos recolectada la cual consta de 80 observaciones y 105 variables de mujeres con cáncer de mama en la ciudad de Cali, concretando algunos parámetros para las simulaciones y el MCMC para obtener la convergencia de los parámetros esperados. Donde se parametrizara el numero de muestras de calentamiento (BURNIN1), el número de iteraciones(SAMPLE1) y el numero de cadenas (NCHAINS1).
options(mc.cores = parallel::detectCores())
datos2 <- readRDS("data/datos2.RDS")
datos <- datos2
set.seed(535535)
BURNIN1 = 12000
SAMPLE1 = 20000
NCHAINS1 = 14
BURNIN = 3000 # 2500
SAMPLE = 7000 # 6500
CHAINS = 6 # 6MODELOS PLANTEADOS
A partir del analisis de los modelos propuestos por Wilson & Cleary se tienen en cuenta para el planteamiento de un modelo base y los modelos mas completos donde final se obtiene el modelo final.
Se establece después de las prueba de modelos frecuentistas y sus efectos indirectos, finalmente con la investigación en la literatura, el modelo bayesiano con sus variables latentes.
Modelo base: primer modelo planteado. Modelo base bio: modelo base mas la variable latente biologica. Modelo base bio ind: modelo base mas la variable latente biologica y sus respectivas variables moderadoras. model_base_bio_ind_econ: modelo base mas la variable latente biologica,con sus respectivas variables moderadoras y variable latente ecnomica. MODELO DE REFENCIA: modelo final planteado a partir de los resultados que se observaron. Modelo indic: Modelo final con efectos indirectos
model_base <- '
funcionalidad =~
prior("normal(0,15)")*f_br23 + prior("normal(0,15)")*f_c30
sintomas =~
prior("normal(0,15)")*s_br23 + prior("normal(0,15)")*s_c30
# regressions
cv_gral ~ salud
salud ~ funcionalidad
funcionalidad ~ sintomas
# residual correlations
'
#fitref_bio = model_base_bio
model_base_bio <- '
# measurement model
funcionalidad =~ prior("normal(0,15)")*f_br23 + prior("normal(0,15)")*f_c30
sintomas =~ prior("normal(0,15)")*s_br23 + prior("normal(0,15)")*s_c30
biologicas =~ her2_pos + edad + comorb + estadio_avz
# regressions
cv_gral ~ salud
salud ~ funcionalidad
funcionalidad ~ sintomas
sintomas ~ biologicas
# residual correlations
'
#fitbase_bio_ind = model_base_bio_ind
model_base_bio_ind <- '
# measurement model
funcionalidad =~ prior("normal(0,15)")*f_br23 + prior("normal(0,15)")*f_c30
sintomas =~ prior("normal(0,15)")*s_br23 + prior("normal(0,15)")*s_c30
biologicas =~ her2_pos + edad + comorb + estadio_avz
# regressions
cv_gral ~ salud + sintomas + funcionalidad
salud ~ funcionalidad
funcionalidad ~ sintomas
sintomas ~ biologicas
# residual correlations
'
model_base_bio_ind_econ <- '
# measurement model
funcionalidad =~
prior("normal(0,15)")*f_br23 + prior("normal(0,15)")*f_c30
sintomas =~
prior("normal(0,15)")*s_br23 + prior("normal(0,15)")*s_c30
biologicas =~ her2_pos + edad + comorb + estadio_avz
socioeconom =~ estr_bajo + rs_subsid + hasta_sec + trabaja
# regressions
cv_gral ~ salud + sintomas + funcionalidad + companero
salud ~ funcionalidad
funcionalidad ~ sintomas
sintomas ~ biologicas
# residual correlations
'
model_ref_bio_ind_sinedad <- '
# measurement model
funcionalidad =~ prior("normal(0,15)")*f_br23 + prior("normal(0,15)")*f_c30
sintomas =~ prior("normal(0,15)")*s_br23 + prior("normal(0,15)")*s_c30
biologicas =~ her2_pos + comorb + estadio_avz
# regressions
cv_gral ~ salud + sintomas + funcionalidad
salud ~ funcionalidad
funcionalidad ~ sintomas
sintomas ~ biologicas
# residual correlations
'
model_ref_bio_ind_sinedad_indi <- '
# measurement model
funcionalidad =~ prior("normal(0,15)")*f_br23 + prior("normal(0,15)")*f_c30
sintomas =~ prior("normal(0,15)")*s_br23 + prior("normal(0,15)")*s_c30
biologicas =~ her2_pos + comorb + estadio_avz
# regressions
cv_gral ~ d*salud + e*sintomas + c*funcionalidad
salud ~ funcionalidad
funcionalidad ~ b*sintomas
sintomas ~ a*biologicas
# indirect effect
ab:= a*b
abc:= a*b*c
abcd:=a*b*c*d
bc:= b*c
# total effect
indtot := ab + abc + abcd
tot := indtot + e
'Exploracion de funcion bsem
De el paquete Blavaan que ayuda a ejecutar la estimacion de los modelos bayesianos para ecuaciones de modelos estructurales, por medio Markov Chain Monte Carlo (MCMC) el cual es un metodo de simulacion para generar muestras de las distribuciones a posteriori y estimar cantidades de interes a posteriori.
