Percepción sobre la Desigualdad en el Acceso a la Salud
Autor: Claudio Intimayta-Escalante
Importanción y Preparación de ENADES
#Cargando Paquetes para Analisis
lapply(c("haven",
"devtools",
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"survey",
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"ordinal",
"knitr",
"AICcmodavg",
"gtsummary"),
require,
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## [1] TRUE#Importar Datos
DF <- read_dta("E:/Desigualdad_Ordinal_ENADES/OXFAM_IEP_ENADES_2024.dta")
#Filtrar Variables
Data <- subset(DF, select = c(P24,etnicidad,etnicidad2,edad,sexo,region,ur,hijos18,edu,ocupa,NSE1,P08,hogar,dep,ponderab,P16))#Etiquetar Variables
Data <- Data %>%
mutate(ETNICO = case_when(
etnicidad %in% 1:2 ~ 1,
etnicidad %in% 3:4 ~ 2,
etnicidad %in% 6:8 ~ 3,
etnicidad %in% 5 ~ 4,
etnicidad %in% 9:98 ~ 4,
etnicidad == 99 ~ NA_integer_,
TRUE ~ NA_real_
))
Data$"ETNICO" <- factor(Data$"ETNICO",
levels = c(1,2,3,4),
labels = c("Blanco_Mestizo","Quechua_Mestizo","Afroperuanos","Otros"))
Data <- Data %>%
mutate(GRUPO_ETARIO = case_when(
edad %in% 18:29 ~ 1,
edad %in% 30:64 ~ 2,
edad %in% 65:90 ~ 3,
TRUE ~ NA_real_
))
Data$"GRUPO_ETARIO" <- factor(Data$"GRUPO_ETARIO",
levels = c(1,2,3),
labels = c("18_29","30_64","65_mas"))
Data <- Data %>%
mutate(SEXO = case_when(
sexo %in% 2 ~ 0,
sexo %in% 1 ~ 1,
TRUE ~ NA_real_
))
Data$"SEXO" <- factor(Data$"SEXO",
levels = c(0,1),
labels = c("Femenino","Masculino"))
Data$"REGION" <- factor(Data$"region",
levels = c(1,2,3),
labels = c("Costa","Sierra","Selva"))
Data$"AREA" <- factor(Data$"ur",
levels = c(1,2),
labels = c("Urbano","Rural"))
Data$"HIJOS_MENORES" <- factor(Data$"hijos18",
levels = c(1,2),
labels = c("Si","No"))
Data <- Data %>%
mutate(EDUCA = case_when(
edu %in% 1:3 ~ 1,
edu %in% 4:5 ~ 2,
edu %in% 6:10 ~ 3,
TRUE ~ NA_real_
))
Data$"EDUCACION" <- factor(Data$"EDUCA",
levels = c(1,2,3),
labels = c("Sin_Edu_Prim","Secundaria","Superior"))
Data <- Data %>%
mutate(OCUPA = case_when(
ocupa %in% 1:2 ~ 1,
ocupa %in% 3:7 ~ 0,
ocupa %in% 8 ~ 1,
ocupa == 99 ~ NA_integer_,
TRUE ~ NA_real_
))
Data$"OCUPACION" <- factor(Data$"OCUPA",
levels = c(0,1),
labels = c("No","Si"))
Data$"RIQUEZA" <- factor(Data$"NSE1",
levels = c(1,2,3,4,5),
labels = c("A","B","C","D","E"))
Data <- Data %>%
mutate(INGRESOS = case_when(
P16 %in% 1:2 ~ 1,
P16 %in% 3:4 ~ 0,
P16 == 99 ~ NA_integer_,
TRUE ~ NA_real_
))
Data$"ALCANZA" <- factor(Data$"INGRESOS",
levels = c(0,1),
labels = c("No","Si"))
Data <- Data %>%
mutate(HOGAR = case_when(
hogar %in% 1:2 ~ 1,
hogar %in% 3:98 ~ 0,
hogar == 99 ~ NA_integer_,
TRUE ~ NA_real_
))
Data$"HACINAMIENTO" <- factor(Data$"HOGAR",
levels = c(0,1),
labels = c("No","Si"))
Data <- Data %>%
mutate(CAPITAL = case_when(
dep %in% 1:14 ~ 0,
dep %in% 15 ~ 1,
dep %in% 16:25 ~ 0,
TRUE ~ NA_real_
))
Data$"LIMA" <- factor(Data$"CAPITAL",
levels = c(0,1),
labels = c("Provincia","Lima"))
Data$"PONDERA" <- as.numeric(Data$"ponderab")
Data <- Data %>%
mutate(Acceso = case_when(
