Abundance and Diversity of Sea Star in Dapa
Kyle Kenneth Ruaya
2024-05-04
library(readxl)
library(dplyr)##
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library(rstatix)##
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## The following object is masked from 'package:stats':
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## filterData
library(readxl)
Data <- read_excel("D:/SNSHS STATS/Gelia/GELIA.xlsx")
View(Data)library(rmarkdown)
paged_table(Data)Normality Test
res_aov <- aov(`Shannon's Index` ~ Location,
data = Data
)
shapiro.test(res_aov$residuals)##
## Shapiro-Wilk normality test
##
## data: res_aov$residuals
## W = 0.70657, p-value = 0.001693Data is not normally distributed.
res_aov <- aov(`Shannon's Index` ~ `Substrate Type`,
data = Data
)
shapiro.test(res_aov$residuals)##
## Shapiro-Wilk normality test
##
## data: res_aov$residuals
## W = 0.87266, p-value = 0.1313Data is normally distributed.
res_aov <- aov(`Population` ~ Location,
data = Data
)
shapiro.test(res_aov$residuals)##
## Shapiro-Wilk normality test
##
## data: res_aov$residuals
## W = 0.92565, p-value = 0.441Data is normally distributed.
res_aov <- aov(`Population` ~ `Substrate Type`,
data = Data
)
shapiro.test(res_aov$residuals)##
## Shapiro-Wilk normality test
##
## data: res_aov$residuals
## W = 0.95023, p-value = 0.6926Data is not normally distributed.
Homogeneity of Variance
leveneTest(`Shannon's Index` ~ Location,
data = Data)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 3.9631 0.07997 .
## 6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1The p-value is greater than the 0.05 level of significance. Thus, the homogeneity assumption of the variance is met.
leveneTest(`Shannon's Index` ~ `Substrate Type`,
data = Data)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 0.5872 0.5849
## 6The p-value is greater than the 0.05 level of significance. Thus, the homogeneity assumption of the variance is met.
leveneTest(`Population` ~ Location,
data = Data)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 0.1624 0.8537
## 6The p-value is greater than the 0.05 level of significance. Thus, the homogeneity assumption of the variance is met.
leveneTest(`Population` ~ `Substrate Type`,
data = Data)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 0.2591 0.78
## 6The p-value is greater than the 0.05 level of significance. Thus, the homogeneity assumption of the variance is met.
Shannon’s Index (Location)
ggboxplot(Data, x = "Location", y = "Shannon's Index",
color = "Location", palette = c("#FC4E07", "#00FF00", "#4E07FC"),
order = c("Catabaan", "RSK", "Boulevard"),
ylab = "Shannon's Index", xlab = "Location")
ggline(Data, x = "Location", y = "Shannon's Index",
add = c("mean_se", "jitter"),
order = c("Catabaan", "RSK", "Boulevard"),
ylab = "Shannon's Index", xlab = "Location")
library("gplots")##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowessplotmeans(`Shannon's Index` ~ Location, data = Data, frame = TRUE,
xlab = "Location", ylab = "Shannon's Index",
main="Mean Plot with 95% CI")## Warning in arrows(x, li, x, pmax(y - gap, li), col = barcol, lwd = lwd, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(x, li, x, pmax(y - gap, li), col = barcol, lwd = lwd, :
## zero-length arrow is of indeterminate angle and so skipped## Warning in arrows(x, ui, x, pmin(y + gap, ui), col = barcol, lwd = lwd, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(x, ui, x, pmin(y + gap, ui), col = barcol, lwd = lwd, :
## zero-length arrow is of indeterminate angle and so skipped## Warning in plot.xy(xy.coords(x, y), type = type, ...): "frame" is not a
## graphical parameter## Warning in axis(1, at = 1:length(means), labels = legends, ...): "frame" is not
## a graphical parameter## Warning in plot.xy(xy.coords(x, y), type = type, ...): "frame" is not a
## graphical parameter
PlantGrowth <- Data %>%
reorder_levels(Location, order = c("Catabaan", "RSK", "Boulevard"))PlantGrowth %>%
group_by(Location) %>%
get_summary_stats(`Shannon's Index`, type = "common")## # A tibble: 3 × 11
## Location variable n min max median iqr mean sd se ci
## <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Catabaan Shannon's In… 3 0 0 0 0 0 0 0 0
## 2 RSK Shannon's In… 3 0 0 0 0 0 0 0 0
## 3 Boulevard Shannon's In… 3 0 0.377 0.199 0.189 0.192 0.189 0.109 0.469ggboxplot(PlantGrowth, x = "Location", y = "Shannon's Index", fill="Location")
Kruskal-wallis Test
res.kruskal <- PlantGrowth %>% kruskal_test(`Shannon's Index` ~ Location)
res.kruskal## # A tibble: 1 × 6
## .y. n statistic df p method
## * <chr> <int> <dbl> <int> <dbl> <chr>
## 1 Shannon's Index 9 4.50 2 0.105 Kruskal-WallisBased on the p-value, there is no significant difference was observed between the group pairs in terms of length.
