Ejercicios 13.8-13.14

Author

González García AY

Bases utilizadas

library(readxl)
Non_Parametric <- read_excel("Bases/Bases de datos/Non_Parametric.xlsx")
View(Non_Parametric)
library("RColorBrewer")

Resolución de ejercicio 13.8

#prueba de U-mann whitney
wilcox.test(Non_Parametric$BiomarkerA~Non_Parametric$Patients, paired=F)

    Wilcoxon rank sum test with continuity correction

data:  Non_Parametric$BiomarkerA by Non_Parametric$Patients
W = 4901.5, p-value = 0.08608
alternative hypothesis: true location shift is not equal to 0
#comparación entre casos y controles biomarcadoa A
boxplot(Non_Parametric$BiomarkerA~Non_Parametric$Patients,
        main= "Comparación biomarcador A entre casos y controles",
        col= brewer.pal(n = 3, name = "Accent"),
        xlab = "Grupos",
        ylab = "Concentracioens de biomarcadorA",
        frame=F)

#prueba de U-mann whitney
wilcox.test(Non_Parametric$BiomarkerB~Non_Parametric$Patients, paired=F)

    Wilcoxon rank sum test with continuity correction

data:  Non_Parametric$BiomarkerB by Non_Parametric$Patients
W = 5826, p-value = 2.144e-05
alternative hypothesis: true location shift is not equal to 0
#comparación entre casos y controles biomarcador B
boxplot(Non_Parametric$BiomarkerB~Non_Parametric$Patients,
        main= "Comparación biomarcador A entre casos y controles",
        col= brewer.pal(n = 3, name = "Accent"),
        xlab = "Grupos",
        ylab = "Concentracioens de biomarcadorB",
        frame=F)

#prueba de U-mann whitney
wilcox.test(Non_Parametric$Biomarcador1~Non_Parametric$Patients, paired=F)

    Wilcoxon rank sum test with continuity correction

data:  Non_Parametric$Biomarcador1 by Non_Parametric$Patients
W = 2117, p-value = 8.644e-06
alternative hypothesis: true location shift is not equal to 0
#comparación entre casos y controles biomarcador 1
boxplot(Non_Parametric$Biomarcador1~Non_Parametric$Patients,
        main= "Comparación biomarcador 1 entre casos y controles",
        col= brewer.pal(n = 3, name = "Accent"),
        xlab = "Grupos",
        ylab = "Concentracioens de biomarcador1",
        frame=F)

#prueba de U-mann whitney
wilcox.test(Non_Parametric$Biomarcador2~Non_Parametric$Patients, paired=F)

    Wilcoxon rank sum test with continuity correction

data:  Non_Parametric$Biomarcador2 by Non_Parametric$Patients
W = 689, p-value = 0.2694
alternative hypothesis: true location shift is not equal to 0
#comparación entre casos y controles biomarcador 2
boxplot(Non_Parametric$Biomarcador2~Non_Parametric$Patients,
        main= "Comparación biomarcador 2 entre casos y controles",
        col= brewer.pal(n = 3, name = "Accent"),
        xlab = "Grupos",
        ylab = "Concentracioens de biomarcador2",
        frame=F)

#prueba de U-mann whitney
wilcox.test(Non_Parametric$Biomarcador3~Non_Parametric$Patients, paired=F)

    Wilcoxon rank sum test with continuity correction

data:  Non_Parametric$Biomarcador3 by Non_Parametric$Patients
W = 4355, p-value = 0.8265
alternative hypothesis: true location shift is not equal to 0
#comparación entre casos y controles biomarcador 1
boxplot(Non_Parametric$Biomarcador3~Non_Parametric$Patients,
        main= "Comparación biomarcador 3 entre casos y controles",
        col= brewer.pal(n = 3, name = "Accent"),
        xlab = "Grupos",
        ylab = "Concentracioens de biomarcador3",
        frame=F)

Resolución ejercicio 13.9

Umannwhit4 <- function(x, y) {
  print(y)  
  par(mfrow = c(1,1))  
  boxplot(x, main = "",  ylab = "", xlab = "Grupos experimentales")
  #U-Mann Whitney test
  wilcox.test(x, paired=F)
}
#Biomarcador A y pacientes
Umannwhit4(Non_Parametric$BiomarkerA~Non_Parametric$Patients, Non_Parametric$BiomarkerA~Non_Parametric$Patients)  
Non_Parametric$BiomarkerA ~ Non_Parametric$Patients