P24 %in% 1 ~ 3,
P24 %in% 2 ~ 2,
P24 %in% 3 ~ 1,
P24 %in% 4 ~ 0,
P24 == 99 ~ NA_integer_,
TRUE ~ NA_real_
))
Data$"ACCESO" <- factor(Data$"Acceso",
levels = c(0,1,2,3),
labels = c("Nada_Desigual","Poco_Desigual","Algo_Desigual","Muy_Desigual"))
Data$ID <- 1:nrow(Data)
Data$ID <- as.numeric(Data$ID)
Data$STRATA <- 1Filtro de la Base de Datos y Evaluacion General
Evaluación de valores faltantes y estructura de losd atos
#Filtro de Bases de Datos
DATA <- subset(Data, select = c(PONDERA,
LIMA,
HACINAMIENTO,
ALCANZA,
RIQUEZA,
OCUPACION,
EDUCACION,
HIJOS_MENORES,
AREA,
REGION,
SEXO,
GRUPO_ETARIO,
ETNICO,
ACCESO,
ID,
STRATA))
Design <- svydesign(id =~ ID, strata =~ STRATA, weights=~ PONDERA, data=DATA, nest=TRUE)
rm(Data,DF)
#Creación de Tabla Descriptiva Ponderada
tab1 <- svyCreateTableOne(factorVars = c("SEXO",
"GRUPO_ETARIO",
"EDUCACION",
"RIQUEZA",
"AREA",
"LIMA",
"OCUPACION",
"HIJOS_MENORES",
"ALCANZA",
"ETNICO",
"ACCESO"),
data = Design)
#Imprimir la Tabla Descriptiva
Tabla_1 <- print(tab1,
contDigits = 2,
catDigits = 2,
showAllLevels = TRUE,
quote = FALSE)##
## level Overall
## n 1508.00
## PONDERA (mean (SD)) 1.03 (0.18)
## LIMA (%) Provincia 944.96 (62.66)
## Lima 563.04 (37.34)
## HACINAMIENTO (%) No 1259.12 (83.73)
## Si 244.66 (16.27)
## ALCANZA (%) No 782.45 (53.74)
## Si 673.48 (46.26)
## RIQUEZA (%) A 27.29 ( 1.81)
## B 188.03 (12.47)
## C 441.21 (29.26)
## D 421.15 (27.93)
## E 430.34 (28.54)
## OCUPACION (%) No 571.77 (38.22)
## Si 924.28 (61.78)
## EDUCACION (%) Sin_Edu_Prim 237.90 (15.78)
## Secundaria 561.23 (37.22)
## Superior 708.88 (47.01)
## HIJOS_MENORES (%) Si 692.11 (45.90)
## No 815.89 (54.10)
## AREA (%) Urbano 1174.73 (77.90)
## Rural 333.27 (22.10)
## REGION (%) Costa 853.29 (56.58)
## Sierra 455.17 (30.18)
## Selva 199.53 (13.23)
## SEXO (%) Femenino 757.02 (50.20)
## Masculino 750.98 (49.80)
## GRUPO_ETARIO (%) 18_29 418.16 (27.73)
## 30_64 962.56 (63.83)
## 65_mas 127.27 ( 8.44)
## ETNICO (%) Blanco_Mestizo 1041.18 (72.49)
## Quechua_Mestizo 173.96 (12.11)
## Afroperuanos 53.29 ( 3.71)
## Otros 167.80 (11.68)
## ACCESO (%) Nada_Desigual 42.33 ( 2.85)
## Poco_Desigual 188.87 (12.70)
## Algo_Desigual 256.85 (17.27)
## Muy_Desigual 999.62 (67.19)
## ID (mean (SD)) 755.60 (436.38)
## STRATA (mean (SD)) 1.00 (0.00)rm(Tabla_1)
rm(tab1)
## PONDERA LIMA RIQUEZA EDUCACION HIJOS_MENORES AREA REGION SEXO GRUPO_ETARIO
## 1365 1 1 1 1 1 1 1 1 1
## 60 1 1 1 1 1 1 1 1 1
## 41 1 1 1 1 1 1 1 1 1
## 6 1 1 1 1 1 1 1 1 1
## 12 1 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1 1 1
## 5 1 1 1 1 1 1 1 1 1
## 2 1 1 1 1 1 1 1 1 1
## 11 1 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1 1 1
## 4 1 1 1 1 1 1 1 1 1
## 0 0 0 0 0 0 0 0 0
## ID STRATA HACINAMIENTO OCUPACION ACCESO ALCANZA ETNICO
## 1365 1 1 1 1 1 1 1 0
## 60 1 1 1 1 1 1 0 1
## 41 1 1 1 1 1 0 1 1
## 6 1 1 1 1 1 0 0 2
## 12 1 1 1 1 0 1 1 1
## 1 1 1 1 1 0 1 0 2
## 5 1 1 1 1 0 0 1 2
## 2 1 1 1 1 0 0 0 3
## 11 1 1 1 0 1 1 1 1
## 1 1 1 1 0 1 1 0 2
## 4 1 1 0 1 1 1 1 1
## 0 0 4 12 20 54 70 160Distribucion de Variables según Percepción de Desigualdad
Integración de factor de ponderación para evaluar distribución de datos entre categorias de la percepción sobre la desigualdad de acceso a la salud