Pairwise Comparisons
res1<- PlantGrowth %>%
dunn_test(`Shannon's Index` ~ Location, p.adjust.method = "bonferroni")
res1## # A tibble: 3 × 9
## .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 Shannon's Index Cataba… RSK 3 3 0 1 1 ns
## 2 Shannon's Index Cataba… Boule… 3 3 1.84 0.0662 0.199 ns
## 3 Shannon's Index RSK Boule… 3 3 1.84 0.0662 0.199 nsShannon’s Index (Substrate Type)
group_by(Data, `Substrate Type`) %>%
summarise(
count = n(),
mean = mean(`Shannon's Index`, na.rm = TRUE),
sd = sd(`Shannon's Index`, na.rm = TRUE)
)## # A tibble: 3 × 4
## `Substrate Type` count mean sd
## <chr> <int> <dbl> <dbl>
## 1 Intertidal Rocky Shores 3 0.126 0.218
## 2 Sandy Beaches 3 0 0
## 3 Sea Grass Beds 3 0.0663 0.115library("ggpubr")
ggboxplot(Data, x = "Substrate Type", y = "Shannon's Index",
color = "Substrate Type", palette = c("#00AFBB", "#E7B800", "#FC4E07"),
order = c("Intertidal Rocky Shores", "Sandy Beaches", "Sea Grass Beds"),
ylab = "Shannon's Index", xlab = "Substrate Type")
ggline(Data, x = "Substrate Type", y = "Shannon's Index",
add = c("mean_se", "jitter"),
order = c("Intertidal Rocky Shores", "Sandy Beaches", "Sea Grass Beds"),
ylab = "Shannon's Index", xlab = "Substrate Type")
library("gplots")
plotmeans(`Shannon's Index` ~ `Substrate Type`, data = Data, frame = TRUE,
xlab = "Substrate Type", ylab = "Shannon's Index",
main="Mean Plot with 95% CI")## Warning in arrows(x, li, x, pmax(y - gap, li), col = barcol, lwd = lwd, :
## zero-length arrow is of indeterminate angle and so skipped## Warning in arrows(x, ui, x, pmin(y + gap, ui), col = barcol, lwd = lwd, :
## zero-length arrow is of indeterminate angle and so skipped## Warning in plot.xy(xy.coords(x, y), type = type, ...): "frame" is not a
## graphical parameter## Warning in axis(1, at = 1:length(means), labels = legends, ...): "frame" is not
## a graphical parameter## Warning in plot.xy(xy.coords(x, y), type = type, ...): "frame" is not a
## graphical parameter
One-way ANOVA
res.aov <- aov(`Shannon's Index` ~ `Substrate Type`, data = Data)
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## `Substrate Type` 2 0.02371 0.01186 0.587 0.585
## Residuals 6 0.12115 0.02019Since p-value = 0.585 < 0.05, it is conclusive that we fail to reject the null hypothesis, that is, there is no significant difference between the substrate type in terms of Shannon’s index
Population (Location)
group_by(Data, `Location`) %>%
summarise(
count = n(),
mean = mean(`Population`, na.rm = TRUE),
sd = sd(`Population`, na.rm = TRUE)
)## # A tibble: 3 × 4
## Location count mean sd
## <chr> <int> <dbl> <dbl>
## 1 Boulevard 3 36.3 4.04
## 2 Catabaan 3 24 6.56
## 3 RSK 3 21.7 5.69library("ggpubr")
ggboxplot(Data, x = "Location", y = "Population",
color = "Location", palette = c("#00AFBB", "#E7B800", "#FC4E07"),
order = c("Catabaan", "RSK", "Boulevard"),
ylab = "Population", xlab = "Location")
ggline(Data, x = "Location", y = "Population",
add = c("mean_se", "jitter"),
