###Load libraries#####
library(tidyverse)
## Warning: package 'tidyverse' was built under R version 3.3.3
## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr
## Warning: package 'ggplot2' was built under R version 3.3.3
## Warning: package 'purrr' was built under R version 3.3.3
## Conflicts with tidy packages ----------------------------------------------
## filter(): dplyr, stats
## lag(): dplyr, stats
library(lme4)
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
## The following object is masked from 'package:tidyr':
##
## expand
library(knitr)
library(broom)
library(forcats)
library(lmerTest)
## Warning: package 'lmerTest' was built under R version 3.3.3
##
## Attaching package: 'lmerTest'
## The following object is masked from 'package:lme4':
##
## lmer
## The following object is masked from 'package:stats':
##
## step
library(MuMIn)
## Warning: package 'MuMIn' was built under R version 3.3.3
library(knitr)
setwd ("P:/MTM2 Oranga Taiao/EMP Data/EMP Data/Biotic Index calculator/Index calculation/MTM new Data")
source('theme_javier.R')
theme_set(theme_javier(8))
options(digits = 3)
###read data#####
all_ind <-
read_csv('all_indices_truncated.csv', col_types = cols(Date = 'D')) %>%
mutate(Type = factor(Type))
dat_mult <-
all_ind %>%
dplyr::select(
Region,
n,
council,
estuary,
site,
cesy,
ces,
year,
AMBI:MEDOCC,
BQI:TBI,
logN,
S,
Type,
metals,
sqrtTP,
sqrtmud
) %>%
drop_na() %>%
mutate(Region = fct_collapse(
Region,
`North Eastern` = c('North Eastern', 'Western North Island')
)) %>%
dplyr::select(-MEDOCC)
dat_mult_centered <-
dat_mult %>%
mutate(
TBI = sqrt(TBI),
S = sqrt(S),
BENTIX = sqrt(max(BENTIX) * 1.05 - BENTIX) * -1,
AMBI = AMBI * -1,
AMBI_S = AMBI_S * -1
) %>%
gather(index, value, AMBI:S) %>%
group_by(index) %>%
mutate(value = scale(value))
ggplot(dat_mult_centered, aes(x = value)) +
geom_histogram() +
facet_wrap( ~ index)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##Data structure####
kable(with(dat_mult_centered, table(council, Region)))
| bayofplentyregionalcouncil |
0 |
0 |
396 |
0 |
| environmentcanterbury |
0 |
0 |
0 |
27 |
| environmentcanterburyccc |
0 |
0 |
0 |
18 |
| environmentsouthland |
0 |
0 |
0 |
522 |
| greaterwellington |
72 |
0 |
0 |
0 |
| hawkesbayregionalcouncil |
0 |
432 |
0 |
0 |
| marlboroughdistrictcouncil |
72 |
0 |
0 |
0 |
| nelsoncitycouncil |
27 |
0 |
0 |
0 |
| northlandregionalcouncil |
0 |
0 |
432 |
0 |
| tasmandistrictcouncil |
135 |
0 |
0 |
0 |
kable(with(dat_mult_centered, table(cesy, Region)))
| bayofplentyregionalcouncil tauranga 1 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 10 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 13 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 14 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 16 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 18 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 2 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 20 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 27 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 28 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 29 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 3 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 32 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 34 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 35 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 37 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 38 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 40 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 42 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 45 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 46 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 47 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 48 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 5 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 50 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 51 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 53 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 55 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 56 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 57 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 60 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 62 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 64 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 66 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 67 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 68 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 69 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 7 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 70 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 71 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 73 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 74 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 75 2011 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 8 2011 |