#Tabla Bivariada
table_2A <- DATA %>%
tbl_summary(
include = c(SEXO,
GRUPO_ETARIO,
EDUCACION,
RIQUEZA,
AREA,
LIMA,
OCUPACION,
HIJOS_MENORES,
ALCANZA,
ETNICO),
by = ACCESO,
percent = "row",
statistic = list(
all_categorical() ~ "{n}/{N}"),
digits = everything() ~ 0
) ## 20 observations missing `ACCESO` have been removed. To include these observations, use `forcats::fct_na_value_to_level()` on `ACCESO` column before passing to `tbl_summary()`.table_2A| Characteristic | Nada_Desigual, N = 421 | Poco_Desigual, N = 1861 | Algo_Desigual, N = 2561 | Muy_Desigual, N = 1,0041 |
|---|---|---|---|---|
| SEXO | ||||
| Femenino | 26/762 | 98/762 | 148/762 | 490/762 |
| Masculino | 16/726 | 88/726 | 108/726 | 514/726 |
| GRUPO_ETARIO | ||||
| 18_29 | 16/393 | 66/393 | 89/393 | 222/393 |
| 30_64 | 26/972 | 108/972 | 145/972 | 693/972 |
| 65_mas | 0/123 | 12/123 | 22/123 | 89/123 |
| EDUCACION | ||||
| Sin_Edu_Prim | 8/224 | 53/224 | 32/224 | 131/224 |
| Secundaria | 25/549 | 77/549 | 107/549 | 340/549 |
| Superior | 9/715 | 56/715 | 117/715 | 533/715 |
| RIQUEZA | ||||
| A | 1/42 | 3/42 | 5/42 | 33/42 |
| B | 3/168 | 15/168 | 25/168 | 125/168 |
| C | 7/448 | 36/448 | 89/448 | 316/448 |
| D | 11/415 | 55/415 | 79/415 | 270/415 |
| E | 20/415 | 77/415 | 58/415 | 260/415 |
| AREA | ||||
| Urbano | 32/1,225 | 144/1,225 | 217/1,225 | 832/1,225 |
| Rural | 10/263 | 42/263 | 39/263 | 172/263 |
| LIMA | ||||
| Provincia | 28/939 | 146/939 | 166/939 | 599/939 |
| Lima | 14/549 | 40/549 | 90/549 | 405/549 |
| OCUPACION | ||||
| No | 13/572 | 73/572 | 110/572 | 376/572 |
| Si | 29/904 | 111/904 | 144/904 | 620/904 |
| Unknown | 0 | 2 | 2 | 8 |
| HIJOS_MENORES | ||||
| Si | 22/689 | 90/689 | 116/689 | 461/689 |
| No | 20/799 | 96/799 | 140/799 | 543/799 |
| ALCANZA | ||||
| No | 25/771 | 100/771 | 136/771 | 510/771 |
| Si | 16/670 | 76/670 | 108/670 | 470/670 |
| Unknown | 1 | 10 | 12 | 24 |
| ETNICO | ||||
| Blanco_Mestizo | 24/1,027 | 119/1,027 | 176/1,027 | 708/1,027 |
| Quechua_Mestizo | 9/174 | 22/174 | 25/174 | 118/174 |
| Afroperuanos | 2/53 | 8/53 | 8/53 | 35/53 |
| Otros | 7/167 | 27/167 | 30/167 | 103/167 |
| Unknown | 0 | 10 | 17 | 40 |
| 1 n/N | ||||
#Tabla Bivariada
Design <- update(Design, ACCESO = forcats::fct_na_value_to_level(ACCESO, level = "Missing"))
style_percent_2digits <- purrr::partial(gtsummary::style_percent, digits = 2)
table_2B <- Design %>%
tbl_svysummary(
include = c(SEXO,
GRUPO_ETARIO,
EDUCACION,
RIQUEZA,
AREA,
LIMA,
OCUPACION,
HIJOS_MENORES,
ALCANZA,
ETNICO),
by = ACCESO,
percent = "row",
statistic = list(gtsummary::all_categorical() ~ "{n} ({p}%)"),
digits = everything() ~ 2 # Apply to all columns
) ## Warning: There were 23 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `df_stats = pmap(...)`.
