library(ggplot2)
library(ggthemes)
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library(showtext)
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library(tidyverse)
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## v purrr 0.3.4
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library(lme4)
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library(car)
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#Ejercicio 1
#Cargar datos
Datos1<-read.csv("pacientes.csv", header=TRUE)
#Modelo lineal generalizado mixto
pacientes_cov <- glmer(Recuperacion_Postcovid~BhLinfocitosAbsolutos+edad+peso+(1|sexo),data=Datos1, family="binomial")
## boundary (singular) fit: see help('isSingular')
summary(pacientes_cov)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: Recuperacion_Postcovid ~ BhLinfocitosAbsolutos + edad + peso +
## (1 | sexo)
## Data: Datos1
##
## AIC BIC logLik deviance df.resid
## 87.0 100.1 -38.5 77.0 97
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.4773 -0.4104 -0.3758 -0.3257 3.5053
##
## Random effects:
## Groups Name Variance Std.Dev.
## sexo (Intercept) 0 0
## Number of obs: 102, groups: sexo, 2
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.1090463 2.2572238 -0.491 0.623
## BhLinfocitosAbsolutos -0.3600108 0.4414253 -0.816 0.415
## edad 0.0009554 0.0210235 0.045 0.964
## peso -0.0048660 0.0189265 -0.257 0.797
##
## Correlation of Fixed Effects:
## (Intr) BhLnfA edad
## BhLnfctsAbs -0.602
## edad -0.637 0.300
## peso -0.809 0.327 0.142
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
AIC(pacientes_cov)
## [1] 86.99109
#Modelo nulo
modelo.null <- glmer(Recuperacion_Postcovid ~ 1+(1|sexo), data=Datos1, family="binomial")
## boundary (singular) fit: see help('isSingular')
summary(modelo.null)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: Recuperacion_Postcovid ~ 1 + (1 | sexo)
## Data: Datos1
##
## AIC BIC logLik deviance df.resid
## 81.8 87.1 -38.9 77.8 100
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.3822 -0.3822 -0.3822 -0.3822 2.6165
##
## Random effects:
## Groups Name Variance Std.Dev.
## sexo (Intercept) 0 0
## Number of obs: 102, groups: sexo, 2
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.9237 0.2969 -6.479 9.24e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
AIC(modelo.null)
## [1] 81.8285
#AIC del modelo nulo es menor, no se encuentran diferencias significativas, no hay efecto
#Comparación de modelos con prueba ANOVA
anova(modelo.null, pacientes_cov, test = "Chi")
## Data: Datos1
## Models:
## modelo.null: Recuperacion_Postcovid ~ 1 + (1 | sexo)
## pacientes_cov: Recuperacion_Postcovid ~ BhLinfocitosAbsolutos + edad + peso + (1 | sexo)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## modelo.null 2 81.828 87.078 -38.914 77.828
## pacientes_cov 5 86.991 100.116 -38.496 76.991 0.8374 3 0.8405
#ANOVA de modelo mixto para obtener valores de P de variables
Anova(pacientes_cov)
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Recuperacion_Postcovid
## Chisq Df Pr(>Chisq)
## BhLinfocitosAbsolutos 0.6651 1 0.4147
## edad 0.0021 1 0.9638
## peso 0.0661 1 0.7971
#Gráfico de barras No. pacientes recuperados y no recuperados
recuperacion<-table(Datos1$Recuperacion_Postcovid)
recuperacion
##
## 0 1
## 89 13
windows()