0 |
0 |
9 |
0 |
| environmentcanterbury avonheathcote a 2001 |
0 |
0 |
0 |
9 |
| environmentcanterbury avonheathcote b 2001 |
0 |
0 |
0 |
9 |
| environmentcanterbury avonheathcote c 2001 |
0 |
0 |
0 |
9 |
| environmentcanterburyccc avonheathcote dischargepoint 2011 |
0 |
0 |
0 |
9 |
| environmentcanterburyccc avonheathcote pleasantpointjetty 2011 |
0 |
0 |
0 |
9 |
| environmentsouthland awarua a 2005 |
0 |
0 |
0 |
9 |
| environmentsouthland awarua b 2005 |
0 |
0 |
0 |
9 |
| environmentsouthland bluff a 2005 |
0 |
0 |
0 |
9 |
| environmentsouthland bluff b 2005 |
0 |
0 |
0 |
9 |
| environmentsouthland fortrose a 2004 |
0 |
0 |
0 |
9 |
| environmentsouthland fortrose a 2005 |
0 |
0 |
0 |
9 |
| environmentsouthland fortrose a 2006 |
0 |
0 |
0 |
9 |
| environmentsouthland fortrose b 2004 |
0 |
0 |
0 |
9 |
| environmentsouthland fortrose b 2009 |
0 |
0 |
0 |
9 |
| environmentsouthland haldane a 2006 |
0 |
0 |
0 |
9 |
| environmentsouthland haldane a 2009 |
0 |
0 |
0 |
9 |
| environmentsouthland haldane a 2010 |
0 |
0 |
0 |
9 |
| environmentsouthland haldane a 2011 |
0 |
0 |
0 |
9 |
| environmentsouthland haldane b 2013 |
0 |
0 |
0 |
9 |
| environmentsouthland jacobsriver a 2003 |
0 |
0 |
0 |
9 |
| environmentsouthland jacobsriver a 2004 |
0 |
0 |
0 |
9 |
| environmentsouthland jacobsriver a 2005 |
0 |
0 |
0 |
9 |
| environmentsouthland jacobsriver a 2006 |
0 |
0 |
0 |
9 |
| environmentsouthland jacobsriver a 2011 |
0 |
0 |
0 |
9 |
| environmentsouthland jacobsriver b 2003 |
0 |
0 |
0 |
9 |
| environmentsouthland jacobsriver b 2004 |
0 |
0 |
0 |
9 |
| environmentsouthland jacobsriver b 2005 |
0 |
0 |
0 |
9 |
| environmentsouthland jacobsriver b 2006 |
0 |
0 |
0 |
9 |
| environmentsouthland jacobsriver b 2011 |
0 |
0 |
0 |
9 |
| environmentsouthland jacobsriver c 2003 |
0 |
0 |
0 |
9 |
| environmentsouthland jacobsriver c 2004 |
0 |
0 |
0 |
9 |
| environmentsouthland jacobsriver c 2005 |
0 |
0 |
0 |
9 |
| environmentsouthland jacobsriver c 2006 |
0 |
0 |
0 |
9 |
| environmentsouthland jacobsriver c 2011 |
0 |
0 |
0 |
9 |
| environmentsouthland jacobsriver d 2012 |
0 |
0 |
0 |
9 |
| environmentsouthland jacobsriver d 2013 |
0 |
0 |
0 |
9 |
| environmentsouthland jacobsriver e 2012 |
0 |
0 |
0 |
9 |
| environmentsouthland newriver a 2001 |
0 |
0 |
0 |
9 |
| environmentsouthland newriver b 2001 |
0 |
0 |
0 |
9 |
| environmentsouthland newriver b 2003 |
0 |
0 |
0 |
9 |
| environmentsouthland newriver b 2004 |
0 |
0 |
0 |
9 |
| environmentsouthland newriver b 2005 |
0 |
0 |
0 |
9 |
| environmentsouthland newriver b 2010 |
0 |
0 |
0 |
9 |
| environmentsouthland newriver c 2001 |
0 |
0 |
0 |
9 |
| environmentsouthland newriver c 2003 |
0 |
0 |
0 |
9 |
| environmentsouthland newriver c 2004 |
0 |
0 |
0 |
9 |
| environmentsouthland newriver c 2005 |
0 |
0 |
0 |
9 |
| environmentsouthland newriver c 2010 |
0 |
0 |
0 |
9 |
| environmentsouthland newriver d 2001 |
0 |
0 |
0 |
9 |
| environmentsouthland newriver d 2003 |
0 |
0 |
0 |
9 |
| environmentsouthland newriver d 2004 |
0 |
0 |
0 |
9 |
| environmentsouthland newriver d 2005 |
0 |
0 |
0 |
9 |
| environmentsouthland newriver d 2010 |
0 |
0 |
0 |
9 |
| environmentsouthland newriver e 2013 |
0 |
0 |
0 |
9 |
| environmentsouthland newriver f 2013 |
0 |
0 |
0 |
9 |
| environmentsouthland waikawa a 2005 |
0 |
0 |
0 |
9 |
| environmentsouthland waikawa a 2006 |
0 |
0 |
0 |
9 |
| environmentsouthland waikawa a 2007 |
0 |
0 |
0 |
9 |
| environmentsouthland waikawa a 2008 |
0 |
0 |
0 |
9 |
| environmentsouthland waikawa b 2005 |
0 |
0 |
0 |
9 |
| environmentsouthland waikawa b 2006 |
0 |
0 |
0 |
9 |
| environmentsouthland waikawa b 2007 |
0 |
0 |
0 |
9 |
| environmentsouthland waikawa b 2008 |
0 |
0 |
0 |
9 |
| greaterwellington pauatahanui a 2009 |
9 |
0 |
0 |
0 |
| greaterwellington pauatahanui a 2010 |
9 |
0 |
0 |
0 |
| greaterwellington pauatahanui b 2009 |