## Caused by warning in `svymean.survey.design2()`:
## ! Sample size greater than population size: are weights correctly scaled?
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 22 remaining warnings.table_2B| Characteristic | Nada_Desigual, N = 421 | Poco_Desigual, N = 1891 | Algo_Desigual, N = 2571 | Muy_Desigual, N = 1,0001 | Missing, N = 201 |
|---|---|---|---|---|---|
| SEXO | |||||
| Femenino | 26.18 (3.46%) | 98.46 (13.01%) | 146.82 (19.39%) | 472.88 (62.47%) | 12.67 (1.67%) |
| Masculino | 16.15 (2.15%) | 90.41 (12.04%) | 110.03 (14.65%) | 526.73 (70.14%) | 7.66 (1.02%) |
| GRUPO_ETARIO | |||||
| 18_29 | 16.43 (3.93%) | 71.79 (17.17%) | 93.53 (22.37%) | 232.53 (55.61%) | 3.88 (0.93%) |
| 30_64 | 25.91 (2.69%) | 105.21 (10.93%) | 142.63 (14.82%) | 677.90 (70.43%) | 10.92 (1.13%) |
| 65_mas | 0.00 (0.00%) | 11.86 (9.32%) | 20.70 (16.26%) | 89.18 (70.07%) | 5.53 (4.34%) |
| EDUCACION | |||||
| Sin_Edu_Prim | 8.09 (3.40%) | 54.20 (22.78%) | 32.96 (13.86%) | 137.93 (57.98%) | 4.72 (1.98%) |
| Secundaria | 25.39 (4.52%) | 79.49 (14.16%) | 107.58 (19.17%) | 337.51 (60.14%) | 11.27 (2.01%) |
| Superior | 8.86 (1.25%) | 55.18 (7.78%) | 116.32 (16.41%) | 524.18 (73.95%) | 4.34 (0.61%) |
| RIQUEZA | |||||
| A | 0.91 (3.32%) | 1.88 (6.90%) | 3.87 (14.18%) | 20.63 (75.60%) | 0.00 (0.00%) |
| B | 3.13 (1.66%) | 15.91 (8.46%) | 27.98 (14.88%) | 138.52 (73.67%) | 2.49 (1.32%) |
| C | 6.48 (1.47%) | 34.78 (7.88%) | 86.89 (19.69%) | 307.33 (69.66%) | 5.73 (1.30%) |
| D | 12.06 (2.86%) | 57.20 (13.58%) | 78.60 (18.66%) | 270.32 (64.19%) | 2.97 (0.71%) |
| E | 19.76 (4.59%) | 79.09 (18.38%) | 59.52 (13.83%) | 262.82 (61.07%) | 9.14 (2.12%) |
| AREA | |||||
| Urbano | 30.26 (2.58%) | 136.26 (11.60%) | 208.64 (17.76%) | 787.59 (67.04%) | 11.97 (1.02%) |
| Rural | 12.07 (3.62%) | 52.61 (15.78%) | 48.21 (14.47%) | 212.02 (63.62%) | 8.36 (2.51%) |
| LIMA | |||||
| Provincia | 28.67 (3.03%) | 147.89 (15.65%) | 162.16 (17.16%) | 595.57 (63.03%) | 10.68 (1.13%) |
| Lima | 13.66 (2.43%) | 40.98 (7.28%) | 94.70 (16.82%) | 404.05 (71.76%) | 9.65 (1.71%) |
| OCUPACION | |||||
| No | 13.48 (2.36%) | 72.35 (12.65%) | 107.94 (18.88%) | 370.89 (64.87%) | 7.12 (1.24%) |
| Si | 28.86 (3.12%) | 114.74 (12.41%) | 146.75 (15.88%) | 620.72 (67.16%) | 13.21 (1.43%) |
| Unknown | 0 | 2 | 2 | 8 | 0 |
| HIJOS_MENORES | |||||
| Si | 22.72 (3.28%) | 90.37 (13.06%) | 116.27 (16.80%) | 454.71 (65.70%) | 8.04 (1.16%) |
| No | 19.61 (2.40%) | 98.50 (12.07%) | 140.59 (17.23%) | 544.91 (66.79%) | 12.29 (1.51%) |
| ALCANZA | |||||
| No | 25.20 (3.22%) | 101.31 (12.95%) | 135.79 (17.35%) | 508.87 (65.04%) | 11.29 (1.44%) |
| Si | 16.16 (2.40%) | 78.16 (11.61%) | 108.73 (16.14%) | 468.22 (69.52%) | 2.22 (0.33%) |