barplot(recuperacion, main = "Pacientes con Síndrome \nPostcovid", sub = "", xlab = "Estado de pacientes", ylab = "No. de pacientes", xaxt = "n",
col = c("#009688", "#9E9E9E"))
legend(x = "topright", legend = c("Recuperado", "No recuperado"), fill = c("#009688", "#9E9E9E"),
title = "")
#Gráfico de barras No. pacientes recuperados y no recuperados y sexo de pacientes
sexo_recuperacion<-table(Datos1$Recuperacion_Postcovid, Datos1$sexo)
sexo_recuperacion
##
## 1 2
## 0 47 42
## 1 9 4
windows()
barplot(sexo_recuperacion, main = "Sexo de Pacientes con Síndrome Postcovid", xlab = "Sexo", ylab = "No. de Pacientes",
col = c("#009688", "#9E9E9E"))
legend(x = "topright", legend = c("Recuperado", "No recuperado"), fill = c("#009688", "#9E9E9E"),
title = "")
#Boxplot de Cantidad de Linfocitos en pacientes
windows()
ggplot(data = Datos1, aes(x = factor(Recuperacion_Postcovid), y = BhLinfocitosAbsolutos, fill=factor(Recuperacion_Postcovid))) +
geom_boxplot(outlier.color="#222424") +
scale_fill_manual(values=c("#009688", "#9E9E9E"), labels = c("Recuperado", "No recuperado")) +
geom_jitter(color="#222424", size=1, alpha=1) +
labs(title="Cantidad de linfocitos en pacientes \ncon Síndrome Post-covid",
x="Recuperación Post-covid", y="Linfocitos B") +
theme_few()+
theme(plot.title=element_text(face="bold", hjust = 0.5),
axis.text.x = element_text(size=rel(1.35)),
axis.title.x = element_text(face="bold", hjust=0.5, size=15),
axis.title.y = element_text(face="bold", hjust=0.5, size=15),
legend.title=element_text(face="bold", color="black", hjust=0.5))+
labs(fill="Pacientes con Síndrome \nPost-covid")+
scale_x_discrete(
"",
labels = c(
"0" = "Recuperado",
"1" = "No Recuperado"))
#Edad
windows()
ggplot(data = Datos1, aes(x = factor(Recuperacion_Postcovid), y = edad, fill=factor(Recuperacion_Postcovid))) +
geom_boxplot(outlier.color="#222424", shape=17) +
scale_fill_manual(values=c("#009688", "#9E9E9E"), labels = c("Recuperado", "No recuperado")) +
geom_jitter(color="#222424", size=1, alpha=1) +
labs(title="Edad de pacientes con \nSíndrome Post-covid",
x="Recuperación Post-covid", y="Edad en años") +
theme_few()+
theme(plot.title=element_text(face="bold", hjust = 0.5),
axis.text.x = element_text(size=rel(1.35)),
axis.title.x = element_text(face="bold", hjust=0.5, size=15),
axis.title.y = element_text(face="bold", hjust=0.5, size=15),
legend.title=element_text(face="bold", color="black", hjust=0.5))+
labs(fill="Pacientes con Síndrome \nPost-covid")+
scale_x_discrete(
"",
labels = c(
"0" = "Recuperado",
"1" = "No Recuperado"))
#Peso
windows()
ggplot(data = Datos1, aes(x = factor(Recuperacion_Postcovid), y = peso, fill=factor(Recuperacion_Postcovid))) +
geom_boxplot(outlier.color="gray") +
scale_fill_manual(values=c("#009688", "#9E9E9E"), labels = c("Recuperado", "No recuperado")) +
geom_jitter(color="#222424", size=1, alpha=1) +
labs(title="Peso de pacientes con \nSíndrome Post-covid",
x="Recuperación Post-covid", y="Peso en kg") +
theme_few()+
theme(plot.title=element_text(face="bold", hjust = 0.5),
axis.text.x = element_text(size=rel(1.35)),
axis.title.x = element_text(face="bold", hjust=0.5, size=15),
axis.title.y = element_text(face="bold", hjust=0.5, size=15),
legend.title=element_text(face="bold", color="black", hjust=0.5))+
labs(fill="Pacientes con Síndrome \nPost-covid")+
scale_x_discrete(
"",
labels = c(
"0" = "Recuperado",
"1" = "No Recuperado"))
