Moorea HOBO temperature data analysis
Carsten Grupstra
March 12, 2019
This R Markdown document contains the HoBo data between August 2017 and March 2019.
TLDR: I compared temperature data between fringe, back and fore reefs in Moorea, based on our HoBo data. Fringe reefs on average are warmer and have more extreme daily temperature ranges. Fore reefs are much more stable, both in term of daily and yearly temperature ranges. Back reefs are intermediately warm and variable. However, these patterns change in relative strength between months. 2019 seems to be a warmer year for the fringe and back reefs.
Set up workspace, load, manipulate and visualize raw data
Load and manipulate raw data
Set working directory using ‘setwd()’, load Tidyverse and other packges using library() and read file using read.csv().
library(tidyverse)
library(cowplot)
library(lme4)
library(FDB1)
library(olsrr)
hobotemp <- read.csv('hobodata_march_2019.csv') Check whether the dataset loaded correctly using head(), which will show us the first few rows of the dataset.
head(hobotemp)## unique lter zone number date temp light
## 1 1 0 back 1 9/16/2017 12:00 27.862 16533.4
## 2 2 0 back 2 9/16/2017 14:00 27.272 6544.5
## 3 3 0 back 3 9/16/2017 16:00 27.075 3444.5
## 4 4 0 back 4 9/16/2017 18:00 26.781 10.8
## 5 5 0 back 5 9/16/2017 20:00 26.585 0.0
## 6 6 0 back 6 9/16/2017 22:00 26.585 0.0All the correct variables are there!
Now let’s check the number of rows using nrow() to see whether it’s the same as the excel file.
nrow(hobotemp)## [1] 109356Great. Time to transform it and do the analysis.
Transform the date/time column into separate columns for time and date. Also check whether it worked using head().
hobotemplongdate <- tidyr::separate(hobotemp, 'date',
into = c('longdate', 'time'),
sep= ' ')
head(hobotemplongdate)## unique lter zone number longdate time temp light
## 1 1 0 back 1 9/16/2017 12:00 27.862 16533.4
## 2 2 0 back 2 9/16/2017 14:00 27.272 6544.5
## 3 3 0 back 3 9/16/2017 16:00 27.075 3444.5
## 4 4 0 back 4 9/16/2017 18:00 26.781 10.8
## 5 5 0 back 5 9/16/2017 20:00 26.585 0.0
## 6 6 0 back 6 9/16/2017 22:00 26.585 0.0That worked.
Add a variable for island side, ‘side’ contingent on the lter sites. I’ll use mutate() and ifelse() for this. A bit clunky but it works.
hobotemplongdate <- dplyr::mutate(hobotemplongdate, side = ifelse(lter == 0.0, "north", ifelse(lter == 1.0, "north", ifelse(lter == 2.0, "north", ifelse(lter == 3.0, "east", ifelse(lter == 3.5, "east", ifelse(lter == 4.0, "east", ifelse(lter == 5.0, "west", ifelse(lter == 5.5, "west", ifelse(lter == 6.0, "west", "diff")))))))))) ## Warning: package 'bindrcpp' was built under R version 3.4.4Now see how many north, east and wests there are.
count(hobotemplongdate, var=side)## # A tibble: 3 x 2
## var n
## <chr> <int>
## 1 east 34566
## 2 north 39266
## 3 west 35524Check the table
head(hobotemplongdate)## unique lter zone number longdate time temp light side
## 1 1 0 back 1 9/16/2017 12:00 27.862 16533.4 north
## 2 2 0 back 2 9/16/2017 14:00 27.272 6544.5 north
## 3 3 0 back 3 9/16/2017 16:00 27.075 3444.5 north
## 4 4 0 back 4 9/16/2017 18:00 26.781 10.8 north
## 5 5 0 back 5 9/16/2017 20:00 26.585 0.0 north
## 6 6 0 back 6 9/16/2017 22:00 26.585 0.0 northFigure of raw temperature data
I want to plot the temperature data over time separately for each lter site (0-6) and reef zone (fringe, back, fore). I will use the ggplot function within Tidyverse for this.
ntempgraph <- ggplot(data=hobotemplongdate,
aes(x=as.Date(longdate, format = "%m / %d / %Y"),
y=temp, colour=zone)) +
geom_point(size=1, alpha = 1/10)+ theme_bw()+
facet_grid(fct_relevel(zone, "fringe", "back", "fore")~lter)+
theme(axis.text.x = element_text(angle=45, margin = margin(t=20, r=100)))+
labs(title="Raw temperature data", y="Temperature (°C)", x="Date")
ntempgraph
It seems like there is a difference in mean temperature, daily temperature ranges, and temperature minima and maxima. In the next section I will test for these differences in several ways.
Analysis of daily average temperatures
In this section I will analyse average temperature values across sites and zones. First I will manipulate the dataset to calculate daily means, then I will visualize these data and test for significance.
Manipulate date data
Separate the ‘longdate’ column into separate columns for month, day and year using the separate() function. I will then combine the year and month columns using the unite() function to facilitate between-year comparisons. By doing this I can calculate monthly or daily averages later on. Check the dataset using the head() function to see the first few rows and the variable names.
hobofull <- hobotemplongdate %>%
tidyr::separate('longdate',
into = c('month', 'day', 'year'),
sep= '/',
remove = FALSE)%>%
tidyr::unite('monthyear',
c("month", "year"),
sep= '/',
remove = FALSE)
head(hobofull)## unique lter zone number longdate monthyear month day year time temp
## 1 1 0 back 1 9/16/2017 9/2017 9 16 2017 12:00 27.862
## 2 2 0 back 2 9/16/2017 9/2017 9 16 2017 14:00 27.272
## 3 3 0 back 3 9/16/2017 9/2017 9 16 2017 16:00 27.075
## 4 4 0 back 4 9/16/2017 9/2017 9 16 2017 18:00 26.781
## 5 5 0 back 5 9/16/2017 9/2017 9 16 2017 20:00 26.585
## 6 6 0 back 6 9/16/2017 9/2017 9 16 2017 22:00 26.585
## light side
## 1 16533.4 north
## 2 6544.5 north
## 3 3444.5 north
## 4 10.8 north
## 5 0.0 north
## 6 0.0 northThere are now separate columns for month, day, year and month/year. Perfect.
Calculate daily means for temperature
I want to calculate daily temperature means for each site and zone. I am using the group_by() and summarise() function to do this.
hobofullmean <- hobofull %>%
group_by(year, month, day, monthyear, zone, lter, longdate, side)%>%
summarise(meantemp = mean(temp))
head(hobofullmean)## # A tibble: 6 x 9
## # Groups: year, month, day, monthyear, zone, lter, longdate [6]
## year month day monthyear zone lter longdate side meantemp
## <chr> <chr> <chr> <chr> <fct> <dbl> <chr> <chr> <dbl>
## 1 2017 10 1 10/2017 back 0 10/1/2017 north 27.3
## 2 2017 10 1 10/2017 back 3 10/1/2017 east 27.2
## 3 2017 10 1 10/2017 back 3.5 10/1/2017 east 27.2
## 4 2017 10 1 10/2017 back 4 10/1/2017 east 26.9
## 5 2017 10 1 10/2017 back 5 10/1/2017 west 27.0
## 6 2017 10 1 10/2017 back 5.5 10/1/2017 west 27.1Looks good
Figure of daily means for temperature
Now I will plot the daily mean temperature for each reef zone separately but averaged over lter sites. I will use ggplot with the geom_smooth() function to get a line with 95% confidence intervals.
meanplot <- ggplot(hobofullmean,
aes(x=as.Date(longdate, format= "%m / %d / %Y"), y=meantemp))+
geom_smooth(aes(colour=zone))+
theme_bw()+
scale_x_date(breaks="1 month")+
theme(axis.text.x = element_text(angle=45, margin = margin(t=20, r=100)))+
labs(title= "Daily temperature means", y="Daily mean temperature (°C) with 95% CI", x="Date")
meanplot
#labels = date_format("%Y-%m-%d"),
breaks = scales::pretty_breaks(n = 18)Interestingly, mean daily temperatures seem to increase relatively steadily from the fore reef to the back reef and fringing reef. Temperature differences are most pronounced in January and April. Temperature differences are more pronounced in the fall (October - Jan) and nearly absent in the spring (April - July)
Figure of daily means per island side
meanplot <- ggplot(hobofullmean,
aes(x=as.Date(longdate, format= "%m / %d / %Y"), y=meantemp))+
geom_smooth(aes(colour=zone))+
theme_bw()+
scale_x_date(breaks="1 month")+
theme(axis.text.x = element_text(angle=45, margin = margin(t=20, r=100)))+
labs(title= "Daily temperature means", y="Daily mean temperature (°C) with 95% CI", x="Date")+
facet_grid(~side)
meanplot
#labels = date_format("%Y-%m-%d"),
breaks = scales::pretty_breaks(n = 18)Calculate monthly temperature means
Given that the daily temperatures vary so much over time, I want to calculate average ranges for separate time intervals. I will use the group_by() and the summarise() function again, this time to calculate mean daily temperature per month.
This should leave us with a much smaller dataset. We have 9 sites with back and fringe zones (=18), 3 with back, fringe and fore reef zones (=21). And up to 12 months. That means there should be a maximum of (max. 12*21= 252) 252 rows. However, we don’t have data for all months for every site. The total number of rows should therefore be <252. I will check this using the nrow() function to count the number of rows.
hobofullmeanmonth <- hobofullmean %>%
group_by(year, month, monthyear, lter, zone, side)%>%
summarise(meanmonth = mean(meantemp))
nrow(hobofullmeanmonth)## [1] 323Number of rows = 209 which < 252. Makes sense.
Test for significance between zones, months and LTER sites
Now I want to test for significant differences between zones. I will use an anova using the function aov().
hobomeanmonthaov <- aov(meanmonth~zone*as.factor(monthyear), data=hobofullmeanmonth)
summary(hobomeanmonthaov)## Df Sum Sq Mean Sq F value Pr(>F)
## zone 2 15.7 7.845 15.022 6.59e-07 ***
## as.factor(monthyear) 18 358.0 19.889 38.086 < 2e-16 ***
## zone:as.factor(monthyear) 36 20.0 0.555 1.063 0.379
## Residuals 266 138.9 0.522
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1There is a significant effect of zone and month on daily temperature means.
What are the assumptions of a Two-Way ANOVA?
- Your dependent variable - here, “temperature”, should be continuous * Your two independent variables - here, “month” and “zone”, should be in categorical, independent groups.
- Sample independence - that each sample has been drawn independently of the other samples
- Variance Equality - That the variance of data in the different groups should be the same
- Normality - That each sample is taken from a normally distributed population
I will look at these assumptions using plot(hobomeanmonthaov) by looking at * residuals vs fitted values to check for heteroscedasticity, * q-q plot to see whether the residuals are normally distributed, * A histogram of the residuals to double-check normal distribution of the residuals
plot(hobomeanmonthaov)
hist(hobomeanmonthaov$resid)
The Residuals vs Fitted curve indicates that there may be some heteroscedasticity at higher and lower fitted values. However, heteroscadisticity supposedly is not a very big problem for ANOVA.
The q-q plot and histogram both indicate that the distribution has long tails.
bartlett.test(meanmonth~zone, data=hobofullmeanmonth)##
## Bartlett test of homogeneity of variances
##
## data: meanmonth by zone
## Bartlett's K-squared = 16.938, df = 2, p-value = 0.0002099Post-hoc tests
Now I will use a post-hoc test to compare individual zones, lter sites, and months. I am mostly interested in looking at zones, but I included sites and months just to be comprehensive.
HSD <- TukeyHSD(hobomeanmonthaov, ordered=FALSE)
HSD## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = meanmonth ~ zone * as.factor(monthyear), data = hobofullmeanmonth)
##
## $zone
## diff lwr upr p adj
## fore-back -0.4084620 -0.681025900 -0.1358980 0.0014070
## fringe-back 0.2170591 0.008469481 0.4256487 0.0392279
## fringe-fore 0.6255210 0.355660921 0.8953811 0.0000003
##
## $`as.factor(monthyear)`
## diff lwr upr p adj
## 1/2019-1/2018 0.66292786 -0.246554447 1.572410163 0.4910284
## 10/2017-1/2018 -2.32231199 -3.202914441 -1.441709531 0.0000000
## 10/2018-1/2018 -1.86399517 -2.744597628 -0.983392718 0.0000000
## 11/2017-1/2018 -1.32815939 -2.208761843 -0.447556933 0.0000299
## 11/2018-1/2018 -0.63666659 -1.546148897 0.272815713 0.5693448
## 12/2017-1/2018 -0.43572217 -1.316324625 0.444880285 0.9616671
## 12/2018-1/2018 0.72246872 -0.187013586 1.631951023 0.3264749
## 2/2018-1/2018 -0.88544999 -1.766052447 -0.004847537 0.0470324
## 2/2019-1/2018 0.58885028 -0.320632023 1.498332586 0.7085283
## 3/2018-1/2018 -0.33307467 -1.180010995 0.513861648 0.9967991
## 3/2019-1/2018 0.83460116 -0.091976280 1.761178605 0.1373243
## 4/2018-1/2018 -0.28170723 -1.149992960 0.586578500 0.9997358
## 5/2018-1/2018 -0.76794038 -1.636226109 0.100345351 0.1592392
## 6/2018-1/2018 -1.45596726 -2.324252991 -0.587681531 0.0000014
## 7/2018-1/2018 -1.95595320 -2.824238933 -1.087667473 0.0000000
## 8/2018-1/2018 -2.23207937 -3.079015693 -1.385143051 0.0000000
## 9/2017-1/2018 -2.09328592 -2.973888377 -1.212683467 0.0000000
## 9/2018-1/2018 -2.30644325 -3.174728980 -1.438157519 0.0000000
## 10/2017-1/2019 -2.98523984 -3.894722149 -2.075757540 0.0000000
## 10/2018-1/2019 -2.52692303 -3.436405336 -1.617440726 0.0000000
## 11/2017-1/2019 -1.99108725 -2.900569551 -1.081604941 0.0000000
## 11/2018-1/2019 -1.29959445 -2.237067352 -0.362121549 0.0002355
## 12/2017-1/2019 -1.09865003 -2.008132333 -0.189167723 0.0035726
## 12/2018-1/2019 0.05954086 -0.877932042 0.997013762 1.0000000
## 2/2018-1/2019 -1.54837785 -2.457860155 -0.638895546 0.0000008
## 2/2019-1/2019 -0.07407758 -1.011550478 0.863395325 1.0000000
## 3/2018-1/2019 -0.99600253 -1.872928133 -0.119076929 0.0095939
## 3/2019-1/2019 0.17167330 -0.782393326 1.125739935 1.0000000
## 4/2018-1/2019 -0.94463509 -1.842197057 -0.047073119 0.0273476
## 5/2018-1/2019 -1.43086824 -2.328430206 -0.533306268 0.0000064
## 6/2018-1/2019 -2.11889512 -3.016457088 -1.221333150 0.0000000
## 7/2018-1/2019 -2.61888106 -3.516443030 -1.721319092 0.0000000
## 8/2018-1/2019 -2.89500723 -3.771932832 -2.018081628 0.0000000
## 9/2017-1/2019 -2.75621378 -3.665696085 -1.846731476 0.0000000
## 9/2018-1/2019 -2.96937111 -3.866933077 -2.071809139 0.0000000
## 10/2018-10/2017 0.45831681 -0.422285642 1.338919268 0.9393133
## 11/2017-10/2017 0.99415260 0.113550144 1.874755054 0.0104960
## 11/2018-10/2017 1.68564539 0.776163089 2.595127699 0.0000000
## 12/2017-10/2017 1.88658982 1.005987362 2.767192272 0.0000000
## 12/2018-10/2017 3.04478070 2.135298400 3.954263009 0.0000000
## 2/2018-10/2017 1.43686199 0.556259539 2.317464449 0.0000032
## 2/2019-10/2017 2.91116227 2.001679963 3.820644573 0.0000000
## 3/2018-10/2017 1.98923731 1.142300992 2.836173634 0.0000000
## 3/2019-10/2017 3.15691315 2.230335706 4.083490591 0.0000000
## 4/2018-10/2017 2.04060476 1.172319026 2.908890486 0.0000000
## 5/2018-10/2017 1.55437161 0.686085877 2.422657337 0.0000001
## 6/2018-10/2017 0.86634473 -0.001941005 1.734630456 0.0512504
## 7/2018-10/2017 0.36635878 -0.501926947 1.234644513 0.9927004
## 8/2018-10/2017 0.09023261 -0.756703707 0.937168936 1.0000000
## 9/2017-10/2017 0.22902606 -0.651576391 1.109628519 0.9999893
## 9/2018-10/2017 0.01586874 -0.852416994 0.884154467 1.0000000
## 11/2017-10/2018 0.53583579 -0.344766670 1.416438240 0.7996934
## 11/2018-10/2018 1.22732858 0.317846276 2.136810885 0.0004254
## 12/2017-10/2018 1.42827300 0.547670548 2.308875458 0.0000038
## 12/2018-10/2018 2.58646389 1.676981586 3.495946196 0.0000000
## 2/2018-10/2018 0.97854518 0.097942726 1.859147636 0.0132056
## 2/2019-10/2018 2.45284545 1.543363150 3.362327759 0.0000000
## 3/2018-10/2018 1.53092050 0.683984178 2.377856821 0.0000001
## 3/2019-10/2018 2.69859634 1.772018893 3.625173778 0.0000000
## 4/2018-10/2018 1.58228794 0.714002213 2.450573673 0.0000001
## 5/2018-10/2018 1.09605479 0.227769064 1.964340524 0.0016241
## 6/2018-10/2018 0.40802791 -0.460257818 1.276313642 0.9768238
## 7/2018-10/2018 -0.09195803 -0.960243760 0.776327700 1.0000000
## 8/2018-10/2018 -0.36808420 -1.215020521 0.478852122 0.9898383
## 9/2017-10/2018 -0.22929075 -1.109893204 0.651311706 0.9999891
## 9/2018-10/2018 -0.44244808 -1.310733807 0.425837653 0.9497167
## 11/2018-11/2017 0.69149280 -0.217989509 1.600975100 0.4087605
## 12/2017-11/2017 0.89243722 0.011834763 1.773039673 0.0430233
## 12/2018-11/2017 2.05062811 1.141145801 2.960110411 0.0000000
## 2/2018-11/2017 0.44270940 -0.437893060 1.323311850 0.9555417
## 2/2019-11/2017 1.91700967 1.007527364 2.826491974 0.0000000
## 3/2018-11/2017 0.99508471 0.148148393 1.842021036 0.0056663
## 3/2019-11/2017 2.16276055 1.236183108 3.089337993 0.0000000
## 4/2018-11/2017 1.04645216 0.178166428 1.914737888 0.0037170
## 5/2018-11/2017 0.56021901 -0.308066722 1.428504739 0.7141741
## 6/2018-11/2017 -0.12780787 -0.996093603 0.740477857 1.0000000
## 7/2018-11/2017 -0.62779382 -1.496079546 0.240491915 0.5068648
## 8/2018-11/2017 -0.90391998 -1.750856306 -0.056983663 0.0228710
## 9/2017-11/2017 -0.76512653 -1.645728990 0.115475920 0.1825036
## 9/2018-11/2017 -0.97828386 -1.846569592 -0.109998132 0.0108109
## 12/2017-11/2018 0.20094442 -0.708537882 1.110426727 0.9999992
## 12/2018-11/2018 1.35913531 0.421662409 2.296608212 0.0000820
## 2/2018-11/2018 -0.24878340 -1.158265705 0.660698905 0.9999772
## 2/2019-11/2018 1.22551687 0.288043972 2.162989776 0.0008232
## 3/2018-11/2018 0.30359192 -0.573333683 1.180517521 0.9993647
## 3/2019-11/2018 1.47126776 0.517201124 2.425334386 0.0000164
## 4/2018-11/2018 0.35495936 -0.542602607 1.252521331 0.9965764
## 5/2018-11/2018 -0.13127379 -1.028835756 0.766288182 1.0000000
## 6/2018-11/2018 -0.81930067 -1.716862637 0.078261300 0.1226491
## 7/2018-11/2018 -1.31928661 -2.216848580 -0.421724642 0.0000582
## 8/2018-11/2018 -1.59541278 -2.472338382 -0.718487178 0.0000001
## 9/2017-11/2018 -1.45661933 -2.366101635 -0.547137025 0.0000056
## 9/2018-11/2018 -1.66977666 -2.567338626 -0.772214688 0.0000000
## 12/2018-12/2017 1.15819089 0.248708583 2.067673193 0.0013754
## 2/2018-12/2017 -0.44972782 -1.330330278 0.430874632 0.9486934
## 2/2019-12/2017 1.02457245 0.115090146 1.934054756 0.0108306
## 3/2018-12/2017 0.10264750 -0.744288825 0.949583818 1.0000000
## 3/2019-12/2017 1.27032333 0.343745890 2.196900775 0.0003016
## 4/2018-12/2017 0.15401494 -0.714270790 1.022300670 1.0000000
## 5/2018-12/2017 -0.33221821 -1.200503940 0.536067521 0.9977117
## 6/2018-12/2017 -1.02024509 -1.888530821 -0.151959361 0.0056594
## 7/2018-12/2017 -1.52023103 -2.388516764 -0.651945303 0.0000003
## 8/2018-12/2017 -1.79635720 -2.643293524 -0.949420881 0.0000000
## 9/2017-12/2017 -1.65756375 -2.538166208 -0.776961298 0.0000000
## 9/2018-12/2017 -1.87072108 -2.739006810 -1.002435350 0.0000000
## 2/2018-12/2018 -1.60791871 -2.517401015 -0.698436406 0.0000002
## 2/2019-12/2018 -0.13361844 -1.071091339 0.803854465 1.0000000
## 3/2018-12/2018 -1.05554339 -1.932468994 -0.178617790 0.0037970
## 3/2019-12/2018 0.11213244 -0.841934186 1.066199075 1.0000000
## 4/2018-12/2018 -1.00417595 -1.901737917 -0.106613979 0.0119802
## 5/2018-12/2018 -1.49040910 -2.387971066 -0.592847128 0.0000019
## 6/2018-12/2018 -2.17843598 -3.075997948 -1.280874010 0.0000000
## 7/2018-12/2018 -2.67842192 -3.575983890 -1.780859952 0.0000000
## 8/2018-12/2018 -2.95454809 -3.831473692 -2.077622488 0.0000000
## 9/2017-12/2018 -2.81575464 -3.725236945 -1.906272336 0.0000000
## 9/2018-12/2018 -3.02891197 -3.926473937 -2.131349999 0.0000000
## 2/2019-2/2018 1.47430027 0.564817969 2.383782579 0.0000039
## 3/2018-2/2018 0.55237532 -0.294561002 1.399311640 0.6965225
## 3/2019-2/2018 1.72005115 0.793473712 2.646628597 0.0000000
## 4/2018-2/2018 0.60374276 -0.264542968 1.472028492 0.5821441
## 5/2018-2/2018 0.11750961 -0.750776117 0.985795343 1.0000000
## 6/2018-2/2018 -0.57051727 -1.438802999 0.297768462 0.6840796
## 7/2018-2/2018 -1.07050321 -1.938788941 -0.202217481 0.0025008
## 8/2018-2/2018 -1.34662938 -2.193565701 -0.499693058 0.0000069
## 9/2017-2/2018 -1.20783593 -2.088438385 -0.327233475 0.0002986
## 9/2018-2/2018 -1.42099326 -2.289278988 -0.552707527 0.0000029
## 3/2018-2/2019 -0.92192495 -1.798850557 -0.044999353 0.0277187
## 3/2019-2/2019 0.24575088 -0.708315750 1.199817512 0.9999908
## 4/2018-2/2019 -0.87055751 -1.768119480 0.027004457 0.0692124
## 5/2018-2/2019 -1.35679066 -2.254352630 -0.459228692 0.0000282
## 6/2018-2/2019 -2.04481754 -2.942379511 -1.147255573 0.0000000
## 7/2018-2/2019 -2.54480348 -3.442365454 -1.647241516 0.0000000
## 8/2018-2/2019 -2.82092965 -3.697855256 -1.944004052 0.0000000
## 9/2017-2/2019 -2.68213620 -3.591618509 -1.772653899 0.0000000
## 9/2018-2/2019 -2.89529353 -3.792855500 -1.997731562 0.0000000
## 3/2019-3/2018 1.16767584 0.273032776 2.062318897 0.0008498
## 4/2018-3/2018 0.05136744 -0.782755184 0.885490071 1.0000000
## 5/2018-3/2018 -0.43486571 -1.268988333 0.399256922 0.9384041
## 6/2018-3/2018 -1.12289259 -1.957015215 -0.288769960 0.0004482
## 7/2018-3/2018 -1.62287853 -2.457001157 -0.788755902 0.0000000
## 8/2018-3/2018 -1.89900470 -2.710880047 -1.087129350 0.0000000
## 9/2017-3/2018 -1.76021125 -2.607147570 -0.913274928 0.0000000
## 9/2018-3/2018 -1.97336858 -2.807491204 -1.139245949 0.0000000
## 4/2018-3/2019 -1.11630839 -2.031188266 -0.201428519 0.0030038
## 5/2018-3/2019 -1.60254154 -2.517421415 -0.687661669 0.0000003
## 6/2018-3/2019 -2.29056842 -3.205448297 -1.375688550 0.0000000
## 7/2018-3/2019 -2.79055437 -3.705434239 -1.875674493 0.0000000
## 8/2018-3/2019 -3.06668053 -3.961323595 -2.172037474 0.0000000
## 9/2017-3/2019 -2.92788709 -3.854464528 -2.001309642 0.0000000
## 9/2018-3/2019 -3.14104441 -4.055924286 -2.226164539 0.0000000
## 5/2018-4/2018 -0.48623315 -1.342024908 0.369558610 0.8761315
## 6/2018-4/2018 -1.17426003 -2.030051790 -0.318468272 0.0002961
## 7/2018-4/2018 -1.67424597 -2.530037732 -0.818454214 0.0000000
## 8/2018-4/2018 -1.95037214 -2.784494770 -1.116249514 0.0000000
## 9/2017-4/2018 -1.81157869 -2.679864422 -0.943292962 0.0000000
## 9/2018-4/2018 -2.02473602 -2.880527779 -1.168944261 0.0000000
## 6/2018-5/2018 -0.68802688 -1.543818641 0.167764877 0.3049914
## 7/2018-5/2018 -1.18801282 -2.043804583 -0.332221065 0.0002282
## 8/2018-5/2018 -1.46413899 -2.298261621 -0.630016365 0.0000003
## 9/2017-5/2018 -1.32534554 -2.193631273 -0.457059813 0.0000217
## 9/2018-5/2018 -1.53850287 -2.394294629 -0.682711112 0.0000001
## 7/2018-6/2018 -0.49998594 -1.355777701 0.355805817 0.8481228
## 8/2018-6/2018 -0.77611211 -1.610234739 0.058010516 0.1034451
## 9/2017-6/2018 -0.63731866 -1.505604392 0.230967069 0.4774049
## 9/2018-6/2018 -0.85047599 -1.706267748 0.005315770 0.0535407
## 8/2018-7/2018 -0.27612617 -1.110248797 0.557996459 0.9996520
## 9/2017-7/2018 -0.13733272 -1.005618449 0.730953011 1.0000000
## 9/2018-7/2018 -0.35049005 -1.206281805 0.505301712 0.9948183
## 9/2017-8/2018 0.13879345 -0.708142872 0.985729771 1.0000000
## 9/2018-8/2018 -0.07436388 -0.908486505 0.759758750 1.0000000
## 9/2018-9/2017 -0.21315733 -1.081443058 0.655128403 0.9999956
##
## $`zone:as.factor(monthyear)`
## diff