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}); Cargamos los datos
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 652814 34.9 1332068 71.2 879926 47.0
Vcells 1170238 9.0 8388608 64.0 2075161 15.9Show code
load("data2_lca215_sin_po_alt_ano_2023_05_14.RData")Cargamos los paquetes
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knitr::opts_chunk$set(echo = TRUE)
if(!require(poLCA)){install.packages("poLCA")}
if(!require(poLCAParallel)){devtools::install_github("QMUL/poLCAParallel@package")}
if(!require(compareGroups)){install.packages("compareGroups")}
if(!require(parallel)){install.packages("parallel")}
if(!require(Hmisc)){install.packages("Hmisc")}
if(!require(tidyverse)){install.packages("tidyverse")}
try(if(!require(sjPlot)){install.packages("sjPlot")})
if(!require(emmeans)){install.packages("emmeans")}
if(!require(nnet)){install.packages("nnet")}
if(!require(here)){install.packages("here")}
if(!require(doParallel)){install.packages("doParallel")}
if(!require(progress)){install.packages("progress")}
if(!require(caret)){install.packages("caret")}
if(!require(rpart)){install.packages("rpart")}
if(!require(rpart.plot)){install.packages("rpart.plot")}
if(!require(partykit)){install.packages("partykit")}
if(!require(randomForest)){install.packages("randomForest")}
if(!require(ggcorrplot)){install.packages("ggcorrplot")}
if(!require(polycor)){install.packages("polycor")}
if(!require(tableone)){install.packages("tableone")}
if(!require(broom)){install.packages("broom")}
if(!require(plotly)){install.packages("plotly")}
if(!require(rsvg)){install.packages("rsvg")}
if(!require(DiagrammeRsvg)){install.packages("DiagrammeRsvg")}
if(!require(glca)){install.packages("glca")}
#if(!require(poLCA)){githubinstall::gh_install_packages("poLCA", ref = github_pull("14"))}
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:Definimos ciertas constantes
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clus_iter= 500 #500
n_thread <- parallel::detectCores()
nrep <- clus_iter # number of different initial values (could be n_thread too)
n_class_max <- 10 # maximum number of classes to investigate
n_bootstrap <- 100 #30 # 50 number of bootstrap samples
print(n_thread)[1] 8Análisis de clases latentes
Especificar modelo
Los valores ceros se declararon datos perdidos.
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f_adj<- item(CAUSAL, EDAD_MUJER_REC, PAIS_ORIGEN_REC, HITO1_EDAD_GEST_SEM_REC, MACROZONA, PREV_TRAMO_REC) ~ outcomeAnálisis de clases latentes
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#Biemer, P. P., & Wiesen, C. (2002). Measurement error evaluation of self-reported drug use: a latent class analysis of the US National Household Survey on Drug Abuse. Journal of the Royal Statistical Society: Series A (Statistics in Society), 165(1), 97–119. doi:10.1111/1467-985x.00612
#lca_entropia(x="ppio", seed= 2125, k= 8, f= f_preds, dat= mydata_preds, nbr_repet= 30, na_rm= T)
#3
#<div style="border: 1px solid #ddd; padding: 5px; overflow-y: scroll; height:400px; overflow-x: scroll; width:100%">
# f is the selected variables
# dat is the data
# nb_var is the number of selected variables
# k is the number of latent class generated
# nbr_repet is the number of repetition to
# reach the convergence of EM algorithm
# x es el código para las variables de los modelos
#seed es el numero random para las semillas. ej: 4345.
#Modo de calcular el mejor modelo.