| Unknown | 1 | 9 | 12 | 23 | 7 |
| ETNICO | |||||
| Blanco_Mestizo | 24.31 (2.33%) | 121.12 (11.63%) | 178.48 (17.14%) | 705.51 (67.76%) | 11.75 (1.13%) |
| Quechua_Mestizo | 9.21 (5.30%) | 22.50 (12.93%) | 24.36 (14.01%) | 115.70 (66.51%) | 2.18 (1.25%) |
| Afroperuanos | 2.03 (3.81%) | 7.63 (14.31%) | 7.03 (13.20%) | 35.74 (67.06%) | 0.86 (1.61%) |
| Otros | 6.78 (4.04%) | 26.33 (15.69%) | 30.01 (17.89%) | 102.00 (60.79%) | 2.67 (1.59%) |
| Unknown | 0 | 11 | 17 | 41 | 3 |
| 1 n (%) | |||||
#Modelo Ordinal General
polr_both = polr(ACCESO ~ ETNICO,
data = DATA,
weight = PONDERA)## Warning in eval(family$initialize): non-integer #successes in a binomial glm!df_both = data.frame(ETNICO = rep(c("Blanco_Mestizo", "Quechua_Mestizo","Afroperuanos","Otros"), each = 4),
ACCESO = rep(c(1:4), 4))
polr_both_probs = cbind(df_both, predict(polr_both, newdata = df_both,
type = "probs", se = TRUE))
polr_both_probs = polr_both_probs[,-2]
polr_both_probs = unique(polr_both_probs)
polr_both_probs_df = reshape2::melt(polr_both_probs, id.vars = "ETNICO",
variable.name = "ACCESO", value.name = "Probabilidad")
ggplot(polr_both_probs_df, aes(x = ACCESO, y = Probabilidad,
color = ETNICO, group = ETNICO)) +
geom_line() +
geom_point() +
xlab("¿Desigualdad en el Acceso a la Salud?")
Desarrollo de Modelos de Regresión Ordinal
Desarrollo de Modelo Ordinal con todas las Variables y filtrando algunas
"Modelo Total"## [1] "Modelo Total"Model_Total = polr(ACCESO ~ factor(SEXO)+
factor(GRUPO_ETARIO)+
factor(EDUCACION)+
factor(RIQUEZA)+
factor(REGION)+
factor(AREA)+
factor(LIMA)+
factor(HACINAMIENTO)+
factor(HIJOS_MENORES)+
factor(OCUPACION)+
factor(ALCANZA)+
factor(ETNICO),
data = DATA, Hess = TRUE,
weight = PONDERA)## Warning in eval(family$initialize): non-integer #successes in a binomial glm!ctable <- coef(summary(Model_Total))
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
ctable <- cbind(ctable, "p value" = p)
OR <- exp(coef(Model_Total))
OR_CI <- exp(cbind(OR = coef(Model_Total), confint(Model_Total)))## Waiting for profiling to be done...result_table <- cbind(OR_CI, "p value" = p)## Warning in base::cbind(...): number of rows of result is not a multiple of
## vector length (arg 2)result_table## OR 2.5 % 97.5 % p value
## factor(SEXO)Masculino 1.3972183 1.0932044 1.787362 7.625548e-03
## factor(GRUPO_ETARIO)30_64 2.1011545 1.6141560 2.735379 3.373935e-08
## factor(GRUPO_ETARIO)65_mas 2.8830773 1.7181519 4.972935 8.976861e-05
## factor(EDUCACION)Secundaria 1.6346841 1.1383046 2.342717 7.556395e-03
## factor(EDUCACION)Superior 2.9815280 1.9770936 4.496224 1.830769e-07
## factor(RIQUEZA)B 0.9524831 0.3443569 2.344360 9.196783e-01
## factor(RIQUEZA)C 1.0805980 0.4018941 2.559905 