    Wilcoxon rank sum test with continuity correction

data:  Non_Parametric$BiomarkerA by Non_Parametric$Patients
W = 4901.5, p-value = 0.08608
alternative hypothesis: true location shift is not equal to 0
#Biomarcador B y pacientes
  Umannwhit4(Non_Parametric$BiomarkerB~Non_Parametric$Patients, Non_Parametric$BiomarkerB~Non_Parametric$Patients)  
Non_Parametric$BiomarkerB ~ Non_Parametric$Patients


    Wilcoxon rank sum test with continuity correction

data:  Non_Parametric$BiomarkerB by Non_Parametric$Patients
W = 5826, p-value = 2.144e-05
alternative hypothesis: true location shift is not equal to 0
#biomarcador1 y pacientes
    Umannwhit4(Non_Parametric$Biomarcador1~Non_Parametric$Patients, Non_Parametric$Biomarcador1~Non_Parametric$Patients)  
Non_Parametric$Biomarcador1 ~ Non_Parametric$Patients


    Wilcoxon rank sum test with continuity correction

data:  Non_Parametric$Biomarcador1 by Non_Parametric$Patients
W = 2117, p-value = 8.644e-06
alternative hypothesis: true location shift is not equal to 0
#biomarcador2 y pacientes
    Umannwhit4(Non_Parametric$Biomarcador2~Non_Parametric$Patients, Non_Parametric$Biomarcador2~Non_Parametric$Patients)      
Non_Parametric$Biomarcador2 ~ Non_Parametric$Patients


    Wilcoxon rank sum test with continuity correction

data:  Non_Parametric$Biomarcador2 by Non_Parametric$Patients
W = 689, p-value = 0.2694
alternative hypothesis: true location shift is not equal to 0
    #biomarcador3 y pacientes
    Umannwhit4(Non_Parametric$Biomarcador3~Non_Parametric$Patients, Non_Parametric$Biomarcador3~Non_Parametric$Patients)         
Non_Parametric$Biomarcador3 ~ Non_Parametric$Patients


    Wilcoxon rank sum test with continuity correction

data:  Non_Parametric$Biomarcador3 by Non_Parametric$Patients
W = 4355, p-value = 0.8265
alternative hypothesis: true location shift is not equal to 0
       #weight y pacientes
    Umannwhit4(Non_Parametric$Weight~Non_Parametric$Patients, Non_Parametric$Weight~Non_Parametric$Patients)         
Non_Parametric$Weight ~ Non_Parametric$Patients

    Wilcoxon rank sum test with continuity correction

data:  Non_Parametric$Weight by Non_Parametric$Patients
W = 4282.5, p-value = 0.9836
alternative hypothesis: true location shift is not equal to 0
     #weight y pacientes
    Umannwhit4(Non_Parametric$Weight~Non_Parametric$Patients, Non_Parametric$Weight~Non_Parametric$Patients)
Non_Parametric$Weight ~ Non_Parametric$Patients


    Wilcoxon rank sum test with continuity correction

data:  Non_Parametric$Weight by Non_Parametric$Patients
W = 4282.5, p-value = 0.9836
alternative hypothesis: true location shift is not equal to 0
    #height y pacientes
    Umannwhit4(Non_Parametric$Height~Non_Parametric$Patients, Non_Parametric$Height~Non_Parametric$Patients)
Non_Parametric$Height ~ Non_Parametric$Patients


    Wilcoxon rank sum test with continuity correction

data:  Non_Parametric$Height by Non_Parametric$Patients
W = 4005.5, p-value = 0.4613
alternative hypothesis: true location shift is not equal to 0
    #bmi y pacientes
    Umannwhit4(Non_Parametric$BMI~Non_Parametric$Patients, Non_Parametric$BMI~Non_Parametric$Patients)
Non_Parametric$BMI ~ Non_Parametric$Patients


    Wilcoxon rank sum test with continuity correction

data:  Non_Parametric$BMI by Non_Parametric$Patients
W = 4295, p-value = 0.9563
alternative hypothesis: true location shift is not equal to 0
    #age y pacientes
    Umannwhit4(Non_Parametric$Age~Non_Parametric$Patients, Non_Parametric$Age~Non_Parametric$Patients)
Non_Parametric$Age ~ Non_Parametric$Patients


