ANEXOS INFORME 2. ANALISIS EMPIRICO - DISEÑO ANIDADO

ANEXOS

Cargar de base de datos y librerias

library(readxl)
dtPFA <- read_excel("C:/Users/PC/Downloads/DATOS INFORME 2 ANIDADOS.xlsx", 
                    sheet = "EjPFA")
head(dtPFA)

## # A tibble: 6 × 3
##   ExposicionPFA Superficie CAntimicrobiano
##   <chr>         <chr>                <dbl>
## 1 Indirecto     PVC                   13.3
## 2 Indirecto     PVC                   13.0
## 3 Indirecto     PVC                   13.5
## 4 Indirecto     AC                    22.9
## 5 Indirecto     AC                    13.4
## 6 Indirecto     AC                    17.6

library(lme4)

## Cargando paquete requerido: Matrix

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.2.1     ✔ readr     2.2.0
## ✔ forcats   1.0.1     ✔ stringr   1.6.0
## ✔ ggplot2   4.0.3     ✔ tibble    3.3.1
## ✔ lubridate 1.9.5     ✔ tidyr     1.3.2
## ✔ purrr     1.2.2

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ tidyr::expand() masks Matrix::expand()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ✖ tidyr::pack()   masks Matrix::pack()
## ✖ tidyr::unpack() masks Matrix::unpack()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(lmerTest)

## 
## Adjuntando el paquete: 'lmerTest'
## 
## The following object is masked from 'package:lme4':
## 
##     lmer
## 
## The following object is masked from 'package:stats':
## 
##     step

library(ggplot2)
library(agricolae)

##Indicar variables como factor

dtPFA$ExposicionPFA <- as.factor(dtPFA$ExposicionPFA)
dtPFA$Superficie   <- as.factor(dtPFA$Superficie)
head(dtPFA)

## # A tibble: 6 × 3
##   ExposicionPFA Superficie CAntimicrobiano
##   <fct>         <fct>                <dbl>
## 1 Indirecto     PVC                   13.3
## 2 Indirecto     PVC                   13.0
## 3 Indirecto     PVC                   13.5
## 4 Indirecto     AC                    22.9
## 5 Indirecto     AC                    13.4
## 6 Indirecto     AC                    17.6

##Grafica BOXPLOT caja y bigote

ggplot(dtPFA, aes(x = Superficie, y = CAntimicrobiano, fill = ExposicionPFA)) +
  geom_boxplot(alpha = 0.7) +
  facet_wrap(~ ExposicionPFA) +
  scale_fill_manual(
    name = "ExposicionPFA",
    values = c(
      "Directo" = "#FFA07A",  # rosa pastel
      "Indirecto" = "#A7C7E7"   # azul pastel
    ),
    labels = c(
      "1" = "Directo",
      "2" = "Indirecto"
    )
  ) +
  labs(
    x = "Superficie",
    y = "Capacidad antimicrobiana ( % )"
  ) +
  theme_minimal() +
  theme(
    legend.position = "right"
  )

#LMER. Donde: A Factor fijo = operador y un B factor aleatorio=pieza anidado a operador

anovaPFA <- lmer(CAntimicrobiano ~ ExposicionPFA + (1 | ExposicionPFA:Superficie), 
                    data = dtPFA)
summary(anovaPFA)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CAntimicrobiano ~ ExposicionPFA + (1 | ExposicionPFA:Superficie)
##    Data: dtPFA
## 
## REML criterion at convergence: 89.8
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.79788 -0.26556 -0.03621  0.37370  2.08831 
## 
## Random effects:
##  Groups                   Name        Variance Std.Dev.
##  ExposicionPFA:Superficie (Intercept) 32.146   5.670   
##  Residual                              5.988   2.447   
## Number of obs: 18, groups:  ExposicionPFA:Superficie, 6
## 
## Fixed effects:
##                        Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)              33.338      3.374   4.000   9.882 0.000588 ***
## ExposicionPFAIndirecto  -18.761      4.771   4.000  -3.932 0.017069 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## ExpscnPFAIn -0.707

#AOV PARA FIJO

aovPFA <- aov(CAntimicrobiano ~ ExposicionPFA/Superficie,
           data=dtPFA)
summary(aovPFA)

##                          Df Sum Sq Mean Sq F value   Pr(>F)    
## ExposicionPFA             1 1583.9  1583.9   264.5 1.54e-09 ***
## ExposicionPFA:Superficie  4  409.7   102.4    17.1 6.74e-05 ***
## Residuals                12   71.9     6.0                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#Calculo % de componentes

total <- 32.146+5.988
total

## [1] 38.134

P_superficie <- 32.146/total*100
P_superficie

## [1] 84.29748

P_Residuos <- 5.988/total*100
P_Residuos 

## [1] 15.70252

##SUPUESTOS #NORMALIDAD - SHAPIRO WILK

library (dplyr)
library (ggplot2)
resid <- residuals(anovaPFA)
shapiro.test(resid)

## 
##  Shapiro-Wilk normality test
## 
## data:  resid
## W = 0.94578, p-value = 0.3627

qqnorm(resid)

#HOMOGENEIDAD DE VARIANZA - TEST DE LEVENE

library(car)

## Cargando paquete requerido: carData

## Registered S3 method overwritten by 'car':
##   method           from
##   na.action.merMod lme4

## 
## Adjuntando el paquete: 'car'

## The following object is masked from 'package:dplyr':
## 
##     recode

## The following object is masked from 'package:purrr':
## 
##     some

dtPFA$Grupo <- interaction(dtPFA$ExposicionPFA, dtPFA$Superficie)

leveneTest(CAntimicrobiano ~ Grupo, data = dtPFA)

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  5  1.5708 0.2413
##       12