Hello DRUGs,
I'm new to R and would appreciate some expert advice on prepping files for,
and running, PCA...
My data set consists of aquatic invertebrate and zooplankton count data and
physicochemical measurements from an ecotoxicology study. Four chemical
treatments were applied to mesocosm tanks, 4 replicates per treatment (16
tanks total), then data were collected weekly over a 3 month period.
I cleaned the data in excel by removing columns with all zero values, and
all rows with NA values.
All zooplankton values were volume normalized, then log normalized. All
other data was log normalized in excel prior to analysis in R. All vectorss
are numeric. I've attached the .csv file to this email rather that using
dput(dataframe). I hope that's acceptable.
My questions are:
1. Did I do the cleaning step appropriately? I know that there are ways to
run PCA's using data that contain NA values (pcaMethods), but wasn't
able
to get the code to work...
(I understand that this isn't strictly an R question, but any help would be
appreciated.)
2. Does my code look correct for the PCA and visualization (see below)?
Thanks in advance,
Sarah
#read data
mesocleaned <- read.csv("MesoCleanedforPCA.9.16.16.csv")
#run PCA
meso.pca <- prcomp(mesocleaned,
center = TRUE,
scale. = TRUE)
# print method
print(meso.pca)
#compute standard deviation of each principal component
std_dev <- meso.pca$sdev
#compute variance
pr_var <- std_dev^2
#check variance of first 10 components
pr_var[1:10]
#proportion of variance explained
prop_varex <- pr_var/sum(pr_var)
prop_varex[1:20]
#The first principal component explains 12.7% of the variance
#The second explains 8.1%
#visualize
biplot(meso.pca)
#for visualization, make Treatment vector a factor instead of numeric
meso.treatment <- as.factor(mesocleaned[, 3])
#ggbiplot to visualize by Treatment group
#reference: https://www.r-bloggers.com/computing-and-visualizing-pca-in-r/
library(devtools)
install_github("ggbiplot", "vqv")
library(ggbiplot)
print(ggbiplot(meso.pca, obs.scale = 1, var.scale = 1, groups meso.treatment,
ellipse = TRUE, circle = TRUE))
g <- ggbiplot(meso.pca, obs.scale = 1, var.scale = 1,
groups = meso.treatment, ellipse = TRUE,
circle = TRUE)
g <- g + scale_color_brewer(name = deparse(substitute(Treatments)), palette
= 'Dark2') #must change meso.treatment to a factor for this to work
g <- g + theme(legend.direction = 'horizontal',
legend.position = 'top')
print(g)
#Circle plot
#plot each variables coefficients inside a unit circle to get insight on a
possible interpretation for PCs.
#reference: https://www.r-bloggers.com/computing-and-visualizing-pca-in-r/
theta <- seq(0,2*pi,length.out = 100)
circle <- data.frame(x = cos(theta), y = sin(theta))
p <- ggplot(circle,aes(x,y)) + geom_path()
loadings <- data.frame(meso.pca$rotation,
.names = row.names(meso.pca$rotation))
p + geom_text(data=loadings,
mapping=aes(x = PC1, y = PC2, label = .names, colour .names)) +
coord_fixed(ratio=1) +
labs(x = "PC1", y = "PC2")
It was not acceptable. Files with a .csv extension are stripped by the list. If
you rename it as .txt it should survive. It appears that you have a controlled
experimental design with explanatory and response variables, so why are you
using pca which lumps them together? Alternatives might be canonical
correlations or canonical correspondence analysis that would let you analyze the
count data in terms of the treatments.
-------------------------------------
David L Carlson
Department of Anthropology
Texas A&M University
College Station, TX 77840-4352
-----Original Message-----
From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Sarah Stinson
Sent: Wednesday, September 21, 2016 12:28 AM
To: r-help at r-project.org
Subject: [R] Help with PCA data file prep and R code
Hello DRUGs,
I'm new to R and would appreciate some expert advice on prepping files for,
and running, PCA...
My data set consists of aquatic invertebrate and zooplankton count data and
physicochemical measurements from an ecotoxicology study. Four chemical
treatments were applied to mesocosm tanks, 4 replicates per treatment (16
tanks total), then data were collected weekly over a 3 month period.
I cleaned the data in excel by removing columns with all zero values, and
all rows with NA values.
All zooplankton values were volume normalized, then log normalized. All
other data was log normalized in excel prior to analysis in R. All vectorss
are numeric. I've attached the .csv file to this email rather that using
dput(dataframe). I hope that's acceptable.
My questions are:
1. Did I do the cleaning step appropriately? I know that there are ways to
run PCA's using data that contain NA values (pcaMethods), but wasn't
able
to get the code to work...
(I understand that this isn't strictly an R question, but any help would be
appreciated.)
2. Does my code look correct for the PCA and visualization (see below)?
Thanks in advance,
Sarah
#read data
mesocleaned <- read.csv("MesoCleanedforPCA.9.16.16.csv")
#run PCA
meso.pca <- prcomp(mesocleaned,
center = TRUE,
scale. = TRUE)
# print method
print(meso.pca)
#compute standard deviation of each principal component
std_dev <- meso.pca$sdev
#compute variance
pr_var <- std_dev^2
#check variance of first 10 components
pr_var[1:10]
#proportion of variance explained
prop_varex <- pr_var/sum(pr_var)
prop_varex[1:20]
#The first principal component explains 12.7% of the variance
#The second explains 8.1%
#visualize
biplot(meso.pca)
#for visualization, make Treatment vector a factor instead of numeric
meso.treatment <- as.factor(mesocleaned[, 3])
#ggbiplot to visualize by Treatment group
#reference: https://www.r-bloggers.com/computing-and-visualizing-pca-in-r/
library(devtools)
install_github("ggbiplot", "vqv")
library(ggbiplot)
print(ggbiplot(meso.pca, obs.scale = 1, var.scale = 1, groups meso.treatment,
ellipse = TRUE, circle = TRUE))
g <- ggbiplot(meso.pca, obs.scale = 1, var.scale = 1,
groups = meso.treatment, ellipse = TRUE,
circle = TRUE)
g <- g + scale_color_brewer(name = deparse(substitute(Treatments)), palette
= 'Dark2') #must change meso.treatment to a factor for this to work
g <- g + theme(legend.direction = 'horizontal',
legend.position = 'top')
print(g)
#Circle plot
#plot each variables coefficients inside a unit circle to get insight on a
possible interpretation for PCs.
#reference: https://www.r-bloggers.com/computing-and-visualizing-pca-in-r/
theta <- seq(0,2*pi,length.out = 100)
circle <- data.frame(x = cos(theta), y = sin(theta))
p <- ggplot(circle,aes(x,y)) + geom_path()
loadings <- data.frame(meso.pca$rotation,
.names = row.names(meso.pca$rotation))
p + geom_text(data=loadings,
mapping=aes(x = PC1, y = PC2, label = .names, colour .names)) +
coord_fixed(ratio=1) +
labs(x = "PC1", y = "PC2")
______________________________________________
R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Hello DRUGs,
I'm new to R and would appreciate some expert advice on prepping files for,
and running, PCA...
My data set consists of aquatic invertebrate and zooplankton count data and
physicochemical measurements from an ecotoxicology study. Four chemical
treatments were applied to mesocosm tanks, 4 replicates per treatment (16
tanks total), then data were collected weekly over a 3 month period.
I cleaned the data in excel by removing columns with all zero values, and
all rows with NA values.
All zooplankton values were volume normalized, then log normalized. All
other data was log normalized in excel prior to analysis in R. All vectorss
are numeric. I've attached the .txt file to this email rather that using
dput(dataframe).
My questions are:
1. Did I do the cleaning step appropriately? I know that there are ways to
run PCA's using data that contain NA values (pcaMethods), but wasn't
able
to get the code to work...
(I understand that this isn't strictly an R question, but any help would be
appreciated.)
2. Does my code look correct for the PCA and visualization (see below)?
Thanks in advance,
Sarah
#read data
mesocleaned <- read.csv("MesoCleanedforPCA.9.16.16.csv")
#run PCA
meso.pca <- prcomp(mesocleaned,
center = TRUE,
scale. = TRUE)
# print method
print(meso.pca)
#compute standard deviation of each principal component
std_dev <- meso.pca$sdev
#compute variance
pr_var <- std_dev^2
#check variance of first 10 components
pr_var[1:10]
#proportion of variance explained
prop_varex <- pr_var/sum(pr_var)
prop_varex[1:20]
#The first principal component explains 12.7% of the variance
#The second explains 8.1%
#visualize
biplot(meso.pca)
#for visualization, make Treatment vector a factor instead of numeric
meso.treatment <- as.factor(mesocleaned[, 3])
#ggbiplot to visualize by Treatment group
#reference: https://www.r-bloggers.com/computing-and-visualizing-pca-in-r/
library(devtools)
install_github("ggbiplot", "vqv")
library(ggbiplot)
print(ggbiplot(meso.pca, obs.scale = 1, var.scale = 1, groups meso.treatment,
ellipse = TRUE, circle = TRUE))
g <- ggbiplot(meso.pca, obs.scale = 1, var.scale = 1,
groups = meso.treatment, ellipse = TRUE,
circle = TRUE)
g <- g + scale_color_brewer(name = deparse(substitute(Treatments)), palette
= 'Dark2') #must change meso.treatment to a factor for this to work
g <- g + theme(legend.direction = 'horizontal',
legend.position = 'top')
print(g)
#Circle plot
#plot each variables coefficients inside a unit circle to get insight on a
possible interpretation for PCs.
#reference: https://www.r-bloggers.com/computing-and-visualizing-pca-in-r/
theta <- seq(0,2*pi,length.out = 100)
circle <- data.frame(x = cos(theta), y = sin(theta))
p <- ggplot(circle,aes(x,y)) + geom_path()
loadings <- data.frame(meso.pca$rotation,
.names = row.names(meso.pca$rotation))
p + geom_text(data=loadings,
mapping=aes(x = PC1, y = PC2, label = .names, colour .names)) +
coord_fixed(ratio=1) +
labs(x = "PC1", y = "PC2")
On Tue, Sep 20, 2016 at 10:28 PM, Sarah Stinson <sastinson at ucdavis.edu>
wrote:
> Hello DRUGs,
> I'm new to R and would appreciate some expert advice on prepping files
> for, and running, PCA...
>
> My data set consists of aquatic invertebrate and zooplankton count data
> and physicochemical measurements from an ecotoxicology study. Four chemical
> treatments were applied to mesocosm tanks, 4 replicates per treatment (16
> tanks total), then data were collected weekly over a 3 month period.
>
> I cleaned the data in excel by removing columns with all zero values, and
> all rows with NA values.
> All zooplankton values were volume normalized, then log normalized. All
> other data was log normalized in excel prior to analysis in R. All vectorss
> are numeric. I've attached the .csv file to this email rather that
using
> dput(dataframe). I hope that's acceptable.
>
> My questions are:
>
> 1. Did I do the cleaning step appropriately? I know that there are ways to
> run PCA's using data that contain NA values (pcaMethods), but
wasn't able
> to get the code to work...
> (I understand that this isn't strictly an R question, but any help
would
> be appreciated.)