order = c("Catabaan", "RSK", "Boulevard"),
ylab = "Population", xlab = "Location")
library("gplots")
plotmeans(`Population` ~ `Location`, data = Data, frame = TRUE,
xlab = "Location", ylab = "Population",
main="Mean Plot with 95% CI")## Warning in plot.xy(xy.coords(x, y), type = type, ...): "frame" is not a
## graphical parameter## Warning in axis(1, at = 1:length(means), labels = legends, ...): "frame" is not
## a graphical parameter## Warning in plot.xy(xy.coords(x, y), type = type, ...): "frame" is not a
## graphical parameter
One-way ANOVA
res.aov <- aov(`Population` ~ `Location`, data = Data)
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Location 2 372.7 186.33 6.098 0.0359 *
## Residuals 6 183.3 30.56
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Since p-value = 0.0359 < 0.05, it is conclusive that we reject the null hypothesis, that is, there is significant difference between the location in terms of population.
TukeyHSD(res.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Population ~ Location, data = Data)
##
## $Location
## diff lwr upr p adj
## Catabaan-Boulevard -12.333333 -26.18154 1.5148774 0.0759949
## RSK-Boulevard -14.666667 -28.51488 -0.8184559 0.0400674
## RSK-Catabaan -2.333333 -16.18154 11.5148774 0.8661040Population (Substrate Type)
group_by(Data, `Substrate Type`) %>%
summarise(
count = n(),
mean = mean(`Population`, na.rm = TRUE),
sd = sd(`Population`, na.rm = TRUE)
)## # A tibble: 3 × 4
## `Substrate Type` count mean sd
## <chr> <int> <dbl> <dbl>
## 1 Intertidal Rocky Shores 3 27.7 4.51
## 2 Sandy Beaches 3 24 11.3
## 3 Sea Grass Beds 3 30.3 10.0library("ggpubr")
ggboxplot(Data, x = "Substrate Type", y = "Population",
color = "Substrate Type", palette = c("#00AFBB", "#E7B800", "#FC4E07"),
order = c("Intertidal Rocky Shores", "Sandy Beaches", "Sea Grass Beds"),
ylab = "Population", xlab = "Substrate Type")
ggline(Data, x = "Substrate Type", y = "Population",
add = c("mean_se", "jitter"),
order = c("Intertidal Rocky Shores", "Sandy Beaches", "Sea Grass Beds"),
ylab = "Population", xlab = "Substrate Type")
library("gplots")
plotmeans(`Population` ~ `Substrate Type`, data = Data, frame = TRUE,
xlab = "Substrate Type", ylab = "Population",
main="Mean Plot with 95% CI")## Warning in plot.xy(xy.coords(x, y), type = type, ...): "frame" is not a
## graphical parameter## Warning in axis(1, at = 1:length(means), labels = legends, ...): "frame" is not
## a graphical parameter## Warning in plot.xy(xy.coords(x, y), type = type, ...): "frame" is not a
## graphical parameter
One-way ANOVA
res.aov <- aov(`Population` ~ `Substrate Type`, data = Data)
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## `Substrate Type` 2 60.7 30.33 0.367 0.707
## Residuals 6 495.3 82.56Since p-value = 0.707 > 0.05, it is conclusive that we fail to reject the null hypothesis, that is, there is no significant difference between the substrate type in terms of population.
