#Base de datos -Bajo Peso al Nacer(Hosmer & Lemeshow)
library(readxl)
library(dplyr)
datos <- read_excel("D:/DISCOE/CLASES/JAVERIANA_SALUD/MSC_EPI_CLINICA/BIOESTADISTICA/Low_bw.xlsx")
##Comparación del peso promedio entre madres que Fuman y no Fuman (varianzas iguales)
library(EnvStats)
#Estadisticas descriptivas
summary(datos$bwt)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 709 2414 2977 2944 3475 4990
summaryFull(datos$bwt)
## datos$bwt
## N 189.00000
## Mean 2944.00000
## Median 2977.00000
## 10% Trimmed Mean 2961.00000
## Geometric Mean 2841.00000
## Skew -0.20860
## Kurtosis -0.08175
## Min 709.00000
## Max 4990.00000
## Range 4281.00000
## 1st Quartile 2414.00000
## 3rd Quartile 3475.00000
## Standard Deviation 729.00000
## Geometric Standard Deviation 1.32600
## Interquartile Range 1061.00000
## Median Absolute Deviation 819.90000
## Coefficient of Variation 0.24760
## attr(,"class")
## [1] "summaryStats"
## attr(,"stats.in.rows")
## [1] TRUE
## attr(,"drop0trailing")
## [1] TRUE
grupos=group_by(datos,smoke)
summarise(grupos, mean=mean(bwt), var=var(bwt), n=n())
## # A tibble: 2 x 4
## smoke mean var n
## <chr> <dbl> <dbl> <int>
## 1 No 3055. 566119. 115
## 2 Yes 2772. 435346. 74
#Prueba F -Prueba de comparación de varianzas (Ho:varianzas iguales vs Ha:varianza diferentes)
var.test(datos$bwt~datos$smoke, alternative="t", conf.level=0.95)
##
## F test to compare two variances
##
## data: datos$bwt by datos$smoke
## F = 1.3004, num df = 114, denom df = 73, p-value = 0.2275
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.8476388 1.9566446
## sample estimates:
## ratio of variances
## 1.30039
#Prueba t
t.test(datos$bwt~datos$smoke, mu=0, alternative="t", conf.level=0.95, var.equal=T )
##
## Two Sample t-test
##
## data: datos$bwt by datos$smoke
## t = 2.6428, df = 187, p-value = 0.00892
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 71.66693 493.65152
## sample estimates:
## mean in group No mean in group Yes
## 3054.957 2772.297
##Comparación de la edad promedio entre madres con recien nacidos con bajo peso y sin bajo peso (varianzas diferentes)
#grupos por bajo peso al nacer
grupos=group_by(datos,low)
summarise(grupos, mean=mean(bwt), var=var(bwt), n=n())
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 2 x 4
## low mean var n
## <chr> <dbl> <dbl> <int>
## 1 No 3329. 228585. 130
## 2 Yes 2097. 152785. 59
#Prueba F (varianzas)
var.test(datos$age~datos$low, alternative="t", conf.level=0.95)
##
## F test to compare two variances
##
## data: datos$age by datos$low
## F = 1.5323, num df = 129, denom df = 58, p-value = 0.06885
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.9669261 2.3389614
## sample estimates:
## ratio of variances
## 1.532254
#Prueba t
t.test(datos$age~datos$low, mu=0, alternative="t", conf.level=0.95, var.equal=F)
##
## Welch Two Sample t-test
##
## data: datos$age by datos$low
## t = 1.7737, df = 136.94, p-value = 0.07834
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1558349 2.8687423
## sample estimates:
## mean in group No mean in group Yes
## 23.66154 22.30508
##Observacines dependientes (datos pareados)
library(readxl)
#Base de datos -Peso antes y después
datos2 <- read_excel("D:/DISCOE/CLASES/JAVERIANA_SALUD/MSC_EPI_CLINICA/BIOESTADISTICA/DatosPareados.xlsx")
#Prueba t (Promedio de las diferencias)
t.test(datos2$Peso_1,datos2$Peso_3, mu=0, alternative="t", conf.level=0.95, paired=T)
##
## Paired t-test
##
## data: datos2$Peso_1 and datos2$Peso_3
## t = 4.5503, df = 65, p-value = 2.403e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.215718 3.117615
## sample estimates:
## mean of the differences
## 2.166667
##Comparación de Proporciones
#tabla cruzada bajo peso y presencia irritabilidad uterina
prop.table(table(datos$low, datos$ui), 1)
##
## No Yes
## No 0.8923077 0.1076923
## Yes 0.7627119 0.2372881
prop.test(table(datos$low, datos$ui), alternative="t", correct=TRUE)
##
## 2-sample test for equality of proportions with continuity correction
##
## data: table(datos$low, datos$ui)
## X-squared = 4.4227, df = 1, p-value = 0.03546
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.003651494 0.262843150
## sample estimates:
## prop 1 prop 2
## 0.8923077 0.7627119