> 2. Does my code look correct for the PCA and visualization (see below)?
>
> Thanks in advance,
> Sarah
>
> #read data
> mesocleaned <- read.csv("MesoCleanedforPCA.9.16.16.csv")
>
> #run PCA
> meso.pca <- prcomp(mesocleaned,
> center = TRUE,
> scale. = TRUE)
>
> # print method
> print(meso.pca)
>
> #compute standard deviation of each principal component
> std_dev <- meso.pca$sdev
>
> #compute variance
> pr_var <- std_dev^2
>
> #check variance of first 10 components
> pr_var[1:10]
>
> #proportion of variance explained
> prop_varex <- pr_var/sum(pr_var)
> prop_varex[1:20]
>
> #The first principal component explains 12.7% of the variance
> #The second explains 8.1%
>
> #visualize
> biplot(meso.pca)
>
> #for visualization, make Treatment vector a factor instead of numeric
> meso.treatment <- as.factor(mesocleaned[, 3])
>
> #ggbiplot to visualize by Treatment group
> #reference: https://www.r-bloggers.com/computing-and-visualizing-pca-in-r/
>
> library(devtools)
> install_github("ggbiplot", "vqv")
> library(ggbiplot)
>
> print(ggbiplot(meso.pca, obs.scale = 1, var.scale = 1, groups >
meso.treatment, ellipse = TRUE, circle = TRUE))
> g <- ggbiplot(meso.pca, obs.scale = 1, var.scale = 1,
> groups = meso.treatment, ellipse = TRUE,
> circle = TRUE)
> g <- g + scale_color_brewer(name = deparse(substitute(Treatments)),
> palette = 'Dark2') #must change meso.treatment to a factor for this
to work
> g <- g + theme(legend.direction = 'horizontal',
> legend.position = 'top')
> print(g)
>
> #Circle plot
> #plot each variables coefficients inside a unit circle to get insight on a
> possible interpretation for PCs.
> #reference: https://www.r-bloggers.com/computing-and-visualizing-pca-in-r/
>
> theta <- seq(0,2*pi,length.out = 100)
> circle <- data.frame(x = cos(theta), y = sin(theta))
> p <- ggplot(circle,aes(x,y)) + geom_path()
>
> loadings <- data.frame(meso.pca$rotation,
> .names = row.names(meso.pca$rotation))
> p + geom_text(data=loadings,
> mapping=aes(x = PC1, y = PC2, label = .names, colour >
.names)) +
> coord_fixed(ratio=1) +
> labs(x = "PC1", y = "PC2")
>
>
-------------- next part --------------
Tank # Date Treatment Temperature meter (C ) pH DO mg DO percent EC SC
Ephemeroptera Dysticidae Tipulidae Chironomidae Chaoboridae Anopholes Culex
Diptera Zygoptera Anisoptera Odonata Gastropoda Corixidae Hyalella Nematoda
Hydrachnidae Cyclopoida Calanoida Nauplia Bosminidae Chydoridae Ceriodaphnia
Ostracoda Keratellahiemalis Keratellacochlearis Anuraeopsisspec. Notholcaspec.
Brachionusangularis Brachionusquadridentatus Mytilinaventralis Platyiaspatulus
Trichocercassp. Polyarthrasp. Rotifera
1 08.26.15 control 19.4 8.72 8.15 85.9 719 799 0 0 0 1.361727836 1.230448921
0.698970004 1.968482949 2.155336037 0 0.954242509 0.954242509 0.954242509 0 0 0
0.477121255 2.653490783 0 2.178294218 2.874953569 2.778188332 2.778188332
3.719554865 0 0 0 0 0 3.254826811 0 0 0 0 3.254826811
2 08.26.15 control 18.9 9.3 9.23 96.9 634 718 1.462397998 0 0 1.113943352
0.84509804 0 0 1.278753601 0.698970004 1.397940009 1.462397998 0 0 0.954242509
0.698970004 0.477121255 3.254826811 0 2.178294218 0 3.537257874 3.430837555
3.874431973 0 0 0 0 0 3.988361943 2.178294218 0 0 0 6.166656161
3 08.26.15 control 18.7 9.23 8.05 84.7 642 731 2.012837225 0 0 1.491361694
0.954242509 0.698970004 2.103803721 2.23299611 0 1.278753601 1.278753601
0.477121255 0 0.954242509 0 0.698970004 0 0 2.134399087 0 3.034691437
2.976756765 3.034691437 0 0 0 0 0 0 0 0 0 0 0
4 08.26.15 control 18.5 9.46 9.07 95.7 633 724 2.021189299 0 0 1.176091259
0.84509804 0.477121255 0 1.431363764 0.698970004 1.230448921 1.322219295
0.698970004 0 0 0.698970004 0.477121255 0 0 2.148536707 0 2.623596445
4.245835955 2.448021484 2.148536707 0 0 0 0 2.148536707 2.148536707 0 0 0
6.445610121
5 08.26.15 high diuron 18.4 9.34 8.56 90.4 622 711 1.875061263 0 0 1.544068044
1.875061263 0 1.230448921 2.11058971 1.431363764 0.954242509 1.544068044
1.72427587 0 1.653212514 0 0.954242509 2.734062209 0 2.134399087 0 0 4.188133326
3.245390563 0 0 0 0 0 2.134399087 0 0 2.134399087 0 4.268798174
6 08.26.15 high diuron 18.7 9.27 8.47 89.69 614 699 1.959041392 0 0 1.431363764
1.113943352 0 1.113943352 1.770852012 0 1.176091259 1.176091259 0.698970004 0
0.698970004 0.84509804 0.84509804 2.734062209 0 0 3.131521252 2.609390445
3.307505558 3.511545308 0 0 0 0 0 3.687591987 0 0 0 0 3.687591987
7 08.26.15 high diuron 18.6 9.17 8.26 87.3 680 773 1.591064607 0 0 1.113943352
1.113943352 0 0.698970004 1.462397998 0 1.176091259 1.176091259 0 0 0.477121255
0.698970004 0.698970004 2.107452407 0 0 0 0 3.672342151 2.406783568 0 0 0 0 0
4.450416436 0 0 2.582307069 0 7.032723505
8 08.26.15 high diuron 18.4 9.26 8.86 93.6 640 732 2.012837225 0 0 1.113943352
1.230448921 0.698970004 1.230448921 1.69019608 0.477121255 1.230448921
1.278753601 0 0 0 0 0.84509804 0 0 2.43383264 0 3.210649025 3.493067717
2.909886331 0 0 0 0 0 3.963756437 2.134399087 0 0 0 6.098155524
9 08.26.15 low diuron bifenthrin 18.1 9.16 7.84 82.7 695 800 1.230448921 0 0
1.397940009 1.770852012 0 1.851258349 2.184691431 0 0.954242509 0.954242509
0.698970004 0 0.698970004 0 0 2.859734288 0 2.462693138 0 2.638285261
3.201829649 3.063629264 0 0 0 2.16315712 0 2.16315712 0 0 2.16315712 0
6.48947136
10 08.26.15 low diuron bifenthrin 18.2 9.11 6.81 72.5 653 751 1.886490725 0 0
1.959041392 0.84509804 0 1.462397998 2.096910013 0 1.041392685 1.041392685
0.477121255 0 0 0.84509804 0 2.148536707 0 2.623596445 2.148536707 3.187115801
3.29179066 2.845031715 0 0 0 0 0 4.467674841 0 0 0 0 4.467674841
11 08.26.15 low diuron bifenthrin 18.2 9.26 8.52 91 609 700 1.740362689 0
0.698970004 1.568201724 1.278753601 0.698970004 1.113943352 1.875061263
0.477121255 1.230448921 1.278753601 0.954242509 0 0 0.477121255 0.477121255
3.020916086 0 2.778188332 0 3.537257874 4.321552724 3.883031008 0 0 0 0 0 0
2.178294218 0 0 0 2.178294218
12 08.26.15 low diuron bifenthrin 18.2 9.38 7.71 82.6 629 725 1.633468456 0 0
1.230448921 0.698970004 0.477121255 2.247973266 2.298853076 0 1.176091259
1.176091259 0.698970004 0 0 0.698970004 1.113943352 0 0 0 0 0 2.685606569
2.986188468 0 0 0 0 0 3.438195948 0 0 0 0 3.438195948
13 08.26.15 high diuron bifenthrin 17.5 9.37 9.29 98.2 609 710 1.51851394 0 0
1.612783857 1.591064607 0 1.041392685 1.949390007 0 0.954242509 0.954242509 0 0
0.477121255 1.113943352 0 2.193985593 0 2.193985593 0 2.493624149 3.653692654
2.89072715 0 0 0 0 0 0 0 0 0 0 0
14 08.26.15 high diuron bifenthrin 18 9.35 9.01 97.2 618 714 0.954242509 0 0
1.397940009 1.176091259 0 2.117271296 2.227886705 1.176091259 1.041392685
1.397940009 0.477121255 0 1.491361694 0 0 3.036695572 0 3.036695572 0 3.63845613
4.55669828 4.010784544 0 0 0 0 0 2.193985593 0 2.193985593 0 0 4.387971186
15 08.26.15 high diuron bifenthrin 18.3 9.38 8.66 94.5 657 753 1.949390007 0 0
1.431363764 0 0 0 1.431363764 0.477121255 1.230448921 1.278753601 0.698970004 0
0 0 0.698970004 2.448021484 0 0 0 0 2.845031715 2.623596445 0 0 0 0 0
3.400885834 0 0 0 0 3.400885834
16 08.26.15 high diuron bifenthrin 17.9 9.29 8.95 95.2 693 801 1.653212514 0 0
1.838849091 1.113943352 0 0.698970004 1.929418926 0.698970004 1.230448921
1.322219295 0.954242509 0 0 0 0.477121255 3.020916086 0 2.95403827 0 0
3.891463082 3.755263878 0 0 0 0 0 0 2.178294218 0 0 0 2.178294218
1 09.02.15 control 20 9.05 9.92 108.9 700 773 1.041392685 0 0 1.397940009
1.322219295 1.361727836 2.495544338 2.57863921 0 1.041392685 1.041392685 0 0 0 0
0.477121255 3.479283934 0 3.980780285 0 3.07202262 3.4186077 3.807675738 0 0 0 0
0 2.817044357 2.120713597 0 0 2.120713597 7.058471551
2 09.02.15 control 19.6 9.3 10.1 109.8 635 708 1.812913357 0 0 1.361727836
0.477121255 0 0.84509804 1.491361694 0 1.041392685 1.041392685 0.84509804 0
1.113943352 0 0 3.07202262 0 2.120713597 0 3.020916086 3.348055856 3.81644831 0
0 0 0 0 4.482914298 2.420095946 0 0 2.720299768 9.623310012
3 09.02.15 control 19.3 9.2 9.01 97.5 656 737 1.672097858 0 0 1.919078092
1.770852012 1.230448921 1.838849091 2.352182518 0.477121255 1.568201724
1.591064607 0.698970004 0 0.698970004 0.954242509 0.954242509 2.120713597 0
2.817044357 0 2.420095946 3.685703314 3.196869365 0 0 0 0 0 4.844145455