8.677528e-01
## factor(RIQUEZA)D 0.8904107 0.3258479 2.155534 8.070968e-01
## factor(RIQUEZA)E 0.8877639 0.3163661 2.224996 8.085503e-01
## factor(REGION)Sierra 1.1367297 0.8156218 1.583964 4.488124e-01
## factor(REGION)Selva 0.9317406 0.6261868 1.390999 7.281632e-01
## factor(AREA)Rural 1.3256678 0.9581391 1.841999 9.064974e-02
## factor(LIMA)Lima 1.4881204 1.0738619 2.060951 1.675975e-02
## factor(HACINAMIENTO)Si 1.2497423 0.8956721 1.763071 1.962622e-01
## factor(HIJOS_MENORES)No 1.0681422 0.8251389 1.383336 6.168273e-01
## factor(OCUPACION)Si 0.9759538 0.7589351 1.253030 8.490131e-01
## factor(ALCANZA)Si 0.8292060 0.6271216 1.095288 1.877280e-01
## factor(ETNICO)Quechua_Mestizo 0.9894543 0.6791221 1.455389 9.564621e-01
## factor(ETNICO)Afroperuanos 1.1377730 0.6023586 2.270954 7.011842e-01
## factor(ETNICO)Otros 0.9039804 0.6299099 1.309986 5.883945e-01"Modelo Parcial - Sin Hacinamiento o Region"## [1] "Modelo Parcial - Sin Hacinamiento o Region"Model_Parcial_1 = polr(ACCESO ~ factor(SEXO)+
factor(GRUPO_ETARIO)+
factor(EDUCACION)+
factor(RIQUEZA)+
factor(AREA)+
factor(LIMA)+
factor(HIJOS_MENORES)+
factor(OCUPACION)+
factor(ALCANZA)+
factor(ETNICO),
data = DATA, Hess = TRUE,
weight = PONDERA)## Warning in eval(family$initialize): non-integer #successes in a binomial glm!ctable <- coef(summary(Model_Total))
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
ctable <- cbind(ctable, "p value" = p)
OR <- exp(coef(Model_Total))
OR_CI <- exp(cbind(OR = coef(Model_Total), confint(Model_Total)))## Waiting for profiling to be done...result_table <- cbind(OR_CI, "p value" = p)## Warning in base::cbind(...): number of rows of result is not a multiple of
## vector length (arg 2)result_table## OR 2.5 % 97.5 % p value
## factor(SEXO)Masculino 1.3972183 1.0932044 1.787362 7.625548e-03
## factor(GRUPO_ETARIO)30_64 2.1011545 1.6141560 2.735379 3.373935e-08
## factor(GRUPO_ETARIO)65_mas 2.8830773 1.7181519 4.972935 8.976861e-05
## factor(EDUCACION)Secundaria 1.6346841 1.1383046 2.342717 7.556395e-03
## factor(EDUCACION)Superior 2.9815280 1.9770936 4.496224 1.830769e-07
## factor(RIQUEZA)B 0.9524831 0.3443569 2.344360 9.196783e-01
## factor(RIQUEZA)C 1.0805980 0.4018941 2.559905 8.677528e-01
## factor(RIQUEZA)D 0.8904107 0.3258479 2.155534 8.070968e-01
## factor(RIQUEZA)E 0.8877639 0.3163661 2.224996 8.085503e-01
## factor(REGION)Sierra 1.1367297 0.8156218 1.583964 4.488124e-01
## factor(REGION)Selva 0.9317406 0.6261868 1.390999 7.281632e-01
## factor(AREA)Rural 1.3256678 0.9581391 1.841999 9.064974e-02
## factor(LIMA)Lima 1.4881204 1.0738619 2.060951 1.675975e-02
## factor(HACINAMIENTO)Si 1.2497423 0.8956721 1.763071 1.962622e-01
## factor(HIJOS_MENORES)No 1.0681422 