setwd("~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/UIS/Pasantías")
library(rstatix)
##
## Attaching package: 'rstatix'
## The following object is masked from 'package:stats':
##
## filter
library(ggplot2)
library(ggpubr)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ tibble 3.1.6 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.4 ✓ stringr 1.4.0
## ✓ readr 2.1.1 ✓ forcats 0.5.1
## ✓ purrr 0.3.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks rstatix::filter(), stats::filter()
## x dplyr::lag() masks stats::lag()
cava <- read.table("dacava2.csv", header=T, sep=",")
res.alt <- t.test(alt ~ Tratamiento, data = cava, var.equal=FALSE)
res.LR <- t.test(LR ~ Tratamiento, data = cava, var.equal=FALSE)
res.diam <- t.test(diam ~ Tratamiento, data = cava, var.equal=FALSE)
res.AFE <- t.test(AFE ~ Tratamiento, data = cava, var.equal=FALSE)
res.PFE <- t.test(PFE ~ Tratamiento, data = cava, var.equal=FALSE)
res.MSR <- t.test(MSR ~ Tratamiento, data = cava, var.equal=FALSE)
res.MST <- t.test(MST ~ Tratamiento, data = cava, var.equal=FALSE)
res.MSF <- t.test(MSF ~ Tratamiento, data = cava, var.equal=FALSE)
res.MSPA <- t.test(MSPA ~ Tratamiento, data = cava, var.equal=FALSE)
res.MT <- t.test(MT ~ Tratamiento, data = cava, var.equal=FALSE)
res.FMR <- t.test(FMR ~ Tratamiento, data = cava, var.equal=FALSE)
res.FMT <- t.test(FMT ~ Tratamiento, data = cava, var.equal=FALSE)
res.FMF <- t.test(FMF ~ Tratamiento, data = cava, var.equal=FALSE)
res.RPA <- t.test(RPA ~ Tratamiento, data = cava, var.equal=FALSE)
res.RT <- t.test(RT ~ Tratamiento, data = cava, var.equal=FALSE)
res.RH <- t.test(RH ~ Tratamiento, data = cava, var.equal=FALSE)
res.TH <- t.test(TH ~ Tratamiento, data = cava, var.equal=FALSE)
res.esbeltez <- t.test(esbeltez ~ Tratamiento, data = cava, var.equal=FALSE)
res.dickson <- t.test(dickson ~ Tratamiento, data = cava, var.equal=FALSE)
res.alt
##
## Welch Two Sample t-test
##
## data: alt by Tratamiento
## t = 11.981, df = 6.4039, p-value = 1.271e-05
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## 11.24089 16.90196
## sample estimates:
## mean in group 1 mean in group 2
## 41.98571 27.91429
res.LR
##
## Welch Two Sample t-test
##
## data: LR by Tratamiento
## t = 1.4528, df = 11.785, p-value = 0.1724
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## -4.338684 21.595827
## sample estimates:
## mean in group 1 mean in group 2
## 46.08571 37.45714
res.diam
##
## Welch Two Sample t-test
##
## data: diam by Tratamiento
## t = 6.6918, df = 10.708, p-value = 3.892e-05
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## 7.730794 15.346349
## sample estimates:
## mean in group 1 mean in group 2
## 37.08286 25.54429
res.AFE
##
## Welch Two Sample t-test
##
## data: AFE by Tratamiento
## t = -1.218, df = 11.427, p-value = 0.2478
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## -0.8992095 0.2566378
## sample estimates:
## mean in group 1 mean in group 2
## 1.869981 2.191267
res.PFE
##
## Welch Two Sample t-test
##
## data: PFE by Tratamiento
## t = 1.3721, df = 11.991, p-value = 0.1952
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## -0.04554154 0.20042941
## sample estimates:
## mean in group 1 mean in group 2
## 0.5555582 0.4781143
res.MSR
##
## Welch Two Sample t-test
##
## data: MSR by Tratamiento
## t = 6.1651, df = 7.7435, p-value = 0.0003077
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## 10.90740 24.06402
## sample estimates:
## mean in group 1 mean in group 2
## 27.52143 10.03571
res.MST
##
## Welch Two Sample t-test
##
## data: MST by Tratamiento
## t = 6.8004, df = 7.5146, p-value = 0.0001838
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## 8.511627 17.396944
## sample estimates:
## mean in group 1 mean in group 2
## 20.935714 7.981429
res.MSF
##
## Welch Two Sample t-test
##
## data: MSF by Tratamiento
## t = 5.0773, df = 6.6087, p-value = 0.001706
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## 5.549096 15.445190
## sample estimates:
## mean in group 1 mean in group 2
## 15.465714 4.968571
res.MSPA