2.120713597 0 0 3.117743317 10.08260237
4 09.02.15 control 19.1 9.44 9.78 105.3 645 727 1.72427587 0 0 1.230448921
1.041392685 0 1.591064607 1.826074803 1.176091259 1.278753601 1.51851394
0.84509804 0 0 1.113943352 0.698970004 0 0 0 0 2.734062209 4.479519572
3.493067717 0 0 0 0 0 3.649811532 2.609390445 0 0 0 6.259201977
5 09.02.15 high diuron 19.1 9.26 9.17 98.4 625 704 1.826074803 0.477121255 0 0 0
0 2.828015064 2.828015064 1.176091259 1.113943352 1.431363764 1.491361694 0
1.462397998 0 1.041392685 3.020916086 0 2.120713597 2.595636597 2.420095946
4.056969373 2.720299768 0 0 0 0 0 2.120713597 0 0 0 0 2.120713597
6 09.02.15 high diuron 19.2 9.3 9.97 107.8 624 701 1.875061263 0 0.698970004
2.029383778 1.812913357 0.477121255 1.612783857 2.348304863 1.113943352
0.698970004 1.230448921 0.84509804 0 0 0.698970004 0 0 0 0 0 0 3.307505558
2.43383264 0 0 0 0 0 2.976756765 0 0 0 0 2.976756765
7 09.02.15 high diuron 19.3 9.19 8.89 96 703 787 1.176091259 0 0 1.653212514
1.875061263 0.698970004 1.113943352 2.130333768 0 0.84509804 0.84509804 0 0
0.84509804 0 0 2.706961648 0 2.582307069 0 2.107452407 4.548105156 4.116918472 0
0 0 0 0 4.669905172 2.803701077 0 2.107452407 2.882768563 12.46382722
8 09.02.15 high diuron 19 9.33 10.19 109.9 643 726 1.908485019 0 0 1.041392685
1.397940009 0 1.462397998 1.799340549 0.84509804 1.361727836 1.462397998 0 0 0 0
1.041392685 0 0 0 0 0 3.263776724 2.96298326 0 0 0 0 0 3.159105895 0 0 0 0
3.159105895
9 09.02.15 low diuron bifenthrin 19 9.13 9.52 102.2 714 805 1.707570176
0.698970004 0 1.176091259 0 0.698970004 2.392696953 2.423245874 0 1.230448921
1.230448921 0 0 0 0.698970004 0.477121255 2.830811959 0 2.43383264 0 3.034691437
4.396035584 3.277557615 0 0 0 0 0 3.131521252 0 0 0 2.134399087 5.265920339
10 09.02.15 low diuron bifenthrin 19 9.04 8.42 90.5 672 759 1.707570176 0 0
1.908485019 1.755874856 0 1.361727836 2.201397124 0.477121255 0 0.477121255 0 0
0 0.698970004 0.84509804 2.120713597 0 2.120713597 0 3.479283934 3.760940019
3.020916086 0 0 0 0 0 4.532398122 2.120713597 0 2.420095946 0 9.073207665
11 09.02.15 low diuron bifenthrin 19.1 9.22 9.95 102.5 616 694 1.113943352 0 0
1.544068044 0.954242509 0.698970004 1.462397998 1.875061263 0.84509804
0.954242509 1.176091259 0 0 1.113943352 0 0 0 0 0 0 2.477881497 4.038766558
3.217060206 0 0 0 0 0 2.477881497 0 0 0 0 2.477881497
12 09.02.15 low diuron bifenthrin 19.2 9.3 9.69 104.4 656 738 1.51851394 0 0
1.491361694 0.84509804 0.477121255 1.612783857 1.908485019 0 1.113943352
1.113943352 0.477121255 0 0 0 0.477121255 3.963756437 0 3.63645061 0 3.493067717
3.172884771 3.172884771 0 0 0 0 0 3.930591821 2.134399087 0 2.734062209 0
8.799053117
13 09.02.15 high diuron bifenthrin 19.2 9.25 9.86 106.9 627 705 1.51851394 0 0
1.397940009 1.322219295 0.698970004 1.785329835 2.045322979 0.477121255
0.698970004 0.84509804 0 0 1.113943352 1.041392685 0 2.448021484 0 0 0
3.29179066 3.808266122 2.448021484 0 0 0 0 0 0 0 0 0 2.92410953 2.92410953
14 09.02.15 high diuron bifenthrin 19.5 9.33 10.39 112.6 622 696 0.698970004 0 0
1.633468456 0.698970004 0.84509804 2.527629901 2.589949601 1.278753601
1.041392685 1.462397998 0.698970004 0 1.69019608 0 0 2.720299768 0 0 0
3.159105895 3.91680518 3.321739096 0 0 0 0 0 3.321739096 0 0 0 0 3.321739096
15 09.02.15 high diuron bifenthrin 19.6 9.32 9.7 105.4 682 760 1.875061263 0 0
1.491361694 0.954242509 0 1.462397998 1.826074803 0.477121255 1.113943352
1.176091259 0 0 0 0 1.041392685 2.734062209 0 0 0 0 2.830811959 3.034691437 0 0
0 0 0 0 0 0 0 0 0
16 09.02.15 high diuron bifenthrin 19 9.28 10.26 110 703 795 1.653212514 0 0
1.86332286 1.544068044 0 1.176091259 2.08278537 0.477121255 1.041392685
1.113943352 1.041392685 0 0 0.477121255 1.113943352 3.277557615 0 2.134399087 0
2.609390445 4.065736094 4.184307167 0 0 0 0 0 2.134399087 2.134399087 0 0 0
4.268798174
1 09.09.15 control 18.2 9.19 9.94 105.9 759 871 1.462397998 0 0 1.397940009
1.113943352 0 1.672097858 1.939519253 0 0 0 0.698970004 0 0 0.698970004
0.84509804 3.755263878 0 4.425840268 0 2.178294218 4.324643654 3.590488842 0 0 0
0 0 0 3.175693866 0 2.778188332 2.178294218 8.132176416
2 09.09.15 control 17 9.42 10.93 113.6 643 759 1.176091259 0 0 1.230448921
1.113943352 0.477121255 1.278753601 1.707570176 0.84509804 1.041392685
1.230448921 0.698970004 0 0.954242509 0.698970004 0.84509804 2.653490783 0 0 0
3.217060206 3.652621882 4.104857016 0 0 0 0 0 3.321739096 2.178294218 0
2.477881497 0 7.977914811
3 09.09.15 control 16.6 9.18 9.47 97 662 789 1.397940009 0 0 0.477121255
0.698970004 0.84509804 2.484299839 2.501059262 0.698970004 0.84509804
1.041392685 0 0 0.954242509 0 0.954242509 3.048918942 0 2.148536707 0
2.748276803 4.285342352 3.607945875 0 0 0 0 0 5.053929606 0 0 0 0 5.053929606
4 09.09.15 control 16.3 9.38 9.92 101.2 658 789 1.041392685 0.477121255 0
1.361727836 0.954242509 0.477121255 1.113943352 1.653212514 1.278753601
0.84509804 1.397940009 0.698970004 0 0 0.84509804 0.477121255 0 0 0 0
2.638285261 4.490591816 3.41560334 0 0 2.16315712 0 0 4.490591816 2.16315712 0 0
0 8.816906056
5 09.09.15 high diuron 16.4 9.28 8.77 89.9 647 772 1.612783857 0 0 1.041392685
0.698970004 0 2.037426498 2.089905111 1.799340549 1.176091259 1.886490725
1.672097858 0 2.257678575 0 0 4.050503902 0 3.891463082 0 2.178294218
3.573459963 3.078856296 0 0 0 0 0 2.95403827 2.178294218 0 2.477881497
3.454310174 11.06452416
6 09.09.15 high diuron 16.7 9.28 8.86 91.1 645 768 1.462397998 0 0 1.230448921
0.84509804 0.84509804 2.1430148 2.227886705 1.322219295 0.954242509 1.462397998
0.477121255 0 0 0 1.041392685 2.178294218 0 0 0 2.477881497 3.755263878
3.931329703 0 0 0 0 0 3.254826811 0 0 2.178294218 2.178294218 7.611415247
7 09.09.15 high diuron 16.7 9.17 8.46 87.1 708 841 1.591064607 0.477121255 0
1.653212514 0 0.477121255 1.908485019 2.103803721 0.698970004 0.954242509
1.113943352 0.698970004 0 0.477121255 0 0.698970004 2.95403827 0 3.020916086 0
2.477881497 4.392905494 3.694005781 0 0 0 0 0 4.491388329 2.477881497 0
2.178294218 3.020916086 12.16848013
8 09.09.15 high diuron 16.1 9.3 9.23 93.9 661 796 1.568201724 0.477121255 0
1.176091259 0.477121255 0.84509804 1.838849091 1.968482949 1.51851394
1.041392685 1.633468456 0 0 0 0.698970004 0.84509804 3.020916086 0 3.078856296 0
0 4.463220652 2.178294218 0 0 0 0 0 4.073067772 0 0 0 0 4.073067772
9 09.09.15 low diuron bifenthrin 16.3 9.25 9.22 94.1 713 852 1.568201724 0 0
1.230448921 1.113943352 0.954242509 2.423245874 2.478566496 0 0.954242509
0.954242509 0.477121255 0 0 0.477121255 1.113943352 2.778188332 0 2.778188332 0
3.217060206 4.460976309 3.175693866 0 0 0 0 0 2.653490783 0 0 0 2.178294218
4.831785001
10 09.09.15 low diuron bifenthrin 16.1 9.18 9.14 93.2 675 813 1.361727836 0 0
0.84509804 1.041392685 0.84509804 1.653212514 1.826074803 1.431363764
1.278753601 1.653212514 0.954242509 0 0 0 1.041392685 0 0 0 0 3.41560334
4.164515118 2.938815545 0 0 0 0 0 4.417859177 0 0 2.16315712 0 6.581016297
11 09.09.15 low diuron bifenthrin 16 9.36 9.92 100.8 620 748 1.322219295 0 0
1.041392685 1.113943352 0.84509804 2.068185862 2.161368002 1.322219295
0.954242509 1.462397998 0 0 0.84509804 0 0.954242509 3.321739096 0 3.111015108 0
3.463010735 4.110712303 3.684799662 0 0 0 0 0 2.210273092 2.210273092 0
2.810321315 4.000023777 11.23089128
12 09.09.15 low diuron bifenthrin 16.2 9.46 10.83 110.1 642 771 1.322219295 0 0
1.113943352 0.698970004 0 1.672097858 1.799340549 1.322219295 1.113943352
1.51851394 0 0.477121255 0 0.477121255 0.698970004 2.653490783 0 0 0 2.874953569
2.477881497 3.217060206 0 0 0 0 0 2.178294218 0 0 2.178294218 0 4.356588436
13 09.09.15 high diuron bifenthrin 15.3 9.35 10.1 100.6 618 758 1.322219295 0 0
0.698970004 0.84509804 1.230448921 2.206825876 2.271841607 0.954242509
0.477121255 1.041392685 0.477121255 0 1.322219295 0.698970004 1.113943352
3.145751342 0 3.094637628 0 2.79395675 3.863355592 3.622665561 0 0 0 0 0 0
2.89072715 0 2.193985593 3.606279127 8.69099187
14 09.09.15 high diuron bifenthrin 15.9 9.46 10.58 106.9 616 747 1.230448921 0 0
1.812913357 1.041392685 0.698970004 2.053078443 2.281033367 1.491361694