#z_ #
#2023-01-20
#https://github.com/QMUL/poLCAParallel/blob/master/exec/3_blrt.R
#0h s
seed<-2125
old <- Sys.time()
lca22 <- glca(f_adj, data = mydata_preds3, nclass = 2, seed = seed, verbose = FALSE, n.init = 5e2, decreasing=T, maxiter = 1e4,testiter = 500)
lca23 <- glca(f_adj, data = mydata_preds3, nclass = 3, seed = seed, verbose = FALSE, n.init = 5e2, decreasing=T, maxiter = 1e4,testiter = 500)
lca24 <- glca(f_adj, data = mydata_preds3, nclass = 4, seed = seed, verbose = FALSE, n.init = 5e2, decreasing=T, maxiter = 1e4,testiter = 500)
lca25 <- glca(f_adj, data = mydata_preds3, nclass = 5, seed = seed, verbose = FALSE, n.init = 5e2, decreasing=T, maxiter = 1e4,testiter = 500)
lca26 <- glca(f_adj, data = mydata_preds3, nclass = 6, seed = seed, verbose = FALSE, n.init = 5e2, decreasing=T, maxiter = 1e4,testiter = 500)
lca27 <- glca(f_adj, data = mydata_preds3, nclass = 7, seed = seed, verbose = FALSE, n.init = 5e2, decreasing=T, maxiter = 1e4,testiter = 500)
lca28 <- glca(f_adj, data = mydata_preds3, nclass = 8, seed = seed, verbose = FALSE, n.init = 5e2, decreasing=T, maxiter = 1e4,testiter = 500)
lca29 <- glca(f_adj, data = mydata_preds3, nclass = 9, seed = seed, verbose = FALSE, n.init = 5e2, decreasing=T, maxiter = 1e4,testiter = 500)
lca210 <- glca(f_adj, data = mydata_preds3, nclass = 10, seed = seed, verbose = FALSE, n.init = 5e2, decreasing=T, maxiter = 1e4,testiter = 500)
new_med<-(Sys.time())
paste0("The model took ",round(new_med-old,2)," until every LCA was computed")[1] "The model took 1.55 until every LCA was computed"Luego calculamos la razón de verosimilitud mediante remuestreo bootstrap (BLRT) entre los distintos modelos con el que asume una clase menos.
Resultados
Hicimos un gráfico de los resultados
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manualcolors <- c('indianred1', 'cornflowerblue', 'gray50', 'darkolivegreen4', 'slateblue2',
'firebrick4', 'goldenrod4')
levels4 <- c("logLik", "Gsq", "AIC", "CAIC", "BIC", "entropy", "Res.Df")
labels4 <- c('Log-Verosimilitud', 'Chi2', 'Criterio de Información\nde Akaike(AIC)'','AIC Corregido'','Criterio de Información\nBayesiano (BIC)')''Entropía'a'Grados de libertad residuales')s')
fig_lca_fit4<- cbind.data.frame(rn=2:10,gof2$gtable) %>%
data.frame() %>%
dplyr::mutate_if(is.character, as.numeric) %>% # convert character columns to numeric
tidyr::pivot_longer(cols = -rn,names_to = "indices", values_to = "value", values_drop_na = F) %>%
dplyr::mutate(indices = factor(indices, levels = levels4, labels = labels4)) %>%
dplyr::filter(grepl("(AIC|BIC)",indices, ignore.case=T))%>%
dplyr::mutate(ModelIndex= factor(rn, levels=2:10)) %>%
ggplot(aes(x = ModelIndex, y = value, group = indices, color = indices, linetype = indices)) +
geom_line(size = 1.5) +
scale_color_manual(values = manualcolors) +
#scale_linetype_manual(values = c("solid", "dashed", "dotted")) +
labs(x = "Número de clases"", y=="Valor"", color=="Medida"", linetype=="Medida"))++
#facet_wrap(.~indices, scales = "free_y", nrow = 4, ncol = 1) +
theme_bw()
fig_lca_fit4
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ggsave("_fig1_comparison_glca_sin_po_ano.png",fig_lca_fit4, dpi=600)
#International Journal of Workplace Health Management (Zhang et al., 2018).Luego en una tabla
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cbind.data.frame(rn=2:10,gof2$gtable) %>%#
dplyr::select(rn, everything()) %>%
dplyr::mutate_if(is.character, as.numeric) %>% # convert character columns to numeric
knitr::kable(format="markdown", caption="Índices de ajuste modelos")| rn | logLik | AIC | CAIC | BIC | entropy | Res.Df | Gsq |