#codigo para funcion sem
set.seed(535535)
fitbase = bsem(
model = model_base,
data = datos,
auto.var = TRUE,
auto.fix.first = TRUE,
auto.cov.lv.x = TRUE,
save.lvs = TRUE,
inits = "prior",
sample = BURNIN1,
burnin = SAMPLE1,
n.chains = NCHAINS1)## Computing posterior predictives...#save(fitfitref.Rdata, file = "data/fitfitref.Rdata.Rdata")
#fitbase load("data/fitref.Rdata")# para cargar
#set.seed(535535)
#fitref_bio <- bsem(
# model = model_base_bio,
# data = datos,
# auto.var = TRUE,
# auto.fix.first = TRUE,
# auto.cov.lv.x = TRUE,
# save.lvs = TRUE,
# inits = "prior",
# sample = BURNIN1,
# burnin = SAMPLE1,
# n.chains = NCHAINS1)
#save(fitref_bio, file = "data/fitref_bio.Rdata") #Modelo estable
load("data/fitref_bio.Rdata")# para cargar
#set.seed(535535)
#fitbase_bio_ind <- bsem(
# model = model_base_bio_ind,
# data = datos,
# auto.var = TRUE,
# auto.fix.first = TRUE,
# auto.cov.lv.x = TRUE,
# save.lvs = TRUE,
# inits = "prior",
# sample = BURNIN1,
# burnin = SAMPLE1,
# n.chains = NCHAINS1)
#save(fitbase_bio_ind, file = "data/fitbase_bio_ind.Rdata") #Modelo estable
load("data/fitbase_bio_ind.Rdata")# para cargar
set.seed(535535)
fitbase_bio_ind_econ <- bsem(
model = model_base_bio_ind_econ,
data = datos,
auto.var = TRUE,
auto.fix.first = TRUE,
auto.cov.lv.x = TRUE,
save.lvs = TRUE,
inits = "prior",
sample = BURNIN1,
burnin = SAMPLE1,
n.chains = NCHAINS1)## Computing posterior predictives...#set.seed(535535)
#fitref_bio_ind_sinedad <- bsem(
# model = model_ref_bio_ind_sinedad,
# data = datos,
# auto.var = TRUE,
# auto.fix.first = TRUE,
# auto.cov.lv.x = TRUE,
# save.lvs = TRUE,
# inits = "prior",
# sample = BURNIN1,
# burnin = SAMPLE1,
# n.chains = NCHAINS1)
load("data/fitref_bio_ind_sinedad.Rdata") #estable
set.seed(535535)
fit_ref_bio_ind_sinedad_indi <- bsem(
model = model_ref_bio_ind_sinedad_indi,
data = datos,
auto.var = TRUE,
auto.fix.first = TRUE,
auto.cov.lv.x = TRUE,
save.lvs = TRUE,
inits = "prior",
sample = BURNIN1,
burnin = SAMPLE1,
n.chains = NCHAINS1)## Computing posterior predictives...El resumen del modelo el cual nos indica diferentes resultados de interes
summary(fitbase,standardized = TRUE, rsquare=TRUE)## ** WARNING ** blavaan (0.4-1) did NOT converge after 8000 adapt+burnin iterations
## ** WARNING ** Proceed with caution
##
## Number of observations 80
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value -1017.126 0.062
##
## Latent Variables:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## funcionalidad =~
## f_br23 1.000 0.169 0.393
## f_c30 1.476 0.162 1.198 1.835 0.250 0.709
## sintomas =~
## s_br23 1.000 0.321 0.564
## s_c30 -0.025 7.523 -25.688 3.489 -0.008 -0.035
## Rhat Prior
##
##
## 1.006 normal(0,15)
##
##
## 3.588 normal(0,15)
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## cv_gral ~
## salud 0.899 0.046 0.809 0.989 0.899 0.857
## salud ~
## funcionalidad 2.608 0.390 1.905 3.431 0.442 0.328
## funcionalidad ~
## sintomas 0.003 4.663 -2.239 15.950 0.006 0.006
## Rhat Prior
##
## 1.000 normal(0,10)
##
## 1.002 normal(0,10)
##
## 3.529 normal(0,10)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## .f_br23 2.343 0.072 2.200 2.484 2.343 5.434
## .f_c30 2.438 0.087 2.264 2.608 2.438 6.914
## .s_br23 1.765 0.064 1.640 1.891 1.765 3.098
## .s_c30 2.164 0.089 1.989 2.338 2.164 9.364
## .cv_gral 0.200 0.215 -0.221 0.622 0.200 0.141
## .salud 4.337 0.204 3.932 4.734 4.337 3.221
## .funcionalidad 0.000 0.000 0.000
## sintomas 0.000 0.000 0.000
## Rhat Prior
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,10)
## 1.000 normal(0,10)
##
##
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## .f_br23 0.157 0.030 0.108 0.224 0.157 0.846
## .f_c30 0.062 0.028 0.009 0.120 0.062 0.497
## .s_br23 0.221 0.059 0.148 0.390 0.221 0.682
## .s_c30 0.053 0.041 0.000 0.141 0.053 0.999
## .cv_gral 0.529 0.088 0.384 0.729 0.529 0.265
## .salud 1.617 0.283 1.145 2.254 1.617 0.892
## .funcionalidad 0.029 0.020 0.000 0.070 1.000 1.000
## sintomas 0.103 0.053 0.001 0.209 1.000 1.000
## Rhat Prior
## 1.002 gamma(1,.5)[sd]
## 1.009 gamma(1,.5)[sd]
## 1.544 gamma(1,.5)[sd]
## 1.008 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.002 gamma(1,.5)[sd]
## 1.320 gamma(1,.5)[sd]
##
## R-Square:
## Estimate
## f_br23 0.154
## f_c30 0.503
## s_br23 0.318
## s_c30 0.001
## cv_gral 0.735
## salud 0.108
## funcionalidad 0.000summary(fitref_bio,standardized = TRUE, rsquare=TRUE)# estable## blavaan (0.4-1) results of 9000 samples after 19000 adapt/burnin iterations
##
## Number of observations 80
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value -1106.965 0.000
##
## Latent Variables:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## funcionalidad =~
## f_br23 1.000 0.467 0.763
## f_c30 1.476 0.163 1.201 1.838 0.690 0.941
## sintomas =~
## s_br23 1.000 0.296 0.545
## s_c30 2.369 0.472 1.702 3.463 0.701 0.955
## biologicas =~
## her2_pos 1.000 0.038 0.075
## edad -0.410 10.649 -21.435 20.481 -0.016 -0.001
## comorb 0.095 6.019 -13.148 13.522 0.004 0.008
## estadio_avz -1.185 9.459 -19.096 17.958 -0.045 -0.102
## Rhat Prior
##
##
## 1.000 normal(0,15)
##
##
## 1.002 normal(0,15)
##
##
## 1.001 normal(0,10)
## 1.001 normal(0,10)
## 1.001 normal(0,10)
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## cv_gral ~