1.113943352 1.633468456 0.698970004 0 1.431363764 1.041392685 0.477121255
3.020916086 2.178294218 2.874953569 0 3.497761337 4.418458594 3.497761337 0 0 0
0 0 3.666859203 0 0 2.477881497 0 6.1447407
15 09.09.15 high diuron bifenthrin 16.2 9.36 8.67 88.3 695 834 1.397940009
0.477121255 0 1.041392685 0.84509804 0 1.431363764 1.633468456 0.954242509
0.954242509 1.230448921 0 0 0 0 1.176091259 3.694005781 0 2.178294218 0
2.653490783 3.590488842 3.430837555 2.477881497 0 0 0 0 2.178294218 0 0 0 0
4.656175715
16 09.09.15 high diuron bifenthrin 15.6 9.34 9.32 93.5 707 862 1.176091259
0.477121255 0 0.477121255 1.278753601 0.698970004 1.633468456 1.826074803 0
1.041392685 1.041392685 0 0 0 0 1.230448921 0 0 0 0 2.778188332 0 3.175693866 0
0 0 0 0 2.178294218 0 0 0 0 2.178294218
1 09.16.15 control 17.1 9.28 10.76 109.8 746 880 0 0.698970004 0 2.029383778
0.84509804 0 1.544068044 2.167317335 0.477121255 1.176091259 1.230448921 0 0 0 0
0.954242509 3.517958449 0 3.321739096 0 2.477881497 4.032776746 2.95403827 0 0 0
0 0 2.477881497 0 0 2.178294218 2.178294218 6.834469933
2 09.16.15 control 17.4 9.43 11.47 119 638 749 1.322219295 0 0.477121255
1.113943352 1.361727836 0 0.84509804 1.653212514 0.477121255 1.041392685
1.113943352 0.84509804 0 0.954242509 0 0.477121255 0 0 2.178294218 0 2.778188332
4.109936144 3.47657894 0 0 0 0 0 2.178294218 0 0 0 2.95403827 5.132332488
3 09.16.15 control 17.1 9.3 10.85 112.6 656 771 1.544068044 0.477121255 0
1.278753601 1.361727836 0.698970004 1.568201724 1.919078092 0.698970004
0.954242509 1.113943352 0.84509804 0 1.397940009 1.041392685 0.698970004
2.43383264 0 2.134399087 0 2.734062209 4.070756467 3.453572465 0 0 0 0 0
4.430069522 2.134399087 0 0 2.609390445 9.173859054
4 09.16.15 control 16.8 9.47 11.33 116.5 652 773 1.612783857 0 0 1.113943352
0.698970004 1.176091259 1.230448921 1.672097858 1.041392685 0.698970004
1.176091259 0.477121255 0 0 0.698970004 0 2.595636597 0 0 0 2.420095946
4.701751883 3.923643782 0 0 0 0 0 5.023212025 0 0 0 0 5.023212025
5 09.16.15 high diuron 16.9 9.26 9.72 100.6 632 747 1.431363764 0 0 1.672097858
0.698970004 0 1.113943352 1.799340549 1.826074803 0.954242509 1.875061263
1.612783857 0 2.285557309 0 0 3.522022409 0 2.762974214 0 2.16315712 2.462693138
2.859734288 0 0 0 0 0 2.462693138 2.16315712 0 2.16315712 3.274338334
10.06334571
6 09.16.15 high diuron 16.5 9.14 9.82 100.3 658 786 1.785329835 0.477121255 0
1.113943352 0.954242509 0.477121255 1.322219295 1.653212514 0.954242509
1.041392685 1.278753601 0 0 0 0.477121255 1.176091259 2.845031715 0 3.100028348
0 2.448021484 4.252674994 3.321739096 0 0 0 0 0 4.186861681 0 0 0 0 4.186861681
7 09.16.15 high diuron 16.5 9.1 8.84 90.3 739 881 1.278753601 0.477121255 0
1.51851394 0.84509804 0.477121255 1.113943352 1.72427587 0 0.84509804 0.84509804
0 0 0.954242509 0.477121255 0.698970004 0 0 0 0 0 4.562093163 0 0 0 0 0 0
4.455086149 0 0 0 2.623596445 7.078682594
8 09.16.15 high diuron 16.4 9.23 10.47 106.6 803 671 1.672097858 0 0 1.361727836
1.041392685 0.698970004 0.954242509 1.653212514 1.041392685 1.041392685
1.322219295 0 0 0 0.477121255 0.954242509 3.100028348 0 3.349754879 0
2.448021484 4.294681899 2.748276803 0 0 0 0 0 3.224880838 0 0 0 0 3.224880838
9 09.16.15 low diuron bifenthrin 16.6 9.18 9.59 99.9 710 844 1.361727836 0 0
1.919078092 1.113943352 0 1.431363764 2.08278537 0 1.041392685 1.041392685 0 0 0
0 0.84509804 2.909886331 0 2.609390445 0 2.734062209 4.467674841 3.33552091 0 0
0 0 0 0 0 0 0 2.830811959 2.830811959
10 09.16.15 low diuron bifenthrin 16.6 9.21 10.35 106.4 689 821 1.462397998 0 0
1.770852012 1.041392685 0.477121255 2.1430148 2.324282455 0.954242509
0.698970004 1.113943352 0.698970004 0 0 0 1.230448921 0 0 2.623596445 0
2.845031715 4.089958729 0 0 0 0 0 0 4.609344417 0 0 0 0 4.609344417
11 09.16.15 low diuron bifenthrin 16.5 9.41 11.42 116.7 625 746 1.322219295 0 0
1.278753601 0.477121255 0 0.954242509 1.491361694 1.041392685 1.041392685
1.322219295 0 0 0.954242509 0 0.84509804 2.193985593 0 2.193985593 0 0
4.023748275 3.094637628 0 0 0 0 0 2.669250603 0 0 0 2.79395675 5.463207353
12 09.16.15 low diuron bifenthrin 16.4 9.83 13.4 132.8 662 793 1.113943352
0.477121255 0 1.361727836 0.698970004 0.477121255 1.397940009 1.72427587
0.84509804 1.113943352 1.278753601 0 0 0 0 0.84509804 3.931329703 0 4.239886962
0 3.351688516 3.652621882 3.217060206 0 0 0 0 0 2.178294218 0 0 3.020916086 0
5.199210304
13 09.16.15 high diuron bifenthrin 16.8 9.2 10.82 111 625 740 1.633468456 0 0
0.698970004 1.397940009 0.477121255 1.397940009 1.740362689 0.84509804
1.113943352 1.278753601 0 0 0.84509804 0.84509804 0.954242509 0 0 3.379705162 0
0 3.946305134 2.778188332 0 0 0 0 0 0 0 0 0 0 0
14 09.16.15 high diuron bifenthrin 17 9.45 12.32 127.1 645 762 0 0 0 1.176091259
0.698970004 0 1.278753601 1.568201724 0.954242509 0.954242509 1.230448921
0.698970004 0 1.431363764 0.477121255 0.84509804 2.859734288 0 3.005690901 0
3.986283636 4.568415706 0 3.005690901 0 0 0 0 3.522022409 0 0 2.16315712 0
8.69087043
15 09.16.15 high diuron bifenthrin 16.9 9.35 10.66 109.9 715 844 1.361727836 0 0
1.51851394 0.698970004 0 0.477121255 1.591064607 0.84509804 1.278753601
1.397940009 0 0 0 0 0.698970004 3.306506523 0 2.16315712 0 2.16315712
3.461344151 3.522022409 0 0 0 0 0 0 0 0 0 0 0
16 09.16.15 high diuron bifenthrin 16.5 9.3 9.92 101.5 744 888 1.322219295 0 0
1.113943352 1.230448921 0.698970004 1.176091259 1.672097858 0.477121255
0.84509804 0.954242509 0 0 0.477121255 0 0.698970004 2.95403827 0 2.178294218 0
0 4.621018572 3.946305134 0 0 0 0 0 0 0 0 0 0 0
1 09.23.15 control 16 9.04 9.15 92.5 766 920 1.230448921 0.954242509 0
2.328379603 1.361727836 0 2.426511261 2.701567985 0 1.361727836 1.361727836
0.477121255 0 0 0.477121255 1.176091259 3.351688516 0 4.014295091 3.555736028
2.178294218 3.899734556 3.020916086 0 0 0 0 0 3.454310174 0 0 2.778188332 0
6.232498506
2 09.23.15 control 15.4 9.24 9.87 99.1 642 783 0.84509804 0 0 1.812913357
1.462397998 1.041392685 1.361727836 2.096910013 1.113943352 1.113943352
1.397940009 0 0 0.698970004 0 0 2.778188332 0 0 0 0 4.765361031 3.622665561 0
2.477881497 0 0 0 3.573459963 0 0 2.178294218 0 8.229635678
3 09.23.15 control 15 9.18 9.22 89.7 658 812 1.431363764 0.477121255 0
2.136720567 1.707570176 0 1.397940009 2.324282455 1.041392685 1.653212514
1.740362689 0.84509804 0 0 0 0.698970004 3.048918942 0 3.100028348 3.758300383
2.92410953 4.008806171 3.677011042 0 0 0 0 0 4.678204242 0 0 2.148536707 0
6.826740949
4 09.23.15 control 14.9 9.29 8.69 85.6 665 824 1.51851394 0.477121255 0
1.755874856 1.51851394 1.230448921 1.568201724 2.149219113 0.954242509
0.954242509 1.230448921 0.477121255 0 0 0 0.698970004 2.477881497 0 0
3.590488842 0 4.327712741 3.856705622 0 0 0 0 0 4.962160135 0 0 0 0 4.962160135
5 09.23.15 high diuron 15.2 9.13 8.6 85.8 655 807 1.278753601 0 0 1.176091259
1.544068044 1.041392685 0 1.770852012 2.012837225 1.431363764 2.11058971
1.838849091 0 1.875061263 0 1.113943352 3.488004637 0 3.937882551 2.845031715 0
3.3760724 3.187115801 0 0 0 0 0 2.448021484 0 0 2.845031715 0 5.293053199
6 09.23.15 high diuron 15 9.02 8.26 81.9 666 823 1.278753601 0 0 1.886490725
1.176091259 0.698970004 1.812913357 2.247973266 1.278753601 0.84509804
1.397940009 0 0 0 0 0.477121255 2.762974214 0 2.462693138 3.622665561
2.859734288 3.916091553 3.201829649 0 0 0 0 0 4.358840104 0 0 2.16315712 0
6.521997224
7 09.23.15 high diuron 15.2 8.9 7.09 70.8 763 938 1.278753601 0 0 1.755874856
0.698970004 1.176091259 2.021189299 2.252853031 0.477121255 1.431363764
1.462397998 1.278753601 0 0.84509804 0 0.477121255 2.762974214 0 2.638285261
4.185498207 0 4.377666155 3.783484804 0 0 0 0 0 4.413033818 0 0 0 0 4.413033818
8 09.23.15 high diuron 14.8 9.03 8.52 84.5 689 856 1.230448921 0 0 1.633468456 0
1.041392685 0.84509804 1.770852012 1.278753601 0.698970004 1.361727836 0 0 0 0
0.477121255 3.522022409 0 0 0 0 3.71654993 3.892613384 0 0 0 0 0 2.16315712 0 0
0 0 2.16315712
9 09.23.15 low diuron bifenthrin 15.1 8.98 7.96 78.8 718 886 1.431363764 0 0
1.633468456 1.361727836 1.230448921 1.707570176 2.123851641 0 1.322219295
1.322219295 0.698970004 0 0 0.698970004 0.698970004 2.653490783 2.178294218 0
