ANCOVA

library(readxl)
ANCOVApH <- read_excel("C:/Users/juanc/OneDrive/Escritorio/2021-1/Suelos/Disenio/ANCOVApH.xlsx")
ANCOVApH

## # A tibble: 35 x 5
##    Tratamientos    pH   CIC     y     x
##    <chr>        <dbl> <dbl> <dbl> <dbl>
##  1 T3            7.6   15.6     1     1
##  2 T4            7.82  18.4     2     1
##  3 T1            6.3   18.8     3     1
##  4 T4            7.9   19.1     4     1
##  5 T0            5.5   21.5     5     1
##  6 T2            7.4   23.7     6     1
##  7 T4            7.89  25.7     7     1
##  8 T4            7.89  15.4     1     2
##  9 T4            7.89  16.7     2     2
## 10 T3            7.6   17.4     3     2
## # ... with 25 more rows

library(dplyr)

## Warning: package 'dplyr' was built under R version 4.0.5

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)
df= ANCOVApH
df %>% ggplot(aes(x = x, y=y, colour=pH))+
   geom_point(size = 15,shape=15)+
   scale_color_continuous(type = 'viridis')

df %>% ggplot(aes(x = x, y=y, colour=CIC))+
   geom_point(size = 15,shape=15)+
   scale_color_continuous(type = 'viridis')

tapply(df$pH,df$Tratamientos,mean)

##       T0       T1       T2       T3       T4 
## 5.700000 6.544000 7.380000 7.633636 7.865000

tapply(df$CIC,df$Tratamientos,mean)

##       T0       T1       T2       T3       T4 
## 24.18714 23.61904 20.97290 21.52820 20.25314

library(lattice)
xyplot(df$pH~df$CIC,pch=16)

xyplot(df$pH~df$CIC|df$Tratamientos,pch=19)

ANCOVA

ANCOVA <- aov(df$pH~df$CIC+df$Tratamientos)
summary(ANCOVA)

##                 Df Sum Sq Mean Sq F value  Pr(>F)    
## df$CIC           1  1.375   1.375   50.97 7.4e-08 ***
## df$Tratamientos  4 16.194   4.048  150.07 < 2e-16 ***
## Residuals       29  0.782   0.027                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

shapiro.test(ANCOVA$residuals)

## 
##  Shapiro-Wilk normality test
## 
## data:  ANCOVA$residuals
## W = 0.93542, p-value = 0.04066

bartlett.test(ANCOVA$residuals,df$Tratamientos)

## 
##  Bartlett test of homogeneity of variances
## 
## data:  ANCOVA$residuals and df$Tratamientos
## Bartlett's K-squared = 30.934, df = 4, p-value = 3.157e-06