##
## Welch Two Sample t-test
##
## data: MSPA by Tratamiento
## t = 6.4141, df = 6.9432, p-value = 0.0003749
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## 14.79147 32.11139
## sample estimates:
## mean in group 1 mean in group 2
## 36.40143 12.95000
res.MT
##
## Welch Two Sample t-test
##
## data: MT by Tratamiento
## t = 6.9089, df = 7.259, p-value = 0.0001943
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## 27.02674 54.84755
## sample estimates:
## mean in group 1 mean in group 2
## 63.92286 22.98571
res.FMR
##
## Welch Two Sample t-test
##
## data: FMR by Tratamiento
## t = -0.091936, df = 12, p-value = 0.9283
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## -0.06519907 0.05991962
## sample estimates:
## mean in group 1 mean in group 2
## 0.4318555 0.4344953
res.FMT
##
## Welch Two Sample t-test
##
## data: FMT by Tratamiento
## t = -0.88446, df = 11.998, p-value = 0.3938
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## -0.06359336 0.02687121
## sample estimates:
## mean in group 1 mean in group 2
## 0.3299907 0.3483518
res.FMF
##
## Welch Two Sample t-test
##
## data: FMF by Tratamiento
## t = 0.96437, df = 11.503, p-value = 0.3547
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## -0.0266750 0.0686766
## sample estimates:
## mean in group 1 mean in group 2
## 0.2381537 0.2171529
res.RPA
##
## Welch Two Sample t-test
##
## data: RPA by Tratamiento
## t = -0.1021, df = 11.967, p-value = 0.9204
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## -0.2000787 0.1821721
## sample estimates:
## mean in group 1 mean in group 2
## 0.7730241 0.7819774
res.RT
##
## Welch Two Sample t-test
##
## data: RT by Tratamiento
## t = 0.42105, df = 11.917, p-value = 0.6812
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## -0.2686185 0.3971826
## sample estimates:
## mean in group 1 mean in group 2
## 1.335927 1.271645
res.RH
##
## Welch Two Sample t-test
##
## data: RH by Tratamiento
## t = -0.61856, df = 11.903, p-value = 0.5479
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## -0.8361277 0.4666168
## sample estimates:
## mean in group 1 mean in group 2
## 1.896133 2.080888
res.TH
##
## Welch Two Sample t-test
##
## data: TH by Tratamiento
## t = -0.61856, df = 11.903, p-value = 0.5479
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## -0.8361277 0.4666168
## sample estimates:
## mean in group 1 mean in group 2
## 1.896133 2.080888
res.esbeltez
##
## Welch Two Sample t-test
##
## data: esbeltez by Tratamiento
## t = -0.54544, df = 9.3049, p-value = 0.5983
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## -0.1437014 0.0876426
## sample estimates:
## mean in group 1 mean in group 2
## 0.8854827 0.9135121
res.dickson
##
## Welch Two Sample t-test
##
## data: dickson by Tratamiento
## t = 7.5164, df = 9.1487, p-value = 3.319e-05
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## 24.43568 45.40235
## sample estimates:
## mean in group 1 mean in group 2
## 57.35265 22.43364
## Creando test especifico para las variables significativas (alt, diam, MSR, MST, MSF, MSPA, MT, dickson)
# alt
stat.test <- cava %>%
t_test(alt ~ Tratamiento) %>%
add_significance()
stat.test
## # A tibble: 1 × 9
## .y. group1 group2 n1 n2 statistic df p p.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 alt 1 2 7 7 12.0 6.40 0.0000127 ****
# Creando box-plot para variables significativas
bxp <- ggboxplot(
cava, x = "Tratamiento", y = "alt",
ylab = "Altura planta (cm)", xlab = "Tratamientos", add = "jitter"
)
# Add p-value and significance levels
stat.test <- stat.test %>% add_xy_position(x = "Tratamiento")
bxp +
stat_pvalue_manual(stat.test, tip.length = 0) +
labs(subtitle = get_test_label(stat.test, detailed = TRUE))
# diam
stat.test <- cava %>%
t_test(diam ~ Tratamiento) %>%
add_significance()
stat.test
## # A tibble: 1 × 9
## .y. group1 group2 n1 n2 statistic df p p.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 diam 1 2 7 7 6.69 10.7 0.0000389 ****
# Creando box-plot para variables significativas
bxp <- ggboxplot(
cava, x = "Tratamiento", y = "diam",