2.778188332 0 4.376819337 3.537257874 0 0 0 0 0 0 0 0 0 0 0
10 09.23.15 low diuron bifenthrin 14.8 9.08 8.73 84.9 674 838 1.230448921 0 0
1.919078092 0.477121255 1.278753601 1.707570176 2.184691431 0.477121255
1.113943352 1.176091259 0.477121255 0 0 0 1.041392685 3.114740106 0 2.859734288
4.079278285 3.274338334 3.704317857 3.558224138 0 0 0 0 0 4.463375008 0 0 0 0
4.463375008
11 09.23.15 low diuron bifenthrin 14.6 9.21 9.4 91.9 622 777 1.591064607
0.477121255 0 1.230448921 0.698970004 0.698970004 1.113943352 1.568201724
1.230448921 1.113943352 1.462397998 0.954242509 0 0.477121255 0.477121255 0
3.129968574 0 2.874953569 3.175693866 3.020916086 4.02054344 2.874953569 0 0 0 0
0 2.178294218 0 0 2.653490783 0 4.831785001
12 09.23.15 low diuron bifenthrin 14.7 9.41 11.46 112.5 648 807 1.431363764 0 0
1.361727836 0.477121255 0.84509804 1.397940009 1.740362689 1.361727836
1.041392685 1.51851394 0 0 0 0 0.698970004 3.762299079 0 3.063629264 3.005690901
3.23959547 3.502723188 2.762974214 0 0 0 0 0 0 0 0 3.23959547 0 3.23959547
13 09.23.15 high diuron bifenthrin 14.6 9.19 9.18 90.3 627 782 1.113943352 0 0
1.230448921 1.041392685 0 1.230448921 1.633468456 0.477121255 1.113943352
1.176091259 0.954242509 0 0.698970004 0 0.477121255 3.406023444 0 3.637901957
3.47657894 2.874953569 3.743684067 3.289570338 0 0 0 0 0 2.178294218 0 0
3.847563528 3.175693866 9.201551612
14 09.23.15 high diuron bifenthrin 14.6 9.28 10.06 98.4 635 791 0 0 0
1.770852012 1.041392685 0 0.698970004 1.86332286 1.785329835 1.113943352
1.86332286 1.431363764 0 1.176091259 0 0.954242509 3.289570338 2.653490783
2.778188332 3.454310174 3.808939864 3.788258555 3.129968574 0 0 0 0 0
3.254826811 0 0 0 0 3.254826811
15 09.23.15 high diuron bifenthrin 14.9 9.17 9.29 92.2 717 887 1.113943352 0 0
1.986771734 1.113943352 0.698970004 1.361727836 2.130333768 0.954242509
0.84509804 1.176091259 0 0 0 0 1.113943352 3.719554865 0 3.537257874 2.874953569
2.178294218 3.666859203 2.778188332 0 0 0 0 0 0 0 0 2.874953569 0 2.874953569
16 09.23.15 high diuron bifenthrin 14.7 9.04 8.2 81.1 753 938 1.278753601
0.477121255 0 1.51851394 0.84509804 0 0.477121255 1.612783857 0 0.954242509
0.954242509 0 0 0 0 0.698970004 2.193985593 0 0 4.212414172 2.669250603
3.696435596 3.696435596 0 0 0 0 0 0 0 0 0 0 0
1 09.30.15 control 16.9 9.16 9.52 97 826 976 0.477121255 0 0 1.612783857
1.361727836 0 1.740362689 2.068185862 0 0.698970004 0.698970004 0 0 0
0.477121255 0 2.509962945 0 2.810321315 3.52993616 0 4.386583438 3.654842849 0 0
0 0 0 4.681813958 0 0 0 2.685606569 7.367420527
2 09.30.15 control 16.4 9.43 10.53 106.7 697 833 0.84509804 0 0 1.397940009
1.176091259 0.698970004 0.698970004 1.672097858 0.477121255 0.84509804
0.954242509 0 0 0.477121255 0 0 2.193985593 0 2.193985593 0 0 4.389873027
4.228650394 0 0 0 0 0 0 0 0 2.193985593 0 2.193985593
3 09.30.15 control 15.9 9.32 9.56 95.9 697 844 1.113943352 0 0 1.51851394
1.278753601 0.477121255 1.278753601 1.851258349 0.477121255 1.278753601
1.322219295 0 0 0 0 0.698970004 3.23959547 0 3.063629264 3.850421379 3.522022409
4.253609914 3.23959547 0 0 0 0 2.938815545 3.575252858 0 0 0 0 6.514068403
4 09.30.15 control 15.8 9.3 10.27 103.1 711 863 0.84509804 0 0 1.462397998
0.954242509 0.477121255 0 1.591064607 0.698970004 0.698970004 0.954242509 0 0 0
0.477121255 0 3.036695572 0 3.395492959 0 0 3.63845613 4.036336228 0 0 0 0 0 0 0
0 0 0 0
5 09.30.15 high diuron 15.7 9.32 8.89 89.7 697 847 0.477121255 0 0 0 0.477121255
0 1.278753601 1.322219295 1.431363764 0.698970004 1.491361694 0.477121255 0
1.361727836 0 0.954242509 3.915819386 0 3.652621882 3.537257874 0 3.606875129
2.178294218 0 0 0 0 2.874953569 2.178294218 0 0 3.175693866 0 8.228941653
6 09.30.15 high diuron 15.6 9.13 8.55 86.4 720 878 1.176091259 0 0 1.397940009
0.477121255 0.84509804 1.322219295 1.72427587 0.954242509 1.041392685
1.278753601 0 0 0 0 0 3.217060206 0 2.477881497 0 0 4.751753017 4.08925319 0 0 0
0 0 3.743684067 0 0 2.477881497 0 6.221565564
7 09.30.15 high diuron 15.9 9.06 8.26 85.4 813 984 1.322219295 0.477121255 0
1.653212514 0 0 1.113943352 1.755874856 0 0.84509804 0.84509804 0 0 0.477121255
0 0 2.79395675 0 0 3.036695572 3.232845063 3.77105542 4.173498602 0 0 0 0 0
4.048569574 0 0 0 0 4.048569574
8 09.30.15 high diuron 15.4 9.24 9.26 94.3 730 894 0.698970004 0.477121255 0
1.041392685 0 1.322219295 1.278753601 1.69019608 1.230448921 1.230448921
1.51851394 0 0 0 0 0 3.814514106 0 3.191477784 0 0 3.9394362 3.513550674 0 0 0 0
0 0 0 0 0 0 0
9 09.30.15 low diuron bifenthrin 15.5 9.18 8.48 85.1 771 942 1.397940009 0 0
1.176091259 0.698970004 0.84509804 1.322219295 1.653212514 0 0.698970004
0.698970004 0 0 0 0.698970004 0.477121255 3.289570338 0 3.020916086 2.95403827
3.175693866 4.200737197 3.838224843 0 0 0 0 0 0 0 0 0 2.178294218 2.178294218
10 09.30.15 low diuron bifenthrin 15.2 9.22 9.53 95.4 712 876 0.698970004 0 0
0.84509804 0 0 2.103803721 2.123851641 0.477121255 0.477121255 0.698970004 0 0 0
0 0.477121255 3.145751342 0 2.193985593 0 0 5.089378261 3.55304764 0 0 0 0 0 0 0
0 0 0 0
11 09.30.15 low diuron bifenthrin 15.2 9.39 9.73 97.1 657 810 1.230448921
0.477121255 0 0.698970004 0 1.176091259 1.322219295 1.591064607 0.954242509
1.176091259 1.361727836 0 0 0 0 0 3.383859397 0 2.986188468 4.070948404
3.321739096 3.438195948 2.986188468 0 0 0 0 0 0 0 0 3.053071206 2.509962945
5.563034151
12 09.30.15 low diuron bifenthrin 15.1 9.66 11.27 112.7 688 849 0.84509804 0 0
1.041392685 1.041392685 0 0.698970004 1.397940009 0 0.698970004 0.698970004 0 0
0 0 0.698970004 3.890224169 0 3.44662607 3.77105542 3.668412697 4.042495974
3.44662607 0 0 0 0 0 2.193985593 0 0 3.589250089 3.036695572 8.819931254
13 09.30.15 high diuron bifenthrin 15 9.4 9.61 95.7 680 841 1.113943352 0 0
1.041392685 0 0.477121255 1.361727836 1.544068044 0.477121255 0.698970004
0.84509804 0 0 0.477121255 0 0 3.77105542 0 3.191477784 3.759475536 2.89072715
3.814514106 3.270612452 0 0 0 0 0 0 0 0 3.305356642 3.305356642 6.610713284
14 09.30.15 high diuron bifenthrin 14.9 9.48 10.91 108.6 676 837 0.477121255 0 0
0.954242509 0 0.477121255 0.954242509 1.278753601 1.278753601 0.84509804
1.397940009 0 0 0.84509804 0 0.477121255 2.810321315 0 2.986188468 3.684799662
3.888885991 4.260690931 3.053071206 0 0 0 0 0 3.24922607 0 0 2.509962945 0
5.759189015
15 09.30.15 high diuron bifenthrin 15.1 9.37 8.95 89.4 771 950 0.477121255 0 0
1.397940009 0 0.698970004 0.477121255 1.491361694 0 0.698970004 0.698970004 0 0
0 0 0 3.622665561 0 4.082685823 3.463010735 2.509962945 3.550133719 2.907096879
0 3.20785785 0 0 0 3.52993616 0 0 2.685606569 0 9.423400579
16 09.30.15 high diuron bifenthrin 14.7 9.27 9.23 92 795 989 0.84509804 0 0
1.544068044 1.176091259 1.041392685 1.397940009 1.919078092 0 0.954242509
0.954242509 0 0 0 0 0.84509804 3.162130259 0 2.907096879 3.897839689 0
4.058884931 3.940032281 0 0 0 0 0 0 0 0 0 2.986188468 2.986188468
1 10.07.15 control 16 9.26 8.84 89.9 844 1015 1.361727836 0.477121255 0
1.770852012 1.431363764 0 1.230448921 2.012837225 0.84509804 1.278753601
1.397940009 0 0 0 0.477121255 1.113943352 3.283969356 0 3.321739096 0 0
3.958401906 4.081235877 0 0 0 0 0 2.845031715 2.720299768 0 0 0 5.565331483
2 10.07.15 control 15.7 9.36 9.12 92.6 701 852 0.698970004 0 0 1.785329835
1.491361694 0.477121255 0.698970004 1.986771734 1.278753601 1.113943352
1.491361694 0 0 0.477121255 0.477121255 0.477121255 3.053071206 0 2.210273092
2.509962945 3.906612514 4.510798112 4.24503986 0 2.210273092 2.210273092 0 0
2.210273092 2.210273092 0 0 0 8.841092368
3 10.07.15 control 15.2 9.18 8.7 86.6 679 835 1.230448921 0 0 1.755874856
1.51851394 0 0.954242509 1.995635195 1.230448921 1.491361694 1.672097858
0.84509804 0 0 0 0.698970004 0 0 2.178294218 0 0 4.628732526 4.067535754 0 0 0 0
0 2.477881497 0 0 0 0 2.477881497
4 10.07.15 control 15.2 9.28 8.68 86.4 721 887 1.041392685 0 0 1.929418926
0.954242509 1.176091259 1.322219295 2.103803721 1.176091259 1.176091259
1.462397998 0.698970004 0 0 0 0 2.827328802 0 3.480038322 3.804473725 2.22720349
4.436825486 3.826746714 0 0 0 0 0 4.249952288 0 0 2.22720349 0 6.477155778
5 10.07.15 high diuron 15 9.12 7.82 77.8 694 857 0.84509804 0 0 1.230448921