9 |
0 |
0 |
0 |
| greaterwellington pauatahanui b 2010 |
9 |
0 |
0 |
0 |
| greaterwellington porirua a 2009 |
9 |
0 |
0 |
0 |
| greaterwellington porirua a 2010 |
9 |
0 |
0 |
0 |
| greaterwellington porirua b 2009 |
9 |
0 |
0 |
0 |
| greaterwellington porirua b 2010 |
9 |
0 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri a 2006 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri a 2007 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri a 2008 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri a 2009 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri a 2010 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri a 2011 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri a 2012 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri a 2013 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri a 2014 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri a 2015 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri b 2006 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri b 2007 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri b 2008 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri b 2009 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri b 2010 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri b 2011 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri b 2012 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri b 2013 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri b 2014 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri b 2015 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri c 2006 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri c 2007 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri c 2008 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri d 2007 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri d 2008 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri d 2009 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri d 2010 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri d 2011 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri d 2012 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri d 2013 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri d 2014 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri d 2015 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri e 2009 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri e 2010 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri e 2011 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri e 2012 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri e 2013 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri e 2014 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri e 2015 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil porangahau a 2007 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil porangahau a 2008 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil porangahau a 2009 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil porangahau a 2010 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil porangahau a 2011 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil porangahau a 2012 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil porangahau a 2013 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil porangahau a 2014 |
0 |
9 |
0 |
0 |
| hawkesbayregionalcouncil porangahau a 2015 |
0 |
9 |
0 |
0 |
| marlboroughdistrictcouncil havelock a 2001 |
9 |
0 |
0 |
0 |
| marlboroughdistrictcouncil havelock a 2014 |
9 |
0 |
0 |
0 |
| marlboroughdistrictcouncil havelock a 2015 |
9 |
0 |
0 |
0 |
| marlboroughdistrictcouncil havelock b 2001 |
9 |
0 |
0 |
0 |
| marlboroughdistrictcouncil havelock b 2014 |
9 |
0 |
0 |
0 |
| marlboroughdistrictcouncil havelock b 2015 |
9 |
0 |
0 |
0 |
| marlboroughdistrictcouncil havelock c 2015 |
9 |
0 |
0 |
0 |
| marlboroughdistrictcouncil havelock d 2015 |
9 |
0 |
0 |
0 |
| nelsoncitycouncil delaware a 2009 |
9 |
0 |
0 |
0 |
| nelsoncitycouncil delaware b 2009 |
9 |
0 |
0 |
0 |
| nelsoncitycouncil delaware c 2009 |
9 |
0 |
0 |
0 |