## salud 0.899 0.046 0.809 0.989 0.899 0.909
## salud ~
## funcionalidad 2.610 0.396 1.905 3.458 1.220 0.692
## funcionalidad ~
## sintomas -1.463 0.329 -2.198 -0.982 -0.926 -0.926
## sintomas ~
## biologicas 0.676 8.859 -17.433 17.857 0.087 0.087
## Rhat Prior
##
## 1.000 normal(0,10)
##
## 1.000 normal(0,10)
##
## 1.002 normal(0,10)
##
## 1.001 normal(0,10)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## .f_br23 2.342 0.072 2.201 2.482 2.342 3.822
## .f_c30 2.436 0.088 2.265 2.608 2.436 3.323
## .s_br23 1.765 0.063 1.641 1.889 1.765 3.253
## .s_c30 2.165 0.090 1.989 2.341 2.165 2.948
## .her2_pos 0.438 0.057 0.326 0.549 0.438 0.859
## .edad 53.719 1.343 51.094 56.371 53.719 4.482
## .comorb 0.337 0.055 0.230 0.444 0.337 0.710
## .estadio_avz 0.337 0.054 0.230 0.443 0.337 0.762
## .cv_gral 0.201 0.214 -0.220 0.622 0.201 0.115
## .salud 4.334 0.205 3.935 4.738 4.334 2.459
## .funcionalidad 0.000 0.000 0.000
## .sintomas 0.000 0.000 0.000
## biologicas 0.000 0.000 0.000
## Rhat Prior
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,10)
## 1.000 normal(0,10)
##
##
##
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## .f_br23 0.157 0.029 0.108 0.223 0.157 0.419
## .f_c30 0.061 0.028 0.008 0.119 0.061 0.114
## .s_br23 0.207 0.036 0.148 0.287 0.207 0.703
## .s_c30 0.048 0.039 0.000 0.132 0.048 0.088
## .her2_pos 0.258 0.042 0.188 0.353 0.258 0.994
## .edad 143.678 22.422 105.911 193.423 143.678 1.000
## .comorb 0.226 0.050 0.109 0.318 0.226 1.000
## .estadio_avz 0.194 0.064 0.020 0.301 0.194 0.990
## .cv_gral 0.529 0.086 0.387 0.724 0.529 0.174
## .salud 1.620 0.285 1.145 2.259 1.620 0.521
## .funcionalidad 0.031 0.020 0.000 0.072 0.142 0.142
## .sintomas 0.087 0.049 0.003 0.190 0.992 0.992
## biologicas 0.001 0.004 0.000 0.009 1.000 1.000
## Rhat Prior
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.003 gamma(1,.5)[sd]
## 1.001 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.001 gamma(1,.5)[sd]
## 1.004 gamma(1,.5)[sd]
##
## R-Square:
## Estimate
## f_br23 0.581
## f_c30 0.886
## s_br23 0.297
## s_c30 0.912
## her2_pos 0.006
## edad 0.000
## comorb 0.000
## estadio_avz 0.010
## cv_gral 0.826
## salud 0.479
## funcionalidad 0.858
## sintomas 0.008summary(fitbase_bio_ind,standardized = TRUE, rsquare=TRUE)#estable## blavaan (0.4-1) results of 5000 samples after 12000 adapt/burnin iterations
##
## Number of observations 80
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value -1105.079 0.002
##
## Latent Variables:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## funcionalidad =~
## f_br23 1.000 0.472 0.770
## f_c30 1.452 0.161 1.179 1.810 0.685 0.927
## sintomas =~
## s_br23 1.000 0.296 0.546
## s_c30 2.352 0.473 1.674 3.504 0.695 0.931
## biologicas =~
## her2_pos 1.000 0.038 0.074
## edad -0.286 10.575 -20.969 20.455 -0.011 -0.001
## comorb 0.089 6.322 -13.992 14.069 0.003 0.007
## estadio_avz -1.036 9.409 -19.014 18.052 -0.039 -0.088
## Rhat Prior
##
##
## 1.000 normal(0,15)
##
##
## 1.001 normal(0,15)
##
##
## 1.000 normal(0,10)
## 1.001 normal(0,10)
## 1.001 normal(0,10)
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## cv_gral ~
## salud 0.758 0.067 0.625 0.890 0.758 0.778
## sintomas -0.551 5.600 -13.150 12.150 -0.163 -0.095
## funcionalidad 0.364 3.658 -7.878 8.620 0.172 0.100
## salud ~
## funcionalidad 2.605 0.394 1.905 3.448 1.229 0.695
## funcionalidad ~
## sintomas -1.539 0.331 -2.325 -1.044 -0.965 -0.965
## sintomas ~
## biologicas 0.571 8.797 -17.460 17.916 0.073 0.073
## Rhat Prior
##
## 1.000 normal(0,10)
## 1.001 normal(0,10)
## 1.001 normal(0,10)
##
## 1.000 normal(0,10)
##
## 1.001 normal(0,10)
##
## 1.000 normal(0,10)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## .f_br23 2.342 0.072 2.199 2.484 2.342 3.825
## .f_c30 2.437 0.088 2.264 2.610 2.437 3.300
## .s_br23 1.765 0.063 1.641 1.888 1.765 3.258
## .s_c30 2.164 0.090 1.988 2.342 2.164 2.899
## .her2_pos 0.438 0.057 0.326 0.549 0.438 0.859
## .edad 53.730 1.342 51.089 56.380 53.730 4.483
## .comorb 0.337 0.054 0.230 0.445 0.337 0.710
## .estadio_avz 0.337 0.055 0.230 0.445 0.337 0.758
## .cv_gral 0.810 0.305 0.214 1.418 0.810 0.471
## .salud 4.335 0.206 3.933 4.737 4.335 2.454
## .funcionalidad 0.000 0.000 0.000
## .sintomas 0.000 0.000 0.000
## biologicas 0.000 0.000 0.000
## Rhat Prior
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,10)
## 1.000 normal(0,10)
##
##
##
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## .f_br23 0.153 0.029 0.104 0.217 0.153 0.407
## .f_c30 0.077 0.028 0.025 0.136 0.077 0.141
## .s_br23 0.206 0.036 0.147 0.287 0.206 0.702
## .s_c30 0.074 0.040 0.001 0.149 0.074 0.133
## .her2_pos 0.258 0.043 0.187 0.355 0.258 0.994
## .edad 143.662 22.366 105.926 193.163 143.662 1.000
## .comorb 0.226 0.049 0.116 0.320 0.226 1.000
## .estadio_avz 0.197 0.063 0.023 0.303 0.197 0.992
## .cv_gral 0.444 0.099 0.232 0.638 0.444 0.150
## .salud 1.611 0.285 1.139 2.252 1.611 0.516
## .funcionalidad 0.015 0.017 0.000 0.058 0.069 0.069
## .sintomas 0.087 0.048 0.003 0.190 0.995 0.995
## biologicas 0.001 0.004 0.000 0.010 1.000 1.000
## Rhat Prior
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.002 gamma(1,.5)[sd]
## 1.001 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.001 gamma(1,.5)[sd]
## 1.001 gamma(1,.5)[sd]
## 1.001 gamma(1,.5)[sd]
##
## R-Square:
## Estimate
## f_br23 0.593
## f_c30 0.859
## s_br23 0.298
## s_c30 0.867
## her2_pos 0.006
## edad 0.000
## comorb 0.000
## estadio_avz 0.008
## cv_gral 0.850
## salud 0.484
## funcionalidad 0.931
## sintomas 0.005summary(fitbase_bio_ind_econ,standardized = TRUE, rsquare=TRUE)#estacle## ** WARNING ** blavaan (0.4-1) did NOT converge after 8000 adapt+burnin iterations
## ** WARNING ** Proceed with caution
##
## Number of observations 80
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value -1773.345 0.005