0.84509804 0 1.230448921 1.591064607 2.1430148 1.041392685 2.173186268
1.462397998 0 2.460897843 0 0 3.053071206 0 0 0 0 4.517232025 2.810321315 0 0 0
0 0 0 0 0 0 0 0
6 10.07.15 high diuron 14.8 9.04 8.25 83.3 710 882 1.041392685 0 0 1.431363764
1.176091259 0.954242509 0.954242509 1.755874856 1.397940009 1.397940009
1.69019608 1.041392685 0 0 0 0.84509804 2.509962945 0 3.383859397 0 2.210273092
4.448153382 3.963510753 0 0 0 0 0 0 0 0 2.685606569 0 2.685606569
7 10.07.15 high diuron 15.5 8.97 7.5 81.8 844 1030 1.322219295 0 0 1.397940009
0.477121255 0.477121255 1.361727836 1.707570176 0 0.698970004 0.698970004 0 0
0.477121255 0 0 2.509962945 0 0 3.321739096 2.685606569 3.438195948 0 0 0 0 0 0
4.064958552 0 0 0 0 4.064958552
8 10.07.15 high diuron 15.2 9.13 8.42 83.7 758 932 0.698970004 0 0 1.86332286 0
1.041392685 1.361727836 2.021189299 1.278753601 0.84509804 1.397940009 0 0 0
0.477121255 0 3.841119731 0 2.509962945 0 2.907096879 4.16665964 3.870405024 0 0
0 0 0 0 0 0 0 0 0
9 10.07.15 low diuron bifenthrin 15.4 9.06 7.54 76.4 795 974 0.84509804 0 0
1.672097858 1.041392685 2.012837225 0.477121255 2.212187604 0.477121255
1.113943352 1.176091259 0.698970004 0 0 0 0.954242509 3.525781433 0 2.702605456
0 0 4.289102307 3.224880838 0 0 0 0 0 0 0 0 0 0 0
10 10.07.15 low diuron bifenthrin 14.9 9.11 8.42 83.1 716 887 1.278753601 0 0
1.707570176 0.477121255 1.86332286 0.698970004 2.11058971 0.954242509
1.176091259 1.361727836 0 0 0 0 0 2.493624149 0 2.89072715 3.589250089
2.969815295 4.51549395 3.55304764 0 0 0 0 0 2.493624149 0 0 2.493624149 0
4.987248298
11 10.07.15 low diuron bifenthrin 14.8 9.23 8.18 81.2 664 825 1.113943352
0.477121255 0 1.431363764 0 0.84509804 0 1.591064607 1.397940009 1.041392685
1.544068044 0.698970004 0 0 0 0.698970004 4.207615585 0 3.286994242 2.210273092
2.907096879 3.931915331 3.963510753 0 0 0 0 0 2.210273092 0 0 2.509962945 0
4.720236037
12 10.07.15 low diuron bifenthrin 14.8 9.48 10.18 100.5 698 867 0.698970004 0 0
1.462397998 0 1.230448921 0.477121255 1.672097858 0.84509804 1.278753601
1.397940009 0 0 0 0 0 2.810321315 0 3.053071206 0 2.907096879 0 4.207615585 0 0
0 0 0 2.509962945 0 0 2.810321315 0 5.32028426
13 10.07.15 high diuron bifenthrin 14.8 9.27 9 88.5 685 851 0.954242509 0 0
1.397940009 0 0.477121255 1.230448921 1.672097858 0.84509804 1.113943352
1.278753601 0.477121255 0 1.176091259 0 0 3.55304764 0 2.79395675 0 0
4.072048626 2.89072715 0 0 0 0 0 0 0 0 0 0 0
14 10.07.15 high diuron bifenthrin 14.7 9.3 9.36 92.2 681 848 0.698970004 0 0
1.799340549 1.041392685 0.698970004 0 1.886490725 1.591064607 1.278753601
1.755874856 1.176091259 0 1.176091259 0.698970004 0.477121255 2.493624149 0
2.193985593 0 3.367475882 3.696435596 3.305356642 0 0 0 0 0 0 0 0 0 0 0
15 10.07.15 high diuron bifenthrin 14.7 9.18 8.36 82.5 786 978 0.84509804 0 0
0.954242509 0.84509804 1.041392685 0 1.397940009 1.113943352 1.176091259
1.431363764 0 0 0 0 0.954242509 3.77105542 0 3.983635031 4.392612953 2.79395675
3.395492959 3.232845063 0 2.89072715 0 0 0 0 0 0 2.79395675 0 5.6846839
16 10.07.15 high diuron bifenthrin 14.5 9.13 7.9 76.8 819 1024 0.477121255 0 0
1.591064607 0 0.477121255 1.278753601 1.770852012 0.698970004 0.954242509
1.113943352 0 0 0 0 0.954242509 3.087803747 0 0 0 0 3.356485276 4.074896229 0 0
0 0 0 0 0 0 0 0 0
1 10.14.15 control 17.3 8.7 10.8 112.3 795 926 1.176091259 0.698970004 0
1.919078092 1.322219295 0 1.113943352 2.06069784 0.84509804 1.041392685
1.230448921 0 0 0 0 1.113943352 3.424357849 0 3.349754879 0 0 4.279002505
4.132244516 0 0 0 0 0 0 0 0 3.424357849 2.623596445 6.047954294
2 10.14.15 control 16.8 9 9.83 101.4 712 842 1.176091259 0 0 1.397940009
1.113943352 0 0 1.568201724 1.361727836 1.230448921 1.591064607 0 0 0.477121255
0 0.698970004 2.874953569 0 2.874953569 0 3.652621882 4.286016164 3.454310174 0
0 0 0 0 0 0 0 2.477881497 0 2.477881497
3 10.14.15 control 16.7 9.11 10.04 103.4 832 833 1.230448921 0.698970004 0
1.653212514 0.84509804 0 0.698970004 1.740362689 1.113943352 1.491361694
1.633468456 0.84509804 0 0 0 1.113943352 3.308381604 0 3.145751342 0 3.76688019
4.297194573 4.012575277 0 0 0 0 0 2.107452407 0 0 2.406783568 2.107452407
6.621688382
4 10.14.15 control 16.6 9.22 9.3 95.5 732 872 0.698970004 0.698970004 0
1.397940009 0.698970004 0.477121255 0.477121255 1.51851394 0.954242509
1.113943352 1.322219295 0.477121255 0 0 0.477121255 0 3.020916086 0 2.874953569
0 2.874953569 4.124826563 4.258213311 0 0 0 0 0 0 0 0 0 0 0
5 10.14.15 high diuron 16.6 9.13 8.73 89.5 711 848 1.113943352 0 0 1.176091259
0.477121255 0 1.041392685 1.462397998 1.612783857 0 1.612783857 1.322219295 0
2.247973266 0 0 3.673806579 0 3.196869365 0 2.720299768 3.865659232 4.17434595 0
0 0 0 0 0 0 0 0 0 0
6 10.14.15 high diuron 16.4 9.02 9.33 95.5 704 842 1.462397998 0 0 1.113943352
1.041392685 0 0 1.361727836 1.612783857 1.397940009 1.812913357 0 0 0.477121255
0 1.113943352 3.070089763 0 3.546964569 0 2.827328802 4.591989031 3.877892045 0
0 0 0 0 2.22720349 0 0 0 2.22720349 4.45440698
7 10.14.15 high diuron 17.5 8.99 8.73 91.3 833 971 1.278753601 0 0 1.740362689 0
0 1.278753601 1.875061263 0 0.84509804 0.84509804 0 0 1.041392685 0 0
2.638285261 0 0 0 0 5.003401096 3.502723188 0 0 0 0 0 4.324537435 0 0 2.16315712
0 6.487694555
8 10.14.15 high diuron 17.1 9.1 9.83 101.8 739 870 0.477121255 0 0 1.707570176 0
0 0.954242509 1.770852012 1.544068044 0.698970004 1.591064607 0.477121255 0 0 0
0.698970004 4.118005144 0 3.529268725 0 0 4.295575158 3.386651147 0 0 0 0 0 0 0
0 0 2.134399087 2.134399087
9 10.14.15 low diuron bifenthrin 17.3 9.1 9.16 95.5 790 925 1.278753601 0 0
1.812913357 1.176091259 0.477121255 0.954242509 1.949390007 0 1.278753601
1.278753601 0.954242509 0 0 0.477121255 1.113943352 3.114740106 0 2.16315712 0
3.439075657 3.439075657 3.908405665 0 0 0 0 0 0 0 0 0 0 0
10 10.14.15 low diuron bifenthrin 16.3 9.07 9.33 95 722 864 1.113943352
0.477121255 0 1.230448921 0.477121255 0.477121255 0.477121255 1.361727836
1.361727836 1.230448921 1.591064607 0.84509804 0 0 0 1.230448921 2.845031715 0
2.148536707 0 2.92410953 2.748276803 4.469737932 0 0 0 0 0 0 0 0 0 0 0
11 10.14.15 low diuron bifenthrin 16.5 9.12 9.03 92.6 667 796 1.230448921
0.477121255 0 1.041392685 0.698970004 0.477121255 0.477121255 1.278753601
1.397940009 0.954242509 1.51851394 0 0 0.477121255 0 1.113943352 3.424357849 0
2.148536707 0 0 3.3760724 4.194686515 0 0 0 0 0 2.148536707 0 0 2.92410953 0
5.072646237
12 10.14.15 low diuron bifenthrin 16.3 9.36 11.02 112.3 674 809 1.176091259
0.477121255 0 1.72427587 1.230448921 0.477121255 0.477121255 1.875061263
1.041392685 0.954242509 1.278753601 0 0 0 0.477121255 0.84509804 2.609390445 0 0
0 2.609390445 3.245390563 4.085478494 0 0 0 0 0 0 0 0 0 0 0
13 10.14.15 high diuron bifenthrin 16 9.27 10.22 103.8 683 826 1.113943352
0.477121255 0 1.113943352 0 0.954242509 1.176091259 1.568201724 1.612783857
1.278753601 1.770852012 0 0 1.397940009 0.477121255 0.698970004 3.650687813 0
2.148536707 2.623596445 0 4.30382482 3.467807974 0 0 0 0 0 0 0 0 0 0 0
14 10.14.15 high diuron bifenthrin 16 9.26 9.99 101.2 686 829 0.84509804
0.698970004 0 1.278753601 0 0.477121255 0.477121255 1.431363764 1.612783857
0.954242509 1.69019608 0.698970004 0 1.278753601 0 0.698970004 2.462693138 0 0
2.16315712 3.336455451 4.494631664 3.80368494 0 0 0 0 0 0 0 0 2.16315712 0
2.16315712
15 10.14.15 high diuron bifenthrin 16.3 9.12 8.68 88.7 784 940 1.113943352 0 0
2.004321374 1.397940009 0 0.477121255 2.103803721 1.278753601 1.51851394
1.707570176 0 0 0 0 1.278753601 3.931066923 0 3.336455451 0 2.462693138
3.575252858 4.364302762 0 0 0 0 0 0 0 0 3.841467813 0 3.841467813
16 10.14.15 high diuron bifenthrin 16 9.09 9.49 96.2 798 963 0.698970004
0.477121255 0 1.462397998 0.698970004 0 0 1.51851394 0 1.041392685 1.041392685
0.698970004 0 0 0 1.113943352 3.773021114 0 2.859734288 0 0 3.439075657
3.306506523 0 0 0 0 0 0 0 0 0 0 0
1 10.21.15 control 14.3 9.07 11.06 107.4 755 950 1.397940009 0 0 1.755874856
1.176091259 0 1.361727836 1.968482949 0.477121255 0.954242509 1.041392685 0 0 0
0.477121255 0.954242509 3.573459963 0 3.666859203 2.178294218 2.653490783
4.224647889 3.555736028 0 0 0 0 0 0 0 0 0 0 0