    Wilcoxon rank sum test with continuity correction

data:  Non_Parametric$Age by Non_Parametric$Patients
W = 3901, p-value = 0.3066
alternative hypothesis: true location shift is not equal to 0

Resolución de ejercicio 13.10

BioMB_antess <- (Non_Parametric$BiomarkerB_3M)
BioMB_despuess <- (Non_Parametric$BiomarkerB_6M)

  #U-Mann Whitney test
  wilcox.test(BioMB_antess, BioMB_despuess, paired=T, conf.int = T)

    Wilcoxon signed rank test with continuity correction

data:  BioMB_antess and BioMB_despuess
V = 0, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -0.01497391 -0.01009440
sample estimates:
(pseudo)median 
   -0.01008769 
boxplot(BioMB_antess, BioMB_despuess, main = "Comparacion de biomarcador B antes y despues",  ylab = "", xlab = c("antes","despues"),col= brewer.pal(n = 3, name = "Accent"))

Resolución ejercicio 13.11

BioMB_despues12 <- (Non_Parametric$BiomarkerB_12M)

  #U-Mann Whitney test
  wilcox.test(BioMB_despuess, BioMB_despues12, paired=T, conf.int = T)

    Wilcoxon signed rank test with continuity correction

data:  BioMB_despuess and BioMB_despues12
V = 15225, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 0.01500467 0.01998204
sample estimates:
(pseudo)median 
    0.01993708 
boxplot(BioMB_despuess, BioMB_despues12, main = "Comparacion de biomarcador B 6 meses y 12 despues",  ylab = "", xlab = c("antes","despues"),col= brewer.pal(n = 3, name = "Accent"))

Resolución de ejercicio 13.12 y 13.13

Kruskal wallis

Umannwhit6 <- function(x, y) {
  print(y)  
  par(mfrow = c(1,1))  
  boxplot(x, main = "Comparación de biomarcador A ",  ylab = "", xlab = "Grupos experimentales")
  #Kruskal wallis
  kruskal.test(x, )
}
#Biomarcador A y pacientes
Umannwhit6(Non_Parametric$BiomarkerA~Non_Parametric$Groups, Non_Parametric$BiomarkerA~Non_Parametric$Groups)  
Non_Parametric$BiomarkerA ~ Non_Parametric$Groups


    Kruskal-Wallis rank sum test

data:  Non_Parametric$BiomarkerA by Non_Parametric$Groups
Kruskal-Wallis chi-squared = 5.5597, df = 2, p-value = 0.06205
#Biomarcador B y pacientes
Umannwhit6(Non_Parametric$BiomarkerB~Non_Parametric$Groups, Non_Parametric$BiomarkerB~Non_Parametric$Groups)  
Non_Parametric$BiomarkerB ~ Non_Parametric$Groups


    Kruskal-Wallis rank sum test

data:  Non_Parametric$BiomarkerB by Non_Parametric$Groups
Kruskal-Wallis chi-squared = 19.964, df = 2, p-value = 4.622e-05
#BMI y pacientes
Umannwhit6(Non_Parametric$BMI~Non_Parametric$Groups, Non_Parametric$BMI~Non_Parametric$Groups)  
Non_Parametric$BMI ~ Non_Parametric$Groups


    Kruskal-Wallis rank sum test

data:  Non_Parametric$BMI by Non_Parametric$Groups
Kruskal-Wallis chi-squared = 0.35034, df = 2, p-value = 0.8393
#EDAD y pacientes
Umannwhit6(Non_Parametric$Age~Non_Parametric$Groups, Non_Parametric$Age~Non_Parametric$Groups)  
Non_Parametric$Age ~ Non_Parametric$Groups


    Kruskal-Wallis rank sum test

data:  Non_Parametric$Age by Non_Parametric$Groups
Kruskal-Wallis chi-squared = 4.1703, df = 2, p-value = 0.1243

Resolucion 13.14

# Datos de librería 

library(MASS)
data("Pima.tr")
library(ggstatsplot)
Warning: package 'ggstatsplot' was built under R version 4.2.3
You can cite this package as:
     Patil, I. (2021). Visualizations with statistical details: The 'ggstatsplot' approach.
     Journal of Open Source Software, 6(61), 3167, doi:10.21105/joss.03167
library(ggplot2)
Warning: package 'ggplot2' was built under R version 4.2.3
library(gplots)
Warning: package 'gplots' was built under R version 4.2.3