##
## Latent Variables:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## funcionalidad =~
## f_br23 1.000 0.137 0.329
## f_c30 1.462 0.163 1.185 1.827 0.200 0.592
## sintomas =~
## s_br23 1.000 0.287 0.521
## s_c30 0.112 7.025 -23.747 3.456 0.032 0.120
## biologicas =~
## her2_pos 1.000 0.060 0.118
## edad -3.316 11.549 -25.836 19.130 -0.200 -0.017
## comorb -0.705 5.033 -11.341 10.634 -0.042 -0.088
## estadio_avz -1.738 8.696 -17.763 16.866 -0.105 -0.226
## socioeconom =~
## estr_bajo 1.000 0.132 0.263
## rs_subsid 3.279 2.531 0.930 10.335 0.432 0.722
## hasta_sec 5.388 3.767 1.698 15.937 0.709 0.965
## trabaja 0.429 0.984 -1.351 2.752 0.057 0.112
## Rhat Prior
##
##
## 1.007 normal(0,15)
##
##
## 3.599 normal(0,15)
##
##
## 1.010 normal(0,10)
## 1.016 normal(0,10)
## 1.032 normal(0,10)
##
##
## 1.001 normal(0,10)
## 1.001 normal(0,10)
## 1.000 normal(0,10)
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## cv_gral ~
## salud 0.760 0.069 0.623 0.894 0.760 0.817
## sintomas 0.027 5.733 -12.332 13.366 0.008 0.006
## funcionalidad 0.583 3.352 -6.939 8.545 0.080 0.065
## companero -0.001 0.162 -0.321 0.317 -0.001 -0.001
## salud ~
## funcionalidad 2.616 0.394 1.915 3.460 0.358 0.271
## funcionalidad ~
## sintomas -0.133 4.378 -2.292 14.662 -0.280 -0.280
## sintomas ~
## biologicas 1.482 8.177 -16.192 16.634 0.311 0.311
## Rhat Prior
##
## 1.000 normal(0,10)
## 1.008 normal(0,10)
## 1.001 normal(0,10)
## 1.000 normal(0,10)
##
## 1.002 normal(0,10)
##
## 3.545 normal(0,10)
##
## 1.042 normal(0,10)
##
## Covariances:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## biologicas ~~
## socioeconom -0.002 0.005 -0.016 0.003 -0.266 -0.266
## Rhat Prior
##
## 1.020 beta(1,1)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## .f_br23 2.342 0.072 2.200 2.483 2.342 5.631
## .f_c30 2.437 0.087 2.265 2.607 2.437 7.212
## .s_br23 1.765 0.064 1.640 1.891 1.765 3.207
## .s_c30 2.164 0.089 1.990 2.340 2.164 8.094
## .her2_pos 0.438 0.057 0.325 0.550 0.438 0.860
## .edad 53.725 1.343 51.062 56.341 53.725 4.508
## .comorb 0.338 0.055 0.230 0.444 0.338 0.702
## .estadio_avz 0.337 0.054 0.230 0.443 0.337 0.729
## .estr_bajo 0.562 0.057 0.450 0.674 0.562 1.124
## .rs_subsid 0.362 0.056 0.253 0.471 0.362 0.606
## .hasta_sec 0.650 0.055 0.542 0.757 0.650 0.884
## .trabaja 0.600 0.057 0.488 0.712 0.600 1.185
## .cv_gral 0.806 0.312 0.197 1.432 0.806 0.656
## .salud 4.336 0.204 3.933 4.736 4.336 3.284
## .funcionalidad 0.000 0.000 0.000
## .sintomas 0.000 0.000 0.000
## biologicas 0.000 0.000 0.000
## socioeconom 0.000 0.000 0.000
## Rhat Prior
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,10)
## 1.000 normal(0,10)
##
##
##
##
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## .f_br23 0.154 0.029 0.106 0.220 0.154 0.892
## .f_c30 0.074 0.028 0.020 0.134 0.074 0.650
## .s_br23 0.221 0.059 0.148 0.389 0.221 0.728
## .s_c30 0.070 0.040 0.001 0.147 0.070 0.986
## .her2_pos 0.255 0.042 0.185 0.350 0.255 0.986
## .edad 142.017 22.942 103.312 192.465 142.017 1.000
## .comorb 0.229 0.040 0.164 0.316 0.229 0.992
## .estadio_avz 0.203 0.045 0.119 0.294 0.203 0.949
## .estr_bajo 0.233 0.040 0.167 0.321 0.233 0.931
## .rs_subsid 0.171 0.036 0.101 0.244 0.171 0.479
## .hasta_sec 0.038 0.039 0.000 0.139 0.038 0.070
## .trabaja 0.253 0.042 0.183 0.347 0.253 0.988
## .cv_gral 0.454 0.101 0.242 0.653 0.454 0.301
## .salud 1.616 0.287 1.141 2.262 1.616 0.927
## .funcionalidad 0.017 0.018 0.000 0.060 0.922 0.922
## .sintomas 0.074 0.045 0.000 0.168 0.903 0.903
## biologicas 0.004 0.009 0.000 0.032 1.000 1.000
## socioeconom 0.017 0.017 0.001 0.063 1.000 1.000
## Rhat Prior
## 1.002 gamma(1,.5)[sd]
## 1.014 gamma(1,.5)[sd]
## 1.550 gamma(1,.5)[sd]
## 1.001 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.001 gamma(1,.5)[sd]
## 1.003 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.002 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.004 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.007 gamma(1,.5)[sd]
## 1.205 gamma(1,.5)[sd]
## 1.008 gamma(1,.5)[sd]
## 1.001 gamma(1,.5)[sd]
##
## R-Square:
## Estimate
## f_br23 0.108
## f_c30 0.350
## s_br23 0.272
## s_c30 0.014
## her2_pos 0.014
## edad 0.000
## comorb 0.008
## estadio_avz 0.051
## estr_bajo 0.069
## rs_subsid 0.521
## hasta_sec 0.930
## trabaja 0.012
## cv_gral 0.699
## salud 0.073
## funcionalidad 0.078
## sintomas 0.097summary(fitref_bio_ind_sinedad,standardized = TRUE, rsquare=TRUE)#esta## blavaan (0.4-1) results of 500 samples after 12000 adapt/burnin iterations
##
## Number of observations 80
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value -719.918 0.066
##
## Latent Variables:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## funcionalidad =~
## f_br23 1.000 0.471 0.770
## f_c30 1.450 0.166 1.167 1.823 0.683 0.926
## sintomas =~
## s_br23 1.000 0.296 0.546
## s_c30 2.339 0.460 1.685 3.447 0.692 0.929
## biologicas =~
## her2_pos 1.000 0.033 0.064
## comorb -0.167 5.726 -13.495 11.956 -0.005 -0.011
## estadio_avz -1.236 9.994 -20.074 18.700 -0.040 -0.093
## Rhat Prior
##
##
## 1.005 normal(0,15)
##
##
## 1.003 normal(0,15)
##
##
## 1.001 normal(0,10)
## 1.020 normal(0,10)
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## cv_gral ~