| northlandregionalcouncil kaipara a 2001 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil kaipara b 2001 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil kaipara c 2001 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-10 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-11 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-15 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-17 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-2 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-20 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-21 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-22 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-3 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-4 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-5 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-7 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-9 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 1 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 10 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 12 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 13 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 14 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 15 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 16 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 17 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 19 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 2 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 20 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 21 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 22 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 3 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 4 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 5 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 6 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 7 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 8 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 9 2016 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ruakaka rua 2008 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ruakaka tam 2008 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil whangarei hat 2008 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil whangarei man 2008 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil whangarei otk 2008 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil whangarei ptl 2008 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil whangaroa kae 2009 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil whangaroa kae 2010 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil whangaroa kae 2011 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil whangaroa kah 2009 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil whangaroa kah 2010 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil whangaroa kah 2011 |
0 |
0 |
9 |
0 |
| tasmandistrictcouncil moutere a 2006 |
9 |
0 |
0 |
0 |
| tasmandistrictcouncil moutere a 2013 |
9 |
0 |
0 |
0 |
| tasmandistrictcouncil moutere b 2006 |
9 |
0 |
0 |
0 |
| tasmandistrictcouncil moutere b 2013 |
9 |
0 |
0 |
0 |
| tasmandistrictcouncil ruataniwha a 2001 |
9 |
0 |
0 |
0 |
| tasmandistrictcouncil ruataniwha b 2001 |
9 |
0 |
0 |
0 |
| tasmandistrictcouncil ruataniwha c 2001 |
9 |
0 |
0 |
0 |
| tasmandistrictcouncil waimea a 2001 |
9 |
0 |
0 |
0 |
| tasmandistrictcouncil waimea a 2014 |
9 |
0 |
0 |
0 |
| tasmandistrictcouncil waimea b 2001 |
9 |
0 |
0 |
0 |
| tasmandistrictcouncil waimea b 2014 |
9 |
0 |
0 |
0 |
| tasmandistrictcouncil waimea c 2001 |
9 |
0 |
0 |
0 |
| tasmandistrictcouncil waimea c 2014 |
9 |
0 |
0 |
0 |
| tasmandistrictcouncil waimea d 2001 |
9 |
0 |
0 |
0 |
| tasmandistrictcouncil waimea d 2014 |
9 |
0 |
0 |
0 |
kable(with(dat_mult_centered, table(ces, Region)))
| bayofplentyregionalcouncil tauranga 1 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 10 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 13 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 14 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 16 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 18 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 2 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 20 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 27 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 28 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 29 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 3 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 32 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 34 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 35 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 37 