Attaching package: 'gplots'
The following object is masked from 'package:stats':

    lowess

Resolución ejemplo 13.1 al 13.13

FunciónTT14 <- function(x, y){
print(y)  
par(mfrow = c(1,3))  
boxplot(x, main = "",  ylab = "", xlab = "Grupos experimentales")
vioplot::vioplot(x, main= "Grafico de violin", ylab="", xlab="")
t.test(x, var.equal=T)
}

FunciónTT14(Pima.tr$bp~Pima.tr$type, Pima.tr$bp~Pima.tr$type)  
Pima.tr$bp ~ Pima.tr$type

    Two Sample t-test

data:  Pima.tr$bp by Pima.tr$type
t = -3.0015, df = 198, p-value = 0.003032
alternative hypothesis: true difference in means between group No and group Yes is not equal to 0
95 percent confidence interval:
 -8.355947 -1.729615
sample estimates:
 mean in group No mean in group Yes 
         69.54545          74.58824 
#ggstatplot

ggbetweenstats(
  data=Pima.tr,
  x= type,
  y= bp,
  title = "Comparación de bp entre paciente con y sin diabetes"
)

#glu y diabetes

FunciónTT14(Pima.tr$glu~Pima.tr$type, Pima.tr$glu~Pima.tr$type) 

Pima.tr$glu ~ Pima.tr$type

    Two Sample t-test

data:  Pima.tr$glu by Pima.tr$type
t = -7.682, df = 198, p-value = 7.075e-13
alternative hypothesis: true difference in means between group No and group Yes is not equal to 0
95 percent confidence interval:
 -40.15520 -23.75033
sample estimates:
 mean in group No mean in group Yes 
         113.1061          145.0588 
#ggstatplot

ggbetweenstats(
  data=Pima.tr,
  x= type,
  y= glu,
  title = "Comparación de glu entre paciente con y sin diabetes"
)


#skin y diabetes
FunciónTT14(Pima.tr$skin~Pima.tr$type, Pima.tr$skin~Pima.tr$type) 

Pima.tr$skin ~ Pima.tr$type

    Two Sample t-test

data:  Pima.tr$skin by Pima.tr$type
t = -3.4712, df = 198, p-value = 0.0006361
alternative hypothesis: true difference in means between group No and group Yes is not equal to 0
95 percent confidence interval:
 -9.272397 -2.553806
sample estimates:
 mean in group No mean in group Yes 
         27.20455          33.11765 
#ggstatplot

ggbetweenstats(
  data=Pima.tr,
  x= type,
  y= skin,
  title = "Comparación de skin entre paciente con y sin diabetes"
)

#bmi y diabetes
FunciónTT14(Pima.tr$bmi~Pima.tr$type, Pima.tr$bmi~Pima.tr$type) 

Pima.tr$bmi ~ Pima.tr$type

    Two Sample t-test

data:  Pima.tr$bmi by Pima.tr$type
t = -4.129, df = 198, p-value = 5.368e-05
alternative hypothesis: true difference in means between group No and group Yes is not equal to 0
95 percent confidence interval:
 -5.370454 -1.898709
sample estimates:
 mean in group No mean in group Yes 
         31.07424          34.70882 
#ggstatplot

ggbetweenstats(
  data=Pima.tr,
  x= type,
  y= bmi,
  title = "Comparación de glu entre paciente con y sin diabetes"
)

#ped y diabtes
FunciónTT14(Pima.tr$ped~Pima.tr$type, Pima.tr$ped~Pima.tr$type) 

Pima.tr$ped ~ Pima.tr$type

    Two Sample t-test

data:  Pima.tr$ped by Pima.tr$type
t = -2.9601, df = 198, p-value = 0.003451
alternative hypothesis: true difference in means between group No and group Yes is not equal to 0
95 percent confidence interval:
 -0.22189897 -0.04445487
sample estimates:
 mean in group No mean in group Yes 
        0.4154848         0.5486618 
#ggstatplot

ggbetweenstats(
  data=Pima.tr,
  x= type,
  y= ped,
  title = "Comparación de glu entre paciente con y sin diabetes"
)

#ejercicio 13.2
Pima.tr$edad1.1 <- cut(Pima.tr$age, 
                            breaks = c(0, 40, 90),
                            labels = c("0-40", "40-90"),
                            right = F, na.rm= TRUE) 