lwr upr
## fore:1/2018-back:1/2018 -0.2628122760 -2.360233941 1.834609389
## fringe:1/2018-back:1/2018 0.2935178091 -1.308414448 1.895450066
## back:1/2019-back:1/2018 0.8449005376 -0.867637080 2.557438156
## fore:1/2019-back:1/2018 -0.3662925627 -2.463714227 1.731129102
## fringe:1/2019-back:1/2018 1.1626021505 -0.549935467 2.875139768
## back:10/2017-back:1/2018 -2.3338019713 -4.046339589 -0.621264353
## fore:10/2017-back:1/2018 -2.4650049283 -4.562426593 -0.367583264
## fringe:10/2017-back:1/2018 -2.0652214382 -3.667153695 -0.463289181
## back:10/2018-back:1/2018 -1.9777854423 -3.628027983 -0.327542902
## fore:10/2018-back:1/2018 -2.0992961470 -4.196717812 -0.001874482
## fringe:10/2018-back:1/2018 -1.4575549746 -3.107797515 0.192687566
## back:11/2017-back:1/2018 -1.3383586470 -3.050896265 0.374178971
## fore:11/2017-back:1/2018 -1.5019452210 -3.599366886 0.595476444
## fringe:11/2017-back:1/2018 -1.0603770497 -2.662309307 0.541555207
## back:11/2018-back:1/2018 -0.9004725358 -2.613010154 0.812065082
## fore:11/2018-back:1/2018 -1.4887211470 -3.586142812 0.608700518
## fringe:11/2018-back:1/2018 0.2202033901 -1.492334228 1.932741008
## back:12/2017-back:1/2018 -0.4386236559 -2.151161274 1.273913962
## fore:12/2017-back:1/2018 -0.7168651434 -2.814286808 1.380556521
## fringe:12/2017-back:1/2018 -0.1331542339 -1.735086491 1.468778023
## back:12/2018-back:1/2018 0.6371263441 -1.075411274 2.349663962
## fore:12/2018-back:1/2018 -0.4054968638 -2.502918528 1.691924801
## fringe:12/2018-back:1/2018 1.5388306452 -0.173706973 3.251368263
## back:2/2018-back:1/2018 -0.8007311668 -2.513268785 0.911806451
## fore:2/2018-back:1/2018 -0.9823457501 -3.079767415 1.115075914
## fringe:2/2018-back:1/2018 -0.7176899962 -2.319622253 0.884242261
## back:2/2019-back:1/2018 0.9156621864 -0.796875432 2.628199804
## fore:2/2019-back:1/2018 -0.6247613145 -2.722182979 1.472660350
## fringe:2/2019-back:1/2018 1.0358809364 -0.676656682 2.748418554
## back:3/2018-back:1/2018 -0.2964190407 -1.859744847 1.266906766
## fore:3/2018-back:1/2018 -0.5453857527 -2.642807417 1.552035912
## fringe:3/2018-back:1/2018 -0.0766010977 -1.678533355 1.525331159
## back:3/2019-back:1/2018 1.1407890220 -0.571748596 2.853326640
## fore:3/2019-back:1/2018 -0.8763568957 -3.298250821 1.545537030
## fringe:3/2019-back:1/2018 1.3235658339 -0.388971784 3.036103452
## back:4/2018-back:1/2018 -0.2424058692 -1.844338126 1.359526388
## fore:4/2018-back:1/2018 -0.3428544803 -2.440276145 1.754567184
## fringe:4/2018-back:1/2018 -0.0997985279 -1.750041069 1.550444013
## back:5/2018-back:1/2018 -0.6987227823 -2.300655039 0.903209475
## fore:5/2018-back:1/2018 -0.7797737455 -2.877195410 1.317647919
## fringe:5/2018-back:1/2018 -0.6413561828 -2.291598724 1.008886358
## back:6/2018-back:1/2018 -1.3771152442 -2.979047501 0.224817013
## fore:6/2018-back:1/2018 -1.4006581840 -3.498079849 0.696763481
## fringe:6/2018-back:1/2018 -1.3691691628 -3.019411704 0.281073378
## back:7/2018-back:1/2018 -1.8732214018 -3.475153659 -0.271289145
## fore:7/2018-back:1/2018 -1.9369063620 -4.034328027 0.160515303
## fringe:7/2018-back:1/2018 -1.8580481866 -3.508290727 -0.207805646
## back:8/2018-back:1/2018 -2.1795517102 -3.781483967 -0.577619453
## fore:8/2018-back:1/2018 -2.2396228760 -4.337044541 -0.142201211
## fringe:8/2018-back:1/2018 -2.0582766671 -3.621602474 -0.494950861
## back:9/2017-back:1/2018 -2.0109233267 -3.723460945 -0.298385709
## fore:9/2017-back:1/2018 -2.2786317900 -4.376053455 -0.181210125
## fringe:9/2017-back:1/2018 -1.8905899626 -3.492522220 -0.288657706
## back:9/2018-back:1/2018 -2.2775215438 -3.927764085 -0.627279003
## fore:9/2018-back:1/2018 -2.3943822581 -4.491803923 -0.296960593
## fringe:9/2018-back:1/2018 -2.0960989743 -3.698031231 -0.494166717
## fringe:1/2018-fore:1/2018 0.5563300851 -1.451798273 2.564458443
## back:1/2019-fore:1/2018 1.1077128136 -0.989708851 3.205134478
## fore:1/2019-fore:1/2018 -0.1034802867 -2.525374212 2.318413639
## fringe:1/2019-fore:1/2018 1.4254144265 -0.672007238 3.522836091
## back:10/2017-fore:1/2018 -2.0709896953 -4.168411360 0.026431969
## fore:10/2017-fore:1/2018 -2.2021926523 -4.624086578 0.219701273
## fringe:10/2017-fore:1/2018 -1.8024091622 -3.810537520 0.205719196
## back:10/2018-fore:1/2018 -1.7149731663 -3.761847122 0.331900789
## fore:10/2018-fore:1/2018 -1.8364838710 -4.258377796 0.585410054
## fringe:10/2018-fore:1/2018 -1.1947426986 -3.241616654 0.852131257
## back:11/2017-fore:1/2018 -1.0755463710 -3.172968036 1.021875294
## fore:11/2017-fore:1/2018 -1.2391329450 -3.661026870 1.182760980
## fringe:11/2017-fore:1/2018 -0.7975647737 -2.805693132 1.210563584
## back:11/2018-fore:1/2018 -0.6376602599 -2.735081924 1.459761405
## fore:11/2018-fore:1/2018 -1.2259088710 -3.647802796 1.195985054
## fringe:11/2018-fore:1/2018 0.4830156661 -1.614405999 2.580437331
## back:12/2017-fore:1/2018 -0.1758113799 -2.273233045 1.921610285
## fore:12/2017-fore:1/2018 -0.4540528674 -2.875946793 1.967841058
## fringe:12/2017-fore:1/2018 0.1296580421 -1.878470316 2.137786400
## back:12/2018-fore:1/2018 0.8999386201 -1.197483045 2.997360285
## fore:12/2018-fore:1/2018 -0.1426845878 -2.564578513 2.279209337
## fringe:12/2018-fore:1/2018 1.8016429211 -0.295778743 3.899064586
## back:2/2018-fore:1/2018 -0.5379188908 -2.635340555 1.559502774
## fore:2/2018-fore:1/2018 -0.7195334741 -3.141427399 1.702360451
## fringe:2/2018-fore:1/2018 -0.4548777202 -2.463006078 1.553250638
## back:2/2019-fore:1/2018 1.1784744624 -0.918947202 3.275896127
## fore:2/2019-fore:1/2018 -0.3619490385 -2.783842964 2.059944887
## fringe:2/2019-fore:1/2018 1.2986932124 -0.798728452 3.396114877
## back:3/2018-fore:1/2018 -0.0336067648 -2.011074874 1.943861345
## fore:3/2018-fore:1/2018 -0.2825734767 -2.704467402 2.139320449
## fringe:3/2018-fore:1/2018 0.1862111782 -1.821917180 2.194339536
## back:3/2019-fore:1/2018 1.4036012980 -0.693820367 3.501022963
## fore:3/2019-fore:1/2018 -0.6135446198 -3.321304345 2.094215106
## fringe:3/2019-fore:1/2018 1.5863781099 -0.511043555 3.683799774
## back:4/2018-fore:1/2018 0.0204064068 -1.987721951 2.028534765
## fore:4/2018-fore:1/2018 -0.0800422043 -2.501936130 2.341851721
## fringe:4/2018-fore:1/2018 0.1630137481 -1.883860207 2.209887704
## back:5/2018-fore:1/2018 -0.4359105063 -2.444038864 1.572217852
## fore:5/2018-fore:1/2018 -0.5169614695 -2.938855395 1.904932456
## fringe:5/2018-fore:1/2018 -0.3785439068 -2.425417862 1.668330049
## back:6/2018-fore:1/2018 -1.1143029682 -3.122431326 0.893825390
## fore:6/2018-fore:1/2018 -1.1378459080 -3.559739833 1.284048017
## fringe:6/2018-fore:1/2018 -1.1063568868 -3.153230842 0.940517069
## back:7/2018-fore:1/2018 -1.6104091258 -3.618537484 0.397719232
## fore:7/2018-fore:1/2018 -1.6740940860 -4.095988011 0.747799839
## fringe:7/2018-fore:1/2018 -1.5952359106 -3.642109866 0.451638045
## back:8/2018-fore:1/2018 -1.9167394342 -3.924867792 0.091388924
## fore:8/2018-fore:1/2018 -1.9768106000 -4.398704525 0.445083325
## fringe:8/2018-fore:1/2018 -1.7954643911 -3.772932500 0.182003718
## back:9/2017-fore:1/2018 -1.7481110508 -3.845532715 0.349310614
## fore:9/2017-fore:1/2018 -2.0158195140 -4.437713439 0.406074411
## fringe:9/2017-fore:1/2018 -1.6277776866 -3.635906045 0.380350671
## back:9/2018-fore:1/2018 -2.0147092678 -4.061583223 0.032164688
## fore:9/2018-fore:1/2018 -2.1315699821 -4.553463907 0.290323943
## fringe:9/2018-fore:1/2018 -1.8332866983 -3.841415056 0.174841660
## back:1/2019-fringe:1/2018 0.5513827285 -1.050549529 2.153314986
## fore:1/2019-fringe:1/2018 -0.6598103719 -2.667938730 1.348317986
## fringe:1/2019-fringe:1/2018 0.8690843414 -0.732847916 2.471016598
## back:10/2017-fringe:1/2018 -2.6273197805 -4.229252037 -1.025387523
## fore:10/2017-fringe:1/2018 -2.7585227375 -4.766651096 -0.750394379
## fringe:10/2017-fringe:1/2018 -2.3587392473 -3.841840329 -0.875638165
## back:10/2018-fringe:1/2018 -2.2713032514 -3.806458718 -0.736147785
## fore:10/2018-fringe:1/2018 -2.3928139561 -4.400942314 -0.384685598
## fringe:10/2018-fringe:1/2018 -1.7510727837 -3.286228250 -0.215917317
## back:11/2017-fringe:1/2018 -1.6318764561 -3.233808713 -0.029944199
## fore:11/2017-fringe:1/2018 -1.7954630302 -3.803591388 0.212665328
## fringe:11/2017-fringe:1/2018 -1.3538948589 -2.836995941 0.129206223
## back:11/2018-fringe:1/2018 -1.1939903450 -2.795922602 0.407941912
## fore:11/2018-fringe:1/2018 -1.7822389561 -3.790367314 0.225889402
## fringe:11/2018-fringe:1/2018 -0.0733144191 -1.675246676 1.528617838
## back:12/2017-fringe:1/2018 -0.7321414651 -2.334073722 0.869790792
## fore:12/2017-fringe:1/2018 -1.0103829525 -3.018511311 0.997745406
## fringe:12/2017-fringe:1/2018 -0.4266720430 -1.909773125 1.056429039
## back:12/2018-fringe:1/2018 0.3436085349 -1.258323722 1.945540792
## fore:12/2018-fringe:1/2018 -0.6990146729 -2.707143031 1.309113685
## fringe:12/2018-fringe:1/2018 1.2453128360 -0.356619421 2.847245093
## back:2/2018-fringe:1/2018 -1.0942489759 -2.696181233 0.507683281
## fore:2/2018-fringe:1/2018 -1.2758635593 -3.283991917 0.732264799
## fringe:2/2018-fringe:1/2018 -1.0112078053 -2.494308887 0.471893277
## back:2/2019-fringe:1/2018 0.6221443772 -0.979787880 2.224076634
## fore:2/2019-fringe:1/2018 -0.9182791236 -2.926407482 1.089849234
## fringe:2/2019-fringe:1/2018 0.7423631272 -0.859569130 2.344295384
## back:3/2018-fringe:1/2018 -0.5899368499 -2.031252027 0.851378328
## fore:3/2018-fringe:1/2018 -0.8389035618 -2.847031920 1.169224796
## fringe:3/2018-fringe:1/2018 -0.3701189069 -1.853219989 1.112982175
## back:3/2019-fringe:1/2018 0.8472712129 -0.754661044 2.449203470
## fore:3/2019-fringe:1/2018 -1.1698747049 -3.514863415 1.175114005
## fringe:3/2019-fringe:1/2018 1.0300480247 -0.571884232 2.631980282
## back:4/2018-fringe:1/2018 -0.5359236783 -2.019024760 0.947177404
## fore:4/2018-fringe:1/2018 -0.6363722894 -2.644500647 1.371756069
## fringe:4/2018-fringe:1/2018 -0.3933163370 -1.928471804 1.141839130
## back:5/2018-fringe:1/2018 -0.9922405914 -2.475341673 0.490860491
## fore:5/2018-fringe:1/2018 -1.0732915547 -3.081419913 0.934836803
## fringe:5/2018-fringe:1/2018 -0.9348739919 -2.470029459 0.600281475
## back:6/2018-fringe:1/2018 -1.6706330533 -3.153734135 -0.187531971
## fore:6/2018-fringe:1/2018 -1.6941759931 -3.702304351 0.313952365
## fringe:6/2018-fringe:1/2018 -1.6626869720 -3.197842439 -0.127531505
## back:7/2018-fringe:1/2018 -2.1667392110 -3.649840293 -0.683638129
## fore:7/2018-fringe:1/2018 -2.2304241711 -4.238552529 -0.222295813
## fringe:7/2018-fringe:1/2018 -2.1515659957 -3.686721462 -0.616410529
## back:8/2018-fringe:1/2018 -2.4730695194 -3.956170601 -0.989968437
## fore:8/2018-fringe:1/2018 -2.5331406851 -4.541269043 -0.525012327
## fringe:8/2018-fringe:1/2018 -2.3517944762 -3.793109654 -0.910479299
## back:9/2017-fringe:1/2018 -2.3044411359 -3.906373393 -0.702508879
## fore:9/2017-fringe:1/2018 -2.5721495991 -4.580277957 -0.564021241
## fringe:9/2017-fringe:1/2018 -2.1841077718 -3.667208854 -0.701006690
## back:9/2018-fringe:1/2018 -2.5710393529 -4.106194820 -1.035883886
## fore:9/2018-fringe:1/2018 -2.6879000672 -4.696028425 -0.679771709
## fringe:9/2018-fringe:1/2018 -2.3896167835 -3.872717866 -0.906515701
## fore:1/2019-back:1/2019 -1.2111931004 -3.308614765 0.886228564
## fringe:1/2019-back:1/2019 0.3177016129 -1.394836005 2.030239231
## back:10/2017-back:1/2019 -3.1787025090 -4.891240127 -1.466164891
## fore:10/2017-back:1/2019 -3.3099054659 -5.407327131 -1.212483801
## fringe:10/2017-back:1/2019 -2.9101219758 -4.512054233 -1.308189719
## back:10/2018-back:1/2019 -2.8226859799 -4.472928521 -1.172443439
## fore:10/2018-back:1/2019 -2.9441966846 -5.041618349 -0.846775020
## fringe:10/2018-back:1/2019 -2.3024555122 -3.952698053 -0.652212972
## back:11/2017-back:1/2019 -2.1832591846 -3.895796802 -0.470721567
## fore:11/2017-back:1/2019 -2.3468457587 -4.444267423 -0.249424094
## fringe:11/2017-back:1/2019 -1.9052775874 -3.507209844 -0.303345330
## back:11/2018-back:1/2019 -1.7453730735 -3.457910691 -0.032835456
## fore:11/2018-back:1/2019 -2.3336216846 -4.431043349 -0.236200020
## fringe:11/2018-back:1/2019 -0.6246971476 -2.337234765 1.087840470
## back:12/2017-back:1/2019 -1.2835241935 -2.996061811 0.429013424
## fore:12/2017-back:1/2019 -1.5617656810 -3.659187346 0.535655984
## fringe:12/2017-back:1/2019 -0.9780547715 -2.579987029 0.623877486
## back:12/2018-back:1/2019 -0.2077741935 -1.920311811 1.504763424
## fore:12/2018-back:1/2019 -1.2503974014 -3.347819066 0.847024263
## fringe:12/2018-back:1/2019 0.6939301075 -1.018607510 2.406467725
## back:2/2018-back:1/2019 -1.6456317044 -3.358169322 0.066905913
## fore:2/2018-back:1/2019 -1.8272462878 -3.924667952 0.270175377
## fringe:2/2018-back:1/2019 -1.5625905338 -3.164522791 0.039341723
## back:2/2019-back:1/2019 0.0707616487 -1.641775969 1.783299267
## fore:2/2019-back:1/2019 -1.4696618521 -3.567083517 0.627759812
## fringe:2/2019-back:1/2019 0.1909803987 -1.521557219 1.903518017
## back:3/2018-back:1/2019 -1.1413195784 -2.704645385 0.422006228
## fore:3/2018-back:1/2019 -1.3902862903 -3.487707955 0.707135374
## fringe:3/2018-back:1/2019 -0.9215016354 -2.523433892 0.680430622
## back:3/2019-back:1/2019 0.2958884844 -1.416649134 2.008426102
## fore:3/2019-back:1/2019 -1.7212574334 -4.143151359 0.700636492
## fringe:3/2019-back:1/2019 0.4786652962 -1.233872322 2.191202914
## back:4/2018-back:1/2019 -1.0873064068 -2.689238664 0.514625850
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## fringe:9/2018-fore:9/2018 0.2982832837 -1.709845074 2.306411642
## p adj
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## fore:1/2019-back:1/2018 1.0000000
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## fore:11/2018-back:1/2018 0.7584286
## fringe:11/2018-back:1/2018 1.0000000
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## fore:2/2018-back:1/2018 0.9998298
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## fore:2/2019-back:1/2018 1.0000000
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## fore:3/2018-back:1/2018 1.0000000
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## fore:3/2019-back:1/2018 0.9999999
## fringe:3/2019-back:1/2018 0.5449303
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## fore:4/2018-back:1/2018 1.0000000
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## back:7/2018-back:4/2018 0.0110276
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## back:8/2018-back:4/2018 0.0002573
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## fringe:8/2018-back:4/2018 0.0006358
## back:9/2017-back:4/2018 0.0102643
## fore:9/2017-back:4/2018 0.0410102
## fringe:9/2017-back:4/2018 0.0090998
## back:9/2018-back:4/2018 0.0001740
## fore:9/2018-back:4/2018 0.0172457
## fringe:9/2018-back:4/2018 0.0007696
## fringe:4/2018-fore:4/2018 1.0000000
## back:5/2018-fore:4/2018 1.0000000
## fore:5/2018-fore:4/2018 1.0000000
## fringe:5/2018-fore:4/2018 1.0000000
## back:6/2018-fore:4/2018 0.9983047
## fore:6/2018-fore:4/2018 0.9999751
## fringe:6/2018-fore:4/2018 0.9990798
## back:7/2018-fore:4/2018 0.5830422
## fore:7/2018-fore:4/2018 0.8898073
## fringe:7/2018-fore:4/2018 0.6592443
## back:8/2018-fore:4/2018 0.1493617
## fore:8/2018-fore:4/2018 0.5086411
## fringe:8/2018-fore:4/2018 0.2486477
## back:9/2017-fore:4/2018 0.4665600
## fore:9/2017-fore:4/2018 0.4529238
## fringe:9/2017-fore:4/2018 0.5524678
## back:9/2018-fore:4/2018 0.1034051
## fore:9/2018-fore:4/2018 0.3028642
## fringe:9/2018-fore:4/2018 0.2349744
## back:5/2018-fringe:4/2018 0.9999992
## fore:5/2018-fringe:4/2018 1.0000000
## fringe:5/2018-fringe:4/2018 1.0000000
## back:6/2018-fringe:4/2018 0.3468975
## fore:6/2018-fringe:4/2018 0.9291162
## fringe:6/2018-fringe:4/2018 0.4484074
## back:7/2018-fringe:4/2018 0.0043218
## fore:7/2018-fringe:4/2018 0.1812293
## fringe:7/2018-fringe:4/2018 0.0094604
## back:8/2018-fringe:4/2018 0.0000962
## fore:8/2018-fringe:4/2018 0.0258225
## fringe:8/2018-fringe:4/2018 0.0002375
## back:9/2017-fringe:4/2018 0.0041105
## fore:9/2017-fringe:4/2018 0.0192862
## fringe:9/2017-fringe:4/2018 0.0035459
## back:9/2018-fringe:4/2018 0.0000652
## fore:9/2018-fringe:4/2018 0.0077434
## fringe:9/2018-fringe:4/2018 0.0002885
## fore:5/2018-back:5/2018 1.0000000
## fringe:5/2018-back:5/2018 1.0000000
## back:6/2018-back:5/2018 0.9999093
## fore:6/2018-back:5/2018 1.0000000
## fringe:6/2018-back:5/2018 0.9999752
## back:7/2018-back:5/2018 0.4781882
## fore:7/2018-back:5/2018 0.9535255
## fringe:7/2018-back:5/2018 0.6073581
## back:8/2018-back:5/2018 0.0510815
## fore:8/2018-back:5/2018 0.5645035
## fringe:8/2018-back:5/2018 0.1058938
## back:9/2017-back:5/2018 0.3872018
## fore:9/2017-back:5/2018 0.4961216
## fringe:9/2017-back:5/2018 0.4381972
## back:9/2018-back:5/2018 0.0332552
## fore:9/2018-back:5/2018 0.3104639
## fringe:9/2018-back:5/2018 0.1073161
## fringe:5/2018-fore:5/2018 1.0000000
## back:6/2018-fore:5/2018 1.0000000
## fore:6/2018-fore:5/2018 1.0000000
## fringe:6/2018-fore:5/2018 1.0000000
## back:7/2018-fore:5/2018 0.9945315
## fore:7/2018-fore:5/2018 0.9997158
## fringe:7/2018-fore:5/2018 0.9972308
## back:8/2018-fore:5/2018 0.7958172
## fore:8/2018-fore:5/2018 0.9669581
## fringe:8/2018-fore:5/2018 0.9114547
## back:9/2017-fore:5/2018 0.9784464
## fore:9/2017-fore:5/2018 0.9509559
## fringe:9/2017-fore:5/2018 0.9925609
## back:9/2018-fore:5/2018 0.6881008
## fore:9/2018-fore:5/2018 0.8721014
## fringe:9/2018-fore:5/2018 0.8950698
## back:6/2018-fringe:5/2018 0.9996925
## fore:6/2018-fringe:5/2018 0.9999999
## fringe:6/2018-fringe:5/2018 0.9999001
## back:7/2018-fringe:5/2018 0.4422272
## fore:7/2018-fringe:5/2018 0.9328969
## fringe:7/2018-fringe:5/2018 0.5643103
## back:8/2018-fringe:5/2018 0.0486315
## fore:8/2018-fringe:5/2018 0.5168603
## fringe:8/2018-fringe:5/2018 0.0999623
## back:9/2017-fringe:5/2018 0.3533831
## fore:9/2017-fringe:5/2018 0.4508545
## fringe:9/2017-fringe:5/2018 0.4046238
## back:9/2018-fringe:5/2018 0.0315717
## fore:9/2018-fringe:5/2018 0.2771604
## fringe:9/2018-fringe:5/2018 0.1002950
## fore:6/2018-back:6/2018 1.0000000
## fringe:6/2018-back:6/2018 1.0000000
## back:7/2018-back:6/2018 1.0000000
## fore:7/2018-back:6/2018 1.0000000
## fringe:7/2018-back:6/2018 1.0000000
## back:8/2018-back:6/2018 0.9951856
## fore:8/2018-back:6/2018 0.9999847
## fringe:8/2018-back:6/2018 0.9997849
## back:9/2017-back:6/2018 0.9999988
## fore:9/2017-back:6/2018 0.9999457
## fringe:9/2017-back:6/2018 1.0000000
## back:9/2018-back:6/2018 0.9787250
## fore:9/2018-back:6/2018 0.9988335
## fringe:9/2018-back:6/2018 0.9995909
## fringe:6/2018-fore:6/2018 1.0000000
## back:7/2018-fore:6/2018 1.0000000
## fore:7/2018-fore:6/2018 1.0000000
## fringe:7/2018-fore:6/2018 1.0000000
## back:8/2018-fore:6/2018 0.9999994
## fore:8/2018-fore:6/2018 1.0000000
## fringe:8/2018-fore:6/2018 1.0000000
## back:9/2017-fore:6/2018 1.0000000
## fore:9/2017-fore:6/2018 0.9999999
## fringe:9/2017-fore:6/2018 1.0000000
## back:9/2018-fore:6/2018 0.9999859
## fore:9/2018-fore:6/2018 0.9999962
## fringe:9/2018-fore:6/2018 1.0000000
## back:7/2018-fringe:6/2018 1.0000000
## fore:7/2018-fringe:6/2018 1.0000000
## fringe:7/2018-fringe:6/2018 1.0000000
## back:8/2018-fringe:6/2018 0.9971064
## fore:8/2018-fringe:6/2018 0.9999886
## fringe:8/2018-fringe:6/2018 0.9998881
## back:9/2017-fringe:6/2018 0.9999993
## fore:9/2017-fringe:6/2018 0.9999594
## fringe:9/2017-fringe:6/2018 1.0000000
## back:9/2018-fringe:6/2018 0.9857922
## fore:9/2018-fringe:6/2018 0.9991027
## fringe:9/2018-fringe:6/2018 0.9997738
## fore:7/2018-back:7/2018 1.0000000
## fringe:7/2018-back:7/2018 1.0000000
## back:8/2018-back:7/2018 1.0000000
## fore:8/2018-back:7/2018 1.0000000
## fringe:8/2018-back:7/2018 1.0000000
## back:9/2017-back:7/2018 1.0000000
## fore:9/2017-back:7/2018 1.0000000
## fringe:9/2017-back:7/2018 1.0000000
## back:9/2018-back:7/2018 1.0000000
## fore:9/2018-back:7/2018 1.0000000
## fringe:9/2018-back:7/2018 1.0000000
## fringe:7/2018-fore:7/2018 1.0000000
## back:8/2018-fore:7/2018 1.0000000
## fore:8/2018-fore:7/2018 1.0000000
## fringe:8/2018-fore:7/2018 1.0000000
## back:9/2017-fore:7/2018 1.0000000
## fore:9/2017-fore:7/2018 1.0000000
## fringe:9/2017-fore:7/2018 1.0000000
## back:9/2018-fore:7/2018 1.0000000
## fore:9/2018-fore:7/2018 1.0000000
## fringe:9/2018-fore:7/2018 1.0000000
## back:8/2018-fringe:7/2018 1.0000000
## fore:8/2018-fringe:7/2018 1.0000000
## fringe:8/2018-fringe:7/2018 1.0000000
## back:9/2017-fringe:7/2018 1.0000000
## fore:9/2017-fringe:7/2018 1.0000000
## fringe:9/2017-fringe:7/2018 1.0000000
## back:9/2018-fringe:7/2018 1.0000000
## fore:9/2018-fringe:7/2018 1.0000000
## fringe:9/2018-fringe:7/2018 1.0000000
## fore:8/2018-back:8/2018 1.0000000
## fringe:8/2018-back:8/2018 1.0000000
## back:9/2017-back:8/2018 1.0000000
## fore:9/2017-back:8/2018 1.0000000
## fringe:9/2017-back:8/2018 1.0000000
## back:9/2018-back:8/2018 1.0000000
## fore:9/2018-back:8/2018 1.0000000
## fringe:9/2018-back:8/2018 1.0000000
## fringe:8/2018-fore:8/2018 1.0000000
## back:9/2017-fore:8/2018 1.0000000
## fore:9/2017-fore:8/2018 1.0000000
## fringe:9/2017-fore:8/2018 1.0000000
## back:9/2018-fore:8/2018 1.0000000
## fore:9/2018-fore:8/2018 1.0000000
## fringe:9/2018-fore:8/2018 1.0000000
## back:9/2017-fringe:8/2018 1.0000000
## fore:9/2017-fringe:8/2018 1.0000000
## fringe:9/2017-fringe:8/2018 1.0000000
## back:9/2018-fringe:8/2018 1.0000000
## fore:9/2018-fringe:8/2018 1.0000000
## fringe:9/2018-fringe:8/2018 1.0000000
## fore:9/2017-back:9/2017 1.0000000
## fringe:9/2017-back:9/2017 1.0000000
## back:9/2018-back:9/2017 1.0000000
## fore:9/2018-back:9/2017 1.0000000
## fringe:9/2018-back:9/2017 1.0000000
## fringe:9/2017-fore:9/2017 1.0000000
## back:9/2018-fore:9/2017 1.0000000
## fore:9/2018-fore:9/2017 1.0000000
## fringe:9/2018-fore:9/2017 1.0000000
## back:9/2018-fringe:9/2017 1.0000000
## fore:9/2018-fringe:9/2017 1.0000000
## fringe:9/2018-fringe:9/2017 1.0000000
## fore:9/2018-back:9/2018 1.0000000
## fringe:9/2018-back:9/2018 1.0000000
## fringe:9/2018-fore:9/2018 1.0000000The zones differ significantly from each other, as do most of the months. LTER sites seem to be more similar. The 0 p-value for comparisons between zones is a limitation of the Tukey_HSD code. Apparently it cannot display more than 8 decimals, or p-values lower than 2e-16.