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 38 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 40 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 42 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 45 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 46 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 47 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 48 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 5 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 50 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 51 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 53 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 55 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 56 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 57 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 60 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 62 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 64 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 66 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 67 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 68 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 69 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 7 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 70 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 71 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 73 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 74 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 75 |
0 |
0 |
9 |
0 |
| bayofplentyregionalcouncil tauranga 8 |
0 |
0 |
9 |
0 |
| environmentcanterbury avonheathcote a |
0 |
0 |
0 |
9 |
| environmentcanterbury avonheathcote b |
0 |
0 |
0 |
9 |
| environmentcanterbury avonheathcote c |
0 |
0 |
0 |
9 |
| environmentcanterburyccc avonheathcote dischargepoint |
0 |
0 |
0 |
9 |
| environmentcanterburyccc avonheathcote pleasantpointjetty |
0 |
0 |
0 |
9 |
| environmentsouthland awarua a |
0 |
0 |
0 |
9 |
| environmentsouthland awarua b |
0 |
0 |
0 |
9 |
| environmentsouthland bluff a |
0 |
0 |
0 |
9 |
| environmentsouthland bluff b |
0 |
0 |
0 |
9 |
| environmentsouthland fortrose a |
0 |
0 |
0 |
27 |
| environmentsouthland fortrose b |
0 |
0 |
0 |
18 |
| environmentsouthland haldane a |
0 |
0 |
0 |
36 |
| environmentsouthland haldane b |
0 |
0 |
0 |
9 |
| environmentsouthland jacobsriver a |
0 |
0 |
0 |
45 |
| environmentsouthland jacobsriver b |
0 |
0 |
0 |
45 |
| environmentsouthland jacobsriver c |
0 |
0 |
0 |
45 |
| environmentsouthland jacobsriver d |
0 |
0 |
0 |
18 |
| environmentsouthland jacobsriver e |
0 |
0 |
0 |
9 |
| environmentsouthland newriver a |
0 |
0 |
0 |
9 |
| environmentsouthland newriver b |
0 |
0 |
0 |
45 |
| environmentsouthland newriver c |
0 |
0 |
0 |
45 |
| environmentsouthland newriver d |
0 |
0 |
0 |
45 |
| environmentsouthland newriver e |
0 |
0 |
0 |
9 |
| environmentsouthland newriver f |
0 |
0 |
0 |
9 |
| environmentsouthland waikawa a |
0 |
0 |
0 |
36 |
| environmentsouthland waikawa b |
0 |
0 |
0 |
36 |
| greaterwellington pauatahanui a |
18 |
0 |
0 |
0 |
| greaterwellington pauatahanui b |
18 |
0 |
0 |
0 |
| greaterwellington porirua a |
18 |
0 |
0 |
0 |
| greaterwellington porirua b |
18 |
0 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri a |
0 |
90 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri b |
0 |
90 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri c |
0 |
27 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri d |
0 |
81 |
0 |
0 |
| hawkesbayregionalcouncil ahuriri e |
0 |
63 |
0 |
0 |
| hawkesbayregionalcouncil porangahau a |
0 |
81 |
0 |
0 |
| marlboroughdistrictcouncil havelock a |
27 |
0 |
0 |
0 |
| marlboroughdistrictcouncil havelock b |
27 |
0 |
0 |
0 |
| marlboroughdistrictcouncil havelock c |
9 |
0 |
0 |
0 |
| marlboroughdistrictcouncil havelock d |
9 |
0 |
0 |
0 |
| nelsoncitycouncil delaware a |
9 |
0 |
0 |
0 |
| nelsoncitycouncil delaware b |
9 |
0 |
0 |
0 |
| nelsoncitycouncil delaware c |
9 |
0 |
0 |
0 |
| northlandregionalcouncil kaipara a |
0 |
0 |
9 |
0 |
| northlandregionalcouncil kaipara b |
0 |
0 |
9 |
0 |