0.8251389 1.383336 6.168273e-01
## factor(OCUPACION)Si 0.9759538 0.7589351 1.253030 8.490131e-01
## factor(ALCANZA)Si 0.8292060 0.6271216 1.095288 1.877280e-01
## factor(ETNICO)Quechua_Mestizo 0.9894543 0.6791221 1.455389 9.564621e-01
## factor(ETNICO)Afroperuanos 1.1377730 0.6023586 2.270954 7.011842e-01
## factor(ETNICO)Otros 0.9039804 0.6299099 1.309986 5.883945e-01"Modelo Parcial - Sin Hacinamiento, Region, Ocupacion o Alcanza"## [1] "Modelo Parcial - Sin Hacinamiento, Region, Ocupacion o Alcanza"Model_Parcial_2 = polr(ACCESO ~ factor(SEXO)+
factor(GRUPO_ETARIO)+
factor(EDUCACION)+
factor(RIQUEZA)+
factor(AREA)+
factor(LIMA)+
factor(HIJOS_MENORES)+
factor(ETNICO),
data = DATA, Hess = TRUE,
weight = PONDERA)## Warning in eval(family$initialize): non-integer #successes in a binomial glm!ctable <- coef(summary(Model_Total))
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
ctable <- cbind(ctable, "p value" = p)
OR <- exp(coef(Model_Total))
OR_CI <- exp(cbind(OR = coef(Model_Total), confint(Model_Total)))## Waiting for profiling to be done...result_table <- cbind(OR_CI, "p value" = p)## Warning in base::cbind(...): number of rows of result is not a multiple of
## vector length (arg 2)result_table## OR 2.5 % 97.5 % p value
## factor(SEXO)Masculino 1.3972183 1.0932044 1.787362 7.625548e-03
## factor(GRUPO_ETARIO)30_64 2.1011545 1.6141560 2.735379 3.373935e-08
## factor(GRUPO_ETARIO)65_mas 2.8830773 1.7181519 4.972935 8.976861e-05
## factor(EDUCACION)Secundaria 1.6346841 1.1383046 2.342717 7.556395e-03
## factor(EDUCACION)Superior 2.9815280 1.9770936 4.496224 1.830769e-07
## factor(RIQUEZA)B 0.9524831 0.3443569 2.344360 9.196783e-01
## factor(RIQUEZA)C 1.0805980 0.4018941 2.559905 8.677528e-01
## factor(RIQUEZA)D 0.8904107 0.3258479 2.155534 8.070968e-01
## factor(RIQUEZA)E 0.8877639 0.3163661 2.224996 8.085503e-01
## factor(REGION)Sierra 1.1367297 0.8156218 1.583964 4.488124e-01
## factor(REGION)Selva 0.9317406 0.6261868 1.390999 7.281632e-01
## factor(AREA)Rural 1.3256678 0.9581391 1.841999 9.064974e-02
## factor(LIMA)Lima 1.4881204 1.0738619 2.060951 1.675975e-02
## factor(HACINAMIENTO)Si 1.2497423 0.8956721 1.763071 1.962622e-01
## factor(HIJOS_MENORES)No 1.0681422 0.8251389 1.383336 6.168273e-01
## factor(OCUPACION)Si 0.9759538 0.7589351 1.253030 8.490131e-01
## factor(ALCANZA)Si 0.8292060 0.6271216 1.095288 1.877280e-01
## factor(ETNICO)Quechua_Mestizo 0.9894543 0.6791221 1.455389 9.564621e-01
## factor(ETNICO)Afroperuanos 1.1377730 0.6023586 2.270954 7.011842e-01
## factor(ETNICO)Otros 0.9039804 0.6299099 1.309986 5.883945e-01Comparación de Modelos de Regresión
Create the figure in the solution for Problem 5, using the data included in the R Markdown file.