|---|---|---|---|---|---|---|---|
| 2 | -22706.84 | 45497.68 | 45801.76 | 45759.76 | 0.9903695 | 3746 | 3536.090 |
| 3 | -22464.96 | 45057.91 | 45521.26 | 45457.26 | 0.8901938 | 3724 | 3052.317 |
| 4 | -22282.17 | 44736.34 | 45358.97 | 45272.97 | 0.9097230 | 3702 | 2686.744 |
| 5 | -22149.63 | 44515.27 | 45297.17 | 45189.17 | 0.9225448 | 3680 | 2421.673 |
| 6 | -22062.06 | 44384.11 | 45325.29 | 45195.29 | 0.8627183 | 3658 | 2246.516 |
| 7 | -22003.44 | 44310.89 | 45411.35 | 45259.35 | 0.8536486 | 3636 | 2129.293 |
| 8 | -21950.00 | 44248.00 | 45507.74 | 45333.74 | 0.7837666 | 3614 | 2022.410 |
| 9 | -21900.74 | 44193.49 | 45612.50 | 45416.50 | 0.7559552 | 3592 | 1923.894 |
| 10 | -21868.96 | 44173.91 | 45752.20 | 45534.20 | 0.7437696 | 3570 | 1860.319 |
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best_model2<-
as.numeric(cbind.data.frame(rn=2:10,gof2$gtable) %>% dplyr::summarise(which.min(BIC)+1))Presentamos el modelo con mejor ajuste
Call:
glca(formula = f_adj, data = mydata_preds3, nclass = 5, n.init = 500,
decreasing = T, testiter = 500, maxiter = 10000, seed = seed,
verbose = FALSE)
Manifest items : CAUSAL EDAD_MUJER_REC PAIS_ORIGEN_REC HITO1_EDAD_GEST_SEM_REC MACROZONA PREV_TRAMO_REC
Covariates (Level 1) : outcome
Categories for manifest items :
Y = 1 Y = 2 Y = 3 Y = 4 Y = 5 Y = 6
CAUSAL 2 3 4
EDAD_MUJER_REC 1 2 3 4 5
PAIS_ORIGEN_REC 1 2 3
HITO1_EDAD_GEST_SEM_REC 1 2 3 4
MACROZONA 1 2 3 4 5 6
PREV_TRAMO_REC 1 2 3 4 5
Model : Latent class analysis
Number of latent classes : 5
Number of observations : 3789
Number of parameters : 108
log-likelihood : -22149.63
G-squared : 2421.673
AIC : 44515.27
BIC : 45189.17
Marginal prevalences for latent classes :
Class 1 Class 2 Class 3 Class 4 Class 5
0.12504 0.54687 0.13337 0.04254 0.15218
Class prevalences by group :
Class 1 Class 2 Class 3 Class 4 Class 5
ALL 0.12504 0.54687 0.13337 0.04254 0.15218
Logistic regression coefficients :
Class 1/5 Class 2/5 Class 3/5 Class 4/5
(Intercept) 0.4116 2.2509 -0.6620 -1.5768
outcome1 -0.6863 -1.1317 0.5664 0.3254
Item-response probabilities :
CAUSAL
Y = 1 Y = 2 Y = 3
Class 1 0.4626 0.5374 0.0000
Class 2 0.4068 0.5932 0.0000
Class 3 0.2021 0.7979 0.0000
Class 4 0.0245 0.0000 0.9755
Class 5 0.0049 0.0000 0.9951
EDAD_MUJER_REC
Y = 1 Y = 2 Y = 3 Y = 4 Y = 5
Class 1 0.0000 0.0089 0.1746 0.5022 0.3142
Class 2 0.0082 0.0181 0.2114 0.4563 0.3060
Class 3 0.0000 0.0000 0.0392 0.4345 0.5262
Class 4 0.0000 0.1647 0.2332 0.4514 0.1507
Class 5 0.0017 0.3483 0.2460 0.2951 0.1089
PAIS_ORIGEN_REC
Y = 1 Y = 2 Y = 3
Class 1 0.0121 0.0001 0.9878
Class 2 0.0059 0.9941 0.0000
Class 3 0.0000 0.9069 0.0931
Class 4 0.0000 0.0007 0.9993
Class 5 0.0000 0.9932 0.0068
HITO1_EDAD_GEST_SEM_REC
Y = 1 Y = 2 Y = 3 Y = 4
Class 1 0.0204 0.1142 0.7360 0.1294
Class 2 0.0290 0.1733 0.6603 0.1374
Class 3 0.0181 0.3704 0.5720 0.0396
Class 4 0.0192 0.9808 0.0000 0.0000
Class 5 0.0088 0.9877 0.0035 0.0000
MACROZONA
Y = 1 Y = 2 Y = 3 Y = 4 Y = 5 Y = 6
Class 1 0.0000 0.5660 0.1060 0.0623 0.2147 0.0510
Class 2 0.0038 0.3205 0.1918 0.1998 0.0949 0.1893
Class 3 0.0000 0.7205 0.0937 0.0498 0.0700 0.0660
Class 4 0.0062 0.5563 0.0870 0.0165 0.2956 0.0384
Class 5 0.0038 0.3850 0.1751 0.1452 0.0862 0.2047
PREV_TRAMO_REC
Y = 1 Y = 2 Y = 3 Y = 4 Y = 5
Class 1 0.0098 0.0007 0.6260 0.2819 0.0817
Class 2 0.0020 0.0305 0.6254 0.3384 0.0037
Class 3 0.0000 0.7586 0.0043 0.2281 0.0091
Class 4 0.0256 0.0061 0.5214 0.1672 0.2796
Class 5 0.0018 0.0696 0.7237 0.1997 0.0053Show code
save.image("data2_lca2_adj216_alt_sin_po_ano.RData")Show code
require(tidyverse)
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))