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A comparison of the effects of five insect pesticides on the

insect biomass in treated plots.

#

Introduction

The data are given in biomass.txt are taken from an experiment

in which the insect pest biomass (g) was measured on plots sprayed

with water (control) or one of five different insecticides.

The goal of the analysis was to determine if the insecticides

vary in their effectiveness and specifically advise on:

- use of insecticide E

- the choice between A and D

- the choice between C and B

The data are organised with an insecticide treatment group in

each column:

'data.frame': 10 obs. of 6 variables:

$ WaterControl: num 350 324 359 255 208 ...

$ A : num 159 146 116 135 137 ...

$ B : num 150.1 154.4 69.5 150.7 212.6 ...

$ C : num 80 266.4 161.2 161.4 51.2 ...

$ D : num 267 110 221 160 198 ...

$ E : num 350 320 359 255 208 ...

Import and tidy data

data are in ../data

biom <- read.table("../data/biomass.txt", header = T)

check structure

str(biom)

'data.frame': 10 obs. of 6 variables:

$ WaterControl: num 350 324 359 255 208 ...

$ A : num 159 146 116 135 137 ...

$ B : num 150.1 154.4 69.5 150.7 212.6 ...

$ C : num 80 266.4 161.2 161.4 51.2 ...

$ D : num 267 110 221 160 198 ...

$ E : num 350 320 359 255 208 ...

The data are organised with an insecticide treatment group in

each column. Put the data into tidy format.

biom <- gather(biom, key = spray, value = biomass)

Exploratory Analysis

quick plot of the data

ggplot(data = biom, aes(x = spray, y = biomass)) + geom_boxplot()

summary statistics

biomsum <- biom %>% group_by(spray) %>% summarise(mean = mean(biomass), median = median(biomass), sd = sd(biomass), n = length(biomass), se = sd / sqrt(n))

conclusion: the sample sizes are equal, 10 is a smallish but

reasonable sample size

the means and medians are similar to each other (expected for

normally distributed data), A has a smaller variance

We have one explanatory variable, "spray" comprising 6 levels

Biomass has decimal places and we would expect such data to be

normally distributed therefore one-way ANOVA is the desired test

- we will check the assumptions after building the model

Statistical Analysis

Carrying out an ANOVA

model <- aov(data = biom, biomass ~ spray) summary(model)

There is a very highly signifcant effect of spray identity on pest

biomass (F = 26.5; d.f., 5, 54; p < 0.001).

Carrying out a Tukey Honest Signifcant differences test

to see where differences bewteen spray treatments lie

ordering can make it easier to understand. Plot included

TukeyHSD(model, ordered = T) plot(TukeyHSD(model, ordered = T), cex.axis = 0.5)

signifcant comparisions

diff lwr upr p adj

D-A 76.505 11.729841 141.28016 0.0118570

E-A 175.515 110.739841 240.29016 0.0000000

WaterControl-A 175.915 111.139841 240.69016 0.0000000

E-C 155.710 90.934841 220.48516 0.0000000

WaterControl-C 156.110 91.334841 220.88516 0.0000000

E-B 154.323 89.547841 219.09816 0.0000001

WaterControl-B 154.723 89.947841 219.49816 0.0000000

E-D 99.010 34.234841 163.78516 0.0004759

WaterControl-D 99.410 34.634841 164.18516 0.0004477

Note smaller values (insect biomass) indicates more

effective control

All sprays are better than the water control except E.

This is probably the most important result.

What advice would you give to a person currently using insecticide E?

Don't bother!! It's no better than water. Switch to any of

the other sprays

Sorting the summary table by the mean can make it easier to

interpret the results

arrange(biomsum, mean)

What advice would you give to a person currently

+ trying to choose between A and D? Choose A because A has sig lower

insect biomass than D

+ trying to choose between C and B? It doesn't matter because there is

no differnce in insect bbiomass. Use other criteria to chose (e.g., price)

We might report this like:

There is a very highly signifcant effect of spray identity on pest

biomass (F = 26.5; d.f., 5, 54; p < 0.001). Post-hoc testing

showed E was no more effective than the control; A, C and B were

all better than the control but could be equally as good as each

other; D would be a better choice than the control or E but

worse than A. See figure 1

Figure

uses the summary data

I reordered the bars to make is easier for me to annotate with

ggplot() + geom_point(data = biom, aes(x = reorder(spray, biomass), y = biomass), position = position_jitter(width = 0.1, height = 0), colour = "gray50") + geom_errorbar(data = biomsum, aes(x = spray, ymin = mean - se, ymax = mean + se), width = 0.3) + geom_errorbar(data = biomsum, aes(x = spray, ymin = mean, ymax = mean), width = 0.2) + ylim(0, 520) + ylab("Pest Biomass (units)") + xlab("Spray treatment") + # E and control are one group annotate("segment", x = 4.5, xend = 6.5, y = 397, yend = 397, colour = "black", size = 1) + annotate("text", x = 5.5, y = 385, label = "N.S", size = 4) + # WaterControl-D and E-D annotate("segment", x = 4, xend = 5.5, y = 410, yend = 410, colour = "black") + annotate("segment", x = 4, xend = 4, y = 410, yend = 400, colour = "black") + annotate("segment", x = 5.5, xend = 5.5, y = 410, yend = 400, colour = "black") + annotate("text", x = 4.5, y = 420, label = "", size = 5) + # WaterControl-B annotate("segment", x = 3, xend = 5.5, y = 440, yend = 440, colour = "black") + annotate("segment", x = 3, xend = 3, y = 440, yend = 430, colour = "black") + annotate("segment", x = 5.5, xend = 5.5, y = 440, yend = 430, colour = "black") + annotate("text", x = 4, y = 450, label = "", size = 5) + # WaterControl-C annotate("segment", x = 2, xend = 5.5, y = 475, yend = 475, colour = "black") + annotate("segment", x = 2, xend = 2, y = 475, yend = 465, colour = "black") + annotate("segment", x = 5.5, xend = 5.5, y = 475, yend = 465, colour = "black") + annotate("text", x = 3.5, y = 485, label = "", size = 5) + # WaterControl-A annotate("segment", x = 1, xend = 5.5, y = 510, yend = 510, colour = "black") + annotate("segment", x = 1, xend = 1, y = 510, yend = 500, colour = "black") + annotate("segment", x = 5.5, xend = 5.5, y = 510, yend = 500, colour = "black") + annotate("text", x = 3.5, y = 520, label = "", size = 5) +
# A-D ** annotate("segment", x = 1, xend = 4, y = 330, yend = 330, colour = "black") + annotate("segment", x = 1, xend = 1, y = 330, yend = 320, colour = "black") + annotate("segment", x = 4, xend = 4, y = 330, yend = 320, colour = "black") + annotate("text", x = 2.5, y = 335, label = "", size = 5) + theme_classic()

Figure 1. The mean pest biomass following various insecticide treatments.

Error bars are +/- 1 S.E. Significant comparisons are indicated.

carlos-r-git/scriptsearch documentation built on Sept. 1, 2020, 6:38 p.m.