Correlation Analysis
library(correlation)##
## Attaching package: 'correlation'## The following object is masked from 'package:rstatix':
##
## cor_testlibrary(corrplot)## corrplot 0.92 loadedlibrary(tidyverse)
library(psych)## Warning: package 'psych' was built under R version 4.3.3##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha## The following object is masked from 'package:car':
##
## logitcorrplot(corr = cor(Data[3:7], method = "spearman"),
addCoef.col = "black",
number.cex = 0.8,
number.digits = 2,
diag = FALSE,
bg = "grey",
outline = "black",
addgrid.col = "white",
mar = c(1,1,1,1))
pairs.panels(Data[3:7],
smooth = FALSE,
scale = FALSE,
density = TRUE,
ellipses = TRUE,
method = "spearman",
pch = 21,
bg = c("red", "yellow", "blue"),
lm = FALSE,
cor = TRUE,
jiggle = FALSE,
factor = 0,
hist.col = 4,
stars = TRUE,
ci = FALSE,
main = "Correlation Matrix of the Variables")
Correlation coefficients whose magnitude are between 0.9 and 1.0
indicate variables which can be considered very highly correlated.
Correlation coefficients whose magnitude are between 0.7 and 0.9
indicate variables which can be considered highly correlated.
Correlation coefficients whose magnitude are between 0.5 and 0.7
indicate variables which can be considered moderately correlated.
Correlation coefficients whose magnitude are between 0.3 and 0.5
indicate variables which have a low correlation. Correlation
coefficients whose magnitude are less than 0.3 have little if any
(linear) correlation.
multiple <- lm( `Shannon's Index`~ `Salinity` + `Water Temperature` + `pH Level`, data = Data)
multiple##
## Call:
## lm(formula = `Shannon's Index` ~ Salinity + `Water Temperature` +
## `pH Level`, data = Data)
##
## Coefficients:
## (Intercept) Salinity `Water Temperature`
## 1.914349 0.005509 0.092164
## `pH Level`
## -0.591327library(performance)
check_model(multiple)
multiple1 <- lm( `Population`~ `Salinity` + `Water Temperature` + `pH Level`, data = Data)
multiple1##
## Call:
## lm(formula = Population ~ Salinity + `Water Temperature` + `pH Level`,
## data = Data)
##
## Coefficients:
## (Intercept) Salinity `Water Temperature`
## 38.8721 0.1209 5.1989
## `pH Level`
## -20.9810library(performance)
check_model(multiple1)
summary(multiple)##
## Call:
## lm(formula = `Shannon's Index` ~ Salinity + `Water Temperature` +
## `pH Level`, data = Data)
##
## Residuals:
## 1 2 3 4 5 6 7 8
## -0.023295 -0.012395 0.168716 0.017811 0.005489 -0.196604 -0.005548 0.017169
## 9
## 0.028657
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.914349 7.497661 0.255 0.809
## Salinity 0.005509 0.010622 0.519 0.626
## `Water Temperature` 0.092164 0.093725 0.983 0.371
## `pH Level` -0.591327 1.306208 -0.453 0.670
##
## Residual standard error: 0.1177 on 5 degrees of freedom
## Multiple R-squared: 0.5216, Adjusted R-squared: 0.2345
## F-statistic: 1.817 on 3 and 5 DF, p-value: 0.2609summary(multiple1)##
## Call:
## lm(formula = Population ~ Salinity + `Water Temperature` + `pH Level`,
## data = Data)
##
## Residuals:
## 1 2 3 4 5 6 7 8 9
## -1.3595 5.7905 -5.0552 -5.4498 -4.4193 0.4412 6.5574 -0.9229 4.4177
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 38.8721 375.3735 0.104 0.922
## Salinity 0.1209 0.5318 0.227 0.829
## `Water Temperature` 5.1989 4.6924 1.108 0.318
## `pH Level` -20.9810 65.3958 -0.321 0.761
##
## Residual standard error: 5.894 on 5 degrees of freedom
## Multiple R-squared: 0.6875, Adjusted R-squared: 0.5001
## F-statistic: 3.667 on 3 and 5 DF, p-value: 0.0979The statistical output displays the coded coefficients, which are the standardized coefficients. pH level has the standardized coefficient with the largest absolute value. This measure suggests that pH level is the most important independent variable in the regression model.