## salud 0.759 0.066 0.628 0.892 0.759 0.786
## sintomas -0.946 5.689 -14.997 10.777 -0.280 -0.164
## funcionalidad 0.079 3.712 -8.697 7.867 0.037 0.022
## salud ~
## funcionalidad 2.603 0.391 1.891 3.435 1.226 0.695
## funcionalidad ~
## sintomas -1.539 0.338 -2.331 -1.031 -0.967 -0.967
## sintomas ~
## biologicas 0.764 8.619 -15.798 18.530 0.084 0.084
## Rhat Prior
##
## 1.006 normal(0,10)
## 1.012 normal(0,10)
## 1.012 normal(0,10)
##
## 1.003 normal(0,10)
##
## 1.003 normal(0,10)
##
## 1.009 normal(0,10)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## .f_br23 2.343 0.072 2.203 2.483 2.343 3.830
## .f_c30 2.438 0.086 2.272 2.606 2.438 3.307
## .s_br23 1.765 0.062 1.643 1.887 1.765 3.255
## .s_c30 2.162 0.088 1.992 2.337 2.162 2.901
## .her2_pos 0.437 0.058 0.321 0.553 0.437 0.859
## .comorb 0.338 0.058 0.229 0.453 0.338 0.704
## .estadio_avz 0.338 0.054 0.233 0.445 0.338 0.780
## .cv_gral 0.807 0.300 0.203 1.386 0.807 0.474
## .salud 4.341 0.207 3.933 4.750 4.341 2.460
## .funcionalidad 0.000 0.000 0.000
## .sintomas 0.000 0.000 0.000
## biologicas 0.000 0.000 0.000
## Rhat Prior
## 1.000 normal(0,32)
## 0.999 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 0.999 normal(0,32)
## 0.999 normal(0,32)
## 1.006 normal(0,10)
## 1.000 normal(0,10)
##
##
##
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## .f_br23 0.152 0.028 0.105 0.215 0.152 0.407
## .f_c30 0.077 0.028 0.023 0.134 0.077 0.142
## .s_br23 0.206 0.036 0.147 0.287 0.206 0.702
## .s_c30 0.076 0.040 0.001 0.151 0.076 0.137
## .her2_pos 0.257 0.041 0.190 0.348 0.257 0.996
## .comorb 0.231 0.040 0.164 0.315 0.231 1.000
## .estadio_avz 0.186 0.072 0.003 0.294 0.186 0.991
## .cv_gral 0.437 0.110 0.160 0.638 0.437 0.151
## .salud 1.611 0.278 1.142 2.228 1.611 0.517
## .funcionalidad 0.014 0.017 0.000 0.058 0.065 0.065
## .sintomas 0.087 0.047 0.003 0.188 0.993 0.993
## biologicas 0.001 0.002 0.000 0.007 1.000 1.000
## Rhat Prior
## 1.002 gamma(1,.5)[sd]
## 1.001 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.005 gamma(1,.5)[sd]
## 0.999 gamma(1,.5)[sd]
## 0.999 gamma(1,.5)[sd]
## 1.020 gamma(1,.5)[sd]
## 1.011 gamma(1,.5)[sd]
## 0.999 gamma(1,.5)[sd]
## 1.008 gamma(1,.5)[sd]
## 1.004 gamma(1,.5)[sd]
## 1.002 gamma(1,.5)[sd]
##
## R-Square:
## Estimate
## f_br23 0.593
## f_c30 0.858
## s_br23 0.298
## s_c30 0.863
## her2_pos 0.004
## comorb 0.000
## estadio_avz 0.009
## cv_gral 0.849
## salud 0.483
## funcionalidad 0.935
## sintomas 0.007summary(fit_ref_bio_ind_sinedad_indi,standardized = TRUE, rsquare=TRUE)## ** WARNING ** blavaan (0.4-1) did NOT converge after 8000 adapt+burnin iterations
## ** WARNING ** Proceed with caution
##
## Number of observations 80
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value -997.790 0.062
##
## Latent Variables:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## funcionalidad =~
## f_br23 1.000 0.417 0.696
## f_c30 -0.023 4.564 -15.333 1.793 -0.010 -0.035
## sintomas =~
## s_br23 1.000 0.293 0.542
## s_c30 2.367 0.483 1.684 3.528 0.694 0.934
## biologicas =~
## her2_pos 1.000 0.034 0.066
## comorb 0.122 6.116 -13.218 13.685 0.004 0.009
## estadio_avz -1.137 9.631 -19.151 18.309 -0.038 -0.087
## Rhat Prior
##
##
## 4.179 normal(0,15)
##
##
## 1.008 normal(0,15)
##
##
## 1.000 normal(0,10)
## 1.001 normal(0,10)
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## cv_gral ~
## salud (d) 0.759 0.068 0.624 0.893 0.759 0.785
## sintomas (e) -0.570 5.312 -12.731 11.637 -0.167 -0.135
## funcionldd (c) 0.372 4.100 -8.557 9.589 0.155 0.126
## salud ~
## funcionldd 0.181 7.491 -25.158 3.422 0.075 0.059
## funcionalidad ~
## sintomas (b) -1.366 0.605 -2.295 0.210 -0.960 -0.960
## sintomas ~
## biologicas (a) 0.567 8.877 -17.859 17.524 0.065 0.065
## Rhat Prior
##
## 1.006 normal(0,10)
## 1.002 normal(0,10)
## 1.001 normal(0,10)
##
## 4.375 normal(0,10)
##
## 1.951 normal(0,10)
##
## 1.001 normal(0,10)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## .f_br23 2.342 0.073 2.199 2.486 2.342 3.909
## .f_c30 2.437 0.088 2.264 2.609 2.437 8.830
## .s_br23 1.765 0.064 1.640 1.890 1.765 3.264
## .s_c30 2.165 0.091 1.987 2.342 2.165 2.914
## .her2_pos 0.437 0.056 0.325 0.549 0.437 0.859
## .comorb 0.338 0.055 0.230 0.446 0.338 0.705
## .estadio_avz 0.337 0.055 0.229 0.445 0.337 0.763
## .cv_gral 0.805 0.310 0.199 1.426 0.805 0.653
## .salud 4.336 0.206 3.927 4.740 4.336 3.405
## .funcionalidad 0.000 0.000 0.000
## .sintomas 0.000 0.000 0.000
## biologicas 0.000 0.000 0.000
## Rhat Prior
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.005 normal(0,10)
## 1.000 normal(0,10)
##
##
##
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## .f_br23 0.185 0.104 0.105 0.524 0.185 0.515
## .f_c30 0.076 0.030 0.017 0.138 0.076 0.999
## .s_br23 0.207 0.036 0.148 0.287 0.207 0.706
## .s_c30 0.070 0.041 0.001 0.147 0.070 0.128
## .her2_pos 0.258 0.042 0.188 0.352 0.258 0.996
## .comorb 0.230 0.043 0.157 0.319 0.230 1.000
## .estadio_avz 0.194 0.064 0.020 0.301 0.194 0.992
## .cv_gral 0.446 0.101 0.234 0.643 0.446 0.294
## .salud 1.616 0.288 1.142 2.262 1.616 0.997
## .funcionalidad 0.014 0.017 0.000 0.057 0.079 0.079
## .sintomas 0.086 0.048 0.002 0.187 0.996 0.996
## biologicas 0.001 0.003 0.000 0.007 1.000 1.000
## Rhat Prior
## 2.849 gamma(1,.5)[sd]
## 1.003 gamma(1,.5)[sd]
## 1.001 gamma(1,.5)[sd]
## 1.042 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.001 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.007 gamma(1,.5)[sd]
## 1.002 gamma(1,.5)[sd]
## 1.039 gamma(1,.5)[sd]
## 1.003 gamma(1,.5)[sd]
## 1.001 gamma(1,.5)[sd]
##
## R-Square:
## Estimate
## f_br23 0.485
## f_c30 0.001
## s_br23 0.294
## s_c30 0.872
## her2_pos 0.004
## comorb 0.000
## estadio_avz 0.008
## cv_gral 0.706
## salud 0.003
## funcionalidad 0.921
## sintomas 0.004
##
## Defined Parameters:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## ab -0.774 13.100 -26.450 24.902 -0.063 -0.063
## abc -0.288 47.521 -93.428 92.852 -0.010 -0.008
## abcd -0.219 35.840 -70.463 70.026 -0.007 -0.006
## bc -0.508 5.317 -10.930 9.913 -0.149 -0.121
## indtot -1.281 85.654 -169.159 166.597 -0.080 -0.077
## tot -1.851 85.573 -169.571 165.870 -0.247 -0.212
## Rhat Prior
##
##
##
##
##
## Informacion del BRMSEA
blavFitIndices(fitbase)## Posterior mean (EAP) of devm-based fit indices:
##
## BRMSEA BGammaHat adjBGammaHat BMc
## 0.196 0.927 0.658 0.887blavFitIndices(fitref_bio)## Posterior mean (EAP) of devm-based fit indices:
##
## BRMSEA BGammaHat adjBGammaHat BMc
## 0.134 0.878 0.796 0.706blavFitIndices(fitbase_bio_ind) #Estable## Posterior mean (EAP) of devm-based fit indices:
##
## BRMSEA BGammaHat adjBGammaHat BMc
## 0.129 0.890 0.807 0.735blavFitIndices(fitbase_bio_ind_econ)## Posterior mean (EAP) of devm-based fit indices:
##
## BRMSEA BGammaHat adjBGammaHat BMc
## 0.195 0.898 0.852 0.656blavFitIndices(fitref_bio_ind_sinedad)## Posterior mean (EAP) of devm-based fit indices:
##
## BRMSEA BGammaHat adjBGammaHat BMc
## 0.099 0.941 0.887 0.869blavFitIndices(fit_ref_bio_ind_sinedad_indi)## Posterior mean (EAP) of devm-based fit indices:
##
## BRMSEA BGammaHat adjBGammaHat BMc
## 0.124 0.925 0.818 0.834Informacion de R hat modelo de referencia
blavInspect(fitref_bio_ind_sinedad, 'rhat')## funcionalidad=~f_c30 sintomas=~s_c30
## 1.0048315 1.0030244
## biologicas=~comorb biologicas=~estadio_avz
## 1.0013575 1.0201912
## cv_gral~salud cv_gral~sintomas
## 1.0056862 1.0119441
## cv_gral~funcionalidad salud~funcionalidad
## 1.0118498 1.0028308
## funcionalidad~sintomas sintomas~biologicas
## 1.0034780 1.0091393
## f_br23~~f_br23 f_c30~~f_c30
## 1.0019487 1.0007276
## s_br23~~s_br23 s_c30~~s_c30
## 0.9996431 1.0054009
## her2_pos~~her2_pos comorb~~comorb
## 0.9994917 0.9994663
## estadio_avz~~estadio_avz cv_gral~~cv_gral
## 1.0202822 1.0107859
## salud~~salud funcionalidad~~funcionalidad
## 0.9992926 1.0084047
## sintomas~~sintomas biologicas~~biologicas
## 1.0036061 1.0021398
## f_br23~1 f_c30~1
## 1.0004614 0.9993499
## s_br23~1 s_c30~1
## 0.9999890 0.9996622
## her2_pos~1 comorb~1
## 0.9999089 0.9993346
## estadio_avz~1 cv_gral~1
## 0.9994897 1.0055727
## salud~1
## 1.0004099Informacion del neff modelo de referencia
blavInspect(fitref_bio_ind_sinedad, 'neff')## funcionalidad=~f_c30 sintomas=~s_c30
## 1350.0703 1194.6285
## biologicas=~comorb biologicas=~estadio_avz
## 1687.1179 368.0325
## cv_gral~salud cv_gral~sintomas
## 1391.6145 578.9260
## cv_gral~funcionalidad salud~funcionalidad
## 562.8230 2027.5925
## funcionalidad~sintomas sintomas~biologicas
## 1182.8548 616.4119
## f_br23~~f_br23 f_c30~~f_c30
## 2970.8817 1772.8345
## s_br23~~s_br23 s_c30~~s_c30
## 3465.0440 1128.2822
## her2_pos~~her2_pos comorb~~comorb
## 3704.4874 3591.1805
## estadio_avz~~estadio_avz cv_gral~~cv_gral
## 249.5160 494.7867
## salud~~salud funcionalidad~~funcionalidad
## 3149.4470 899.0129
## sintomas~~sintomas biologicas~~biologicas
## 1350.5927 1086.4631
## f_br23~1 f_c30~1
## 1102.2056 928.0063
## s_br23~1 s_c30~1
## 1494.5909 941.1430
## her2_pos~1 comorb~1
## 3776.4438 3730.5295
## estadio_avz~1 cv_gral~1
## 3702.1813 1382.5197
## salud~1
## 1369.4601Graficos de modelos sem bayesiano con la funcion semplot
semPaths(
fitbase,
intercepts = FALSE,
residuals = TRUE,
edge.label.cex = 1.5,
intStyle = "multi",
optimizeLatRes = TRUE,
title.color = "black",
groups = "lat",
pastel = TRUE,
exoVar = FALSE,
sizeInt = 5,
edge.color = "black",
esize = 6,
label.prop = 2,
sizeLat = 6,
"std"
)
semPaths(
fitref_bio,
intercepts = FALSE,
residuals = TRUE,
edge.label.cex = 1.5,
intStyle = "multi",
optimizeLatRes = TRUE,
title.color = "black",
groups = "lat",
pastel = TRUE,
exoVar = FALSE,
sizeInt = 5,
edge.color = "black",
esize = 6,
label.prop = 2,
sizeLat = 6,
"std"
)
semPaths(
fitbase_bio_ind,
intercepts = FALSE,
residuals = TRUE,
edge.label.cex = 1.5,
intStyle = "multi",
optimizeLatRes = TRUE,
title.color = "black",
groups = "lat",
pastel = TRUE,
exoVar = FALSE,
sizeInt = 5,
edge.color = "black",
esize = 6,
label.prop = 2,
sizeLat = 6,
"std"
)
semPaths(
fitbase_bio_ind_econ,
intercepts = FALSE,
residuals = TRUE,
edge.label.cex = 1.5,
intStyle = "multi",
optimizeLatRes = TRUE,
title.color = "black",
groups = "lat",
pastel = TRUE,
exoVar = FALSE,
sizeInt = 5,
edge.color = "black",
esize = 6,
label.prop = 2,
sizeLat = 6,
"std"
)
semPaths(
fitref_bio_ind_sinedad,
intercepts = FALSE,
residuals = TRUE,
edge.label.cex = 1.5,
intStyle = "multi",
optimizeLatRes = TRUE,
title.color = "black",
groups = "lat",
pastel = TRUE,
exoVar = FALSE,
sizeInt = 5,
edge.color = "black",
esize = 6,
label.prop = 2,
sizeLat = 6,
"std"
)
graficos de mcmc
plot(fitref_bio_ind_sinedad, par = 1:12, facet_args = list(ncol = 4))
continuacion de graficos mcmc modelos ref
plot(fitref_bio_ind_sinedad,par = 13:20, facet_args = list(ncol = 4))
plot(fitref_bio_ind_sinedad,par = 21:30, facet_args = list(ncol = 4))
Grafico de intervalos todo modelos ref
plot(fitbase, plot.type = "intervals")
plot(fitref_bio, plot.type = "intervals")
plot(fitbase_bio_ind, plot.type = "intervals")
plot(fitbase_bio_ind_econ, plot.type = "intervals")
plot(fitref_bio_ind_sinedad, plot.type = "intervals")
Gráfico de coordenas paralelas de todos modelos ref
plot(fitbase, plot.type = "parcoord")
plot(fitref_bio, plot.type = "parcoord")
plot(fitbase_bio_ind, plot.type = "parcoord")
plot(fitbase_bio_ind_econ, plot.type = "parcoord")
plot(fitref_bio_ind_sinedad, plot.type = "parcoord")
Tabla de indices relevantes para describir el SEM
describe_posterior(fitref_bio_ind_sinedad)## Summary of Posterior Distribution