Analysis of daily temperature ranges
In this section I will explore differences in temperature ranges between the different zones over time. First I will analyse differences in daily temperature ranges between reef zones, sites, and months. In the section thereafter I will look at daily minima and maxima to look at what drives differences in daily temperature ranges.
Calculate daily temperature ranges
I will use the group_by() function to select the variables that I want to keep. I will then use the summarise() function to pick the lowest and highest temperature for each unique date using the min() and max() arguments. Lastly, I will use the mutate() function to create a new column named ‘range’ in which I subtract the minimum temperature from the daily maximum temperature for every unique day for each lter site and reef zone separately.
Then, I will check the dataset using the function head() again.
hobofullrange <- hobofull %>%
group_by(year, month, day, monthyear, zone, lter, longdate, side)%>%
summarise(min_temp = min(temp), max_temp = max(temp))%>%
mutate(range = max_temp-min_temp)
head(hobofullrange, digits=5)## # A tibble: 6 x 11
## # Groups: year, month, day, monthyear, zone, lter, longdate [6]
## year month day monthyear zone lter longdate side min_temp max_temp
## <chr> <chr> <chr> <chr> <fct> <dbl> <chr> <chr> <dbl> <dbl>
## 1 2017 10 1 10/2017 back 0 10/1/20~ north 27.0 28.5
## 2 2017 10 1 10/2017 back 3 10/1/20~ east 26.6 28.8
## 3 2017 10 1 10/2017 back 3.5 10/1/20~ east 26.6 29.2
## 4 2017 10 1 10/2017 back 4 10/1/20~ east 26.6 27.7
## 5 2017 10 1 10/2017 back 5 10/1/20~ west 26.6 27.9
## 6 2017 10 1 10/2017 back 5.5 10/1/20~ west 26.8 28.1
## # ... with 1 more variable: range <dbl>Everything went as planned.
figure of daily temperature ranges
Now I will plot the daily ranges for each reef zone separately but with lter sites combined. I will use ggplot with the geom_smooth() function to get a line with 95% confidence intervals.
rangeplotfull <- ggplot(data=hobofullrange,
aes(x=as.Date(longdate, format = "%m / %d / %Y"),
y=range, colour=zone)) +
geom_smooth()+ theme_bw()+
theme(axis.text.x = element_text(margin = margin(t=5)))+
labs(title="Daily temperature range", y="Mean daily temperature range (°C) with 95% CI ", x="Date")
rangeplotfull
Figure of daily temperature ranges per island side
rangeplotfullside <- ggplot(data=hobofullrange,
aes(x=as.Date(longdate, format = "%m / %d / %Y"),
y=range, colour=zone)) +
geom_smooth()+ theme_bw()+
theme(axis.text.x = element_text(margin = margin(t=5)))+
labs(title="Daily temperature range", y="Mean daily temperature range (°C) with 95% CI ", x="Date")+
facet_grid(~side)
rangeplotfullside
Calculate average daily ranges per month
Given that the daily ranges vary so much over time, I need to calculate average ranges for separate time intervals. I will use the group_by() and the summarise function again, this time to calculate mean daily ranges per month. I will check whether this worked using nrow() to count the number of rows. The number of rows should = 209 again.
hobofullrangemeans <- hobofullrange %>%
group_by(year, month, monthyear, lter, zone, side)%>%
summarise(meanrange = mean(range))
nrow(hobofullrangemeans)## [1] 323Looks like it worked.
Boxplots of mean daily ranges per month
I will make boxplots of the average daily temperature ranges over time calculated per month. I will plot the values for each site on top so that we can see which sites are more or less variable.
#hobofullrangemeans$month <- factor(hobofullrangemeans$month, levels=c("9", "10", "11", "12", "1", "2", "3", "4", "5", "6", "7", "8"))
hrmplot <- ggplot(hobofullrangemeans, aes(x=monthyear, y=meanrange))+
geom_boxplot() + theme_bw()+
geom_point(alpha=3/4, aes(colour=as.factor(lter)))+
facet_grid(~fct_relevel(zone, "fringe", "back", "fore"))+
labs(title= "Monthly averages of daily temperature range", y="Temperature range (°C)", x="Date")+
theme(axis.text.x = element_text(angle=45, margin = margin(t=20, r=100)))
hrmplot
Looks good. Now we can see which of the lter sites contribute most to the temperature ranges. It seems that the fringe reefs for sites 3, 4, 4.5, and 5 are more variable, whereas sites 1, 2, 5.5 and 6 are generally less variable. For the back reefs, it seems that sites 0 and 4 are generally less variable, and sites 3, 3.5 and 2 are more variable.
Test for significance differences between zones, months, and LTER sites
Now I want to test for significant differences between zones. I will use an anova using the function aov()
hobolm <- aov(meanrange~zone*as.factor(monthyear), data=hobofullrangemeans)
summary(hobolm)## Df Sum Sq Mean Sq F value Pr(>F)
## zone 2 59.18 29.591 237.725 <2e-16 ***
## as.factor(monthyear) 18 19.47 1.082 8.690 <2e-16 ***
## zone:as.factor(monthyear) 36 2.07 0.057 0.462 0.997
## Residuals 266 33.11 0.124
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1There is a significant effect of zone and month on daily temperature variability. Great!
Post-hoc tests
Now I will use a post-hoc test to compare individual zones, lter sites, and months. I am mostly interested in looking at zones, but I included sites and months anyway just to be comprehensive.
HSD <- TukeyHSD(hobolm, ordered=FALSE)
HSD## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = meanrange ~ zone * as.factor(monthyear), data = hobofullrangemeans)
##
## $zone
## diff lwr upr p adj
## fore-back -0.9538920 -1.0869627 -0.8208213 0
## fringe-back 0.2582747 0.1564374 0.3601119 0
## fringe-fore 1.2121667 1.0804160 1.3439174 0
##
## $`as.factor(monthyear)`
## diff lwr upr p adj
## 1/2019-1/2018 -0.112344695 -0.556370648 0.33168126 0.9999930
## 10/2017-1/2018 0.224307400 -0.205618880 0.65423368 0.9380017
## 10/2018-1/2018 0.170950619 -0.258975661 0.60087690 0.9963455
## 11/2017-1/2018 -0.058986211 -0.488912492 0.37094007 1.0000000
## 11/2018-1/2018 0.340313513 -0.103712440 0.78433947 0.3933728
## 12/2017-1/2018 -0.057666034 -0.487592315 0.37226025 1.0000000
## 12/2018-1/2018 0.147332724 -0.296693229 0.59135868 0.9996407
## 2/2018-1/2018 -0.311669965 -0.741596245 0.11825632 0.5017069
## 2/2019-1/2018 -0.151217387 -0.595243340 0.29280857 0.9994896
## 3/2018-1/2018 0.169411272 -0.244078584 0.58290113 0.9947946
## 3/2019-1/2018 -0.174632580 -0.627004693 0.27773953 0.9974465
## 4/2018-1/2018 -0.168087984 -0.592001014 0.25582505 0.9964661
## 5/2018-1/2018 -0.444935177 -0.868848206 -0.02102215 0.0282933
## 6/2018-1/2018 -0.428623169 -0.852536199 -0.00471014 0.0441677
## 7/2018-1/2018 -0.362765105 -0.786678134 0.06114792 0.2039298
## 8/2018-1/2018 0.021613137 -0.391876719 0.43510299 1.0000000
## 9/2017-1/2018 0.418588177 -0.011338104 0.84851446 0.0665463
## 9/2018-1/2018 0.148651518 -0.275261512 0.57256455 0.9992476
## 10/2017-1/2019 0.336652095 -0.107373858 0.78067805 0.4141876
## 10/2018-1/2019 0.283295314 -0.160730639 0.72732127 0.7318967
## 11/2017-1/2019 0.053358484 -0.390667470 0.49738444 1.0000000
## 11/2018-1/2019 0.452658208 -0.005033269 0.91034968 0.0564131
## 12/2017-1/2019 0.054678661 -0.389347292 0.49870461 1.0000000
## 12/2018-1/2019 0.259677419 -0.198014057 0.71736890 0.8774441
## 2/2018-1/2019 -0.199325270 -0.643351223 0.24470068 0.9855741
## 2/2019-1/2019 -0.038872692 -0.496564168 0.41881878 1.0000000
## 3/2018-1/2019 0.281755967 -0.146375207 0.70988714 0.6813833
## 3/2019-1/2019 -0.062287885 -0.528080724 0.40350495 1.0000000
## 4/2018-1/2019 -0.055743289 -0.493949516 0.38246294 1.0000000
## 5/2018-1/2019 -0.332590482 -0.770796708 0.10561574 0.4121481
## 6/2018-1/2019 -0.316278474 -0.754484701 0.12192775 0.5102957
## 7/2018-1/2019 -0.250420410 -0.688626637 0.18778582 0.8706566
## 8/2018-1/2019 0.133957832 -0.294173342 0.56208901 0.9998404
## 9/2017-1/2019 0.530932872 0.086906918 0.97495882 0.0042466
## 9/2018-1/2019 0.260996213 -0.177210014 0.69920244 0.8263199
## 10/2018-10/2017 -0.053356781 -0.483283062 0.37656950 1.0000000
## 11/2017-10/2017 -0.283293612 -0.713219892 0.14663267 0.6792403
## 11/2018-10/2017 0.116006113 -0.328019841 0.56003207 0.9999886
## 12/2017-10/2017 -0.281973435 -0.711899715 0.14795285 0.6871601
## 12/2018-10/2017 -0.076974676 -0.521000629 0.36705128 1.0000000
## 2/2018-10/2017 -0.535977365 -0.965903646 -0.10605108 0.0020459
## 2/2019-10/2017 -0.375524787 -0.819550740 0.06850117 0.2214546
## 3/2018-10/2017 -0.054896129 -0.468385985 0.35859373 1.0000000
## 3/2019-10/2017 -0.398939980 -0.851312094 0.05343213 0.1628890
## 4/2018-10/2017 -0.392395385 -0.816308414 0.03151764 0.1084159
## 5/2018-10/2017 -0.669242577 -1.093155607 -0.24532955 0.0000085
## 6/2018-10/2017 -0.652930570 -1.076843599 -0.22901754 0.0000170
## 7/2018-10/2017 -0.587072505 -1.010985535 -0.16315948 0.0002408
## 8/2018-10/2017 -0.202694264 -0.616184120 0.21079559 0.9649141
## 9/2017-10/2017 0.194280776 -0.235645504 0.62420706 0.9845233
## 9/2018-10/2017 -0.075655882 -0.499568912 0.34825715 1.0000000
## 11/2017-10/2018 -0.229936831 -0.659863111 0.19998945 0.9233802
## 11/2018-10/2018 0.169362893 -0.274663060 0.61338885 0.9977981
## 12/2017-10/2018 -0.228616654 -0.658542934 0.20130963 0.9270025
## 12/2018-10/2018 -0.023617895 -0.467643848 0.42040806 1.0000000
## 2/2018-10/2018 -0.482620584 -0.912546865 -0.05269430 0.0114066
## 2/2019-10/2018 -0.322168006 -0.766193959 0.12185795 0.5000275
## 3/2018-10/2018 -0.001539348 -0.415029204 0.41195051 1.0000000
## 3/2019-10/2018 -0.345583199 -0.797955313 0.10678891 0.3996155
## 4/2018-10/2018 -0.339038604 -0.762951633 0.08487443 0.3142584
## 5/2018-10/2018 -0.615885796 -1.039798826 -0.19197277 0.0000778
## 6/2018-10/2018 -0.599573789 -1.023486818 -0.17566076 0.0001484
## 7/2018-10/2018 -0.533715724 -0.957628754 -0.10980269 0.0017055
## 8/2018-10/2018 -0.149337483 -0.562827339 0.26415237 0.9988954
## 9/2017-10/2018 0.247637557 -0.182288723 0.67756384 0.8629012
## 9/2018-10/2018 -0.022299102 -0.446212131 0.40161393 1.0000000
## 11/2018-11/2017 0.399299724 -0.044726229 0.84332568 0.1391929
## 12/2017-11/2017 0.001320177 -0.428606104 0.43124646 1.0000000
## 12/2018-11/2017 0.206318936 -0.237707018 0.65034489 0.9793300
## 2/2018-11/2017 -0.252683754 -0.682610034 0.17724253 0.8415811
## 2/2019-11/2017 -0.092231175 -0.536257129 0.35179478 0.9999997
## 3/2018-11/2017 0.228397483 -0.185092373 0.64188734 0.9003539
## 3/2019-11/2017 -0.115646369 -0.568018482 0.33672574 0.9999918
## 4/2018-11/2017 -0.109101773 -0.533014803 0.31481126 0.9999909
## 5/2018-11/2017 -0.385948965 -0.809861995 0.03796406 0.1254209
## 6/2018-11/2017 -0.369636958 -0.793549988 0.05427607 0.1776888
## 7/2018-11/2017 -0.303778894 -0.727691923 0.12013414 0.5242477
## 8/2018-11/2017 0.080599348 -0.332890508 0.49408920 0.9999999
## 9/2017-11/2017 0.477574388 0.047648107 0.90750067 0.0132725
## 9/2018-11/2017 0.207637729 -0.216275300 0.63155076 0.9651809
## 12/2017-11/2018 -0.397979547 -0.842005500 0.04604641 0.1430468
## 12/2018-11/2018 -0.192980789 -0.650672265 0.26471069 0.9927579
## 2/2018-11/2018 -0.651983478 -1.096009431 -0.20795752 0.0000597
## 2/2019-11/2018 -0.491530899 -0.949222376 -0.03383942 0.0210858
## 3/2018-11/2018 -0.170902241 -0.599033414 0.25722893 0.9961711
## 3/2019-11/2018 -0.514946093 -0.980738932 -0.04915325 0.0142033
## 4/2018-11/2018 -0.508401497 -0.946607724 -0.07019527 0.0069261
## 5/2018-11/2018 -0.785248690 -1.223454916 -0.34704246 0.0000001
## 6/2018-11/2018 -0.768936682 -1.207142909 -0.33073046 0.0000003
## 7/2018-11/2018 -0.703078618 -1.141284844 -0.26487239 0.0000053
## 8/2018-11/2018 -0.318700376 -0.746831549 0.10943080 0.4498674
## 9/2017-11/2018 0.078274664 -0.365751290 0.52230062 1.0000000
## 9/2018-11/2018 -0.191661995 -0.629868222 0.24654423 0.9891039
## 12/2018-12/2017 0.204998759 -0.239027195 0.64902471 0.9806475
## 2/2018-12/2017 -0.254003931 -0.683930211 0.17592235 0.8357136
## 2/2019-12/2017 -0.093551352 -0.537577306 0.35047460 0.9999996
## 3/2018-12/2017 0.227077306 -0.186412550 0.64056716 0.9048461
## 3/2019-12/2017 -0.116966546 -0.569338659 0.33540557 0.9999902
## 4/2018-12/2017 -0.110421950 -0.534334980 0.31349108 0.9999891
## 5/2018-12/2017 -0.387269143 -0.811182172 0.03664389 0.1217780
## 6/2018-12/2017 -0.370957135 -0.794870165 0.05295589 0.1729409
## 7/2018-12/2017 -0.305099071 -0.729012100 0.11881396 0.5158055
## 8/2018-12/2017 0.079279171 -0.334210685 0.49276903 0.9999999
## 9/2017-12/2017 0.476254211 0.046327930 0.90618049 0.0138045
## 9/2018-12/2017 0.206317552 -0.217595477 0.63023058 0.9672481
## 2/2018-12/2018 -0.459002689 -0.903028642 -0.01497674 0.0340945
## 2/2019-12/2018 -0.298550111 -0.756241587 0.15914137 0.6962896
## 3/2018-12/2018 0.022078548 -0.406052626 0.45020972 1.0000000
## 3/2019-12/2018 -0.321965304 -0.787758144 0.14382754 0.5932981
## 4/2018-12/2018 -0.315420709 -0.753626935 0.12278552 0.5155922
## 5/2018-12/2018 -0.592267901 -1.030474128 -0.15406167 0.0004115
## 6/2018-12/2018 -0.575955894 -1.014162120 -0.13774967 0.0007378
## 7/2018-12/2018 -0.510097829 -0.948304056 -0.07189160 0.0065722
## 8/2018-12/2018 -0.125719587 -0.553850761 0.30241159 0.9999354
## 9/2017-12/2018 0.271255452 -0.172770501 0.71528141 0.7944951
## 9/2018-12/2018 0.001318794 -0.436887433 0.43952502 1.0000000
## 2/2019-2/2018 0.160452578 -0.283573375 0.60447853 0.9988877
## 3/2018-2/2018 0.481081237 0.067591380 0.89457109 0.0066253
## 3/2019-2/2018 0.137037385 -0.315334728 0.58940950 0.9998990
## 4/2018-2/2018 0.143581980 -0.280331049 0.56749501 0.9995256
## 5/2018-2/2018 -0.133265212 -0.557178241 0.29064782 0.9998295
## 6/2018-2/2018 -0.116953205 -0.540866234 0.30695982 0.9999741
## 7/2018-2/2018 -0.051095140 -0.475008170 0.37281789 1.0000000
## 8/2018-2/2018 0.333283102 -0.080206755 0.74677296 0.3004851
## 9/2017-2/2018 0.730258141 0.300331861 1.16018442 0.0000009
## 9/2018-2/2018 0.460321483 0.036408453 0.88423451 0.0181903
## 3/2018-2/2019 0.320628658 -0.107502515 0.74875983 0.4380962
## 3/2019-2/2019 -0.023415193 -0.489208033 0.44237765 1.0000000
## 4/2018-2/2019 -0.016870598 -0.455076824 0.42133563 1.0000000
## 5/2018-2/2019 -0.293717790 -0.731924017 0.14448844 0.6495412
## 6/2018-2/2019 -0.277405783 -0.715612010 0.16080044 0.7438922
## 7/2018-2/2019 -0.211547718 -0.649753945 0.22665851 0.9697283
## 8/2018-2/2019 0.172830523 -0.255300650 0.60096170 0.9956254
## 9/2017-2/2019 0.569805563 0.125779610 1.01383152 0.0011867
## 9/2018-2/2019 0.299868904 -0.138337322 0.73807513 0.6119938
## 3/2019-3/2018 -0.344043852 -0.780825014 0.09273731 0.3419362
## 4/2018-3/2018 -0.337499256 -0.744733232 0.06973472 0.2533674
## 5/2018-3/2018 -0.614346449 -1.021580424 -0.20711247 0.0000297
## 6/2018-3/2018 -0.598034441 -1.005268417 -0.19080047 0.0000595
## 7/2018-3/2018 -0.532176377 -0.939410353 -0.12494240 0.0008289
## 8/2018-3/2018 -0.147798135 -0.544170581 0.24857431 0.9983410
## 9/2017-3/2018 0.249176905 -0.164312952 0.66266676 0.8120622
## 9/2018-3/2018 -0.020759754 -0.427993730 0.38647422 1.0000000
## 4/2018-3/2019 0.006544596 -0.440116550 0.45320574 1.0000000
## 5/2018-3/2019 -0.270302597 -0.716963742 0.17635855 0.8067506
## 6/2018-3/2019 -0.253990590 -0.700651735 0.19267056 0.8753520
## 7/2018-3/2019 -0.188132525 -0.634793670 0.25852862 0.9928437
## 8/2018-3/2019 0.196245717 -0.240535445 0.63302688 0.9854385
## 9/2017-3/2019 0.593220756 0.140848643 1.04559287 0.0007728
## 9/2018-3/2019 0.323284098 -0.123377048 0.76994524 0.5048329
## 5/2018-4/2018 -0.276847192 -0.694660436 0.14096605 0.6696896
## 6/2018-4/2018 -0.260535185 -0.678348429 0.15727806 0.7661581
## 7/2018-4/2018 -0.194677121 -0.612490364 0.22313612 0.9787380
## 8/2018-4/2018 0.189701121 -0.217532855 0.59693510 0.9787912
## 9/2017-4/2018 0.586676161 0.162763131 1.01058919 0.0002445
## 9/2018-4/2018 0.316739502 -0.101073741 0.73455275 0.4144233
## 6/2018-5/2018 0.016312007 -0.401501236 0.43412525 1.0000000
## 7/2018-5/2018 0.082170072 -0.335643172 0.49998332 0.9999999
## 8/2018-5/2018 0.466548314 0.059314338 0.87378229 0.0084184
## 9/2017-5/2018 0.863523353 0.439610324 1.28743638 0.0000000
## 9/2018-5/2018 0.593586695 0.175773451 1.01139994 0.0001336
## 7/2018-6/2018 0.065858065 -0.351955179 0.48367131 1.0000000
## 8/2018-6/2018 0.450236306 0.043002331 0.85747028 0.0141906
## 9/2017-6/2018 0.847211346 0.423298317 1.27112438 0.0000000
## 9/2018-6/2018 0.577274687 0.159461444 0.99508793 0.0002538
## 8/2018-7/2018 0.384378242 -0.022855734 0.79161222 0.0905196
## 9/2017-7/2018 0.781353282 0.357440252 1.20526631 0.0000001
## 9/2018-7/2018 0.511416623 0.093603379 0.92922987 0.0028419
## 9/2017-8/2018 0.396975040 -0.016514817 0.81046490 0.0767225
## 9/2018-8/2018 0.127038381 -0.280195595 0.53427236 0.9998470
## 9/2018-9/2017 -0.269936659 -0.693849688 0.15397637 0.7349154
##
## $`zone:as.factor(monthyear)`
## diff lwr upr
## fore:1/2018-back:1/2018 -0.9833817204 -2.007381472 0.0406180311
## fringe:1/2018-back:1/2018 0.2922634409 -0.489829288 1.0743561697
## back:1/2019-back:1/2018 -0.1518387097 -0.987931006 0.6842535863
## fore:1/2019-back:1/2018 -1.1025860215 -2.126585773 -0.0785862700
## fringe:1/2019-back:1/2018 0.2305752688 -0.605517027 1.0666675648
## back:10/2017-back:1/2018 0.2843225806 -0.551769715 1.1204148767
## fore:10/2017-back:1/2018 -0.8716720430 -1.895671795 0.1523277085
## fringe:10/2017-back:1/2018 0.5137836022 -0.268309127 1.2958763310
## back:10/2018-back:1/2018 0.1686643625 -0.637014333 0.9743430585
## fore:10/2018-back:1/2018 -0.8737688172 -1.897768569 0.1502309343
## fringe:10/2018-back:1/2018 0.4966434479 -0.309035248 1.3023221440
## back:11/2017-back:1/2018 -0.0886799283 -0.924772224 0.7474123677
## fore:11/2017-back:1/2018 -0.9098465950 -1.933846347 0.1141531566
## fringe:11/2017-back:1/2018 0.2058520161 -0.576240713 0.9879447450
## back:11/2018-back:1/2018 0.3978867384 -0.438205558 1.2339790344
## fore:11/2018-back:1/2018 -0.8179132616 -1.841913013 0.2060864899
## fringe:11/2018-back:1/2018 0.6701589606 -0.165933335 1.5062512566
## back:12/2017-back:1/2018 -0.0268333333 -0.862925629 0.8092589627
## fore:12/2017-back:1/2018 -0.9292849462 -1.953284698 0.0947148053
## fringe:12/2017-back:1/2018 0.1695618280 -0.612530901 0.9516545568
## back:12/2018-back:1/2018 0.1210752688 -0.715017027 0.9571675648
## fore:12/2018-back:1/2018 -0.9369623656 -1.960962117 0.0870373860
## fringe:12/2018-back:1/2018 0.5240430108 -0.312049285 1.3601353068
## back:2/2018-back:1/2018 -0.3616259601 -1.197718256 0.4744663359
## fore:2/2018-back:1/2018 -1.0955188172 -2.119518569 -0.0715190657
## fringe:2/2018-back:1/2018 -0.0567643529 -0.838857082 0.7253283759
## back:2/2019-back:1/2018 -0.2405723886 -1.076664685 