2 10.21.15 control 13.7 9.25 10.44 100.2 678 865 0.477121255 0.698970004 0
1.707570176 1.041392685 0 0 1.785329835 1.322219295 0.84509804 1.431363764 0 0
0.84509804 0 0 3.47657894 2.178294218 3.020916086 0 3.915819386 4.415969889
4.282636599 0 0 0 0 0 0 0 0 0 0 0
3 10.21.15 control 13.7 9.22 10.95 105.6 664 846 0.698970004 0 0 1.612783857
0.954242509 0 0.698970004 1.740362689 0.954242509 1.176091259 1.361727836 0 0 0
0 0.477121255 3.511545308 0 2.43383264 0 3.71106839 4.905722674 4.03433043 0 0 0
0 0 0 0 0 0 0 0
4 10.21.15 control 13.4 9.31 9.84 94.5 701 901 1.176091259 0.477121255 0
1.72427587 1.041392685 0.954242509 0.477121255 1.86332286 0.84509804 0.84509804
1.113943352 0.477121255 0 0 1.041392685 0 3.454310174 0 2.95403827 0 2.477881497
4.895568802 3.874431973 0 0 0 0 0 2.778188332 0 0 2.477881497 0 5.256069829
5 10.21.15 high diuron 13.4 9.15 8.98 87.8 648 833 0.954242509 0 0 1.361727836 0
0 0.477121255 1.431363764 1.397940009 0 1.397940009 1.322219295 0 2.396199347 0
0.477121255 4.191226204 0 3.722759396 0 2.669250603 3.094637628 3.305356642 0 0
0 0 0 0 0 0 2.193985593 0 2.193985593
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0.698970004 0 0.477121255 1.361727836 1.431363764 1.322219295 1.672097858
0.477121255 0 0.477121255 0 0.477121255 3.321739096 0 0 0 3.224880838
5.026258432 4.154071383 0 0 0 0 0 0 0 0 3.525781433 0 3.525781433
7 10.21.15 high diuron 13.7 9.12 9.89 95.8 766 978 1.278753601 0 0 1.707570176 0
0 0.477121255 1.72427587 0.954242509 0.954242509 1.230448921 0.84509804 0
0.477121255 0.477121255 0.698970004 3.005690901 0 2.462693138 0 2.762974214
5.217832742 4.142466526 0 0 0 0 0 3.952604138 0 0 2.16315712 0 6.115761258
8 10.21.15 high diuron 13.6 9.27 11.15 107.7 690 882 0.84509804 0.477121255 0
1.041392685 0.84509804 0 0.477121255 1.278753601 1.491361694 0 1.491361694 0 0 0
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0 0 0 0 0 0 0 0 0
9 10.21.15 low diuron bifenthrin 13.8 9.19 9.51 92.3 751 955 0.954242509
0.698970004 0 1.397940009 0.84509804 0.954242509 0.954242509 1.672097858 0
0.84509804 0.84509804 0.698970004 0 0 0 1.278753601 3.892613384 0 3.114740106
2.16315712 3.637385367 3.336455451 3.979753448 0 0 0 0 0 0 0 0 0 0 0
10 10.21.15 low diuron bifenthrin 13.2 9.16 10.37 99 681 878 0.954242509 0 0
1.041392685 0.698970004 0 0.477121255 1.230448921 1.361727836 1.361727836
1.653212514 0.954242509 0 0.477121255 0 0.954242509 3.172884771 0 3.277557615
2.134399087 3.649811532 4.403059088 3.562682971 0 0 0 0 0 0 0 0 0 2.43383264
2.43383264
11 10.21.15 low diuron bifenthrin 13.3 9.25 10.3 98.7 637 820 0.954242509
0.698970004 0 1.113943352 0.698970004 0 1.041392685 1.431363764 1.230448921
0.84509804 1.361727836 1.041392685 0 0 0.477121255 0 3.454310174 0 2.178294218 0
3.555736028 3.351688516 4.119919599 0 0 0 0 0 3.537257874 0 0 0 0 3.537257874
12 10.21.15 low diuron bifenthrin 13.1 9.44 11.85 112.8 625 811 1.591064607
0.698970004 0 1.755874856 0.477121255 0.477121255 0 1.785329835 1.591064607
1.113943352 1.707570176 0 0 0 1.041392685 1.113943352 3.992717087 0 2.462693138
0 3.773021114 4.601083972 3.900581303 0 0 0 0 0 0 0 0 0 0 0
13 10.21.15 high diuron bifenthrin 13 9.33 11.08 105.4 645 837 0 0 0 0.954242509
0.698970004 0.698970004 0.477121255 1.278753601 1.51851394 1.176091259
1.672097858 0 0 1.176091259 0 0.698970004 3.517958449 0 2.178294218 0
3.379705162 4.877840265 4.056255722 0 0 0 0 0 0 0 0 0 0 0
14 10.21.15 high diuron bifenthrin 13 9.31 10.82 102.9 652 846 0.477121255 0 0
1.612783857 0.477121255 0.477121255 1.113943352 1.755874856 1.633468456
0.84509804 1.69019608 0.954242509 0 1.041392685 0.477121255 0.477121255
2.16315712 0 0 0 2.462693138 3.502723188 3.704317857 0 0 0 0 0 2.762974214 0 0 0
0 2.762974214
15 10.21.15 high diuron bifenthrin 13 9.13 8.88 84.4 756 980 1.113943352 0 0
1.176091259 0.477121255 0.477121255 0 1.278753601 1.113943352 0.84509804
1.278753601 0 0 0 0 0.698970004 3.175693866 0 0 0 0 0 3.430837555 0 0 0 0 0
4.038766558 0 0 0 0 4.038766558
16 10.21.15 high diuron bifenthrin 13.1 9.17 10.09 96.5 757 979 0.698970004
0.477121255 0 1.544068044 0 0.698970004 0 1.591064607 0.477121255 0 0.477121255
1.041392685 0 0 0 1.361727836 3.916091553 0 2.762974214 0 2.462693138
3.691731253 3.916091553 0 0 0 0 0 0 0 0 0 0 0
1 10.28.15 control 14.3 9.07 10.58 103.1 773 973 1.462397998 0 0 1.51851394
1.176091259 0 1.230448921 1.826074803 0.84509804 0 0.84509804 0.698970004 0 0
0.477121255 0.954242509 3.351688516 0 2.874953569 0 2.778188332 3.694005781
4.162205604 0 0 0 0 0 2.477881497 0 0 0 0 2.477881497
2 10.28.15 control 13.8 9.2 10.74 103.3 706 899 1.322219295 0 0 1.397940009
0.954242509 0.477121255 0.698970004 1.591064607 1.278753601 1.113943352
1.491361694 0.477121255 0 1.041392685 0.477121255 1.041392685 3.411876905 0
2.509962945 0 3.841119731 4.099717782 4.515097961 0 0 0 0 0 2.210273092 0 0 0 0
2.210273092
3 10.28.15 control 13.7 9.21 11.19 106.6 648 823 1.361727836 0.477121255 0
0.84509804 0.477121255 0.477121255 0.954242509 1.278753601 0.84509804
1.041392685 1.230448921 0 0 0.954242509 0 0.954242509 2.830811959 0 3.277557615
0 3.838833437 5.030477548 4.381638351 0 0 0 0 0 0 0 0 0 3.94416306 3.94416306
4 10.28.15 control 13.7 9.27 9.82 94.5 730 931 0.698970004 0.84509804 0
1.633468456 0.954242509 0.698970004 0.698970004 1.770852012 1.322219295
0.477121255 1.361727836 0.698970004 0 0 0.84509804 0.477121255 3.406023444 0
2.95403827 0 3.078856296 5.130131984 3.974790552 0 0 0 0 0 3.078856296 0 0 0
2.874953569 5.953809865
5 10.28.15 high diuron 13.6 9.14 9.12 88.2 705 901 0 0 0 0.477121255 0 0 0
0.477121255 2.149219113 0.477121255 2.155336037 0.84509804 0 2.674861141 0 0
4.246548457 0 3.575252858 0 2.938815545 4.160194042 3.979753448 0 0 0 0 0
2.938815545 0 0 0 0 2.938815545
6 10.28.15 high diuron 13.5 9.1 1074 103.3 683 875 1.397940009 0 0 1.176091259
0.477121255 0 0.698970004 1.322219295 1.591064607 1.278753601 1.755874856
0.477121255 0 0 0 1.041392685 2.748276803 0 2.748276803 0 3.349754879
4.132244516 4.300798516 0 0 0 0 0 3.048918942 0 0 0 0 3.048918942
7 10.28.15 high diuron 13.8 9.11 9.6 92.8 816 1039 1.612783857 0.477121255 0
1.672097858 0 0 0.698970004 1.707570176 0.698970004 0.954242509 1.113943352
0.698970004 0 0.477121255 0 1.041392685 2.874953569 0 2.778188332 0 3.020916086
4.810894434 3.891463082 0 0 0 0 0 3.020916086 0 0 0 0 3.020916086
8 10.28.15 high diuron 13.9 9.27 10.78 104.6 723 918 1.431363764 0.477121255 0
1.113943352 0 0.84509804 1.041392685 1.491361694 1.544068044 0.477121255
1.568201724 0 0 0 0 1.041392685 4.026703165 0 0 0 3.321739096 3.818922553
4.196620891 0 0 0 0 0 0 0 0 0 0 0
9 10.28.15 low diuron bifenthrin 13.9 9.17 8.84 86 786 997 1.491361694 0 0
1.278753601 1.041392685 1.113943352 1.568201724 1.886490725 0 0.84509804
0.84509804 0.477121255 0 0 0.477121255 1.230448921 3.020916086 0 0 0 2.178294218
2.477881497 3.289570338 0 0 0 0 0 0 0 0 0 0 0
10 10.28.15 low diuron bifenthrin 13.5 9.13 10.71 102.7 704 902 0.84509804 0 0
1.230448921 0 0 1.322219295 1.568201724 1.397940009 1.322219295 1.653212514
0.954242509 0 0 0 1.113943352 3.351688516 0 2.178294218 0 2.95403827 4.405869954
3.988361943 0 0 0 0 0 0 0 0 0 0 0
11 10.28.15 low diuron bifenthrin 13.5 9.25 10.61 102 656 840 1.322219295 0 0
0.954242509 1.041392685 0.698970004 0.84509804 1.462397998 1.591064607
0.84509804 1.653212514 0 0 0.84509804 0 0.954242509 3.463010735 0 0 0 3.20785785
3.712822777 3.787443111 0 0 0 0 0 3.162130259 0 0 0 0 3.162130259
12 10.28.15 low diuron bifenthrin 13.3 9.44 11.98 114.5 653 841 1.278753601 0 0
1.397940009 0.477121255 0 0 1.431363764 1.361727836 1.278753601 1.612783857 0 0
0 0.84509804 0.698970004 3.783484804 0 4.029436313 0 3.201829649 4.029436313
3.336455451 0 0 0 0 0 2.16315712 0 0 0 0 2.16315712
13 10.28.15 high diuron bifenthrin 13.3 9.37 11.42 109.2 663 853 1.113943352
0.477121255 0 0.698970004 0 0 0 0.698970004 1.361727836 0.954242509 1.491361694
0.477121255 0 1.51851394 0.477121255 1.230448921 3.575252858 0 0 0 3.005690901
4.510425435 4.099717782 0 0 0 0 0 0 0 0 0 0 0
14 10.28.15 high diuron bifenthrin 13.4 9.3 11.12 105.6 682 878 1.230448921 0 0