##
## Parameter | Component | Median | 95% CI | pd | ROPE | % in ROPE | Rhat | ESS
## -----------------------------------------------------------------------------------------------------------------------------
## funcionalidad=~f_c30 | latent | 1.43 | [ 1.15, 1.78] | 100% | [-0.10, 0.10] | 0% | 1.005 | 1350.00
## sintomas=~s_c30 | latent | 2.26 | [ 1.64, 3.35] | 100% | [-0.10, 0.10] | 0% | 1.003 | 1195.00
## biologicas=~comorb | latent | 0.10 | [-12.27, 12.65] | 51.63% | [-0.10, 0.10] | 3.23% | 1.001 | 1687.00
## biologicas=~estadio_avz | latent | -2.22 | [-20.85, 17.86] | 57.30% | [-0.10, 0.10] | 0.49% | 1.020 | 368.00
## cv_gral~salud | regression | 0.76 | [ 0.64, 0.90] | 100% | [-0.10, 0.10] | 0% | 1.006 | 1392.00
## cv_gral~sintomas | regression | -0.63 | [-13.02, 11.77] | 60.87% | [-0.10, 0.10] | 3.72% | 1.012 | 579.00
## cv_gral~funcionalidad | regression | 0.28 | [ -8.79, 7.54] | 56.93% | [-0.10, 0.10] | 4.84% | 1.012 | 563.00
## salud~funcionalidad | regression | 2.58 | [ 1.84, 3.33] | 100% | [-0.10, 0.10] | 0% | 1.003 | 2028.00
## funcionalidad~sintomas | regression | -1.48 | [ -2.24, -0.99] | 100% | [-0.10, 0.10] | 0% | 1.003 | 1183.00
## sintomas~biologicas | regression | 1.45 | [-15.02, 19.06] | 57.27% | [-0.10, 0.10] | 0.84% | 1.009 | 616.00
## f_br23~~f_br23 | residual | 0.15 | [ 0.10, 0.21] | 100% | [-0.10, 0.10] | 0% | 1.002 | 2971.00
## f_c30~~f_c30 | residual | 0.08 | [ 0.02, 0.13] | 100% | [-0.10, 0.10] | 83.02% | 1.001 | 1773.00
## s_br23~~s_br23 | residual | 0.20 | [ 0.14, 0.28] | 100% | [-0.10, 0.10] | 0% | 1.000 | 3465.00
## s_c30~~s_c30 | residual | 0.08 | [ 0.00, 0.14] | 100% | [-0.10, 0.10] | 76.01% | 1.005 | 1128.00
## her2_pos~~her2_pos | residual | 0.25 | [ 0.18, 0.34] | 100% | [-0.10, 0.10] | 0% | 0.999 | 3704.00
## comorb~~comorb | residual | 0.23 | [ 0.16, 0.31] | 100% | [-0.10, 0.10] | 0% | 0.999 | 3591.00
## estadio_avz~~estadio_avz | residual | 0.20 | [ 0.00, 0.28] | 100% | [-0.10, 0.10] | 13.43% | 1.020 | 250.00
## cv_gral~~cv_gral | residual | 0.44 | [ 0.22, 0.66] | 100% | [-0.10, 0.10] | 0% | 1.011 | 495.00
## salud~~salud | residual | 1.58 | [ 1.10, 2.16] | 100% | [-0.10, 0.10] | 0% | 0.999 | 3149.00
## funcionalidad~~funcionalidad | residual | 7.39e-03 | [ 0.00, 0.05] | 100% | [-0.10, 0.10] | 100% | 1.008 | 899.00
## sintomas~~sintomas | residual | 0.09 | [ 0.00, 0.17] | 100% | [-0.10, 0.10] | 65.59% | 1.004 | 1351.00
## biologicas~~biologicas | residual | 4.18e-04 | [ 0.00, 0.00] | 100% | [-0.10, 0.10] | 100% | 1.002 | 1086.00
## f_br23~1 | intercept | 2.34 | [ 2.21, 2.49] | 100% | [-0.10, 0.10] | 0% | 1.000 | 1102.00
## f_c30~1 | intercept | 2.44 | [ 2.26, 2.59] | 100% | [-0.10, 0.10] | 0% | 0.999 | 928.00
## s_br23~1 | intercept | 1.76 | [ 1.64, 1.88] | 100% | [-0.10, 0.10] | 0% | 1.000 | 1495.00
## s_c30~1 | intercept | 2.16 | [ 2.00, 2.34] | 100% | [-0.10, 0.10] | 0% | 1.000 | 941.00
## her2_pos~1 | intercept | 0.44 | [ 0.32, 0.55] | 100% | [-0.10, 0.10] | 0% | 1.000 | 3776.00
## comorb~1 | intercept | 0.34 | [ 0.23, 0.45] | 100% | [-0.10, 0.10] | 0% | 0.999 | 3731.00
## estadio_avz~1 | intercept | 0.34 | [ 0.23, 0.44] | 100% | [-0.10, 0.10] | 0% | 0.999 | 3702.00
## cv_gral~1 | intercept | 0.81 | [ 0.16, 1.34] | 99.50% | [-0.10, 0.10] | 0% | 1.006 | 1383.00
## salud~1 | intercept | 4.34 | [ 3.91, 4.72] | 100% | [-0.10, 0.10] | 0% | 1.000 | 1369.00sexit(fitref_bio_ind_sinedad)## # Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|.
##
## - funcionalidad=~f_c30 (Median = 1.43, 95% CI [1.15, 1.78]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 100.00% of being large (> 0.30)
## - sintomas=~s_c30 (Median = 2.26, 95% CI [1.64, 3.35]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 100.00% of being large (> 0.30)
## - biologicas=~comorb (Median = 0.10, 95% CI [-12.27, 12.65]) has a 51.63% probability of being positive (> 0), 50.87% of being significant (> 0.05), and 47.07% of being large (> 0.30)
## - biologicas=~estadio_avz (Median = -2.22, 95% CI [-20.85, 17.86]) has a 57.30% probability of being negative (< 0), 57.23% of being significant (< -0.05), and 56.53% of being large (< -0.30)
## - cv_gral~salud (Median = 0.76, 95% CI [0.64, 0.90]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 100.00% of being large (> 0.30)
## - cv_gral~sintomas (Median = -0.63, 95% CI [-13.02, 11.77]) has a 60.87% probability of being negative (< 0), 60.07% of being significant (< -0.05), and 55.63% of being large (< -0.30)
## - cv_gral~funcionalidad (Median = 0.28, 95% CI [-8.79, 7.54]) has a 56.93% probability of being positive (> 0), 55.77% of being significant (> 0.05), and 49.37% of being large (> 0.30)
## - salud~funcionalidad (Median = 2.58, 95% CI [1.84, 3.33]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 100.00% of being large (> 0.30)
## - funcionalidad~sintomas (Median = -1.48, 95% CI [-2.24, -0.99]) has a 100.00% probability of being negative (< 0), 100.00% of being significant (< -0.05), and 100.00% of being large (< -0.30)
## - sintomas~biologicas (Median = 1.45, 95% CI [-15.02, 19.06]) has a 57.27% probability of being positive (> 0), 57.07% of being significant (> 0.05), and 56.27% of being large (> 0.30)
## - f_br23~~f_br23 (Median = 0.15, 95% CI [0.10, 0.21]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 0.03% of being large (> 0.30)
## - f_c30~~f_c30 (Median = 0.08, 95% CI [0.02, 0.13]) has a 100.00% probability of being positive (> 0), 84.43% of being significant (> 0.05), and 0.00% of being large (> 0.30)
## - s_br23~~s_br23 (Median = 0.20, 95% CI [0.14, 0.28]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 1.40% of being large (> 0.30)
## - s_c30~~s_c30 (Median = 0.08, 