0.5955199074
## fore:2/2019-back:1/2018 -1.0095796638 -2.033579415 0.0144200878
## fringe:2/2019-back:1/2018 0.1756240399 -0.660468256 1.0117163359
## back:3/2018-back:1/2018 0.2386790408 -0.524565310 1.0019233919
## fore:3/2018-back:1/2018 -0.9640376344 -1.988037386 0.0599621171
## fringe:3/2018-back:1/2018 0.4440701613 -0.338022568 1.2261628901
## back:3/2019-back:1/2018 -0.1795958489 -1.015688145 0.6564964471
## fore:3/2019-back:1/2018 -1.0179209911 -2.200334056 0.1644920733
## fringe:3/2019-back:1/2018 0.0769156753 -0.759176621 0.9130079713
## back:4/2018-back:1/2018 -0.1675563172 -0.949649046 0.6145364116
## fore:4/2018-back:1/2018 -1.0515243728 -2.075524124 -0.0275246212
## fringe:4/2018-back:1/2018 0.0871311828 -0.718547513 0.8928098788
## back:5/2018-back:1/2018 -0.4814220430 -1.263514772 0.3006706858
## fore:5/2018-back:1/2018 -1.1743387097 -2.198338461 -0.1503389581
## fringe:5/2018-back:1/2018 -0.2134231951 -1.019101891 0.5922555009
## back:6/2018-back:1/2018 -0.4435396505 -1.225632379 0.3385530783
## fore:6/2018-back:1/2018 -1.1764021505 -2.200401902 -0.1524023990
## fringe:6/2018-back:1/2018 -0.2138878648 -1.019566561 0.5917908312
## back:7/2018-back:1/2018 -0.3599116935 -1.142004422 0.4221810353
## fore:7/2018-back:1/2018 -1.1687365591 -2.192736311 -0.1447368076
## fringe:7/2018-back:1/2018 -0.1433986175 -0.949077314 0.6622800785
## back:8/2018-back:1/2018 0.1606226417 -0.621470087 0.9427153706
## fore:8/2018-back:1/2018 -0.8977317390 -1.921731491 0.1262680126
## fringe:8/2018-back:1/2018 0.1687873124 -0.594457039 0.9320316636
## back:9/2017-back:1/2018 0.6236990069 -0.212393289 1.4597913029
## fore:9/2017-back:1/2018 -0.6470793730 -1.671079125 0.3769203785
## fringe:9/2017-back:1/2018 0.5878756807 -0.194217048 1.3699684095
## back:9/2018-back:1/2018 0.2303597542 -0.575318942 1.0360384502
## fore:9/2018-back:1/2018 -0.9693688172 -1.993368569 0.0546309343
## fringe:9/2018-back:1/2018 0.4212585637 -0.360834165 1.2033512926
## fringe:1/2018-fore:1/2018 1.2756451613 0.295240041 2.2560502818
## back:1/2019-fore:1/2018 0.8315430108 -0.192456741 1.8555427623
## fore:1/2019-fore:1/2018 -0.1192043011 -1.301617365 1.0632087633
## fringe:1/2019-fore:1/2018 1.2139569892 0.189957238 2.2379567408
## back:10/2017-fore:1/2018 1.2677043011 0.243704550 2.2917040526
## fore:10/2017-fore:1/2018 0.1117096774 -1.070703387 1.2941227418
## fringe:10/2017-fore:1/2018 1.4971653226 0.516760202 2.4775704430
## back:10/2018-fore:1/2018 1.1520460829 0.152724651 2.1513675152
## fore:10/2018-fore:1/2018 0.1096129032 -1.072800161 1.2920259676
## fringe:10/2018-fore:1/2018 1.4800251684 0.480703736 2.4793466006
## back:11/2017-fore:1/2018 0.8947017921 -0.129297959 1.9187015437
## fore:11/2017-fore:1/2018 0.0735351254 -1.108877939 1.2559481899
## fringe:11/2017-fore:1/2018 1.1892337366 0.208828616 2.1696388570
## back:11/2018-fore:1/2018 1.3812684588 0.357268707 2.4052682103
## fore:11/2018-fore:1/2018 0.1654684588 -1.016944606 1.3478815232
## fringe:11/2018-fore:1/2018 1.6535406810 0.629540929 2.6775404326
## back:12/2017-fore:1/2018 0.9565483871 -0.067451364 1.9805481386
## fore:12/2017-fore:1/2018 0.0540967742 -1.128316290 1.2365098386
## fringe:12/2017-fore:1/2018 1.1529435484 0.172538428 2.1333486689
## back:12/2018-fore:1/2018 1.1044569892 0.080457238 2.1284567408
## fore:12/2018-fore:1/2018 0.0464193548 -1.135993710 1.2288324193
## fringe:12/2018-fore:1/2018 1.5074247312 0.483424980 2.5314244827
## back:2/2018-fore:1/2018 0.6217557604 -0.402243991 1.6457555119
## fore:2/2018-fore:1/2018 -0.1121370968 -1.294550161 1.0702759676
## fringe:2/2018-fore:1/2018 0.9266173675 -0.053787753 1.9070224880
## back:2/2019-fore:1/2018 0.7428093318 -0.281190420 1.7668090833
## fore:2/2019-fore:1/2018 -0.0261979433 -1.208611008 1.1562151211
## fringe:2/2019-fore:1/2018 1.1590057604 0.135006009 2.1830055119
## back:3/2018-fore:1/2018 1.2220607612 0.256624537 2.1874969856
## fore:3/2018-fore:1/2018 0.0193440860 -1.163068978 1.2017571504
## fringe:3/2018-fore:1/2018 1.4274518817 0.447046761 2.4078570022
## back:3/2019-fore:1/2018 0.8037858715 -0.220213880 1.8277856230
## fore:3/2019-fore:1/2018 -0.0345392707 -1.356517265 1.2874387241
## fringe:3/2019-fore:1/2018 1.0602973957 0.036297644 2.0842971473
## back:4/2018-fore:1/2018 0.8158254032 -0.164579717 1.7962305237
## fore:4/2018-fore:1/2018 -0.0681426523 -1.250555717 1.1142704121
## fringe:4/2018-fore:1/2018 1.0705129032 0.071191471 2.0698343355
## back:5/2018-fore:1/2018 0.5019596774 -0.478445443 1.4823647979
## fore:5/2018-fore:1/2018 -0.1909569892 -1.373370054 0.9914560752
## fringe:5/2018-fore:1/2018 0.7699585253 -0.229362907 1.7692799576
## back:6/2018-fore:1/2018 0.5398420699 -0.440563051 1.5202471904
## fore:6/2018-fore:1/2018 -0.1930204301 -1.375433495 0.9893926343
## fringe:6/2018-fore:1/2018 0.7694938556 -0.229827577 1.7688152878
## back:7/2018-fore:1/2018 0.6234700269 -0.356935094 1.6038751474
## fore:7/2018-fore:1/2018 -0.1853548387 -1.367767903 0.9970582257
## fringe:7/2018-fore:1/2018 0.8399831029 -0.159338329 1.8393045352
## back:8/2018-fore:1/2018 1.1440043622 0.163599242 2.1244094826
## fore:8/2018-fore:1/2018 0.0856499815 -1.096763083 1.2680630459
## fringe:8/2018-fore:1/2018 1.1521690329 0.186732809 2.1176052572
## back:9/2017-fore:1/2018 1.6070807273 0.583080976 2.6310804788
## fore:9/2017-fore:1/2018 0.3363023474 -0.846110717 1.5187154118
## fringe:9/2017-fore:1/2018 1.5712574011 0.590852281 2.5516625216
## back:9/2018-fore:1/2018 1.2137414747 0.214420042 2.2130629069
## fore:9/2018-fore:1/2018 0.0140129032 -1.168400161 1.1964259676
## fringe:9/2018-fore:1/2018 1.4046402842 0.424235164 2.3850454046
## back:1/2019-fringe:1/2018 -0.4441021505 -1.226194879 0.3379905783
## fore:1/2019-fringe:1/2018 -1.3948494624 -2.375254583 -0.4144443419
## fringe:1/2019-fringe:1/2018 -0.0616881720 -0.843780901 0.7204045568
## back:10/2017-fringe:1/2018 -0.0079408602 -0.790033589 0.7741518686
## fore:10/2017-fringe:1/2018 -1.1639354839 -2.144340604 -0.1835303634
## fringe:10/2017-fringe:1/2018 0.2215201613 -0.502557007 0.9455973295
## back:10/2018-fringe:1/2018 -0.1235990783 -0.873090153 0.6258919958
## fore:10/2018-fringe:1/2018 -1.1660322581 -2.146437379 -0.1856271376
## fringe:10/2018-fringe:1/2018 0.2043800071 -0.545111067 0.9538710813
## back:11/2017-fringe:1/2018 -0.3809433692 -1.163036098 0.4011493597
## fore:11/2017-fringe:1/2018 -1.2021100358 -2.182515156 -0.2217049154
## fringe:11/2017-fringe:1/2018 -0.0864114247 -0.810488593 0.6376657435
## back:11/2018-fringe:1/2018 0.1056232975 -0.676469431 0.8877160263
## fore:11/2018-fringe:1/2018 -1.1101767025 -2.090581823 -0.1297715820
## fringe:11/2018-fringe:1/2018 0.3778955197 -0.404197209 1.1599882486
## back:12/2017-fringe:1/2018 -0.3190967742 -1.101189503 0.4629959547
## fore:12/2017-fringe:1/2018 -1.2215483871 -2.201953508 -0.2411432666
## fringe:12/2017-fringe:1/2018 -0.1227016129 -0.846778781 0.6013755554
## back:12/2018-fringe:1/2018 -0.1711881720 -0.953280901 0.6109045568
## fore:12/2018-fringe:1/2018 -1.2292258065 -2.209630927 -0.2488206860
## fringe:12/2018-fringe:1/2018 0.2317795699 -0.550313159 1.0138722987
## back:2/2018-fringe:1/2018 -0.6538894009 -1.435982130 0.1282033279
## fore:2/2018-fringe:1/2018 -1.3877822581 -2.368187379 -0.4073771376
## fringe:2/2018-fringe:1/2018 -0.3490277938 -1.073104962 0.3750493745
## back:2/2019-fringe:1/2018 -0.5328358295 -1.314928558 0.2492568994
## fore:2/2019-fringe:1/2018 -1.3018431046 -2.282248225 -0.3214379842
## fringe:2/2019-fringe:1/2018 -0.1166394009 -0.898732130 0.6654533279
## back:3/2018-fringe:1/2018 -0.0535844001 -0.757260923 0.6500921226
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## fore:9/2018-fringe:7/2018 -0.8259701997 -1.825291632 0.1733512325
## fringe:9/2018-fringe:7/2018 0.5646571813 -0.184833893 1.3141482554
## fore:8/2018-back:8/2018 -1.0583543807 -2.038759501 -0.0779492602
## fringe:8/2018-back:8/2018 0.0081646707 -0.695511852 0.7118411934
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## fore:9/2017-back:8/2018 -0.8077020147 -1.788107135 0.1727031057
## fringe:9/2017-back:8/2018 0.4272530389 -0.296824129 1.1513302072
## back:9/2018-back:8/2018 0.0697371125 -0.679753962 0.8192281867
## fore:9/2018-back:8/2018 -1.1299914589 -2.110396579 -0.1495863385
## fringe:9/2018-back:8/2018 0.2606359220 -0.463441246 0.9847130903
## fringe:8/2018-fore:8/2018 1.0665190514 0.101082827 2.0319552758
## back:9/2017-fore:8/2018 1.5214307458 0.497430994 2.5454304974
## fore:9/2017-fore:8/2018 0.2506523660 -0.931760698 1.4330654304
## fringe:9/2017-fore:8/2018 1.4856074196 0.505202299 2.4660125401
## back:9/2018-fore:8/2018 1.1280914932 0.128770061 2.1274129254
## fore:9/2018-fore:8/2018 -0.0716370782 -1.254050143 1.1107759862
## fringe:9/2018-fore:8/2018 1.3189903027 0.338585182 2.2993954232
## back:9/2017-fringe:8/2018 0.4549116944 -0.308332657 1.2181560456
## fore:9/2017-fringe:8/2018 -0.8158666855 -1.781302910 0.1495695389
## fringe:9/2017-fringe:8/2018 0.4190883682 -0.284588154 1.1227648909
## back:9/2018-fringe:8/2018 0.0615724418 -0.668228746 0.7913736293
## fore:9/2018-fringe:8/2018 -1.1381561296 -2.103592354 -0.1727199053
## fringe:9/2018-fringe:8/2018 0.2524712513 -0.451205271 0.9561477740
## fore:9/2017-back:9/2017 -1.2707783799 -2.294778131 -0.2467786283
## fringe:9/2017-back:9/2017 -0.0358233262 -0.817916055 0.7462694026
## back:9/2018-back:9/2017 -0.3933392526 -1.199017949 0.4123394434
## fore:9/2018-back:9/2017 -1.5930678241 -2.617067576 -0.5690680725
## fringe:9/2018-back:9/2017 -0.2024404431 -0.984533172 0.5796522857
## fringe:9/2017-fore:9/2017 1.2349550537 0.254549933 2.2153601741
## back:9/2018-fore:9/2017 0.8774391272 -0.121882305 1.8767605595
## fore:9/2018-fore:9/2017 -0.3222894442 -1.504702509 0.8601236202
## fringe:9/2018-fore:9/2017 1.0683379368 0.087932816 2.0487430572
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## fore:9/2018-fringe:9/2017 -1.5572444979 -2.537649618 -0.5768393774
## fringe:9/2018-fringe:9/2017 -0.1666171169 -0.890694285 0.5574600513
## fore:9/2018-back:9/2018 -1.1997285714 -2.199050004 -0.2004071392
## fringe:9/2018-back:9/2018 0.1908988095 -0.558592265 0.9403898837
## fringe:9/2018-fore:9/2018 1.3906273810 0.410222260 2.3710325014
## p adj
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## fringe:1/2018-back:1/2018 0.9999998
## back:1/2019-back:1/2018 1.0000000
## fore:1/2019-back:1/2018 0.0159122
## fringe:1/2019-back:1/2018 1.0000000
## back:10/2017-back:1/2018 1.0000000
## fore:10/2017-back:1/2018 0.2912585
## fringe:10/2017-back:1/2018 0.8922765
## back:10/2018-back:1/2018 1.0000000
## fore:10/2018-back:1/2018 0.2856542
## fringe:10/2018-back:1/2018 0.9536980
## back:11/2017-back:1/2018 1.0000000
## fore:11/2017-back:1/2018 0.1998671
## fringe:11/2017-back:1/2018 1.0000000
## back:11/2018-back:1/2018 0.9997431
## fore:11/2018-back:1/2018 0.4547593
## fringe:11/2018-back:1/2018 0.4452697
## back:12/2017-back:1/2018 1.0000000
## fore:12/2017-back:1/2018 0.1620713
## fringe:12/2017-back:1/2018 1.0000000
## back:12/2018-back:1/2018 1.0000000
## fore:12/2018-back:1/2018 0.1487213
## fringe:12/2018-back:1/2018 0.9413111
## back:2/2018-back:1/2018 0.9999813
## fore:2/2018-back:1/2018 0.0177363
## fringe:2/2018-back:1/2018 1.0000000
## back:2/2019-back:1/2018 1.0000000
## fore:2/2019-back:1/2018 0.0607298
## fringe:2/2019-back:1/2018 1.0000000
## back:3/2018-back:1/2018 1.0000000
## fore:3/2018-back:1/2018 0.1083258
## fringe:3/2018-back:1/2018 0.9878974
## back:3/2019-back:1/2018 1.0000000
## fore:3/2019-back:1/2018 0.2654498
## fringe:3/2019-back:1/2018 1.0000000
## back:4/2018-back:1/2018 1.0000000
## fore:4/2018-back:1/2018 0.0340202
## fringe:4/2018-back:1/2018 1.0000000
## back:5/2018-back:1/2018 0.9546468
## fore:5/2018-back:1/2018 0.0049973
## fringe:5/2018-back:1/2018 1.0000000
## back:6/2018-back:1/2018 0.9881565
## fore:6/2018-back:1/2018 0.0048267
## fringe:6/2018-back:1/2018 1.0000000
## back:7/2018-back:1/2018 0.9998932
## fore:7/2018-back:1/2018 0.0054896
## fringe:7/2018-back:1/2018 1.0000000
## back:8/2018-back:1/2018 1.0000000
## fore:8/2018-back:1/2018 0.2264002
## fringe:8/2018-back:1/2018 1.0000000
## back:9/2017-back:1/2018 0.6395308
## fore:9/2017-back:1/2018 0.9343545
## fringe:9/2017-back:1/2018 0.6196773
## back:9/2018-back:1/2018 1.0000000
## fore:9/2018-back:1/2018 0.1015229
## fringe:9/2018-back:1/2018 0.9956036
## fringe:1/2018-fore:1/2018 0.0002842
## back:1/2019-fore:1/2018 0.4101802
## fore:1/2019-fore:1/2018 1.0000000
## fringe:1/2019-fore:1/2018 0.0025307
## back:10/2017-fore:1/2018 0.0009638
## fore:10/2017-fore:1/2018 1.0000000
## fringe:10/2017-fore:1/2018 0.0000023
## back:10/2018-fore:1/2018 0.0045041
## fore:10/2018-fore:1/2018 1.0000000
## fringe:10/2018-fore:1/2018 0.0000064
## back:11/2017-fore:1/2018 0.2333975
## fore:11/2017-fore:1/2018 1.0000000
## fringe:11/2017-fore:1/2018 0.0015351
## back:11/2018-fore:1/2018 0.0001085
## fore:11/2018-fore:1/2018 1.0000000
## fringe:11/2018-fore:1/2018 0.0000003
## back:12/2017-fore:1/2018 0.1185000
## fore:12/2017-fore:1/2018 1.0000000
## fringe:12/2017-fore:1/2018 0.0029995
## back:12/2018-fore:1/2018 0.0154589
## fore:12/2018-fore:1/2018 1.0000000
## fringe:12/2018-fore:1/2018 0.0000078
## back:2/2018-fore:1/2018 0.9630367
## fore:2/2018-fore:1/2018 1.0000000
## fringe:2/2018-fore:1/2018 0.1034621
## back:2/2019-fore:1/2018 0.7090257
## fore:2/2019-fore:1/2018 1.0000000
## fringe:2/2019-fore:1/2018 0.0064539
## back:3/2018-fore:1/2018 0.0005672
## fore:3/2018-fore:1/2018 1.0000000
## fringe:3/2018-fore:1/2018 0.0000112
## back:3/2019-fore:1/2018 0.5024403
## fore:3/2019-fore:1/2018 1.0000000
## fringe:3/2019-fore:1/2018 0.0299791
## back:4/2018-fore:1/2018 0.3466332
## fore:4/2018-fore:1/2018 1.0000000
## fringe:4/2018-fore:1/2018 0.0173524
## back:5/2018-fore:1/2018 0.9985151
## fore:5/2018-fore:1/2018 1.0000000
## fringe:5/2018-fore:1/2018 0.5533657
## back:6/2018-fore:1/2018 0.9931901
## fore:6/2018-fore:1/2018 1.0000000
## fringe:6/2018-fore:1/2018 0.5550096
## back:7/2018-fore:1/2018 0.9285260
## fore:7/2018-fore:1/2018 1.0000000
## fringe:7/2018-fore:1/2018 0.3215633
## back:8/2018-fore:1/2018 0.0035243
## fore:8/2018-fore:1/2018 1.0000000
## fringe:8/2018-fore:1/2018 0.0021946
## back:9/2017-fore:1/2018 0.0000009
## fore:9/2017-fore:1/2018 1.0000000
## fringe:9/2017-fore:1/2018 0.0000004
## back:9/2018-fore:1/2018 0.0014915
## fore:9/2018-fore:1/2018 1.0000000
## fringe:9/2018-fore:1/2018 0.0000185
## back:1/2019-fringe:1/2018 0.9878816
## fore:1/2019-fringe:1/2018 0.0000230
## fringe:1/2019-fringe:1/2018 1.0000000
## back:10/2017-fringe:1/2018 1.0000000
## fore:10/2017-fringe:1/2018 0.0024549
## fringe:10/2017-fringe:1/2018 1.0000000
## back:10/2018-fringe:1/2018 1.0000000
## fore:10/2018-fringe:1/2018 0.0023623
## fringe:10/2018-fringe:1/2018 1.0000000
## back:11/2017-fringe:1/2018 0.9995403
## fore:11/2017-fringe:1/2018 0.0012035
## fringe:11/2017-fringe:1/2018 1.0000000
## back:11/2018-fringe:1/2018 1.0000000
## fore:11/2018-fringe:1/2018 0.0063969
## fringe:11/2018-fringe:1/2018 0.9996231
## back:12/2017-fringe:1/2018 0.9999968
## fore:12/2017-fringe:1/2018 0.0008290
## fringe:12/2017-fringe:1/2018 1.0000000
## back:12/2018-fringe:1/2018 1.0000000
## fore:12/2018-fringe:1/2018 0.0007142
## fringe:12/2018-fringe:1/2018 1.0000000
## back:2/2018-fringe:1/2018 0.3347628
## fore:2/2018-fringe:1/2018 0.0000268
## fringe:2/2018-fringe:1/2018 0.9996449
## back:2/2019-fringe:1/2018 0.8378277
## fore:2/2019-fringe:1/2018 0.0001665
## fringe:2/2019-fringe:1/2018 1.0000000
## back:3/2018-fringe:1/2018 1.0000000
## fore:3/2018-fringe:1/2018 0.0004190
## fringe:3/2018-fringe:1/2018 1.0000000
## back:3/2019-fringe:1/2018 0.9664809
## fore:3/2019-fringe:1/2018 0.0052094
## fringe:3/2019-fringe:1/2018 1.0000000
## back:4/2018-fringe:1/2018 0.9298449
## fore:4/2018-fringe:1/2018 0.0000692
## fringe:4/2018-fringe:1/2018 1.0000000
## back:5/2018-fringe:1/2018 0.0181090
## fore:5/2018-fringe:1/2018 0.0000046
## fringe:5/2018-fringe:1/2018 0.8538509
## back:6/2018-fringe:1/2018 0.0397327
## fore:6/2018-fringe:1/2018 0.0000044
## fringe:6/2018-fringe:1/2018 0.8523832
## back:7/2018-fringe:1/2018 0.1749564
## fore:7/2018-fringe:1/2018 0.0000052
## fringe:7/2018-fringe:1/2018 0.9817201
## back:8/2018-fringe:1/2018 1.0000000
## fore:8/2018-fringe:1/2018 0.0015133
## fringe:8/2018-fringe:1/2018 1.0000000
## back:9/2017-fringe:1/2018 0.9999898
## fore:9/2017-fringe:1/2018 0.0877653
## fringe:9/2017-fringe:1/2018 0.9999968
## back:9/2018-fringe:1/2018 1.0000000
## fore:9/2018-fringe:1/2018 0.0003767
## fringe:9/2018-fringe:1/2018 1.0000000
## fore:1/2019-back:1/2019 0.1268941
## fringe:1/2019-back:1/2019 0.9999095
## back:10/2017-back:1/2019 0.9977552
## fore:10/2017-back:1/2019 0.7788273
## fringe:10/2017-back:1/2019 0.2917100
## back:10/2018-back:1/2019 0.9999986
## fore:10/2018-back:1/2019 0.7727902
## fringe:10/2018-back:1/2019 0.4339704
## back:11/2017-back:1/2019 1.0000000
## fore:11/2017-back:1/2019 0.6592629
## fringe:11/2017-back:1/2019 0.9999096
## back:11/2018-back:1/2019 0.8911756
## fore:11/2018-back:1/2019 0.9044966
## fringe:11/2018-back:1/2019 0.0630633
## back:12/2017-back:1/2019 1.0000000
## fore:12/2017-back:1/2019 0.5931377
## fringe:12/2017-back:1/2019 0.9999960
## back:12/2018-back:1/2019 1.0000000
## fore:12/2018-back:1/2019 0.5666568
## fringe:12/2018-back:1/2019 0.4223097
## back:2/2018-back:1/2019 1.0000000
## fore:2/2018-back:1/2019 0.1377472
## fringe:2/2018-back:1/2019 1.0000000
## back:2/2019-back:1/2019 1.0000000
## fore:2/2019-back:1/2019 0.3301266
## fringe:2/2019-back:1/2019 0.9999991
## back:3/2018-back:1/2019 0.9985369
## fore:3/2018-back:1/2019 0.4738977
## fringe:3/2018-back:1/2019 0.5835533
## back:3/2019-back:1/2019 1.0000000
## fore:3/2019-back:1/2019 0.6856045
## fringe:3/2019-back:1/2019 1.0000000
## back:4/2018-back:1/2019 1.0000000
## fore:4/2018-back:1/2019 0.2219645
## fringe:4/2018-back:1/2019 1.0000000
## back:5/2018-back:1/2019 0.9999914
## fore:5/2018-back:1/2019 0.0510335
## fringe:5/2018-back:1/2019 1.0000000
## back:6/2018-back:1/2019 0.9999998
## fore:6/2018-back:1/2019 0.0496163
## fringe:6/2018-back:1/2019 1.0000000
## back:7/2018-back:1/2019 1.0000000
## fore:7/2018-back:1/2019 0.0550596
## fringe:7/2018-back:1/2019 1.0000000
## back:8/2018-back:1/2019 0.9999984
## fore:8/2018-back:1/2019 0.6991198
## fringe:8/2018-back:1/2019 0.9999921
## back:9/2017-back:1/2019 0.1282548
## fore:9/2017-back:1/2019 0.9996143
## fringe:9/2017-back:1/2019 0.1025903
## back:9/2018-back:1/2019 0.9997631
## fore:9/2018-back:1/2019 0.4560353
## fringe:9/2018-back:1/2019 0.6845853
## fringe:1/2019-fore:1/2019 0.0002799
## back:10/2017-fore:1/2019 0.0000969
## fore:10/2017-fore:1/2019 1.0000000
## fringe:10/2017-fore:1/2019 0.0000001
## back:10/2018-fore:1/2019 0.0005021
## fore:10/2018-fore:1/2019 1.0000000
## fringe:10/2018-fore:1/2019 0.0000004
## back:11/2017-fore:1/2019 0.0573200
## fore:11/2017-fore:1/2019 1.0000000
## fringe:11/2017-fore:1/2019 0.0001453
## back:11/2018-fore:1/2019 0.0000091
## fore:11/2018-fore:1/2019 1.0000000
## fringe:11/2018-fore:1/2019 0.0000000
## back:12/2017-fore:1/2019 0.0238913
## fore:12/2017-fore:1/2019 1.0000000
## fringe:12/2017-fore:1/2019 0.0003050
## back:12/2018-fore:1/2019 0.0021334
## fore:12/2018-fore:1/2019 1.0000000
## fringe:12/2018-fore:1/2019 0.0000005
## back:2/2018-fore:1/2019 0.7149129
## fore:2/2018-fore:1/2019 1.0000000
## fringe:2/2018-fore:1/2019 0.0186214
## back:2/2019-fore:1/2019 0.3179102
## fore:2/2019-fore:1/2019 1.0000000
## fringe:2/2019-fore:1/2019 0.0007937
## back:3/2018-fore:1/2019 0.0000468
## fore:3/2018-fore:1/2019 1.0000000
## fringe:3/2018-fore:1/2019 0.0000007
## back:3/2019-fore:1/2019 0.1736758
## fore:3/2019-fore:1/2019 1.0000000
## fringe:3/2019-fore:1/2019 0.0045805
## back:4/2018-fore:1/2019 0.0928489
## fore:4/2018-fore:1/2019 1.0000000
## fringe:4/2018-fore:1/2019 0.0023123
## back:5/2018-fore:1/2019 0.9319803
## fore:5/2018-fore:1/2019 1.0000000
## fringe:5/2018-fore:1/2019 0.1972105
## back:6/2018-fore:1/2019 0.8596560
## fore:6/2018-fore:1/2019 1.0000000
## fringe:6/2018-fore:1/2019 0.1982002
## back:7/2018-fore:1/2019 0.5991522
## fore:7/2018-fore:1/2019 1.0000000
## fringe:7/2018-fore:1/2019 0.0858396
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## fore:8/2018-fore:1/2019 1.0000000