1.851258349 1.041392685 0 1.322219295 2.004321374 1.431363764 0.698970004
1.491361694 0.698970004 0 1.51851394 1.041392685 0.84509804 2.669250603 0 0 0
2.969815295 3.947122153 3.270612452 0 0 0 0 0 2.193985593 0 0 0 0 2.193985593
15 10.28.15 high diuron bifenthrin 13.4 9.05 8.75 84.6 785 1008 1.230448921
0.477121255 0 1.278753601 0 0 0.698970004 1.361727836 1.176091259 0.84509804
1.322219295 0 0 0 0 1.361727836 3.497761337 0 2.95403827 0 2.178294218
2.178294218 4.050503902 0 0 0 0 0 4.951383163 0 0 0 0 4.951383163
16 10.28.15 high diuron bifenthrin 13.5 9.13 9.85 94.8 793 1017 1.041392685
0.477121255 0 1.113943352 1.041392685 0 1.041392685 1.51851394 0 0.84509804
0.84509804 0.477121255 0 0 0 1.230448921 2.874953569 0 0 0 2.178294218
3.020916086 4.001522057 0 0 0 0 0 0 0 0 0 0 0
Looking at your data there are several issues. 1. Tank is an integer, but it sounds like you intend to use it as a categorical measure. If so that it should be a factor, but factors cannot be used in pca. Is Tank 10 10 times more of something than Tank 1? 2. Date is a factor. That means you are not measuring time, just the fact that 2 rows are the same time or different time. Factors cannot be used in pca. 3. Treatment is a factor, but factors cannot be used in pca. 4. Your log transformed data has many 0's and no negative values. Did you add 1 to each value before taking logarithms? First line of your code after reading the data:> meso.pca <- prcomp(mesocleaned, center=TRUE, scale.=TRUE)Error in colMeans(x, na.rm = TRUE) : 'x' must be numeric> scale. = TRUE)------------------------------------- David L Carlson Department of Anthropology Texas A&M University College Station, TX 77840-4352 -----Original Message----- From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Sarah Stinson Sent: Wednesday, September 21, 2016 10:25 AM To: r-help at r-project.org Subject: Re: [R] Help with PCA data file prep and R code Hello DRUGs, I'm new to R and would appreciate some expert advice on prepping files for, and running, PCA... My data set consists of aquatic invertebrate and zooplankton count data and physicochemical measurements from an ecotoxicology study. Four chemical treatments were applied to mesocosm tanks, 4 replicates per treatment (16 tanks total), then data were collected weekly over a 3 month period. I cleaned the data in excel by removing columns with all zero values, and all rows with NA values. All zooplankton values were volume normalized, then log normalized. All other data was log normalized in excel prior to analysis in R. All vectorss are numeric. I've attached the .txt file to this email rather that using dput(dataframe). My questions are: 1. Did I do the cleaning step appropriately? I know that there are ways to run PCA's using data that contain NA values (pcaMethods), but wasn't able to get the code to work... (I understand that this isn't strictly an R question, but any help would be appreciated.) 2. Does my code look correct for the PCA and visualization (see below)? Thanks in advance, Sarah #read data mesocleaned <- read.csv("MesoCleanedforPCA.9.16.16.csv") #run PCA meso.pca <- prcomp(mesocleaned, center = TRUE, scale. = TRUE) # print method print(meso.pca) #compute standard deviation of each principal component std_dev <- meso.pca$sdev #compute variance pr_var <- std_dev^2 #check variance of first 10 components pr_var[1:10] #proportion of variance explained prop_varex <- pr_var/sum(pr_var) prop_varex[1:20] #The first principal component explains 12.7% of the variance #The second explains 8.1% #visualize biplot(meso.pca) #for visualization, make Treatment vector a factor instead of numeric meso.treatment <- as.factor(mesocleaned[, 3]) #ggbiplot to visualize by Treatment group #reference: https://www.r-bloggers.com/computing-and-visualizing-pca-in-r/ library(devtools) install_github("ggbiplot", "vqv") library(ggbiplot) print(ggbiplot(meso.pca, obs.scale = 1, var.scale = 1, groups meso.treatment, ellipse = TRUE, circle = TRUE)) g <- ggbiplot(meso.pca, obs.scale = 1, var.scale = 1, groups = meso.treatment, ellipse = TRUE, circle = TRUE) g <- g + scale_color_brewer(name = deparse(substitute(Treatments)), palette = 'Dark2') #must change meso.treatment to a factor for this to work g <- g + theme(legend.direction = 'horizontal', legend.position = 'top') print(g) #Circle plot #plot each variables coefficients inside a unit circle to get insight on a possible interpretation for PCs. #reference: https://www.r-bloggers.com/computing-and-visualizing-pca-in-r/ theta <- seq(0,2*pi,length.out = 100) circle <- data.frame(x = cos(theta), y = sin(theta)) p <- ggplot(circle,aes(x,y)) + geom_path() loadings <- data.frame(meso.pca$rotation, .names = row.names(meso.pca$rotation)) p + geom_text(data=loadings, mapping=aes(x = PC1, y = PC2, label = .names, colour .names)) + coord_fixed(ratio=1) + labs(x = "PC1", y = "PC2") On Tue, Sep 20, 2016 at 10:28 PM, Sarah Stinson <sastinson at ucdavis.edu> wrote:> Hello DRUGs, > I'm new to R and would appreciate some expert advice on prepping files > for, and running, PCA... > > My data set consists of aquatic invertebrate and zooplankton count data > and physicochemical measurements from an ecotoxicology study. Four chemical > treatments were applied to mesocosm tanks, 4 replicates per treatment (16 > tanks total), then data were collected weekly over a 3 month period. > > I cleaned the data in excel by removing columns with all zero values, and > all rows with NA values. > All zooplankton values were volume normalized, then log normalized. All > other data was log normalized in excel prior to analysis in R. All vectorss > are numeric. I've attached the .csv file to this email rather that using > dput(dataframe). I hope that's acceptable. > > My questions are: > > 1. Did I do the cleaning step appropriately? I know that there are ways to > run PCA's using data that contain NA values (pcaMethods), but wasn't able > to get the code to work... > (I understand that this isn't strictly an R question, but any help would > be appreciated.) > 2. Does my code look correct for the PCA and visualization (see below)? > > Thanks in advance, > Sarah > > #read data > mesocleaned <- read.csv("MesoCleanedforPCA.9.16.16.csv") > > #run PCA > meso.pca <- prcomp(mesocleaned, > center = TRUE, > scale. = TRUE) > > # print method > print(meso.pca) > > #compute standard deviation of each principal component > std_dev <- meso.pca$sdev > > #compute variance > pr_var <- std_dev^2 > > #check variance of first 10 components > pr_var[1:10] > > #proportion of variance explained > prop_varex <- pr_var/sum(pr_var) > prop_varex[1:20] > > #The first principal component explains 12.7% of the variance > #The second explains 8.1% > > #visualize > biplot(meso.pca) > > #for visualization, make Treatment vector a factor instead of numeric > meso.treatment <- as.factor(mesocleaned[, 3]) > > #ggbiplot to visualize by Treatment group > #reference: https://www.r-bloggers.com/computing-and-visualizing-pca-in-r/ > > library(devtools) > install_github("ggbiplot", "vqv") > library(ggbiplot) > > print(ggbiplot(meso.pca, obs.scale = 1, var.scale = 1, groups > meso.treatment, ellipse = TRUE, circle = TRUE)) > g <- ggbiplot(meso.pca, obs.scale = 1, var.scale = 1, > groups = meso.treatment, ellipse = TRUE, > circle = TRUE) > g <- g + scale_color_brewer(name = deparse(substitute(Treatments)), > palette = 'Dark2') #must change meso.treatment to a factor for this to work > g <- g + theme(legend.direction = 'horizontal', > legend.position = 'top') > print(g) > > #Circle plot > #plot each variables coefficients inside a unit circle to get insight on a > possible interpretation for PCs. > #reference: https://www.r-bloggers.com/computing-and-visualizing-pca-in-r/ > > theta <- seq(0,2*pi,length.out = 100) > circle <- data.frame(x = cos(theta), y = sin(theta)) > p <- ggplot(circle,aes(x,y)) + geom_path() > > loadings <- data.frame(meso.pca$rotation, > .names = row.names(meso.pca$rotation)) > p + geom_text(data=loadings, > mapping=aes(x = PC1, y = PC2, label = .names, colour > .names)) + > coord_fixed(ratio=1) + > labs(x = "PC1", y = "PC2") > >