| northlandregionalcouncil kaipara c |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-10 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-11 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-15 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-17 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-2 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-20 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-21 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-22 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-3 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-4 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-5 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-7 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil mangonui man-9 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 1 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 10 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 12 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 13 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 14 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 15 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 16 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 17 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 19 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 2 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 20 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 21 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 22 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 3 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 4 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 5 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 6 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 7 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 8 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ngunguru 9 |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ruakaka rua |
0 |
0 |
9 |
0 |
| northlandregionalcouncil ruakaka tam |
0 |
0 |
9 |
0 |
| northlandregionalcouncil whangarei hat |
0 |
0 |
9 |
0 |
| northlandregionalcouncil whangarei man |
0 |
0 |
9 |
0 |
| northlandregionalcouncil whangarei otk |
0 |
0 |
9 |
0 |
| northlandregionalcouncil whangarei ptl |
0 |
0 |
9 |
0 |
| northlandregionalcouncil whangaroa kae |
0 |
0 |
27 |
0 |
| northlandregionalcouncil whangaroa kah |
0 |
0 |
27 |
0 |
| tasmandistrictcouncil moutere a |
18 |
0 |
0 |
0 |
| tasmandistrictcouncil moutere b |
18 |
0 |
0 |
0 |
| tasmandistrictcouncil ruataniwha a |
9 |
0 |
0 |
0 |
| tasmandistrictcouncil ruataniwha b |
9 |
0 |
0 |
0 |
| tasmandistrictcouncil ruataniwha c |
9 |
0 |
0 |
0 |
| tasmandistrictcouncil waimea a |
18 |
0 |
0 |
0 |
| tasmandistrictcouncil waimea b |
18 |
0 |
0 |
0 |
| tasmandistrictcouncil waimea c |
18 |
0 |
0 |
0 |
| tasmandistrictcouncil waimea d |
18 |
0 |
0 |
0 |
kable(with(dat_mult_centered, table(estuary, Region)))
| ahuriri |
0 |
351 |
0 |
0 |
| avonheathcote |
0 |
0 |
0 |
45 |
| awarua |
0 |
0 |
0 |
18 |
| bluff |
0 |
0 |
0 |
18 |
| delaware |
27 |
0 |
0 |
0 |
| fortrose |
0 |
0 |
0 |
45 |
| haldane |
0 |
0 |
0 |
45 |
| havelock |
72 |
0 |
0 |
0 |
| jacobsriver |
0 |
0 |
0 |
162 |
| kaipara |
0 |
0 |
27 |
0 |
| mangonui |
0 |
0 |
117 |
0 |
| moutere |
36 |
0 |
0 |
0 |
| newriver |
0 |
0 |
0 |
162 |
| ngunguru |
0 |
0 |
180 |
0 |
| pauatahanui |
36 |
0 |
0 |
0 |
| porangahau |
0 |
81 |
0 |
0 |
| porirua |
36 |
0 |
0 |
0 |
| ruakaka |
0 |
0 |
18 |
0 |
| ruataniwha |
27 |
0 |
0 |
0 |
| tauranga |
0 |
0 |
396 |
0 |
| waikawa |
0 |
0 |
0 |
72 |
| waimea |
72 |
0 |
0 |
0 |
| whangarei |
0 |
0 |
36 |
0 |
| whangaroa |
0 |
0 |
54 |
0 |
kable(with(dat_mult_centered, table(estuary, year)))
| ahuriri |
0 |
0 |
0 |
0 |
27 |
36 |
36 |
36 |
36 |
36 |
36 |
36 |
36 |
36 |
0 |
| avonheathcote |
27 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
18 |
0 |
0 |
0 |
0 |
0 |
| awarua |
0 |
0 |
0 |
18 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| bluff |
0 |
0 |
0 |
18 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| delaware |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
27 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| fortrose |
0 |
0 |
18 |
9 |
9 |
0 |
0 |
9 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| haldane |
0 |
0 |
0 |
0 |
9 |
0 |
0 |
9 |
9 |
9 |
0 |
9 |
0 |
0 |
0 |
| havelock |
18 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
18 |
36 |
0 |
| jacobsriver |
0 |
27 |
27 |
27 |
27 |
0 |
0 |
0 |
0 |
27 |
18 |
9 |
0 |
0 |
0 |
| kaipara |
27 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| mangonui |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
117 |
| moutere |
0 |
0 |
0 |
0 |
18 |
0 |
0 |
0 |
0 |
0 |
0 |
18 |
0 |