## fringe:8/2018-fore:1/2019 0.0002076
## back:9/2017-fore:1/2019 0.0000001
## fore:9/2017-fore:1/2019 0.9999995
## fringe:9/2017-fore:1/2019 0.0000000
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## fore:9/2018-fore:1/2019 1.0000000
## fringe:9/2018-fore:1/2019 0.0000012
## back:10/2017-fringe:1/2019 1.0000000
## fore:10/2017-fringe:1/2019 0.0159956
## fringe:10/2017-fringe:1/2019 0.9999999
## back:10/2018-fringe:1/2019 1.0000000
## fore:10/2018-fringe:1/2019 0.0154859
## fringe:10/2018-fringe:1/2019 1.0000000
## back:11/2017-fringe:1/2019 0.9999996
## fore:11/2017-fringe:1/2019 0.0087477
## fringe:11/2017-fringe:1/2019 1.0000000
## back:11/2018-fringe:1/2019 1.0000000
## fore:11/2018-fringe:1/2019 0.0355272
## fringe:11/2018-fringe:1/2019 0.9973441
## back:12/2017-fringe:1/2019 1.0000000
## fore:12/2017-fringe:1/2019 0.0063633
## fringe:12/2017-fringe:1/2019 1.0000000
## back:12/2018-fringe:1/2019 1.0000000
## fore:12/2018-fringe:1/2019 0.0056007
## fringe:12/2018-fringe:1/2019 1.0000000
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## fore:2/2018-fringe:1/2019 0.0003208
## fringe:2/2018-fringe:1/2019 0.9999999
## back:2/2019-fringe:1/2019 0.9894594
## fore:2/2019-fringe:1/2019 0.0015901
## fringe:2/2019-fringe:1/2019 1.0000000
## back:3/2018-fringe:1/2019 1.0000000
## fore:3/2018-fringe:1/2019 0.0035401
## fringe:3/2018-fringe:1/2019 1.0000000
## back:3/2019-fringe:1/2019 0.9994526
## fore:3/2019-fringe:1/2019 0.0220170
## fringe:3/2019-fringe:1/2019 1.0000000
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## fore:4/2018-fringe:1/2019 0.0007384
## fringe:4/2018-fringe:1/2019 1.0000000
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## fore:5/2018-fringe:1/2019 0.0000673
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## fore:6/2018-fringe:1/2019 0.0000645
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## back:7/2018-fringe:1/2019 0.6079744
## fore:7/2018-fringe:1/2019 0.0000754
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## back:8/2018-fringe:1/2019 1.0000000
## fore:8/2018-fringe:1/2019 0.0106277
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## fore:9/2017-fringe:1/2019 0.2754432
## fringe:9/2017-fringe:1/2019 0.9999123
## back:9/2018-fringe:1/2019 1.0000000
## fore:9/2018-fringe:1/2019 0.0032294
## fringe:9/2018-fringe:1/2019 1.0000000
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## fore:10/2018-back:10/2017 0.0065522
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## back:12/2017-back:10/2017 0.9999998
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## fore:2/2018-back:10/2017 0.0001116
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## fore:2/2019-back:10/2017 0.0005921
## fringe:2/2019-back:10/2017 1.0000000
## back:3/2018-back:10/2017 1.0000000
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## fore:3/2019-back:10/2017 0.0107181
## fringe:3/2019-back:10/2017 1.0000000
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## fringe:4/2018-back:10/2017 1.0000000
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## back:4/2018-back:3/2018 0.9837566
## fore:4/2018-back:3/2018 0.0001400
## fringe:4/2018-back:3/2018 1.0000000
## back:5/2018-back:3/2018 0.0358257
## fore:5/2018-back:3/2018 0.0000094
## fringe:5/2018-back:3/2018 0.9502570
## back:6/2018-back:3/2018 0.0756692
## fore:6/2018-back:3/2018 0.0000090
## fringe:6/2018-back:3/2018 0.9495178
## back:7/2018-back:3/2018 0.2928597
## fore:7/2018-back:3/2018 0.0000107
## fringe:7/2018-back:3/2018 0.9975747
## back:8/2018-back:3/2018 1.0000000
## fore:8/2018-back:3/2018 0.0029409
## fringe:8/2018-back:3/2018 1.0000000
## back:9/2017-back:3/2018 0.9989409
## fore:9/2017-back:3/2018 0.1445179
## fringe:9/2017-back:3/2018 0.9992789
## back:9/2018-back:3/2018 1.0000000
## fore:9/2018-back:3/2018 0.0007491
## fringe:9/2018-back:3/2018 1.0000000
## fringe:3/2018-fore:3/2018 0.0000172
## back:3/2019-fore:3/2018 0.5690113
## fore:3/2019-fore:3/2018 1.0000000
## fringe:3/2019-fore:3/2018 0.0395262
## back:4/2018-fore:3/2018 0.4090437
## fore:4/2018-fore:3/2018 1.0000000
## fringe:4/2018-fore:3/2018 0.0234077
## back:5/2018-fore:3/2018 0.9994054
## fore:5/2018-fore:3/2018 1.0000000
## fringe:5/2018-fore:3/2018 0.6215861
## back:6/2018-fore:3/2018 0.9967372
## fore:6/2018-fore:3/2018 1.0000000
## fringe:6/2018-fore:3/2018 0.6232107
## back:7/2018-fore:3/2018 0.9539477
## fore:7/2018-fore:3/2018 1.0000000
## fringe:7/2018-fore:3/2018 0.3806272
## back:8/2018-fore:3/2018 0.0049696
## fore:8/2018-fore:3/2018 1.0000000
## fringe:8/2018-fore:3/2018 0.0031414
## back:9/2017-fore:3/2018 0.0000013
## fore:9/2017-fore:3/2018 1.0000000
## fringe:9/2017-fore:3/2018 0.0000006
## back:9/2018-fore:3/2018 0.0021247
## fore:9/2018-fore:3/2018 1.0000000
## fringe:9/2018-fore:3/2018 0.0000283
## back:3/2019-fringe:3/2018 0.4592357
## fore:3/2019-fringe:3/2018 0.0004567
## fringe:3/2019-fringe:3/2018 0.9998191
## back:4/2018-fringe:3/2018 0.3096072
## fore:4/2018-fringe:3/2018 0.0000024
## fringe:4/2018-fringe:3/2018 0.9997382
## back:5/2018-fringe:3/2018 0.0004464
## fore:5/2018-fringe:3/2018 0.0000001
## fringe:5/2018-fringe:3/2018 0.2250882
## back:6/2018-fringe:3/2018 0.0012100
## fore:6/2018-fringe:3/2018 0.0000001
## fringe:6/2018-fringe:3/2018 0.2236461
## back:7/2018-fringe:3/2018 0.0092446
## fore:7/2018-fringe:3/2018 0.0000001
## fringe:7/2018-fringe:3/2018 0.5063718
## back:8/2018-fringe:3/2018 0.9999992
## fore:8/2018-fringe:3/2018 0.0000722
## fringe:8/2018-fringe:3/2018 0.9999992
## back:9/2017-fringe:3/2018 1.0000000
## fore:9/2017-fringe:3/2018 0.0088550
## fringe:9/2017-fringe:3/2018 1.0000000
## back:9/2018-fringe:3/2018 1.0000000
## fore:9/2018-fringe:3/2018 0.0000152
## fringe:9/2018-fringe:3/2018 1.0000000
## fore:3/2019-back:3/2019 0.7608338
## fringe:3/2019-back:3/2019 1.0000000
## back:4/2018-back:3/2019 1.0000000
## fore:4/2018-back:3/2019 0.2905694
## fringe:4/2018-back:3/2019 1.0000000
## back:5/2018-back:3/2019 0.9999995
## fore:5/2018-back:3/2019 0.0737723
## fringe:5/2018-back:3/2019 1.0000000
## back:6/2018-back:3/2019 1.0000000
## fore:6/2018-back:3/2019 0.0718273
## fringe:6/2018-back:3/2019 1.0000000
## back:7/2018-back:3/2019 1.0000000
## fore:7/2018-back:3/2019 0.0792778
## fringe:7/2018-back:3/2019 1.0000000
## back:8/2018-back:3/2019 0.9999778
## fore:8/2018-back:3/2019 0.7836600
## fringe:8/2018-back:3/2019 0.9999143
## back:9/2017-back:3/2019 0.0848095
## fore:9/2017-back:3/2019 0.9999139
## fringe:9/2017-back:3/2019 0.0646497
## back:9/2018-back:3/2019 0.9987089
## fore:9/2018-back:3/2019 0.5506031
## fringe:9/2018-back:3/2019 0.5612026
## fringe:3/2019-fore:3/2019 0.1307920
## back:4/2018-fore:3/2019 0.6506067
## fore:4/2018-fore:3/2019 1.0000000
## fringe:4/2018-fore:3/2019 0.0952323
## back:5/2018-fore:3/2019 0.9998274
## fore:5/2018-fore:3/2019 1.0000000
## fringe:5/2018-fore:3/2019 0.8074561
## back:6/2018-fore:3/2019 0.9990671
## fore:6/2018-fore:3/2019 1.0000000
## fringe:6/2018-fore:3/2019 0.8085514
## back:7/2018-fore:3/2019 0.9849679
## fore:7/2018-fore:3/2019 1.0000000
## fringe:7/2018-fore:3/2019 0.6143904
## back:8/2018-fore:3/2019 0.0327762
## fore:8/2018-fore:3/2019 1.0000000
## fringe:8/2018-fore:3/2019 0.0247331
## back:9/2017-fore:3/2019 0.0000477
## fore:9/2017-fore:3/2019 1.0000000
## fringe:9/2017-fore:3/2019 0.0000353
## back:9/2018-fore:3/2019 0.0163361
## fore:9/2018-fore:3/2019 1.0000000
## fringe:9/2018-fore:3/2019 0.0006709
## back:4/2018-fringe:3/2019 1.0000000
## fore:4/2018-fringe:3/2019 0.0106052
## fringe:4/2018-fringe:3/2019 1.0000000
## back:5/2018-fringe:3/2019 0.7457535
## fore:5/2018-fringe:3/2019 0.0013015
## fringe:5/2018-fringe:3/2019 1.0000000
## back:6/2018-fringe:3/2019 0.8747181
## fore:6/2018-fringe:3/2019 0.0012537
## fringe:6/2018-fringe:3/2019 0.9999999
## back:7/2018-fringe:3/2019 0.9910613
## fore:7/2018-fringe:3/2019 0.0014403
## fringe:7/2018-fringe:3/2019 1.0000000
## back:8/2018-fringe:3/2019 1.0000000
## fore:8/2018-fringe:3/2019 0.0951344
## fringe:8/2018-fringe:3/2019 1.0000000
## back:9/2017-fringe:3/2019 0.8979831
## fore:9/2017-fringe:3/2019 0.7667736
## fringe:9/2017-fringe:3/2019 0.8992113
## back:9/2018-fringe:3/2019 1.0000000
## fore:9/2018-fringe:3/2019 0.0366582
## fringe:9/2018-fringe:3/2019 0.9999687
## fore:4/2018-back:4/2018 0.1731386
## fringe:4/2018-back:4/2018 1.0000000
## back:5/2018-back:4/2018 0.9999800
## fore:5/2018-back:4/2018 0.0340077
## fringe:5/2018-back:4/2018 1.0000000
## back:6/2018-back:4/2018 0.9999997
## fore:6/2018-back:4/2018 0.0329729
## fringe:6/2018-back:4/2018 1.0000000
## back:7/2018-back:4/2018 1.0000000
## fore:7/2018-back:4/2018 0.0369641
## fringe:7/2018-back:4/2018 1.0000000
## back:8/2018-back:4/2018 0.9999294
## fore:8/2018-back:4/2018 0.6436849
## fringe:8/2018-back:4/2018 0.9997128
## back:9/2017-back:4/2018 0.0423684
## fore:9/2017-back:4/2018 0.9994912
## fringe:9/2017-back:4/2018 0.0266559
## back:9/2018-back:4/2018 0.9967354
## fore:9/2018-back:4/2018 0.3913708
## fringe:9/2018-back:4/2018 0.4064448
## fringe:4/2018-fore:4/2018 0.0056725
## back:5/2018-fore:4/2018 0.9816019
## fore:5/2018-fore:4/2018 1.0000000
## fringe:5/2018-fore:4/2018 0.3270802
## back:6/2018-fore:4/2018 0.9495030
## fore:6/2018-fore:4/2018 1.0000000
## fringe:6/2018-fore:4/2018 0.3284503
## back:7/2018-fore:4/2018 0.7715263
## fore:7/2018-fore:4/2018 1.0000000
## fringe:7/2018-fore:4/2018 0.1598077
## back:8/2018-fore:4/2018 0.0009936
## fore:8/2018-fore:4/2018 1.0000000
## fringe:8/2018-fore:4/2018 0.0005873
## back:9/2017-fore:4/2018 0.0000002
## fore:9/2017-fore:4/2018 1.0000000
## fringe:9/2017-fore:4/2018 0.0000001
## back:9/2018-fore:4/2018 0.0004082
## fore:9/2018-fore:4/2018 1.0000000
## fringe:9/2018-fore:4/2018 0.0000040
## back:5/2018-fringe:4/2018 0.5953909
## fore:5/2018-fringe:4/2018 0.0006065
## fringe:5/2018-fringe:4/2018 0.9999994
## back:6/2018-fringe:4/2018 0.7637192
## fore:6/2018-fringe:4/2018 0.0005829
## fringe:6/2018-fringe:4/2018 0.9999993
## back:7/2018-fringe:4/2018 0.9720063
## fore:7/2018-fringe:4/2018 0.0006754
## fringe:7/2018-fringe:4/2018 1.0000000
## back:8/2018-fringe:4/2018 1.0000000
## fore:8/2018-fringe:4/2018 0.0610500
## fringe:8/2018-fringe:4/2018 1.0000000
## back:9/2017-fringe:4/2018 0.8736003
## fore:9/2017-fringe:4/2018 0.6780746
## fringe:9/2017-fringe:4/2018 0.8689345
## back:9/2018-fringe:4/2018 1.0000000
## fore:9/2018-fringe:4/2018 0.0215723
## fringe:9/2018-fringe:4/2018 0.9999554
## fore:5/2018-back:5/2018 0.7675561
## fringe:5/2018-back:5/2018 1.0000000
## back:6/2018-back:5/2018 1.0000000
## fore:6/2018-back:5/2018 0.7612124
## fringe:6/2018-back:5/2018 1.0000000
## back:7/2018-back:5/2018 1.0000000
## fore:7/2018-back:5/2018 0.7843939
## fringe:7/2018-back:5/2018 0.9999383
## back:8/2018-back:5/2018 0.2037871
## fore:8/2018-back:5/2018 0.9999891
## fringe:8/2018-back:5/2018 0.1338070
## back:9/2017-back:5/2018 0.0000283
## fore:9/2017-back:5/2018 1.0000000
## fringe:9/2017-back:5/2018 0.0000071
## back:9/2018-back:5/2018 0.0977176
## fore:9/2018-back:5/2018 0.9992271
## fringe:9/2018-back:5/2018 0.0008181
## fringe:5/2018-fore:5/2018 0.0839409
## back:6/2018-fore:5/2018 0.6414841
## fore:6/2018-fore:5/2018 1.0000000
## fringe:6/2018-fore:5/2018 0.0844480
## back:7/2018-fore:5/2018 0.3509776
## fore:7/2018-fore:5/2018 1.0000000
## fringe:7/2018-fore:5/2018 0.0317186
## back:8/2018-fore:5/2018 0.0000834
## fore:8/2018-fore:5/2018 1.0000000
## fringe:8/2018-fore:5/2018 0.0000450
## back:9/2017-fore:5/2018 0.0000000
## fore:9/2017-fore:5/2018 0.9999551
## fringe:9/2017-fore:5/2018 0.0000000
## back:9/2018-fore:5/2018 0.0000331
## fore:9/2018-fore:5/2018 1.0000000
## fringe:9/2018-fore:5/2018 0.0000002
## back:6/2018-fringe:5/2018 1.0000000
## fore:6/2018-fringe:5/2018 0.0817198
## fringe:6/2018-fringe:5/2018 1.0000000
## back:7/2018-fringe:5/2018 1.0000000
## fore:7/2018-fringe:5/2018 0.0902259
## fringe:7/2018-fringe:5/2018 1.0000000
## back:8/2018-fringe:5/2018 0.9991754
## fore:8/2018-fringe:5/2018 0.8289934
## fringe:8/2018-fringe:5/2018 0.9975565
## back:9/2017-fringe:5/2018 0.0284212
## fore:9/2017-fringe:5/2018 0.9999794
## fringe:9/2017-fringe:5/2018 0.0179368
## back:9/2018-fringe:5/2018 0.9856163
## fore:9/2018-fringe:5/2018 0.6028781
## fringe:9/2018-fringe:5/2018 0.3035940
## fore:6/2018-back:6/2018 0.6341835
## fringe:6/2018-back:6/2018 1.0000000
## back:7/2018-back:6/2018 1.0000000
## fore:7/2018-back:6/2018 0.6611489
## fringe:7/2018-back:6/2018 0.9999982
## back:8/2018-back:6/2018 0.3398119
## fore:8/2018-back:6/2018 0.9998724
## fringe:8/2018-back:6/2018 0.2419831
## back:9/2017-back:6/2018 0.0000785
## fore:9/2017-back:6/2018 1.0000000
## fringe:9/2017-back:6/2018 0.0000221
## back:9/2018-back:6/2018 0.1780052
## fore:9/2018-back:6/2018 0.9959700
## fringe:9/2018-back:6/2018 0.0021581
## fringe:6/2018-fore:6/2018 0.0822157
## back:7/2018-fore:6/2018 0.3445769
## fore:7/2018-fore:6/2018 1.0000000
## fringe:7/2018-fore:6/2018 0.0307642
## back:8/2018-fore:6/2018 0.0000798
## fore:8/2018-fore:6/2018 1.0000000
## fringe:8/2018-fore:6/2018 0.0000430
## back:9/2017-fore:6/2018 0.0000000
## fore:9/2017-fore:6/2018 0.9999499
## fringe:9/2017-fore:6/2018 0.0000000
## back:9/2018-fore:6/2018 0.0000317
## fore:9/2018-fore:6/2018 1.0000000
## fringe:9/2018-fore:6/2018 0.0000002
## back:7/2018-fringe:6/2018 1.0000000
## fore:7/2018-fringe:6/2018 0.0907642
## fringe:7/2018-fringe:6/2018 1.0000000
## back:8/2018-fringe:6/2018 0.9991510
## fore:8/2018-fringe:6/2018 0.8301892
## fringe:8/2018-fringe:6/2018 0.9974918
## back:9/2017-fringe:6/2018 0.0281772
## fore:9/2017-fringe:6/2018 0.9999800
## fringe:9/2017-fringe:6/2018 0.0177638
## back:9/2018-fringe:6/2018 0.9853483
## fore:9/2018-fringe:6/2018 0.6045132
## fringe:9/2018-fringe:6/2018 0.3018477
## fore:7/2018-back:7/2018 0.3686558
## fringe:7/2018-back:7/2018 1.0000000
## back:8/2018-back:7/2018 0.7300361
## fore:8/2018-back:7/2018 0.9936698
## fringe:8/2018-back:7/2018 0.6208380
## back:9/2017-back:7/2018 0.0006633
## fore:9/2017-back:7/2018 1.0000000
## fringe:9/2017-back:7/2018 0.0002433
## back:9/2018-back:7/2018 0.4933126
## fore:9/2018-back:7/2018 0.9477274
## fringe:9/2018-back:7/2018 0.0153910
## fringe:7/2018-fore:7/2018 0.0344440
## back:8/2018-fore:7/2018 0.0000939
## fore:8/2018-fore:7/2018 1.0000000
## fringe:8/2018-fore:7/2018 0.0000508
## back:9/2017-fore:7/2018 0.0000000
## fore:9/2017-fore:7/2018 0.9999668
## fringe:9/2017-fore:7/2018 0.0000000
## back:9/2018-fore:7/2018 0.0000373
## fore:9/2018-fore:7/2018 1.0000000
## fringe:9/2018-fore:7/2018 0.0000003
## back:8/2018-fringe:7/2018 0.9999973
## fore:8/2018-fringe:7/2018 0.6085487
## fringe:8/2018-fringe:7/2018 0.9999865
## back:9/2017-fringe:7/2018 0.0947620
## fore:9/2017-fringe:7/2018 0.9989616
## fringe:9/2017-fringe:7/2018 0.0697066
## back:9/2018-fringe:7/2018 0.9996296
## fore:9/2018-fringe:7/2018 0.3638471
## fringe:9/2018-fringe:7/2018 0.6136636
## fore:8/2018-back:8/2018 0.0152313
## fringe:8/2018-back:8/2018 1.0000000
## back:9/2017-back:8/2018 0.9751333
## fore:9/2017-back:8/2018 0.3722503
## fringe:9/2017-back:8/2018 0.9764944
## back:9/2018-back:8/2018 1.0000000
## fore:9/2018-back:8/2018 0.0045238
## fringe:9/2018-back:8/2018 1.0000000
## fringe:8/2018-fore:8/2018 0.0101453
## back:9/2017-fore:8/2018 0.0000058
## fore:9/2017-fore:8/2018 1.0000000
## fringe:9/2017-fore:8/2018 0.0000030
## back:9/2018-fore:8/2018 0.0067879
## fore:9/2018-fore:8/2018 1.0000000
## fringe:9/2018-fore:8/2018 0.0001166
## back:9/2017-fringe:8/2018 0.9723335
## fore:9/2017-fringe:8/2018 0.3085302
## fringe:9/2017-fringe:8/2018 0.9726693
## back:9/2018-fringe:8/2018 1.0000000
## fore:9/2018-fringe:8/2018 0.0028478
## fringe:9/2018-fringe:8/2018 1.0000000
## fore:9/2017-back:9/2017 0.0009107
## fringe:9/2017-back:9/2017 1.0000000
## back:9/2018-back:9/2017 0.9995136
## fore:9/2018-back:9/2017 0.0000012
## fringe:9/2018-back:9/2017 1.0000000
## fringe:9/2017-fore:9/2017 0.0006387
## back:9/2018-fore:9/2017 0.2232707
## fore:9/2018-fore:9/2017 1.0000000
## fringe:9/2018-fore:9/2017 0.0129453
## back:9/2018-fringe:9/2017 0.9997272
## fore:9/2018-fringe:9/2017 0.0000006
## fringe:9/2018-fringe:9/2017 1.0000000
## fore:9/2018-back:9/2018 0.0019285
## fringe:9/2018-back:9/2018 1.0000000
## fringe:9/2018-fore:9/2018 0.0000252The post-hoc test generates a lot of text. The main findings are as follows: - All reef zones are significantly different and fore reef is more different from fringe and back reefs than the back and fringe reefs are from each other.