Pima.tr$edad1.1 <- factor(Pima.tr$edad1.1)
#Niveles de glucosa y relación con edad (menor a 40 y mayor a 40)

ggbetweenstats(
  data=Pima.tr,
  x= edad1.1,
  y= bmi,
  title = "Comparación de edad y BMI"
)

Resolución ejercicio 13.3

#librería
library(readxl)
SLE_dataset1 <- read_excel("C:/Users/David/Downloads/SLE dataset1.xlsx")
View(SLE_dataset1)

#resolución

ggbetweenstats(
  data=SLE_dataset1,
  x= Groups_NLSLEvsSLE,
  y= Leptin,
  title = "Comparación de leptina y grupos"
)

ggbetweenstats(
  data=SLE_dataset1,
  x= Groups_NLSLEvsSLE,
  y= Leptin_BMI,
  title = "Comparación de leptina_BMI y grupos"
)

ggbetweenstats(
  data=SLE_dataset1,
  x= Groups_NLSLEvsSLE,
  y= Adiponectin,
  title = "Comparación de leptina y grupos"
)

ggbetweenstats(
  data=SLE_dataset1,
  x= Groups_NLSLEvsSLE,
  y= Adiponectin_BMI,
  title = "Comparación de leptina y grupos"
)

Resolución ejercicios 13.4 y 13.5

#datos de base

library(readxl)
Base_Prueba_t_pareada_Nueva <- read_excel("C:/Users/David/Downloads/Base_Prueba_t_pareada_Nueva.xlsx")
View(Base_Prueba_t_pareada_Nueva)

#resolucion 13.4

ggwithinstats(
  data=Base_Prueba_t_pareada_Nueva[Base_Prueba_t_pareada_Nueva$Time!="6M",],
  x=Time,
  y=Leptin,
  title = "Comparación de leptina a la basal y a los 6M"
  )

#resolucion 13.5

ggwithinstats(
  data=Base_Prueba_t_pareada_Nueva[Base_Prueba_t_pareada_Nueva$Time!="12M",],
  x=Time,
  y=Leptin,
  title = "Comparación de leptina a la basal y a los 12M"
  )

Resolcion 13.6

#bases empleadas

library(MASS)
data("Cushings")
attach(Cushings)
library(gplots)

#resolucion

ggbetweenstats(
  data= Cushings,
  x= Type,
  y= Pregnanetriol,
  title = "Comparación de leptina_BMI y grupos"
)

Resolucion 13.7

ggbetweenstats(
  data=SLE_dataset1,
  x= Groups_NLSLEvsSLE,
  y= Leptin,
  title = "Comparación de leptina y grupos"
)

ggbetweenstats(
  data=SLE_dataset1,
  x= Groups_NLSLEvsSLE,
  y= Leptin_BMI,
  title = "Comparación de leptina_BMI y grupos"
)

ggbetweenstats(
  data=SLE_dataset1,
  x= Groups_NLSLEvsSLE,
  y= Adiponectin,
  title = "Comparación de leptina y grupos"
)

ggbetweenstats(
  data=SLE_dataset1,
  x= Groups_NLSLEvsSLE,
  y= Adiponectin_BMI,
  title = "Comparación de leptina y grupos"
)

Resolución 13.8

#resolucion
ggbetweenstats(
  data=Non_Parametric,
  x= Patients,
  y= BiomarkerA,
  title = "Comparación de biomarcadorA y pacientes"
)

ggbetweenstats(
  data=Non_Parametric,
  x= Patients,
  y= BiomarkerB,
  title = "Comparación de biomarcadorB y pacientes"
)

ggbetweenstats(
  data=Non_Parametric,
  x= Patients,
  y= Biomarcador1,
  title = "Comparación de biomarcador1 y pacientes"
)

ggbetweenstats(
  data=Non_Parametric,
  x= Patients,
  y= Biomarcador2,
  title = "Comparación de biomarcador2 y pacientes"
)

ggbetweenstats(
  data=Non_Parametric,
  x= Patients,
  y= Biomarcador3,
  title = "Comparación de biomarcador3 y pacientes"
)

Ejericicio 13.9

ggbetweenstats(
  data=Non_Parametric,
  x= Patients,
  y= Age,
  title = "Comparación de edad y pacientes"
)

ggbetweenstats(
  data=Non_Parametric,
  x= Patients,
  y= Weight,
  title = "Comparación de peso y pacientes"
)

ggbetweenstats(
  data=Non_Parametric,
  x= Patients,
  y= Height,
  title = "Comparación de altura y pacientes"
)