Modelo_Nulo = clm(ACCESO ~ 1, data=DATA)
Model_Parcial_2 = clm(ACCESO ~ factor(SEXO)+
factor(GRUPO_ETARIO)+
factor(EDUCACION)+
factor(RIQUEZA)+
factor(AREA)+
factor(LIMA)+
factor(HIJOS_MENORES)+
factor(ETNICO),
data = DATA,
weight = PONDERA)
Model_Parcial_1 = clm(ACCESO ~ factor(SEXO)+
factor(GRUPO_ETARIO)+
factor(EDUCACION)+
factor(RIQUEZA)+
factor(AREA)+
factor(LIMA)+
factor(HIJOS_MENORES)+
factor(OCUPACION)+
factor(ALCANZA)+
factor(ETNICO),
data = DATA,
weight = PONDERA)
Model_Total = clm(ACCESO ~ factor(SEXO)+
factor(GRUPO_ETARIO)+
factor(EDUCACION)+
factor(RIQUEZA)+
factor(REGION)+
factor(AREA)+
factor(LIMA)+
factor(HACINAMIENTO)+
factor(HIJOS_MENORES)+
factor(OCUPACION)+
factor(ALCANZA)+
factor(ETNICO),
data = DATA,
weight = PONDERA)
mod_set = list()
mod_set[[1]] = Modelo_Nulo
mod_set[[2]] = Model_Parcial_1
mod_set[[3]] = Model_Parcial_2
mod_set[[4]] = Model_Total
kable(aictab(mod_set, modnames = c("Model Total","Model Parcial 2","Model Parcial 1", "Modelo Nulo")))| Modnames | K | AICc | Delta_AICc | ModelLik | AICcWt | LL | Cum.Wt | |
|---|---|---|---|---|---|---|---|---|
| 4 | Modelo Nulo | 23 | 2458.779 | 0.00000 | 1.0000000 | 0.9937123 | -1205.978 | 0.9937123 |
| 2 | Model Parcial 2 | 20 | 2468.904 | 10.12569 | 0.0063275 | 0.0062877 | -1214.141 | 1.0000000 |
| 3 | Model Parcial 1 | 18 | 2574.205 | 115.42636 | 0.0000000 | 0.0000000 | -1268.859 | 1.0000000 |
| 1 | Model Total | 3 | 2770.395 | 311.61627 | 0.0000000 | 0.0000000 | -1382.189 | 1.0000000 |
## [1] "Imputación del Modelo Total"##
## iter imp variable
## 1 1 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 1 2 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 1 3 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 1 4 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 1 5 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 2 1 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 2 2 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 2 3 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 2 4 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 2 5 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 3 1 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 3 2 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 3 3 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 3 4 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 3 5 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 4 1 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 4 2 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 4 3 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 4 4 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 4 5 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 5 1 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 5 2 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 5 3 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 5 4 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO
## 5 5 HACINAMIENTO ALCANZA OCUPACION ETNICO ACCESO## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!## Term Odds_Ratio Lower.95..CI Upper.95..CI
## 1 factor(SEXO)Masculino 1.3972183 1.09284100 1.7863705
## 2 factor(GRUPO_ETARIO)30_64 2.1011545 1.61428178 2.7348697
## 3 factor(GRUPO_ETARIO)65_mas 2.8830773 1.69720590 4.8975405
## 4 factor(EDUCACION)Secundaria 1.6346841 1.13980494 2.3444293
## 5 factor(EDUCACION)Superior 2.9815280 1.97766107 4.4949610
## 6 factor(RIQUEZA)B 0.9524831 0.36974937 2.4536190
## 7 factor(RIQUEZA)C 1.0805980 0.43391995 2.6910312
## 8 factor(RIQUEZA)D 0.8904107 0.35070946 2.2606496
## 9 factor(RIQUEZA)E 0.8877639 0.33889280 2.3255870
## 10 factor(REGION)Sierra 1.1367297 0.81588099 1.5837534
## 11 factor(REGION)Selva 0.9317406 0.62538157 1.3881775
## 12 factor(AREA)Rural 1.3256678 0.95632523 1.8376542
## 13 factor(LIMA)Lima 1.4881204 1.07442229 2.0611097
## 14 factor(HACINAMIENTO)Si 1.2497423 0.89119404 1.7525430
## 15 factor(HIJOS_MENORES)No 1.0681422 0.82505337 1.3828533
## 16 factor(OCUPACION)Si 0.9759538 0.75962632 1.2538873
## 17 factor(ALCANZA)Si 0.8292060 0.62754322 1.0956736
## 18 factor(ETNICO)Quechua_Mestizo 0.9894543 0.67623353 1.4477542
## 19 factor(ETNICO)Afroperuanos 1.1377730 0.58847945 2.1997836
## 20 factor(ETNICO)Otros 0.9039804 0.62715479 1.3029966
## 21 Nada_Desigual|Poco_Desigual 0.1267112 0.04163081 0.3856693
## 22 Poco_Desigual|Algo_Desigual 0.7856338 0.26633754 2.3174371
## 23 Algo_Desigual|Muy_Desigual 2.1805972 0.73904776 6.4339605
## P_Value
## 1 7.717581e-03
## 2 4.047152e-08
## 3 9.429287e-05
## 4 7.647970e-03
## 5 2.119012e-07
## 6 9.196934e-01
## 7 8.677779e-01
## 8 8.071342e-01
## 9 8.085874e-01
## 10 4.489456e-01
## 11 7.282178e-01
## 12 9.088236e-02
## 13 1.689698e-02
## 14 1.964851e-01
## 15 6.169095e-01
## 16 8.490419e-01
## 17 1.879533e-01
## 18 9.564702e-01
## 19 7.012451e-01
## 20 5.884845e-01
## 21 2.854894e-04
## 22 6.620719e-01
## 23 1.581165e-01