10 out of 36 site:site comparisons are significant. Half of these (5) are comparisons between lter 0 and other lter sites. This indicates that lter 0 is quite different from other lter sites. for the rest, it seems that lter sites are pretty similar overall.
32 out of 66 month:month comparisons are significant. A lot of these significant comparisons seem to be with months 9, 10, 1, 2 and 3.
Analysis of daily temperature minima and maxima
In this section I will look at whether the differences in daily temperature range between back and fringe reefs are driven by differing daily minima or maxima. I will basically be doing the same thing as I did for temperature ranges but for minimum and maximum daily values.
Figure of minimum and maximum temperatures
I am using ggplot again with geom_smooth() to get averages wiht 95% confidence intervals. Beware, this graph will be a little hard to read. The top three lines are daily maximum temperatures and the bottom three lines are daily minimum temperatures.
mintemp <- ggplot(hobofullrange, aes(x=as.Date(longdate, format= "%m / %d / %Y"), y=min_temp))+
geom_smooth(aes(colour=zone))+
theme_bw()+ theme(legend.position = c(0.15,0.82))+
labs(title="Daily temperature minima", y="Daily temperature minima (°C) with 95% CI", x="date")+
theme(axis.text.x = element_text(angle=45, margin = margin(t=20, r=100)))+
coord_cartesian(ylim=c(26,32))
maxtemp <- ggplot(hobofullrange, aes(x=as.Date(longdate, format= "%m / %d / %Y"), y=max_temp))+
geom_smooth(aes(colour=zone))+
theme_bw()+ theme(legend.position="none")+
labs(title="Daily temperature maxima", y="Daily temperature maxima (°C) with 95% CI", x="date")+
coord_cartesian(ylim=c(26,32))+
theme(axis.text.x = element_text(angle=45, margin = margin(t=20, r=100)))
plot_grid(mintemp, maxtemp)
Maximum temperatures seem to be generally higher in the fringing reefs. The higher daily temperature ranges in the fringe reef before january 2018 seem to be caused by higher maximum temperatures, and after april 2018 by lower minimum temperatures.
Calculate average monthly maximum temperatures
Now I will calculate averages for the daily minimum and maximum temperatures to allow for easy comparison and for statistical tests.
I will use the group_by() and summarise() functions to calculate monthly means.
hobofullminmeans <- hobofullrange %>%
group_by(year, month, monthyear, lter, zone)%>%
summarise(meanmin = mean(min_temp))
hobofullmaxmeans <- hobofullrange %>%
group_by(year, month, monthyear, lter, zone)%>%
summarise(meanmax = mean(max_temp))Check the dataframe. There should by 209 rows, just like with the mean daily ranges.
nrow(hobofullminmeans)## [1] 323nrow(hobofullmaxmeans)## [1] 323Looks right.
Test for significance of differences between zones and months
First I will test for differences between temperature minima.
minaov <- aov(meanmin~zone*as.factor(monthyear), data=hobofullminmeans)
summary(minaov)## Df Sum Sq Mean Sq F value Pr(>F)
## zone 2 0.8 0.415 0.773 0.463
## as.factor(monthyear) 18 362.1 20.118 37.432 <2e-16 ***
## zone:as.factor(monthyear) 36 19.0 0.528 0.983 0.501
## Residuals 266 143.0 0.537
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Monthly means for daily temperature minima differ signifcantly between reef zones, months and lter sites.
maxaov <- aov(meanmax~zone*as.factor(monthyear), data=hobofullmaxmeans)
summary(maxaov)## Df Sum Sq Mean Sq F value Pr(>F)
## zone 2 73.9 36.96 65.746 <2e-16 ***
## as.factor(monthyear) 18 335.3 18.63 33.135 <2e-16 ***
## zone:as.factor(monthyear) 36 22.9 0.64 1.134 0.284
## Residuals 266 149.5 0.56
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mean monthly maximum temperatures also differ between reef zones, months and lter sites.
Post-hoc tests
I will use Tukey tests again. First for the minimum temperatures.
minHSD <- TukeyHSD(minaov, ordered=FALSE)
minHSD## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = meanmin ~ zone * as.factor(monthyear), data = hobofullminmeans)
##
## $zone
## diff lwr upr p adj
## fore-back -0.12376965 -0.4002811 0.1527418 0.5429235
## fringe-back 0.01678133 -0.1948292 0.2283919 0.9809263
## fringe-fore 0.14055098 -0.1332175 0.4143195 0.4482743
##
## $`as.factor(monthyear)`
## diff lwr upr p adj
## 1/2019-1/2018 0.698874780 -0.22377949 1.621529047 0.4160251
## 10/2017-1/2018 -2.349091082 -3.24244723 -1.455734929 0.0000000
## 10/2018-1/2018 -1.904517388 -2.79787354 -1.011161235 0.0000000
## 11/2017-1/2018 -1.278602404 -2.17195856 -0.385246251 0.0001120
## 11/2018-1/2018 -0.781305220 -1.70395949 0.141349047 0.2195288
## 12/2017-1/2018 -0.404174573 -1.29753073 0.489181579 0.9843309
## 12/2018-1/2018 0.659117791 -0.26353648 1.581772058 0.5303168
## 2/2018-1/2018 -0.769633776 -1.66298993 0.123722376 0.1943194
## 2/2019-1/2018 0.651417638 -0.27123663 1.574071904 0.5530227
## 3/2018-1/2018 -0.345101016 -1.20430345 0.514101418 0.9958793
## 3/2019-1/2018 0.916836780 -0.02316021 1.856833772 0.0653318
## 4/2018-1/2018 -0.196884786 -1.07774583 0.683976259 0.9999990
## 5/2018-1/2018 -0.574542910 -1.45540396 0.306318135 0.6964018
## 6/2018-1/2018 -1.264953304 -2.14581435 -0.384092259 0.0001034
## 7/2018-1/2018 -1.795474630 -2.67633568 -0.914613585 0.0000000
## 8/2018-1/2018 -2.196989226 -3.05619166 -1.337786791 0.0000000
## 9/2017-1/2018 -2.167850000 -3.06120615 -1.274493848 0.0000000
## 9/2018-1/2018 -2.325370786 -3.20623183 -1.444509741 0.0000000
## 10/2017-1/2019 -3.047965862 -3.97062013 -2.125311596 0.0000000
## 10/2018-1/2019 -2.603392168 -3.52604643 -1.680737902 0.0000000
## 11/2017-1/2019 -1.977477184 -2.90013145 -1.054822917 0.0000000
## 11/2018-1/2019 -1.480180000 -2.43123025 -0.529129751 0.0000127
## 12/2017-1/2019 -1.103049354 -2.02570362 -0.180395087 0.0042589
## 12/2018-1/2019 -0.039756989 -0.99080724 0.911293260 1.0000000
## 2/2018-1/2019 -1.468508557 -2.39116282 -0.545854290 0.0000067
## 2/2019-1/2019 -0.047457143 -0.99850739 0.903593106 1.0000000
## 3/2018-1/2019 -1.043975797 -1.93360184 -0.154349749 0.0057780
## 3/2019-1/2019 0.217961999 -0.74992230 1.185846303 0.9999989
## 4/2018-1/2019 -0.895759566 -1.80632086 0.014801723 0.0597200
## 5/2018-1/2019 -1.273417690 -2.18397898 -0.362856401 0.0001928
## 6/2018-1/2019 -1.963828085 -2.87438937 -1.053266795 0.0000000
## 7/2018-1/2019 -2.494349411 -3.40491070 -1.583788121 0.0000000
## 8/2018-1/2019 -2.895864006 -3.78549005 -2.006237959 0.0000000
## 9/2017-1/2019 -2.866724781 -3.78937905 -1.944070514 0.0000000
## 9/2018-1/2019 -3.024245566 -3.93480686 -2.113684277 0.0000000
## 10/2018-10/2017 0.444573694 -0.44878246 1.337929846 0.9595485
## 11/2017-10/2017 1.070488678 0.17713253 1.963844830 0.0040973
## 11/2018-10/2017 1.567785862 0.64513160 2.490440129 0.0000009
## 12/2017-10/2017 1.944916509 1.05156036 2.838272661 0.0000000
## 12/2018-10/2017 3.008208873 2.08555461 3.930863139 0.0000000
## 2/2018-10/2017 1.579457306 0.68610115 2.472813458 0.0000002
## 2/2019-10/2017 3.000508719 2.07785445 3.923162986 0.0000000
## 3/2018-10/2017 2.003990065 1.14478763 2.863192500 0.0000000
## 3/2019-10/2017 3.265927861 2.32593087 4.205924853 0.0000000
## 4/2018-10/2017 2.152206296 1.27134525 3.033067341 0.0000000
## 5/2018-10/2017 1.774548172 0.89368713 2.655409217 0.0000000
## 6/2018-10/2017 1.084137777 0.20327673 1.964998823 0.0025788
## 7/2018-10/2017 0.553616451 -0.32724459 1.434477496 0.7546949
## 8/2018-10/2017 0.152101856 -0.70710058 1.011304290 1.0000000
## 9/2017-10/2017 0.181241082 -0.71211507 1.074597234 0.9999998
## 9/2018-10/2017 0.023720296 -0.85714075 0.904581341 1.0000000
## 11/2017-10/2018 0.625914984 -0.26744117 1.519271136 0.5677077
## 11/2018-10/2018 1.123212168 0.20055790 2.045866435 0.0031265
## 12/2017-10/2018 1.500342815 0.60698666 2.393698967 0.0000013
## 12/2018-10/2018 2.563635179 1.64098091 3.486289445 0.0000000
## 2/2018-10/2018 1.134883611 0.24152746 2.028239764 0.0014408
## 2/2019-10/2018 2.555935025 1.63328076 3.478589292 0.0000000
## 3/2018-10/2018 1.559416371 0.70021394 2.418618806 0.0000001
## 3/2019-10/2018 2.821354167 1.88135718 3.761351159 0.0000000
## 4/2018-10/2018 1.707632602 0.82677156 2.588493647 0.0000000
## 5/2018-10/2018 1.329974478 0.44911343 2.210835523 0.0000291
## 6/2018-10/2018 0.639564083 -0.24129696 1.520425129 0.4986652
## 7/2018-10/2018 0.109042757 -0.77181829 0.989903802 1.0000000
## 8/2018-10/2018 -0.292471838 -1.15167427 0.566730596 0.9994928
## 9/2017-10/2018 -0.263332612 -1.15668876 0.630023540 0.9999318
## 9/2018-10/2018 -0.420853398 -1.30171444 0.460007647 0.9726573
## 11/2018-11/2017 0.497297184 -0.42535708 1.419951450 0.9182841
## 12/2017-11/2017 0.874427830 -0.01892832 1.767783983 0.0629701
## 12/2018-11/2017 1.937720195 1.01506593 2.860374461 0.0000000
## 2/2018-11/2017 0.508968627 -0.38438752 1.402324780 0.8735627
## 2/2019-11/2017 1.930020041 1.00736577 2.852674308 0.0000000
## 3/2018-11/2017 0.933501387 0.07429895 1.792703822 0.0180576
## 3/2019-11/2017 2.195439183 1.25544219 3.135436175 0.0000000
## 4/2018-11/2017 1.081717618 0.20085657 1.962578663 0.0026832
## 5/2018-11/2017 0.704059494 -0.17680155 1.584920539 0.3153720
## 6/2018-11/2017 0.013649099 -0.86721195 0.894510144 1.0000000
## 7/2018-11/2017 -0.516872227 -1.39773327 0.363988818 0.8433850
## 8/2018-11/2017 -0.918386822 -1.77758926 -0.059184388 0.0224295
## 9/2017-11/2017 -0.889247597 -1.78260375 0.004108556 0.0526015
## 9/2018-11/2017 -1.046768382 -1.92762943 -0.165907337 0.0047071
## 12/2017-11/2018 0.377130646 -0.54552362 1.299784913 0.9949360
## 12/2018-11/2018 1.440423011 0.48937276 2.391473260 0.0000268
## 2/2018-11/2018 0.011671443 -0.91098282 0.934325710 1.0000000
## 2/2019-11/2018 1.432722857 0.48167261 2.383773106 0.0000309
## 3/2018-11/2018 0.436204203 -0.45342184 1.325830251 0.9648325
## 3/2019-11/2018 1.698141999 0.73025770 2.666026303 0.0000003
## 4/2018-11/2018 0.584420434 -0.32614086 1.494981723 0.7225542
## 5/2018-11/2018 0.206762310 -0.70379898 1.117323599 0.9999987
## 6/2018-11/2018 -0.483648085 -1.39420937 0.426913205 0.9277009
## 7/2018-11/2018 -1.014169411 -1.92473070 -0.103608121 0.0127780
## 8/2018-11/2018 -1.415684006 -2.30531005 -0.526057959 0.0000067
## 9/2017-11/2018 -1.386544781 -2.30919905 -0.463890514 0.0000329
## 9/2018-11/2018 -1.544065566 -2.45462686 -0.633504277 0.0000009
## 12/2018-12/2017 1.063292364 0.14063810 1.985946631 0.0076893
## 2/2018-12/2017 -0.365459203 -1.25881536 0.527896949 0.9948865
## 2/2019-12/2017 1.055592211 0.13293794 1.978246477 0.0085961
## 3/2018-12/2017 0.059073557 -0.80012888 0.918275991 1.0000000
## 3/2019-12/2017 1.321011353 0.38101436 2.261008345 0.0001723
## 4/2018-12/2017 0.207289787 -0.67357126 1.088150833 0.9999977
## 5/2018-12/2017 -0.170368337 -1.05122938 0.710492708 0.9999999
## 6/2018-12/2017 -0.860778731 -1.74163978 0.020082314 0.0640633
## 7/2018-12/2017 -1.391300057 -2.27216110 -0.510439012 0.0000084
## 8/2018-12/2017 -1.792814653 -2.65201709 -0.933612218 0.0000000
## 9/2017-12/2017 -1.763675427 -2.65703158 -0.870319275 0.0000000
## 9/2018-12/2017 -1.921196213 -2.80205726 -1.040335168 0.0000000
## 2/2018-12/2018 -1.428751567 -2.35140583 -0.506097301 0.0000146
## 2/2019-12/2018 -0.007700154 -0.95875040 0.943350096 1.0000000
## 3/2018-12/2018 -1.004218808 -1.89384486 -0.114592760 0.0105147
## 3/2019-12/2018 0.257718989 -0.71016532 1.225603292 0.9999848
## 4/2018-12/2018 -0.856002577 -1.76656387 0.054558713 0.0940315
## 5/2018-12/2018 -1.233660701 -2.14422199 -0.323099412 0.0003907
## 6/2018-12/2018 -1.924071095 -2.83463238 -1.013509806 0.0000000
## 7/2018-12/2018 -2.454592422 -3.36515371 -1.544031132 0.0000000
## 8/2018-12/2018 -2.856107017 -3.74573306 -1.966480970 0.0000000
## 9/2017-12/2018 -2.826967791 -3.74962206 -1.904313525 0.0000000
## 9/2018-12/2018 -2.984488577 -3.89504987 -2.073927288 0.0000000
## 2/2019-2/2018 1.421051414 0.49839715 2.343705680 0.0000170
## 3/2018-2/2018 0.424532760 -0.43466967 1.283735194 0.9621749
## 3/2019-2/2018 1.686470556 0.74647356 2.626467548 0.0000001
## 4/2018-2/2018 0.572748990 -0.30811205 1.453610036 0.7015722
## 5/2018-2/2018 0.195090866 -0.68577018 1.075951911 0.9999991
## 6/2018-2/2018 -0.495319528 -1.37618057 0.385541517 0.8854921
## 7/2018-2/2018 -1.025840854 -1.90670190 -0.144979809 0.0065250
## 8/2018-2/2018 -1.427355450 -2.28655788 -0.568153015 0.0000018
## 9/2017-2/2018 -1.398216224 -2.29157238 -0.504860072 0.0000109
## 9/2018-2/2018 -1.555737010 -2.43659805 -0.674875965 0.0000002
## 3/2018-2/2019 -0.996518654 -1.88614470 -0.106892607 0.0117691
## 3/2019-2/2019 0.265419142 -0.70246516 1.233303446 0.9999764
## 4/2018-2/2019 -0.848302423 -1.75886371 0.062258866 0.1022597
## 5/2018-2/2019 -1.225960547 -2.13652184 -0.315399258 0.0004469
## 6/2018-2/2019 -1.916370942 -2.82693223 -1.005809652 0.0000000
## 7/2018-2/2019 -2.446892268 -3.35745356 -1.536330979 0.0000000
## 8/2018-2/2019 -2.848406863 -3.73803291 -1.958780816 0.0000000
## 9/2017-2/2019 -2.819267638 -3.74192190 -1.896613371 0.0000000
## 9/2018-2/2019 -2.976788423 -3.88734971 -2.066227134 0.0000000
## 3/2019-3/2018 1.261937796 0.35433769 2.169537903 0.0002201
## 4/2018-3/2018 0.148216231 -0.69798693 0.994419392 1.0000000
## 5/2018-3/2018 -0.229441893 -1.07564505 0.616761268 0.9999800
## 6/2018-3/2018 -0.919852288 -1.76605545 -0.073649127 0.0179319
## 7/2018-3/2018 -1.450373614 -2.29657677 -0.604170453 0.0000007
## 8/2018-3/2018 -1.851888209 -2.67552189 -1.028254533 0.0000000
## 9/2017-3/2018 -1.822748984 -2.68195142 -0.963546549 0.0000000
## 9/2018-3/2018 -1.980269769 -2.82647293 -1.134066608 0.0000000
## 4/2018-3/2019 -1.113721565 -2.04185157 -0.185591558 0.0040014
## 5/2018-3/2019 -1.491379690 -2.41950970 -0.563249682 0.0000051
## 6/2018-3/2019 -2.181790084 -3.10992009 -1.253660076 0.0000000
## 7/2018-3/2019 -2.712311410 -3.64044142 -1.784181403 0.0000000
## 8/2018-3/2019 -3.113826006 -4.02142611 -2.206225899 0.0000000
## 9/2017-3/2019 -3.084686780 -4.02468377 -2.144689788 0.0000000
## 9/2018-3/2019 -3.242207566 -4.17033757 -2.314077558 0.0000000
## 5/2018-4/2018 -0.377658124 -1.24584425 0.490528000 0.9897374
## 6/2018-4/2018 -1.068068519 -1.93625464 -0.199882394 0.0025991
## 7/2018-4/2018 -1.598589845 -2.46677597 -0.730403720 0.0000001
## 8/2018-4/2018 -2.000104440 -2.84630760 -1.153901279 0.0000000
## 9/2017-4/2018 -1.970965214 -2.85182626 -1.090104169 0.0000000
## 9/2018-4/2018 -2.128486000 -2.99667212 -1.260299875 0.0000000
## 6/2018-5/2018 -0.690410394 -1.55859652 0.177775730 0.3245168
## 7/2018-5/2018 -1.220931720 -2.08911785 -0.352745596 0.0001696
## 8/2018-5/2018 -1.622446316 -2.46864948 -0.776243155 0.0000000
## 9/2017-5/2018 -1.593307090 -2.47416814 -0.712446045 0.0000001
## 9/2018-5/2018 -1.750827876 -2.61901400 -0.882641751 0.0000000
## 7/2018-6/2018 -0.530521326 -1.39870745 0.337664798 0.7941293
## 8/2018-6/2018 -0.932035922 -1.77823908 -0.085832761 0.0149620
## 9/2017-6/2018 -0.902896696 -1.78375774 -0.022035651 0.0377075
## 9/2018-6/2018 -1.060417482 -1.92860361 -0.192231357 0.0029506
## 8/2018-7/2018 -0.401514595 -1.24771776 0.444688566 0.9744657
## 9/2017-7/2018 -0.372375370 -1.25323641 0.508485675 0.9925407
## 9/2018-7/2018 -0.529896155 -1.39808228 0.338289969 0.7956892
## 9/2017-8/2018 0.029139226 -0.83006321 0.888341660 1.0000000
## 9/2018-8/2018 -0.128381560 -0.97458472 0.717821601 1.0000000
## 9/2018-9/2017 -0.157520786 -1.03838183 0.723340259 1.0000000
##
## $`zone:as.factor(monthyear)`
## diff lwr upr
## fore:1/2018-back:1/2018 0.0507365591 -2.077061901 2.178535019
## fringe:1/2018-back:1/2018 0.0858104839 -1.539322434 1.710943402
## back:1/2019-back:1/2018 0.9025537634 -0.834786404 2.639893931
## fore:1/2019-back:1/2018 0.0067688172 -2.121029643 2.134567277
## fringe:1/2019-back:1/2018 0.9543440860 -0.782996082 2.691684254
## back:10/2017-back:1/2018 -2.3595698925 -4.096910060 -0.622229725
## fore:10/2017-back:1/2018 -2.1730483871 -4.300846847 -0.045249927
## fringe:10/2017-back:1/2018 -2.3024112903 -3.927544208 -0.677278373
## back:10/2018-back:1/2018 -2.0149861751 -3.689129051 -0.340843300
## fore:10/2018-back:1/2018 -1.8271666667 -3.954965127 0.300631793
## fringe:10/2018-back:1/2018 -1.7097828784 -3.383925754 -0.035640003
## back:11/2017-back:1/2018 -1.2756148746 -3.012955042 0.461725293
## fore:11/2017-back:1/2018 -1.2291537634 -3.356952223 0.898644697
## fringe:11/2017-back:1/2018 -1.1945495968 -2.819682515 0.430583321
## back:11/2018-back:1/2018 -1.0708259857 -2.808166153 0.666514182
## fore:11/2018-back:1/2018 -1.2583759857 -3.386174446 0.869422474
## fringe:11/2018-back:1/2018 -0.1401537634 -1.877493931 1.597186404
## back:12/2017-back:1/2018 -0.4182204301 -2.155560598 1.319119737
## fore:12/2017-back:1/2018 -0.4322634409 -2.560061901 1.695535019
## fringe:12/2017-back:1/2018 -0.2782701613 -1.903403079 1.346862757
## back:12/2018-back:1/2018 0.5881720430 -1.149168125 2.325512211
## fore:12/2018-back:1/2018 -0.1291559140 -2.256954374 1.998642546
## fringe:12/2018-back:1/2018 1.2372956989 -0.500044469 2.974635866
## back:2/2018-back:1/2018 -0.6793275730 -2.416667740 1.058012595
## fore:2/2018-back:1/2018 -0.6128394777 -2.740637938 1.514958982
## fringe:2/2018-back:1/2018 -0.7913245968 -2.416457515 0.833808321
## back:2/2019-back:1/2018 1.0085295699 -0.728810598 2.745869737
## fore:2/2019-back:1/2018 -0.3036013825 -2.431399843 1.824197078
## fringe:2/2019-back:1/2018 0.8849105223 -0.852429645 2.622250690
## back:3/2018-back:1/2018 -0.3172657166 -1.903233050 1.268701616
## fore:3/2018-back:1/2018 -0.2188225806 -2.346621041 1.908975879
## fringe:3/2018-back:1/2018 -0.2952036290 -1.920336547 1.329929289
## back:3/2019-back:1/2018 1.2103928512 -0.526947316 2.947733019
## fore:3/2019-back:1/2018 -0.5503001403 -3.007270168 1.906669887
## fringe:3/2019-back:1/2018 1.2355033439 -0.501836824 2.972843511
## back:4/2018-back:1/2018 -0.1483287634 -1.773461681 1.476804154
## fore:4/2018-back:1/2018 0.0089240143 -2.118874446 2.136722474
## fringe:4/2018-back:1/2018 -0.2141251920 -1.888268068 1.460017684
## back:5/2018-back:1/2018 -0.4968588710 -2.121991789 1.128274047
## fore:5/2018-back:1/2018 -0.3649731183 -2.492771578 1.762825342
## fringe:5/2018-back:1/2018 -0.6266843318 -2.300827207 1.047458544
## back:6/2018-back:1/2018 -1.1950204301 -2.820153348 0.430112488
## fore:6/2018-back:1/2018 -0.9814204301 -3.109218890 1.146378030
## fringe:6/2018-back:1/2018 -1.3399347158 -3.014077591 0.334208160
## back:7/2018-back:1/2018 -1.7025633065 -3.327696224 -0.077430389
## fore:7/2018-back:1/2018 -1.5292311828 -3.657029643 0.598567277
## fringe:7/2018-back:1/2018 -1.8893073733 -3.563450249 -0.215164498
## back:8/2018-back:1/2018 -2.1765498289 -3.801682747 -0.551416911
## fore:8/2018-back:1/2018 -1.9254540230 -4.053252483 0.202344437
## fringe:8/2018-back:1/2018 -2.1895233751 -3.775490708 -0.603556042
## back:9/2017-back:1/2018 -2.1270595505 -3.864399718 -0.389719383
## fore:9/2017-back:1/2018 -2.0467653217 -4.174563782 0.081033138
## fringe:9/2017-back:1/2018 -2.1390128981 -3.764145816 -0.513879980
## back:9/2018-back:1/2018 -2.3043204301 -3.978463306 -0.630177555
## fore:9/2018-back:1/2018 -2.0514870968 -4.179285557 0.076311363
## fringe:9/2018-back:1/2018 -2.3337495968 -3.958882515 -0.708616679
## fringe:1/2018-fore:1/2018 0.0350739247 -2.002138001 2.072285850
## back:1/2019-fore:1/2018 0.8518172043 -1.275981256 2.979615664
## fore:1/2019-fore:1/2018 -0.0439677419 -2.500937769 2.413002285
## fringe:1/2019-fore:1/2018 0.9036075269 -1.224190933 3.031405987
## back:10/2017-fore:1/2018 -2.4103064516 -4.538104912 -0.282507992
## fore:10/2017-fore:1/2018 -2.2237849462 -4.680754974 0.233185081
## fringe:10/2017-fore:1/2018 -2.3531478495 -4.390359775 -0.315935924
## back:10/2018-fore:1/2018 -2.0657227343 -4.142241407 0.010795938
## fore:10/2018-fore:1/2018 -1.8779032258 -4.334873253 0.579066802
## fringe:10/2018-fore:1/2018 -1.7605194376 -3.837038110 0.315999235
## back:11/2017-fore:1/2018 -1.3263514337 -3.454149894 0.801447026
## fore:11/2017-fore:1/2018 -1.2798903226 -3.736860350 1.177079705
## fringe:11/2017-fore:1/2018 -1.2452861559 -3.282498081 0.791925770
## back:11/2018-fore:1/2018 -1.1215625448 -3.249361005 1.006235915
## fore:11/2018-fore:1/2018 -1.3091125448 -3.766082572 1.147857483
## fringe:11/2018-fore:1/2018 -0.1908903226 -2.318688783 1.936908137
## back:12/2017-fore:1/2018 -0.4689569892 -2.596755449 1.658841471
## fore:12/2017-fore:1/2018 -0.4830000000 -2.939970027 1.973970027
## fringe:12/2017-fore:1/2018 -0.3290067204 -2.366218646 1.708205205
## back:12/2018-fore:1/2018 0.5374354839 -1.590362976 2.665233944
## fore:12/2018-fore:1/2018 -0.1798924731 -2.636862500 2.277077554
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## fringe:9/2018-fore:9/2018 -0.2822625000 -2.319474425 1.754949425