95% CI [1.67e-06, 0.14]) has a 100.00% probability of being positive (> 0), 75.50% of being significant (> 0.05), and 0.00% of being large (> 0.30)
## - her2_pos~~her2_pos (Median = 0.25, 95% CI [0.18, 0.34]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 14.47% of being large (> 0.30)
## - comorb~~comorb (Median = 0.23, 95% CI [0.16, 0.31]) has a 100.00% probability of being positive (> 0), 99.97% of being significant (> 0.05), and 5.20% of being large (> 0.30)
## - estadio_avz~~estadio_avz (Median = 0.20, 95% CI [3.71e-07, 0.28]) has a 100.00% probability of being positive (> 0), 91.87% of being significant (> 0.05), and 1.90% of being large (> 0.30)
## - cv_gral~~cv_gral (Median = 0.44, 95% CI [0.22, 0.66]) has a 100.00% probability of being positive (> 0), 98.73% of being significant (> 0.05), and 92.60% of being large (> 0.30)
## - salud~~salud (Median = 1.58, 95% CI [1.10, 2.16]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 100.00% of being large (> 0.30)
## - funcionalidad~~funcionalidad (Median = 7.39e-03, 95% CI [1.19e-08, 0.05]) has a 100.00% probability of being positive (> 0), 4.80% of being significant (> 0.05), and 0.00% of being large (> 0.30)
## - sintomas~~sintomas (Median = 0.09, 95% CI [9.46e-06, 0.17]) has a 100.00% probability of being positive (> 0), 77.90% of being significant (> 0.05), and 0.00% of being large (> 0.30)
## - biologicas~~biologicas (Median = 4.18e-04, 95% CI [2.30e-11, 4.61e-03]) has a 100.00% probability of being positive (> 0), 0.00% of being significant (> 0.05), and 0.00% of being large (> 0.30)
## - f_br23~1 (Median = 2.34, 95% CI [2.21, 2.49]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 100.00% of being large (> 0.30)
## - f_c30~1 (Median = 2.44, 95% CI [2.26, 2.59]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 100.00% of being large (> 0.30)
## - s_br23~1 (Median = 1.76, 95% CI [1.64, 1.88]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 100.00% of being large (> 0.30)
## - s_c30~1 (Median = 2.16, 95% CI [2.00, 2.34]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 100.00% of being large (> 0.30)
## - her2_pos~1 (Median = 0.44, 95% CI [0.32, 0.55]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 98.90% of being large (> 0.30)
## - comorb~1 (Median = 0.34, 95% CI [0.23, 0.45]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 74.40% of being large (> 0.30)
## - estadio_avz~1 (Median = 0.34, 95% CI [0.23, 0.44]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 76.17% of being large (> 0.30)
## - cv_gral~1 (Median = 0.81, 95% CI [0.16, 1.34]) has a 99.50% probability of being positive (> 0), 99.17% of being significant (> 0.05), and 95.07% of being large (> 0.30)
## - salud~1 (Median = 4.34, 95% CI [3.91, 4.72]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 100.00% of being large (> 0.30)
##
## Parameter | Median | 95% CI | Direction | Significance (> |0.05|) | Large (> |0.30|)
## -----------------------------------------------------------------------------------------------------------------------
## funcionalidad=~f_c30 | 1.43 | [1.15, 1.78] | 1.00 | 1.00 | 1.00
## sintomas=~s_c30 | 2.26 | [1.64, 3.35] | 1.00 | 1.00 | 1.00
## biologicas=~comorb | 0.10 | [-12.27, 12.65] | 0.52 | 0.51 | 0.47
## biologicas=~estadio_avz | -2.22 | [-20.85, 17.86] | 0.57 | 0.57 | 0.57
## cv_gral~salud | 0.76 | [0.64, 0.90] | 1.00 | 1.00 | 1.00
## cv_gral~sintomas | -0.63 | [-13.02, 11.77] | 0.61 | 0.60 | 0.56
## cv_gral~funcionalidad | 0.28 | [-8.79, 7.54] | 0.57 | 0.56 | 0.49
## salud~funcionalidad | 2.58 | [1.84, 3.33] | 1.00 | 1.00 | 1.00
## funcionalidad~sintomas | -1.48 | [-2.24, -0.99] | 1.00 | 1.00 | 1.00
## sintomas~biologicas | 1.45 | [-15.02, 19.06] | 0.57 | 0.57 | 0.56
## f_br23~~f_br23 | 0.15 | [0.10, 0.21] | 1.00 | 1.00 | 3.33e-04
## f_c30~~f_c30 | 0.08 | [0.02, 0.13] | 1.00 | 0.84 | 0.00
## s_br23~~s_br23 | 0.20 | [0.14, 0.28] | 1.00 | 1.00 | 0.01
## s_c30~~s_c30 | 0.08 | [1.67e-06, 0.14] | 1.00 | 0.76 | 0.00
## her2_pos~~her2_pos | 0.25 | [0.18, 0.34] | 1.00 | 1.00 | 0.14
## comorb~~comorb | 0.23 | [0.16, 0.31] | 1.00 | 1.00 | 0.05
## estadio_avz~~estadio_avz | 0.20 | [3.71e-07, 0.28] | 1.00 | 0.92 | 0.02
## cv_gral~~cv_gral | 0.44 | [0.22, 0.66] | 1.00 | 0.99 | 0.93
## salud~~salud | 1.58 | [1.10, 2.16] | 1.00 | 1.00 | 1.00
## funcionalidad~~funcionalidad | 7.39e-03 | [1.19e-08, 0.05] | 1.00 | 0.05 | 0.00
## sintomas~~sintomas | 0.09 | [9.46e-06, 0.17] | 1.00 | 0.78 | 0.00
## biologicas~~biologicas | 4.18e-04 | [2.30e-11, 4.61e-03] | 1.00 | 0.00 | 0.00
## f_br23~1 | 2.34 | [2.21, 2.49] | 1.00 | 1.00 | 1.00
## f_c30~1 | 2.44 | [2.26, 2.59] | 1.00 | 1.00 | 1.00
## s_br23~1 | 1.76 | [1.64, 1.88] | 1.00 | 1.00 | 1.00
## s_c30~1 | 2.16 | [2.00, 2.34] | 1.00 | 1.00 | 1.00
## her2_pos~1 | 0.44 | [0.32, 0.55] | 1.00 | 1.00 | 0.99
## comorb~1 | 0.34 | [0.23, 0.45] | 1.00 | 1.00 | 0.74
## estadio_avz~1 | 0.34 | [0.23, 0.44] | 1.00 | 1.00 | 0.76
## cv_gral~1 | 0.81 | [0.16, 1.34] | 0.99 | 0.99 | 0.95
## salud~1 | 4.34 | [3.91, 4.72] | 1.00 | 1.00 | 1.00Comparacion de todos los modelos vs modelo de referencia
comparison <- bayesfactor_models(fitbase,fitref_bio,fitbase_bio_ind,fitbase_bio_ind_econ, denominator = fitref_bio_ind_sinedad)
comparison## Bayes Factors for Model Comparison
##
## Model BF
## [1] fitbase 8.40e-130
## [2] fitref_bio 8.08e-169
## [3] fitbase_bio_ind 5.33e-168
## [4] fitbase_bio_ind_econ 0.00e+00
##
## * Against Denominator: [5] fitref_bio_ind_sinedad
## * Bayes Factor Type: marginal likelihoods (Laplace approximation)mat_comp <- as.matrix(comparison)
heatmap(mat_comp)