ylab = "Diametro base tallo (mm)", xlab = "Tratamientos", add = "jitter"
)
# Add p-value and significance levels
stat.test <- stat.test %>% add_xy_position(x = "Tratamiento")
bxp +
stat_pvalue_manual(stat.test, tip.length = 0) +
labs(subtitle = get_test_label(stat.test, detailed = TRUE))
# MSR
stat.test <- cava %>%
t_test(MSR ~ Tratamiento) %>%
add_significance()
stat.test
## # A tibble: 1 × 9
## .y. group1 group2 n1 n2 statistic df p p.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 MSR 1 2 7 7 6.17 7.74 0.000308 ***
# Creando box-plot para variables significativas
bxp <- ggboxplot(
cava, x = "Tratamiento", y = "MSR",
ylab = "Biomasa seca raíz (g)", xlab = "Tratamientos", add = "jitter"
)
# Add p-value and significance levels
stat.test <- stat.test %>% add_xy_position(x = "Tratamiento")
bxp +
stat_pvalue_manual(stat.test, tip.length = 0) +
labs(subtitle = get_test_label(stat.test, detailed = TRUE))
# MST
stat.test <- cava %>%
t_test(MST ~ Tratamiento) %>%
add_significance()
stat.test
## # A tibble: 1 × 9
## .y. group1 group2 n1 n2 statistic df p p.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 MST 1 2 7 7 6.80 7.51 0.000184 ***
# Creando box-plot para variables significativas
bxp <- ggboxplot(
cava, x = "Tratamiento", y = "MST",
ylab = "Biomasa seca tallo (g)", xlab = "Tratamientos", add = "jitter"
)
# Add p-value and significance levels
stat.test <- stat.test %>% add_xy_position(x = "Tratamiento")
bxp +
stat_pvalue_manual(stat.test, tip.length = 0) +
labs(subtitle = get_test_label(stat.test, detailed = TRUE))
# MSF
stat.test <- cava %>%
t_test(MSF ~ Tratamiento) %>%
add_significance()
stat.test
## # A tibble: 1 × 9
## .y. group1 group2 n1 n2 statistic df p p.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 MSF 1 2 7 7 5.08 6.61 0.00171 **
# Creando box-plot para variables significativas
bxp <- ggboxplot(
cava, x = "Tratamiento", y = "MSF",
ylab = "Biomasa seca foliar (g)", xlab = "Tratamientos", add = "jitter"
)
# Add p-value and significance levels
stat.test <- stat.test %>% add_xy_position(x = "Tratamiento")
bxp +
stat_pvalue_manual(stat.test, tip.length = 0) +
labs(subtitle = get_test_label(stat.test, detailed = TRUE))
# MSPA
stat.test <- cava %>%
t_test(MSPA ~ Tratamiento) %>%
add_significance()
stat.test
## # A tibble: 1 × 9
## .y. group1 group2 n1 n2 statistic df p p.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 MSPA 1 2 7 7 6.41 6.94 0.000375 ***
# Creando box-plot para variables significativas
bxp <- ggboxplot(
cava, x = "Tratamiento", y = "MSPA",
ylab = "Biomasa seca parte aérea (g)", xlab = "Tratamientos", add = "jitter"
)
# Add p-value and significance levels
stat.test <- stat.test %>% add_xy_position(x = "Tratamiento")
bxp +
stat_pvalue_manual(stat.test, tip.length = 0) +
labs(subtitle = get_test_label(stat.test, detailed = TRUE))
# MT
stat.test <- cava %>%
t_test(MT ~ Tratamiento) %>%
add_significance()
stat.test
## # A tibble: 1 × 9
## .y. group1 group2 n1 n2 statistic df p p.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 MT 1 2 7 7 6.91 7.26 0.000194 ***
# Creando box-plot para variables significativas
bxp <- ggboxplot(
cava, x = "Tratamiento", y = "MT",
ylab = "Biomasa seca total (g)", xlab = "Tratamientos", add = "jitter"
)
# Add p-value and significance levels
stat.test <- stat.test %>% add_xy_position(x = "Tratamiento")
bxp +
stat_pvalue_manual(stat.test, tip.length = 0) +
labs(subtitle = get_test_label(stat.test, detailed = TRUE))
# dickson
stat.test <- cava %>%
t_test(dickson ~ Tratamiento) %>%
add_significance()
stat.test
## # A tibble: 1 × 9
## .y. group1 group2 n1 n2 statistic df p p.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 dickson 1 2 7 7 7.52 9.15 0.0000332 ****
# Creando box-plot para variables significativas
bxp <- ggboxplot(
cava, x = "Tratamiento", y = "dickson",
ylab = "Índice de Dickson", xlab = "Tratamientos", add = "jitter"
)
# Add p-value and significance levels
stat.test <- stat.test %>% add_xy_position(x = "Tratamiento")
bxp +
stat_pvalue_manual(stat.test, tip.length = 0) +
labs(subtitle = get_test_label(stat.test, detailed = TRUE))