## p adj
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## fore:1/2019-back:1/2018 1.0000000
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## fore:10/2017-back:1/2018 0.0369234
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## fore:10/2018-back:1/2018 0.2711344
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## back:11/2018-back:1/2018 0.9537738
## fore:11/2018-back:1/2018 0.9756093
## fringe:11/2018-back:1/2018 1.0000000
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## fore:2/2018-back:1/2018 1.0000000
## fringe:2/2018-back:1/2018 0.9995439
## back:2/2019-back:1/2018 0.9821326
## fore:2/2019-back:1/2018 1.0000000
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## back:4/2018-back:1/2018 1.0000000
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## back:5/2018-back:1/2018 1.0000000
## fore:5/2018-back:1/2018 1.0000000
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## fore:6/2018-back:1/2018 0.9998865
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## fore:7/2018-back:1/2018 0.7306783
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## fore:8/2018-back:1/2018 0.1669076
## fringe:8/2018-back:1/2018 0.0000562
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## fore:9/2017-back:1/2018 0.0835621
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## fore:9/2018-back:1/2018 0.0811861
## fringe:9/2018-back:1/2018 0.0000172
## fringe:1/2018-fore:1/2018 1.0000000
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## fore:11/2017-fore:1/2018 0.9978237
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## fore:11/2018-fore:1/2018 0.9964827
## fringe:11/2018-fore:1/2018 1.0000000
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## fore:2/2019-fore:1/2018 1.0000000
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## fore:3/2019-fore:1/2018 1.0000000
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## back:4/2018-fore:1/2018 1.0000000
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## fore:6/2018-fore:1/2018 0.9999921
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## fore:7/2018-fore:1/2018 0.9173304
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## fore:9/2017-fore:1/2018 0.2845550
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## back:9/2018-fore:1/2018 0.0062075
## fore:9/2018-fore:1/2018 0.2793675
## fringe:9/2018-fore:1/2018 0.0033077
## back:1/2019-fringe:1/2018 0.9990288
## fore:1/2019-fringe:1/2018 1.0000000
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## fore:10/2017-fringe:1/2018 0.0094847
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## fore:10/2018-fringe:1/2018 0.1116194
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## fore:11/2018-fringe:1/2018 0.8865280
## fringe:11/2018-fringe:1/2018 1.0000000
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## fore:2/2018-fringe:1/2018 1.0000000
## fringe:2/2018-fringe:1/2018 0.9807894
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## fore:2/2019-fringe:1/2018 1.0000000
## fringe:2/2019-fringe:1/2018 0.9994212
## back:3/2018-fringe:1/2018 1.0000000
## fore:3/2018-fringe:1/2018 1.0000000
## fringe:3/2018-fringe:1/2018 1.0000000
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## fore:3/2019-fringe:1/2018 1.0000000
## fringe:3/2019-fringe:1/2018 0.7655153
## back:4/2018-fringe:1/2018 1.0000000
## fore:4/2018-fringe:1/2018 1.0000000
## fringe:4/2018-fringe:1/2018 1.0000000
## back:5/2018-fringe:1/2018 0.9999994
## fore:5/2018-fringe:1/2018 1.0000000
## fringe:5/2018-fringe:1/2018 0.9999089
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## fore:6/2018-fringe:1/2018 0.9975414
## fringe:6/2018-fringe:1/2018 0.1479152
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## fore:7/2018-fringe:1/2018 0.4752525
## fringe:7/2018-fringe:1/2018 0.0005414
## back:8/2018-fringe:1/2018 0.0000039
## fore:8/2018-fringe:1/2018 0.0596256
## fringe:8/2018-fringe:1/2018 0.0000012
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## fore:9/2017-fringe:1/2018 0.0252819
## fringe:9/2017-fringe:1/2018 0.0000068
## back:9/2018-fringe:1/2018 0.0000019
## fore:9/2018-fringe:1/2018 0.0244092
## fringe:9/2018-fringe:1/2018 0.0000003
## fore:1/2019-back:1/2019 0.9999916
## fringe:1/2019-back:1/2019 1.0000000
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## fore:9/2018-back:9/2018 1.0000000
## fringe:9/2018-back:9/2018 1.0000000
## fringe:9/2018-fore:9/2018 1.0000000There seems to be a significant difference between each of the zones. While it’s highly significant for fore:fringe, it’s less signficant for fore:back and back:fringe. As can be expected, temperature minima are highly significantly different month-to-month.
Now I’ll do the same thing with the maximum temperatures.
maxHSD <- TukeyHSD(maxaov)
maxHSD## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = meanmax ~ zone * as.factor(monthyear), data = hobofullmaxmeans)
##
## $zone
## diff lwr upr p adj
## fore-back -1.077662 -1.36046476 -0.7948586 0.0000000
## fringe-back 0.275056 0.05863054 0.4914815 0.0084042
## fringe-fore 1.352718 1.07272000 1.6327154 0.0000000
##
## $`as.factor(monthyear)`
## diff lwr upr p adj
## 1/2019-1/2018 0.586530085 -0.35711786 1.53017803 0.7707719
## 10/2017-1/2018 -2.124783681 -3.03846688 -1.21110049 0.0000000
## 10/2018-1/2018 -1.733566768 -2.64724996 -0.81988357 0.0000000
## 11/2017-1/2018 -1.337588615 -2.25127181 -0.42390542 0.0000644
## 11/2018-1/2018 -0.440991707 -1.38463965 0.50265624 0.9780872
## 12/2017-1/2018 -0.461840607 -1.37552380 0.45184259 0.9532707
## 12/2018-1/2018 0.806450516 -0.13719743 1.75009846 0.2058793
## 2/2018-1/2018 -1.081303741 -1.99498693 -0.16762055 0.0050376
## 2/2019-1/2018 0.500200251 -0.44344769 1.44384820 0.9289416
## 3/2018-1/2018 -0.175689745 -1.05444210 0.70306261 0.9999998
## 3/2019-1/2018 0.742204200 -0.21918108 1.70358948 0.3795241
## 4/2018-1/2018 -0.364972770 -1.26587655 0.53593101 0.9954388
## 5/2018-1/2018 -1.019478087 -1.92038187 -0.11857431 0.0101350
## 6/2018-1/2018 -1.693576474 -2.59448025 -0.79267270 0.0000000
## 7/2018-1/2018 -2.158239735 -3.05914351 -1.25733596 0.0000000
## 8/2018-1/2018 -2.175376089 -3.05412845 -1.29662373 0.0000000
## 9/2017-1/2018 -1.749261823 -2.66294502 -0.83557863 0.0000000
## 9/2018-1/2018 -2.176719268 -3.07762305 -1.27581549 0.0000000
## 10/2017-1/2019 -2.711313767 -3.65496171 -1.76766582 0.0000000
## 10/2018-1/2019 -2.320096854 -3.26374480 -1.37644891 0.0000000
## 11/2017-1/2019 -1.924118700 -2.86776665 -0.98047076 0.0000000
## 11/2018-1/2019 -1.027521792 -2.00021183 -0.05483175 0.0260930
## 12/2017-1/2019 -1.048370693 -1.99201864 -0.10472275 0.0132473
## 12/2018-1/2019 0.219920430 -0.75276961 1.19261047 0.9999988
## 2/2018-1/2019 -1.667833826 -2.61148177 -0.72418588 0.0000002
## 2/2019-1/2019 -0.086329834 -1.05901987 0.88636020 1.0000000
## 3/2018-1/2019 -0.762219830 -1.67208805 0.14764839 0.2361522
## 3/2019-1/2019 0.155674114 -0.83423301 1.14558124 1.0000000
## 4/2018-1/2019 -0.951502855 -1.88278266 -0.02022305 0.0391613
## 5/2018-1/2019 -1.606008172 -2.53728798 -0.67472836 0.0000005
## 6/2018-1/2019 -2.280106559 -3.21138637 -1.34882675 0.0000000
## 7/2018-1/2019 -2.744769821 -3.67604963 -1.81349001 0.0000000
## 8/2018-1/2019 -2.761906174 -3.67177439 -1.85203796 0.0000000
## 9/2017-1/2019 -2.335791909 -3.27943985 -1.39214396 0.0000000
## 9/2018-1/2019 -2.763249353 -3.69452916 -1.83196954 0.0000000
## 10/2018-10/2017 0.391216913 -0.52246628 1.30490011 0.9913875
## 11/2017-10/2017 0.787195066 -0.12648813 1.70087826 0.1942307
## 11/2018-10/2017 1.683791975 0.74014403 2.62743992 0.0000002
## 12/2017-10/2017 1.662943074 0.74925988 2.57662627 0.0000001
## 12/2018-10/2017 2.931234197 1.98758625 3.87488214 0.0000000
## 2/2018-10/2017 1.043479940 0.12979675 1.95716313 0.0088303
## 2/2019-10/2017 2.624983932 1.68133599 3.56863188 0.0000000
## 3/2018-10/2017 1.949093937 1.07034158 2.82784629 0.0000000
## 3/2019-10/2017 2.866987881 1.90560260 3.82837316 0.0000000
## 4/2018-10/2017 1.759810911 0.85890713 2.66071469 0.0000000
## 5/2018-10/2017 1.105305595 0.20440182 2.00620937 0.0027276
## 6/2018-10/2017 0.431207208 -0.46969657 1.33211099 0.9721660
## 7/2018-10/2017 -0.033456054 -0.93435983 0.86744772 1.0000000
## 8/2018-10/2017 -0.050592408 -0.92934476 0.82815995 1.0000000
## 9/2017-10/2017 0.375521858 -0.53816134 1.28920505 0.9946010
## 9/2018-10/2017 -0.051935587 -0.95283937 0.84896819 1.0000000
## 11/2017-10/2018 0.395978153 -0.51770504 1.30966135 0.9901484
## 11/2018-10/2018 1.292575061 0.34892712 2.23622301 0.0003076
## 12/2017-10/2018 1.271726161 0.35804297 2.18540935 0.0002149
## 12/2018-10/2018 2.540017284 1.59636934 3.48366523 0.0000000
## 2/2018-10/2018 0.652263027 -0.26142017 1.56594622 0.5316434
## 2/2019-10/2018 2.233767019 1.29011907 3.17741496 0.0000000
## 3/2018-10/2018 1.557877024 0.67912467 2.43662938 0.0000002
## 3/2019-10/2018 2.475770968 1.51438569 3.43715625 0.0000000
## 4/2018-10/2018 1.368593998 0.46769022 2.26949778 0.0000247
## 5/2018-10/2018 0.714088682 -0.18681510 1.61499246 0.3304525
## 6/2018-10/2018 0.039990294 -0.86091348 0.94089407 1.0000000
## 7/2018-10/2018 -0.424672967 -1.32557675 0.47623081 0.9760880
## 8/2018-10/2018 -0.441809321 -1.32056168 0.43694304 0.9555139
## 9/2017-10/2018 -0.015695055 -0.92937825 0.89798814 1.0000000
## 9/2018-10/2018 -0.443152500 -1.34405628 0.45775128 0.9637424
## 11/2018-11/2017 0.896596908 -0.04705104 1.84024485 0.0849561
## 12/2017-11/2017 0.875748008 -0.03793519 1.78943120 0.0779867
## 12/2018-11/2017 2.144039130 1.20039119 3.08768708 0.0000000
## 2/2018-11/2017 0.256284874 -0.65739832 1.16996807 0.9999669
## 2/2019-11/2017 1.837788866 0.89414092 2.78143681 0.0000000
## 3/2018-11/2017 1.161898870 0.28314651 2.04065123 0.0006531
## 3/2019-11/2017 2.079792815 1.11840754 3.04117809 0.0000000
## 4/2018-11/2017 0.972615845 0.07171207 1.87351962 0.0196653
## 5/2018-11/2017 0.318110528 -0.58279325 1.21901431 0.9991761
## 6/2018-11/2017 -0.355987859 -1.25689164 0.54491592 0.9966101
## 7/2018-11/2017 -0.820651121 -1.72155490 0.08025266 0.1248585
## 8/2018-11/2017 -0.837787474 -1.71653983 0.04096488 0.0821874
## 9/2017-11/2017 -0.411673209 -1.32535640 0.50200999 0.9849978
## 9/2018-11/2017 -0.839130653 -1.74003443 0.06177313 0.1024548
## 12/2017-11/2018 -0.020848901 -0.96449685 0.92279904 1.0000000
## 12/2018-11/2018 1.247442222 0.27475218 2.22013226 0.0012012
## 2/2018-11/2018 -0.640312034 -1.58395998 0.30333591 0.6274853
## 2/2019-11/2018 0.941191958 -0.03149808 1.91388200 0.0709048
## 3/2018-11/2018 0.265301962 -0.64456625 1.17517018 0.9999417
## 3/2019-11/2018 1.183195907 0.19328878 2.17310303 0.0042743
## 4/2018-11/2018 0.076018937 -0.85526087 1.00729875 1.0000000
## 5/2018-11/2018 -0.578486380 -1.50976619 0.35279343 0.7716442
## 6/2018-11/2018 -1.252584767 -2.18386458 -0.32130496 0.0004566
## 7/2018-11/2018 -1.717248029 -2.64852784 -0.78596822 0.0000000
## 8/2018-11/2018 -1.734384382 -2.64425260 -0.82451617 0.0000000
## 9/2017-11/2018 -1.308270117 -2.25191806 -0.36462217 0.0002350
## 9/2018-11/2018 -1.735727561 -2.66700737 -0.80444775 0.0000000
## 12/2018-12/2017 1.268291123 0.32464318 2.21193907 0.0004638
## 2/2018-12/2017 -0.619463134 -1.53314633 0.29422006 0.6289994
## 2/2019-12/2017 0.962040858 0.01839291 1.90568880 0.0401682
## 3/2018-12/2017 0.286150863 -0.59260149 1.16490322 0.9997221
## 3/2019-12/2017 1.204044807 0.24265953 2.16543009 0.0018806
## 4/2018-12/2017 0.096867837 -0.80403594 0.99777162 1.0000000
## 5/2018-12/2017 -0.557637479 -1.45854126 0.34326630 0.7766272
## 6/2018-12/2017 -1.231735866 -2.13263964 -0.33083209 0.0003204
## 7/2018-12/2017 -1.696399128 -2.59730291 -0.79549535 0.0000000
## 8/2018-12/2017 -1.713535482 -2.59228784 -0.83478312 0.0000000
## 9/2017-12/2017 -1.287421216 -2.20110441 -0.37373802 0.0001621
## 9/2018-12/2017 -1.714878661 -2.61578244 -0.81397488 0.0000000
## 2/2018-12/2018 -1.887754256 -2.83140220 -0.94410631 0.0000000
## 2/2019-12/2018 -0.306250265 -1.27894030 0.66643977 0.9998258
## 3/2018-12/2018 -0.982140260 -1.89200848 -0.07227204 0.0197063
## 3/2019-12/2018 -0.064246316 -1.05415344 0.92566081 1.0000000
## 4/2018-12/2018 -1.171423286 -2.10270309 -0.24014348 0.0017350
## 5/2018-12/2018 -1.825928602 -2.75720841 -0.89464879 0.0000000
## 6/2018-12/2018 -2.500026989 -3.43130680 -1.56874718 0.0000000
## 7/2018-12/2018 -2.964690251 -3.89597006 -2.03341044 0.0000000
## 8/2018-12/2018 -2.981826604 -3.89169482 -2.07195839 0.0000000
## 9/2017-12/2018 -2.555712339 -3.49936028 -1.61206439 0.0000000
## 9/2018-12/2018 -2.983169783 -3.91444959 -2.05188997 0.0000000
## 2/2019-2/2018 1.581503992 0.63785605 2.52515194 0.0000014
## 3/2018-2/2018 0.905613996 0.02686164 1.78436635 0.0353687
## 3/2019-2/2018 1.823507941 0.86212266 2.78489322 0.0000000
## 4/2018-2/2018 0.716330971 -0.18457281 1.61723475 0.3247632
## 5/2018-2/2018 0.061825654 -0.83907812 0.96272943 1.0000000
## 6/2018-2/2018 -0.612272733 -1.51317651 0.28863105 0.6246131
## 7/2018-2/2018 -1.076935994 -1.97783977 -0.17603222 0.0042662
## 8/2018-2/2018 -1.094072348 -1.97282471 -0.21531999 0.0020957
## 9/2017-2/2018 -0.667958083 -1.58164128 0.24572511 0.4852520
## 9/2018-2/2018 -1.095415527 -1.99631931 -0.19451175 0.0031925
## 3/2018-2/2019 -0.675889996 -1.58575821 0.23397822 0.4539544
## 3/2019-2/2019 0.242003949 -0.74790318 1.23191108 0.9999959
## 4/2018-2/2019 -0.865173021 -1.79645283 0.06610679 0.1049140
## 5/2018-2/2019 -1.519678338 -2.45095815 -0.58839853 0.0000032
## 6/2018-2/2019 -2.193776725 -3.12505653 -1.26249692 0.0000000
## 7/2018-2/2019 -2.658439986 -3.58971980 -1.72716018 0.0000000
## 8/2018-2/2019 -2.675576340 -3.58544456 -1.76570812 0.0000000
## 9/2017-2/2019 -2.249462074 -3.19311002 -1.30581413 0.0000000
## 9/2018-2/2019 -2.676919519 -3.60819933 -1.74563971 0.0000000
## 3/2019-3/2018 0.917893944 -0.01035730 1.84614519 0.0565119
## 4/2018-3/2018 -0.189283025 -1.05474033 0.67617428 0.9999993
## 5/2018-3/2018 -0.843788342 -1.70924565 0.02166896 0.0656097
## 6/2018-3/2018 -1.517886729 -2.38334403 -0.65242943 0.0000003
## 7/2018-3/2018 -1.982549991 -2.84800729 -1.11709269 0.0000000
## 8/2018-3/2018 -1.999686344 -2.84206063 -1.15731206 0.0000000
## 9/2017-3/2018 -1.573572079 -2.45232444 -0.69481972 0.0000001
## 9/2018-3/2018 -2.001029523 -2.86648683 -1.13557222 0.0000000
## 4/2018-3/2019 -1.107176970 -2.05642525 -0.15792869 0.0063687
## 5/2018-3/2019 -1.761682286 -2.71093057 -0.81243401 0.0000000
## 6/2018-3/2019 -2.435780674 -3.38502895 -1.48653239 0.0000000
## 7/2018-3/2019 -2.900443935 -3.84969221 -1.95119566 0.0000000
## 8/2018-3/2019 -2.917580289 -3.84583154 -1.98932904 0.0000000
## 9/2017-3/2019 -2.491466023 -3.45285130 -1.53008074 0.0000000
## 9/2018-3/2019 -2.918923468 -3.86817175 -1.96967519 0.0000000
## 5/2018-4/2018 -0.654505317 -1.54244577 0.23343514 0.4691295
## 6/2018-4/2018 -1.328603704 -2.21654416 -0.44066325 0.0000369
## 7/2018-4/2018 -1.793266965 -2.68120742 -0.90532651 0.0000000
## 8/2018-4/2018 -1.810403319 -2.67586062 -0.94494602 0.0000000
## 9/2017-4/2018 -1.384289054 -2.28519283 -0.48338527 0.0000181
## 9/2018-4/2018 -1.811746498 -2.69968696 -0.92380604 0.0000000
## 6/2018-5/2018 -0.674098387 -1.56203885 0.21384207 0.4116670
## 7/2018-5/2018 -1.138761649 -2.02670211 -0.25082119 0.0012010
## 8/2018-5/2018 -1.155898002 -2.02135531 -0.29044070 0.0005298
## 9/2017-5/2018 -0.729783737 -1.63068752 0.17112004 0.2917741
## 9/2018-5/2018 -1.157241181 -2.04518164 -0.26930072 0.0008750
## 7/2018-6/2018 -0.464663262 -1.35260372 0.42327720 0.9363611
## 8/2018-6/2018 -0.481799615 -1.34725692 0.38365769 0.8940401
## 9/2017-6/2018 -0.055685350 -0.95658913 0.84521843 1.0000000
## 9/2018-6/2018 -0.483142794 -1.37108325 0.40479766 0.9116857
## 8/2018-7/2018 -0.017136354 -0.88259366 0.84832095 1.0000000
## 9/2017-7/2018 0.408977912 -0.49192587 1.30988169 0.9837599
## 9/2018-7/2018 -0.018479533 -0.90641999 0.86946093 1.0000000
## 9/2017-8/2018 0.426114265 -0.45263809 1.30486662 0.9683932
## 9/2018-8/2018 -0.001343179 -0.86680048 0.86411412 1.0000000
## 9/2018-9/2017 -0.427457444 -1.32836122 0.47344633 0.9744729
##
## $`zone:as.factor(monthyear)`
## diff lwr upr
## fore:1/2018-back:1/2018 -0.932645161 -3.108858632 1.243568310
## fringe:1/2018-back:1/2018 0.378073925 -1.284036569 2.040184418
## back:1/2019-back:1/2018 0.750715054 -1.026155805 2.527585912
## fore:1/2019-back:1/2018 -1.095817204 -3.272030675 1.080396267
## fringe:1/2019-back:1/2018 1.184919355 -0.591951504 2.961790213
## back:10/2017-back:1/2018 -2.075247312 -3.852118170 -0.298376453
## fore:10/2017-back:1/2018 -3.044720430 -5.220933901 -0.868506959
## fringe:10/2017-back:1/2018 -1.788627688 -3.450738181 -0.126517195
## back:10/2018-back:1/2018 -1.846321813 -3.558557415 -0.134086210
## fore:10/2018-back:1/2018 -2.700935484 -4.877148955 -0.524722013
## fringe:10/2018-back:1/2018 -1.213139430 -2.925375033 0.499096172
## back:11/2017-back:1/2018 -1.364294803 -3.141165661 0.412576056
## fore:11/2017-back:1/2018 -2.139000358 -4.315213830 0.037213113
## fringe:11/2017-back:1/2018 -0.988697581 -2.650808074 0.673412913
## back:11/2018-back:1/2018 -0.672939247 -2.449810106 1.103931611
## fore:11/2018-back:1/2018 -2.076289247 -4.252502718 0.099924224
## fringe:11/2018-back:1/2018 0.530005197 -1.246865661 2.306876056
## back:12/2017-back:1/2018 -0.445053763 -2.221924622 1.331817095
## fore:12/2017-back:1/2018 -1.361548387 -3.537761858 0.814665084
## fringe:12/2017-back:1/2018 -0.108708333 -1.770818827 1.553402160
## back:12/2018-back:1/2018 0.709247312 -1.067623547 2.486118170
## fore:12/2018-back:1/2018 -1.066118280 -3.242331751 1.110095192
## fringe:12/2018-back:1/2018 1.761338710 -0.015532149 3.538209568
## back:2/2018-back:1/2018 -1.040953533 -2.817824392 0.735917326
## fore:2/2018-back:1/2018 -1.708358295 -3.884571766 0.467855176
## fringe:2/2018-back:1/2018 -0.848088950 -2.510199443 0.814021544
## back:2/2019-back:1/2018 0.767957181 -1.008913677 2.544828040
## fore:2/2019-back:1/2018 -1.313181046 -3.489394517 0.863032425
## fringe:2/2019-back:1/2018 1.060534562 -0.716336296 2.837405421
## back:3/2018-back:1/2018 -0.078586676 -1.700640427 1.543467076
## fore:3/2018-back:1/2018 -1.182860215 -3.359073686 0.993353256
## fringe:3/2018-back:1/2018 0.148866532 -1.513243961 1.810977026
## back:3/2019-back:1/2018 1.030797002 -0.746073856 2.807667861
## fore:3/2019-back:1/2018 -1.568221131 -4.081095998 0.944653735
## fringe:3/2019-back:1/2018 1.312419019 -0.464451839 3.089289878
## back:4/2018-back:1/2018 -0.315885081 -1.977995574 1.346225413
## fore:4/2018-back:1/2018 -1.042600358 -3.218813830 1.133613113
## fringe:4/2018-back:1/2018 -0.126994009 -1.839229612 1.585241593
## back:5/2018-back:1/2018 -0.978280914 -2.640391407 0.683829579
## fore:5/2018-back:1/2018 -1.539311828 -3.715525299 0.636901643
## fringe:5/2018-back:1/2018 -0.840107527 -2.552343129 0.872128076
## back:6/2018-back:1/2018 -1.638560081 -3.300670574 0.023550413
## fore:6/2018-back:1/2018 -2.157822581 -4.334036052 0.018390890
## fringe:6/2018-back:1/2018 -1.553822581 -3.266058183 0.158413022
## back:7/2018-back:1/2018 -2.062475000 -3.724585493 -0.400364507
## fore:7/2018-back:1/2018 -2.697967742 -4.874181213 -0.521754271
## fringe:7/2018-back:1/2018 -2.032705991 -3.744941593 -0.320470388
## back:8/2018-back:1/2018 -2.015927187 -3.678037680 -0.353816694
## fore:8/2018-back:1/2018 -2.823185762 -4.999399233 -0.646972291
## fringe:8/2018-back:1/2018 -2.020736063 -3.642789814 -0.398682311
## back:9/2017-back:1/2018 -1.503360544 -3.280231402 0.273510315
## fore:9/2017-back:1/2018 -2.693844695 -4.870058166 -0.517631224
## fringe:9/2017-back:1/2018 -1.551137217 -3.213247711 0.110973276
## back:9/2018-back:1/2018 -2.073960676 -3.786196278 -0.361725073
## fore:9/2018-back:1/2018 -3.020855914 -5.197069385 -0.844642443
## fringe:9/2018-back:1/2018 -1.912491033 -3.574601526 -0.250380540
## fringe:1/2018-fore:1/2018 1.310719086 -0.772846683 3.394284856
## back:1/2019-fore:1/2018 1.683360215 -0.492853256 3.859573686
## fore:1/2019-fore:1/2018 -0.163172043 -2.676046910 2.349702824
## fringe:1/2019-fore:1/2018 2.117564516 -0.058648955 4.293777987
## back:10/2017-fore:1/2018 -1.142602151 -3.318815622 1.033611321
## fore:10/2017-fore:1/2018 -2.112075269 -4.624950136 0.400799598
## fringe:10/2017-fore:1/2018 -0.855982527 -2.939548296 1.227583243
## back:10/2018-fore:1/2018 -0.913676651 -3.037443537 1.210090234
## fore:10/2018-fore:1/2018 -1.768290323 -4.281165189 0.744584544
## fringe:10/2018-fore:1/2018 -0.280494269 -2.404261154 1.843272616
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## fore:8/2018-fringe:6/2018 -1.269363181 -3.393130067 0.854403704