0 |
0 |
| newriver |
36 |
27 |
27 |
27 |
0 |
0 |
0 |
0 |
27 |
0 |
0 |
18 |
0 |
0 |
0 |
| ngunguru |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
180 |
| pauatahanui |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
18 |
18 |
0 |
0 |
0 |
0 |
0 |
0 |
| porangahau |
0 |
0 |
0 |
0 |
0 |
9 |
9 |
9 |
9 |
9 |
9 |
9 |
9 |
9 |
0 |
| porirua |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
18 |
18 |
0 |
0 |
0 |
0 |
0 |
0 |
| ruakaka |
0 |
0 |
0 |
0 |
0 |
0 |
18 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| ruataniwha |
27 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| tauranga |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
396 |
0 |
0 |
0 |
0 |
0 |
| waikawa |
0 |
0 |
0 |
18 |
18 |
18 |
18 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| waimea |
36 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
36 |
0 |
0 |
| whangarei |
0 |
0 |
0 |
0 |
0 |
0 |
36 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| whangaroa |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
18 |
18 |
18 |
0 |
0 |
0 |
0 |
0 |
###Linear mixed models using lmer#####
EG_models8 <-
dat_mult_centered %>%
do(
fit_index8 = lmer(
value ~ (scale(metals) + scale(sqrtmud) + scale(sqrtTP)) * Type + (1 |
year) + (1 | Region / estuary),
data = .,
,
REML = FALSE,
weights = sqrt(n),
na.action = "na.fail"
)
)
index_coef_all8 <-
tidy(EG_models8, fit_index8, conf.int = T) %>%
data.frame(.) %>%
filter(term != '(Intercept)' &
term != 'sd_Observation.Residual') %>%
mutate(term = factor(term))
kable(index_coef_all8)
| AMBI |
scale(metals) |
-0.134 |
0.122 |
-1.103 |
-0.373 |
0.104 |
fixed |
| AMBI |
scale(sqrtmud) |
-0.557 |
0.115 |
-4.841 |
-0.782 |
-0.331 |
fixed |
| AMBI |
scale(sqrtTP) |
0.016 |
0.146 |
0.110 |
-0.271 |
0.303 |
fixed |
| AMBI |
Type8 |
-0.461 |
0.179 |
-2.573 |
-0.813 |
-0.110 |
fixed |
| AMBI |
scale(metals):Type8 |
-0.054 |
0.210 |
-0.257 |
-0.466 |
0.358 |
fixed |
| AMBI |
scale(sqrtmud):Type8 |
0.259 |
0.165 |
1.568 |
-0.065 |
0.583 |
fixed |
| AMBI |
scale(sqrtTP):Type8 |
-0.001 |
0.206 |
-0.007 |
-0.404 |
0.402 |
fixed |
| AMBI |
sd_(Intercept).estuary:Region |
0.284 |
NA |
NA |
NA |
NA |
estuary:Region |
| AMBI |
sd_(Intercept).year |
0.000 |
NA |
NA |
NA |
NA |
year |
| AMBI |
sd_(Intercept).Region |
0.000 |
NA |
NA |
NA |
NA |
Region |
| AMBI_S |
scale(metals) |
-0.178 |
0.114 |
-1.565 |
-0.401 |
0.045 |
fixed |
| AMBI_S |
scale(sqrtmud) |
-0.750 |
0.108 |
-6.960 |
-0.962 |
-0.539 |
fixed |
| AMBI_S |
scale(sqrtTP) |
0.036 |
0.136 |
0.263 |
-0.230 |
0.302 |
fixed |
| AMBI_S |
Type8 |
-0.319 |
0.178 |
-1.794 |
-0.668 |
0.030 |
fixed |
| AMBI_S |
scale(metals):Type8 |
-0.021 |
0.194 |
-0.111 |
-0.402 |
0.359 |
fixed |
| AMBI_S |
scale(sqrtmud):Type8 |
0.392 |
0.155 |
2.533 |
0.089 |
0.695 |
fixed |
| AMBI_S |
scale(sqrtTP):Type8 |
-0.066 |
0.190 |
-0.348 |
-0.439 |
0.307 |
fixed |
| AMBI_S |
sd_(Intercept).estuary:Region |
0.305 |
NA |
NA |
NA |
NA |
estuary:Region |
| AMBI_S |
sd_(Intercept).year |
0.050 |
NA |
NA |
NA |
NA |
year |
| AMBI_S |
sd_(Intercept).Region |
0.000 |
NA |
NA |
NA |
NA |
Region |
| BENTIX |
scale(metals) |
-0.267 |
0.126 |
-2.119 |
-0.514 |
-0.020 |
fixed |
| BENTIX |
scale(sqrtmud) |
-0.500 |
0.121 |
-4.132 |
-0.737 |
-0.263 |
fixed |
| BENTIX |
scale(sqrtTP) |
0.252 |
0.150 |
1.682 |
-0.042 |
0.546 |
fixed |
| BENTIX |
Type8 |
-0.492 |
0.262 |
-1.881 |
-1.005 |
0.021 |
fixed |
| BENTIX |
scale(metals):Type8 |
0.002 |
0.213 |
0.008 |
-0.417 |
0.420 |
fixed |
| BENTIX |
scale(sqrtmud):Type8 |
0.183 |
0.175 |
1.046 |
-0.160 |
0.526 |
fixed |
| BENTIX |
scale(sqrtTP):Type8 |
-0.087 |
0.210 |
-0.413 |
-0.498 |
0.325 |
fixed |
| BENTIX |
sd_(Intercept).estuary:Region |
0.540 |
NA |
NA |
NA |
NA |
estuary:Region |
| BENTIX |
sd_(Intercept).year |
0.000 |
NA |
NA |
NA |
NA |
year |
| BENTIX |
sd_(Intercept).Region |
0.000 |
NA |
NA |
NA |
NA |
Region |
| BQI |
scale(metals) |
-0.210 |
0.091 |
-2.306 |
-0.388 |
-0.031 |
fixed |
| BQI |
scale(sqrtmud) |
-0.213 |
0.089 |
-2.399 |
-0.387 |
-0.039 |
fixed |
| BQI |
scale(sqrtTP) |
-0.021 |
0.107 |
-0.200 |
-0.232 |
0.189 |
fixed |
| BQI |
Type8 |
-0.276 |
0.246 |
-1.123 |
-0.758 |
0.206 |
fixed |
| BQI |
scale(metals):Type8 |
-0.015 |
0.151 |
-0.101 |
-0.312 |
0.281 |
fixed |
| BQI |
scale(sqrtmud):Type8 |
-0.130 |
0.126 |
-1.030 |
-0.377 |
0.117 |
fixed |
| BQI |
scale(sqrtTP):Type8 |
0.298 |
0.149 |
2.004 |
0.007 |
0.589 |
fixed |
| BQI |