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## fore:7/2018-back:7/2018 -0.635492742 -2.719058511 1.448073028
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## fore:8/2018-back:7/2018 -0.760710762 -2.844276531 1.322855008
## fringe:8/2018-back:7/2018 0.041738937 -1.453720730 1.537198605
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## fore:9/2017-back:7/2018 -0.631369695 -2.714935464 1.452196075
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## fore:9/2018-back:7/2018 -0.958380914 -3.041946683 1.125184856
## fringe:9/2018-back:7/2018 0.149983967 -1.388831336 1.688799270
## fringe:7/2018-fore:7/2018 0.665261751 -1.458505134 2.789028636
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## fore:8/2018-fore:7/2018 -0.125218020 -2.638092887 2.387656847
## fringe:8/2018-fore:7/2018 0.677231679 -1.374522058 2.728985416
## back:9/2017-fore:7/2018 1.194607198 -0.981606273 3.370820669
## fore:9/2017-fore:7/2018 0.004123047 -2.508751820 2.516997914
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## fore:9/2018-fore:7/2018 -0.322888172 -2.835763039 2.189986695
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## back:8/2018-fringe:7/2018 0.016778804 -1.576046360 1.609603968
## fore:8/2018-fringe:7/2018 -0.790479771 -2.914246656 1.333287114
## fringe:8/2018-fringe:7/2018 0.011969928 -1.539010112 1.562949968
## back:9/2017-fringe:7/2018 0.529345447 -1.182890155 2.241581050
## fore:9/2017-fringe:7/2018 -0.661138704 -2.784905589 1.462628181
## fringe:9/2017-fringe:7/2018 0.481568773 -1.111256391 2.074393937
## back:9/2018-fringe:7/2018 -0.041254685 -1.686317441 1.603808070
## fore:9/2018-fringe:7/2018 -0.988149923 -3.111916808 1.135616962
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## fore:8/2018-back:8/2018 -0.807258575 -2.890824344 1.276307195
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## fore:9/2018-back:8/2018 -1.004928727 -3.088494496 1.078637043
## fringe:9/2018-back:8/2018 0.103436154 -1.435379149 1.642251457
## fringe:8/2018-fore:8/2018 0.802449699 -1.249304038 2.854203436
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## fringe:9/2017-fore:8/2018 1.272048545 -0.811517225 3.355614314
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## fore:9/2017-fringe:8/2018 -0.673108632 -2.724862369 1.378645105
## fringe:9/2017-fringe:8/2018 0.469598845 -1.025860822 1.965058513
## back:9/2018-fringe:8/2018 -0.053224613 -1.604204653 1.497755427
## fore:9/2018-fringe:8/2018 -1.000119851 -3.051873588 1.051633886
## fringe:9/2018-fringe:8/2018 0.108245030 -1.387214638 1.603704697
## fore:9/2017-back:9/2017 -1.190484151 -3.366697622 0.985729320
## fringe:9/2017-back:9/2017 -0.047776674 -1.709887167 1.614333820
## back:9/2018-back:9/2017 -0.570600132 -2.282835735 1.141635470
## fore:9/2018-back:9/2017 -1.517495370 -3.693708841 0.658718101
## fringe:9/2018-back:9/2017 -0.409130489 -2.071240983 1.252980004
## fringe:9/2017-fore:9/2017 1.142707477 -0.940858292 3.226273247
## back:9/2018-fore:9/2017 0.619884019 -1.503882866 2.743650904
## fore:9/2018-fore:9/2017 -0.327011219 -2.839886086 2.185863648
## fringe:9/2018-fore:9/2017 0.781353662 -1.302212108 2.864919431
## back:9/2018-fringe:9/2017 -0.522823458 -2.115648622 1.070001705
## fore:9/2018-fringe:9/2017 -1.469718697 -3.553284466 0.613847073
## fringe:9/2018-fringe:9/2017 -0.361353816 -1.900169118 1.177461487
## fore:9/2018-back:9/2018 -0.946895238 -3.070662123 1.176871647
## fringe:9/2018-back:9/2018 0.161469643 -1.431355521 1.754294807
## fringe:9/2018-fore:9/2018 1.108364881 -0.975200889 3.191930650
## p adj
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## fringe:1/2018-back:1/2018 1.0000000
## back:1/2019-back:1/2018 0.9999907
## fore:1/2019-back:1/2018 0.9989842
## fringe:1/2019-back:1/2018 0.8716963
## back:10/2017-back:1/2018 0.0034595
## fore:10/2017-back:1/2018 0.0000381
## fringe:10/2017-back:1/2018 0.0160703
## back:10/2018-back:1/2018 0.0155229
## fore:10/2018-back:1/2018 0.0009086
## fringe:10/2018-back:1/2018 0.7622977
## back:11/2017-back:1/2018 0.5628201
## fore:11/2017-back:1/2018 0.0632705
## fringe:11/2017-back:1/2018 0.9731990
## back:11/2018-back:1/2018 0.9999997
## fore:11/2018-back:1/2018 0.0924172
## fringe:11/2018-back:1/2018 1.0000000
## back:12/2017-back:1/2018 1.0000000
## fore:12/2017-back:1/2018 0.9427633
## fringe:12/2017-back:1/2018 1.0000000
## back:12/2018-back:1/2018 0.9999984
## fore:12/2018-back:1/2018 0.9994709
## fringe:12/2018-back:1/2018 0.0564474
## back:2/2018-back:1/2018 0.9791392
## fore:2/2018-back:1/2018 0.5022068
## fringe:2/2018-back:1/2018 0.9986250
## back:2/2019-back:1/2018 0.9999817
## fore:2/2019-back:1/2018 0.9663985
## fringe:2/2019-back:1/2018 0.9717102
## back:3/2018-back:1/2018 1.0000000
## fore:3/2018-back:1/2018 0.9947222
## fringe:3/2018-back:1/2018 1.0000000
## back:3/2019-back:1/2018 0.9823335
## fore:3/2019-back:1/2018 0.9447515
## fringe:3/2019-back:1/2018 0.6648319
## back:4/2018-back:1/2018 1.0000000
## fore:4/2018-back:1/2018 0.9996955
## fringe:4/2018-back:1/2018 1.0000000
## back:5/2018-back:1/2018 0.9774531
## fore:5/2018-back:1/2018 0.7658475
## fringe:5/2018-back:1/2018 0.9994508
## back:6/2018-back:1/2018 0.0608017
## fore:6/2018-back:1/2018 0.0562222
## fringe:6/2018-back:1/2018 0.1621141
## back:7/2018-back:1/2018 0.0009128
## fore:7/2018-back:1/2018 0.0009323
## fringe:7/2018-back:1/2018 0.0024564
## back:8/2018-back:1/2018 0.0015388
## fore:8/2018-back:1/2018 0.0003067
## fringe:8/2018-back:1/2018 0.0008317
## back:9/2017-back:1/2018 0.3057004
## fore:9/2017-back:1/2018 0.0009662
## fringe:9/2017-back:1/2018 0.1197974
## back:9/2018-back:1/2018 0.0015852
## fore:9/2018-back:1/2018 0.0000480
## fringe:9/2018-back:1/2018 0.0046781
## fringe:1/2018-fore:1/2018 0.9382665
## back:1/2019-fore:1/2018 0.5426027
## fore:1/2019-fore:1/2018 1.0000000
## fringe:1/2019-fore:1/2018 0.0722041
## back:10/2017-fore:1/2018 0.9974205
## fore:10/2017-fore:1/2018 0.3217147
## fringe:10/2017-fore:1/2018 0.9999960
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## fore:10/2018-fore:1/2018 0.7766842
## fringe:10/2018-fore:1/2018 1.0000000
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## fore:11/2017-fore:1/2018 0.9996794
## fringe:11/2017-fore:1/2018 1.0000000
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## fore:11/2018-fore:1/2018 0.9999209
## fringe:11/2018-fore:1/2018 0.8599106
## back:12/2017-fore:1/2018 1.0000000
## fore:12/2017-fore:1/2018 1.0000000
## fringe:12/2017-fore:1/2018 0.9999988
## back:12/2018-fore:1/2018 0.6098588
## fore:12/2018-fore:1/2018 1.0000000
## fringe:12/2018-fore:1/2018 0.0009651
## back:2/2018-fore:1/2018 1.0000000
## fore:2/2018-fore:1/2018 1.0000000
## fringe:2/2018-fore:1/2018 1.0000000
## back:2/2019-fore:1/2018 0.5146930
## fore:2/2019-fore:1/2018 1.0000000
## fringe:2/2019-fore:1/2018 0.1471926
## back:3/2018-fore:1/2018 0.9999941
## fore:3/2018-fore:1/2018 1.0000000
## fringe:3/2018-fore:1/2018 0.9979866
## back:3/2019-fore:1/2018 0.1719953
## fore:3/2019-fore:1/2018 1.0000000
## fringe:3/2019-fore:1/2018 0.0317197
## back:4/2018-fore:1/2018 1.0000000
## fore:4/2018-fore:1/2018 1.0000000
## fringe:4/2018-fore:1/2018 0.9999997
## back:5/2018-fore:1/2018 1.0000000
## fore:5/2018-fore:1/2018 1.0000000
## fringe:5/2018-fore:1/2018 1.0000000
## back:6/2018-fore:1/2018 1.0000000
## fore:6/2018-fore:1/2018 0.9995291
## fringe:6/2018-fore:1/2018 1.0000000
## back:7/2018-fore:1/2018 0.9949657
## fore:7/2018-fore:1/2018 0.7801478
## fringe:7/2018-fore:1/2018 0.9980775
## back:8/2018-fore:1/2018 0.9979133
## fore:8/2018-fore:1/2018 0.6173270
## fringe:8/2018-fore:1/2018 0.9968110
## back:9/2017-fore:1/2018 1.0000000
## fore:9/2017-fore:1/2018 0.7849182
## fringe:9/2017-fore:1/2018 1.0000000
## back:9/2018-fore:1/2018 0.9958034
## fore:9/2018-fore:1/2018 0.3500612
## fringe:9/2018-fore:1/2018 0.9998107
## back:1/2019-fringe:1/2018 1.0000000
## fore:1/2019-fringe:1/2018 0.7656855
## fringe:1/2019-fringe:1/2018 0.9995771
## back:10/2017-fringe:1/2018 0.0000072
## fore:10/2017-fringe:1/2018 0.0000002
## fringe:10/2017-fringe:1/2018 0.0000315
## back:10/2018-fringe:1/2018 0.0000402
## fore:10/2018-fringe:1/2018 0.0000069
## fringe:10/2018-fringe:1/2018 0.0507121
## back:11/2017-fringe:1/2018 0.0247230
## fore:11/2017-fringe:1/2018 0.0016810
## fringe:11/2017-fringe:1/2018 0.2005652
## back:11/2018-fringe:1/2018 0.9337571
## fore:11/2018-fringe:1/2018 0.0028959
## fringe:11/2018-fringe:1/2018 1.0000000
## back:12/2017-fringe:1/2018 0.9993132
## fore:12/2017-fringe:1/2018 0.3382029
## fringe:12/2017-fringe:1/2018 1.0000000
## back:12/2018-fringe:1/2018 1.0000000
## fore:12/2018-fringe:1/2018 0.8067338
## fringe:12/2018-fringe:1/2018 0.3463205
## back:2/2018-fringe:1/2018 0.2844034
## fore:2/2018-fringe:1/2018 0.0490458
## fringe:2/2018-fringe:1/2018 0.4613214
## back:2/2019-fringe:1/2018 1.0000000
## fore:2/2019-fringe:1/2018 0.4113042
## fringe:2/2019-fringe:1/2018 0.9999961
## back:3/2018-fringe:1/2018 1.0000000
## fore:3/2018-fringe:1/2018 0.6284241
## fringe:3/2018-fringe:1/2018 1.0000000
## back:3/2019-fringe:1/2018 0.9999991
## fore:3/2019-fringe:1/2018 0.4507199
## fringe:3/2019-fringe:1/2018 0.9899216
## back:4/2018-fringe:1/2018 0.9999385
## fore:4/2018-fringe:1/2018 0.8364412
## fringe:4/2018-fringe:1/2018 1.0000000
## back:5/2018-fringe:1/2018 0.2155001
## fore:5/2018-fringe:1/2018 0.1399037
## fringe:5/2018-fringe:1/2018 0.5734787
## back:6/2018-fringe:1/2018 0.0002359
## fore:6/2018-fringe:1/2018 0.0014235
## fringe:6/2018-fringe:1/2018 0.0015388
## back:7/2018-fringe:1/2018 0.0000006
## fore:7/2018-fringe:1/2018 0.0000071
## fringe:7/2018-fringe:1/2018 0.0000031
## back:8/2018-fringe:1/2018 0.0000012
## fore:8/2018-fringe:1/2018 0.0000018
## fringe:8/2018-fringe:1/2018 0.0000004
## back:9/2017-fringe:1/2018 0.0064415
## fore:9/2017-fringe:1/2018 0.0000074
## fringe:9/2017-fringe:1/2018 0.0007155
## back:9/2018-fringe:1/2018 0.0000017
## fore:9/2018-fringe:1/2018 0.0000002
## fringe:9/2018-fringe:1/2018 0.0000054
## fore:1/2019-back:1/2019 0.2988476
## fringe:1/2019-back:1/2019 1.0000000
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## fore:10/2017-back:1/2019 0.0000000
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## fore:10/2018-back:1/2019 0.0000006
## fringe:10/2018-back:1/2019 0.0049855
## back:11/2017-back:1/2019 0.0023217
## fore:11/2017-back:1/2019 0.0001665
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## back:11/2018-back:1/2019 0.4463629
## fore:11/2018-back:1/2019 0.0002963
## fringe:11/2018-back:1/2019 1.0000000
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## fore:12/2017-back:1/2019 0.0745711
## fringe:12/2017-back:1/2019 0.9981504
## back:12/2018-back:1/2019 1.0000000
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## fringe:12/2018-back:1/2019 0.9875195
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## fore:2/2018-back:1/2019 0.0066606
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## fore:2/2019-back:1/2019 0.0993295
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## fore:3/2018-back:1/2019 0.1999054
## fringe:3/2018-back:1/2019 0.9999999
## back:3/2019-back:1/2019 1.0000000
## fore:3/2019-back:1/2019 0.1357148
## fringe:3/2019-back:1/2019 1.0000000
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## fore:4/2018-back:1/2019 0.3715965
## fringe:4/2018-back:1/2019 0.9984743
## back:5/2018-back:1/2019 0.0279097
## fore:5/2018-back:1/2019 0.0232596
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## fore:8/2018-back:1/2019 0.0000002
## fringe:8/2018-back:1/2019 0.0000000
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## fore:9/2018-back:1/2019 0.0000000
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## fringe:10/2017-fore:1/2019 1.0000000
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## fore:11/2018-fore:1/2019 0.9999992
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## fore:12/2018-fore:1/2019 1.0000000
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## fore:11/2018-fringe:1/2019 0.0000043
## fringe:11/2018-fringe:1/2019 0.9999999
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## back:9/2017-back:4/2018 0.7440830
## fore:9/2017-back:4/2018 0.0054944
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## back:5/2018-fore:4/2018 1.0000000
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## fringe:6/2018-fore:4/2018 1.0000000
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## fore:7/2018-fore:4/2018 0.8886686
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## fore:8/2018-fore:4/2018 0.7620768
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## back:9/2017-fore:4/2018 1.0000000
## fore:9/2017-fore:4/2018 0.8919245
## fringe:9/2017-fore:4/2018 1.0000000
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## fore:9/2018-fore:4/2018 0.4944102
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## fore:5/2018-fringe:4/2018 0.8757046
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## back:6/2018-fringe:4/2018 0.0985872
## fore:6/2018-fringe:4/2018 0.0899066
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## back:7/2018-fringe:4/2018 0.0014762
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## fore:8/2018-fringe:4/2018 0.0005278
## fringe:8/2018-fringe:4/2018 0.0013242
## back:9/2017-fringe:4/2018 0.4374904
## fore:9/2017-fringe:4/2018 0.0016636
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## back:9/2018-fringe:4/2018 0.0026223
## fore:9/2018-fringe:4/2018 0.0000818
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## back:9/2017-back:5/2018 1.0000000
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## fore:9/2018-back:5/2018 0.0655084
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## fringe:5/2018-fore:5/2018 1.0000000
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## fore:7/2018-fringe:5/2018 0.2308686
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## fore:8/2018-fringe:5/2018 0.1190407
## fringe:8/2018-fringe:5/2018 0.5861197
## back:9/2017-fringe:5/2018 0.9999994
## fore:9/2017-fringe:5/2018 0.2355041
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## fore:9/2018-fringe:5/2018 0.0340447
## fringe:9/2018-fringe:5/2018 0.8572455
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## fringe:8/2018-back:6/2018 1.0000000
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## fore:9/2017-back:6/2018 0.9988385
## fringe:9/2017-back:6/2018 1.0000000
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## fore:7/2018-fringe:6/2018 0.9955863
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## fore:8/2018-fringe:6/2018 0.9710704
## fringe:8/2018-fringe:6/2018 1.0000000
## back:9/2017-fringe:6/2018 1.0000000
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## fringe:8/2018-fore:7/2018 1.0000000
## back:9/2017-fore:7/2018 0.9935861
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## back:9/2018-fore:7/2018 1.0000000
## fore:9/2018-fore:7/2018 1.0000000
## fringe:9/2018-fore:7/2018 0.9999998
## back:8/2018-fringe:7/2018 1.0000000
## fore:8/2018-fringe:7/2018 0.9999998
## fringe:8/2018-fringe:7/2018 1.0000000
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## fore:9/2018-fringe:7/2018 0.9998569
## fringe:9/2018-fringe:7/2018 1.0000000
## fore:8/2018-back:8/2018 0.9999994
## fringe:8/2018-back:8/2018 1.0000000
## back:9/2017-back:8/2018 1.0000000
## fore:9/2017-back:8/2018 1.0000000
## fringe:9/2017-back:8/2018 1.0000000
## back:9/2018-back:8/2018 1.0000000
## fore:9/2018-back:8/2018 0.9996397
## fringe:9/2018-back:8/2018 1.0000000
## fringe:8/2018-fore:8/2018 0.9999992
## back:9/2017-fore:8/2018 0.9636864
## fore:9/2017-fore:8/2018 1.0000000
## fringe:9/2017-fore:8/2018 0.9598507
## back:9/2018-fore:8/2018 1.0000000
## fore:9/2018-fore:8/2018 1.0000000
## fringe:9/2018-fore:8/2018 0.9999746
## back:9/2017-fringe:8/2018 1.0000000
## fore:9/2017-fringe:8/2018 1.0000000
## fringe:9/2017-fringe:8/2018 1.0000000
## back:9/2018-fringe:8/2018 1.0000000
## fore:9/2018-fringe:8/2018 0.9995318
## fringe:9/2018-fringe:8/2018 1.0000000
## fore:9/2017-back:9/2017 0.9940062
## fringe:9/2017-back:9/2017 1.0000000
## back:9/2018-back:9/2017 1.0000000
## fore:9/2018-back:9/2017 0.7951000
## fringe:9/2018-back:9/2017 1.0000000
## fringe:9/2017-fore:9/2017 0.9936993
## back:9/2018-fore:9/2017 1.0000000
## fore:9/2018-fore:9/2017 1.0000000
## fringe:9/2018-fore:9/2017 0.9999998
## back:9/2018-fringe:9/2017 1.0000000
## fore:9/2018-fringe:9/2017 0.7716730
## fringe:9/2018-fringe:9/2017 1.0000000
## fore:9/2018-back:9/2018 0.9999552
## fringe:9/2018-back:9/2018 1.0000000
## fringe:9/2018-fore:9/2018 0.9965990Temperature maxima are strongly significantly different between reef zones and between months.
It seems that differences in daily temperature ranges are mostly driven by differences in daily temperature maxima.