sd_(Intercept).estuary:Region |
0.489 |
NA |
NA |
NA |
NA |
estuary:Region |
| BQI |
sd_(Intercept).year |
0.091 |
NA |
NA |
NA |
NA |
year |
| BQI |
sd_(Intercept).Region |
0.607 |
NA |
NA |
NA |
NA |
Region |
| ITI |
scale(metals) |
-0.154 |
0.123 |
-1.249 |
-0.396 |
0.088 |
fixed |
| ITI |
scale(sqrtmud) |
-0.220 |
0.119 |
-1.857 |
-0.453 |
0.012 |
fixed |
| ITI |
scale(sqrtTP) |
0.327 |
0.144 |
2.266 |
0.044 |
0.610 |
fixed |
| ITI |
Type8 |
-0.451 |
0.258 |
-1.748 |
-0.957 |
0.055 |
fixed |
| ITI |
scale(metals):Type8 |
0.190 |
0.204 |
0.933 |
-0.209 |
0.589 |
fixed |
| ITI |
scale(sqrtmud):Type8 |
-0.215 |
0.167 |
-1.287 |
-0.544 |
0.113 |
fixed |
| ITI |
scale(sqrtTP):Type8 |
-0.018 |
0.199 |
-0.090 |
-0.409 |
0.373 |
fixed |
| ITI |
sd_(Intercept).estuary:Region |
0.535 |
NA |
NA |
NA |
NA |
estuary:Region |
| ITI |
sd_(Intercept).year |
0.199 |
NA |
NA |
NA |
NA |
year |
| ITI |
sd_(Intercept).Region |
0.000 |
NA |
NA |
NA |
NA |
Region |
| logN |
scale(metals) |
-0.129 |
0.095 |
-1.354 |
-0.315 |
0.057 |
fixed |
| logN |
scale(sqrtmud) |
0.330 |
0.094 |
3.513 |
0.146 |
0.514 |
fixed |
| logN |
scale(sqrtTP) |
-0.128 |
0.111 |
-1.155 |
-0.344 |
0.089 |
fixed |
| logN |
Type8 |
-0.802 |
0.212 |
-3.787 |
-1.218 |
-0.387 |
fixed |
| logN |
scale(metals):Type8 |
0.109 |
0.156 |
0.700 |
-0.196 |
0.414 |
fixed |
| logN |
scale(sqrtmud):Type8 |
-0.441 |
0.130 |
-3.380 |
-0.696 |
-0.185 |
fixed |
| logN |
scale(sqrtTP):Type8 |
0.428 |
0.152 |
2.809 |
0.129 |
0.727 |
fixed |
| logN |
sd_(Intercept).estuary:Region |
0.378 |
NA |
NA |
NA |
NA |
estuary:Region |
| logN |
sd_(Intercept).year |
0.162 |
NA |
NA |
NA |
NA |
year |
| logN |
sd_(Intercept).Region |
0.919 |
NA |
NA |
NA |
NA |
Region |
| M_AMBI |
scale(metals) |
-0.132 |
0.103 |
-1.277 |
-0.335 |
0.071 |
fixed |
| M_AMBI |
scale(sqrtmud) |
-0.455 |
0.100 |
-4.535 |
-0.652 |
-0.259 |
fixed |
| M_AMBI |
scale(sqrtTP) |
-0.059 |
0.122 |
-0.486 |
-0.298 |
0.180 |
fixed |
| M_AMBI |
Type8 |
-0.184 |
0.248 |
-0.740 |
-0.670 |
0.303 |
fixed |
| M_AMBI |
scale(metals):Type8 |
-0.068 |
0.173 |
-0.393 |
-0.406 |
0.270 |
fixed |
| M_AMBI |
scale(sqrtmud):Type8 |
0.005 |
0.143 |
0.037 |
-0.275 |
0.285 |
fixed |
| M_AMBI |
scale(sqrtTP):Type8 |
0.281 |
0.169 |
1.662 |
-0.051 |
0.613 |
fixed |
| M_AMBI |
sd_(Intercept).estuary:Region |
0.492 |
NA |
NA |
NA |
NA |
estuary:Region |
| M_AMBI |
sd_(Intercept).year |
0.096 |
NA |
NA |
NA |
NA |
year |
| M_AMBI |
sd_(Intercept).Region |
0.338 |
NA |
NA |
NA |
NA |
Region |
| S |
scale(metals) |
-0.176 |
0.091 |
-1.924 |
-0.355 |
0.003 |
fixed |
| S |
scale(sqrtmud) |
-0.153 |
0.089 |
-1.714 |
-0.328 |
0.022 |
fixed |
| S |
scale(sqrtTP) |
-0.059 |
0.107 |
-0.549 |
-0.269 |
0.151 |
fixed |
| S |
Type8 |
-0.325 |
0.236 |
-1.377 |
-0.787 |
0.137 |
fixed |
| S |
scale(metals):Type8 |
0.008 |
0.150 |
0.056 |
-0.286 |
0.303 |
fixed |
| S |
scale(sqrtmud):Type8 |
-0.215 |
0.126 |
-1.715 |
-0.461 |
0.031 |
fixed |
| S |
scale(sqrtTP):Type8 |
0.351 |
0.147 |
2.383 |
0.062 |
0.640 |
fixed |
| S |
sd_(Intercept).estuary:Region |
0.460 |
NA |
NA |
NA |
NA |
estuary:Region |
| S |
sd_(Intercept).year |
0.137 |
NA |
NA |
NA |
NA |
year |
| S |
sd_(Intercept).Region |
0.585 |
NA |
NA |
NA |
NA |
Region |
| TBI |
scale(metals) |
-0.270 |
0.087 |
-3.102 |
-0.441 |
-0.099 |
fixed |
| TBI |
scale(sqrtmud) |
-0.222 |
0.085 |
-2.601 |
-0.389 |
-0.055 |
fixed |
| TBI |
scale(sqrtTP) |
0.086 |
0.101 |
0.853 |
-0.112 |
0.284 |
fixed |
| TBI |
Type8 |
0.014 |
0.215 |
0.067 |
-0.406 |
0.435 |
fixed |
| TBI |
scale(metals):Type8 |
0.115 |
0.142 |
0.811 |
-0.163 |
0.392 |
fixed |
| TBI |
scale(sqrtmud):Type8 |
-0.144 |
0.118 |
-1.220 |
-0.376 |
0.088 |
fixed |
| TBI |
scale(sqrtTP):Type8 |
0.199 |
0.138 |
1.437 |
-0.072 |
0.470 |
fixed |
| TBI |
sd_(Intercept).estuary:Region |
0.413 |
NA |
NA |
NA |
NA |
estuary:Region |
| TBI |
sd_(Intercept).year |
0.187 |
NA |
NA |
NA |
NA |
year |
| TBI |
sd_(Intercept).Region |
0.470 |
NA |
NA |
NA |
NA |
Region |
##Model summary####
index_Summ8 <-
glance(EG_models8, fit_index8)
kable(index_Summ8)
| AMBI |
1.383 |
-310 |
645 |
686 |
621 |
225 |
| AMBI_S |
1.250 |
-289 |
602 |
643 |
578 |
225 |
| BENTIX |
1.281 |
-303 |
629 |
671 |
605 |
225 |
| BQI |
0.873 |
-223 |
469 |
511 |
445 |
225 |
| ITI |
1.203 |
-292 |
609 |
650 |
585 |
225 |
| logN |
0.922 |
-235 |
493 |
535 |
469 |
225 |
| M_AMBI |
1.011 |
-253 |
529 |
571 |
505 |
225 |
| S |
0.869 |
-222 |
469 |
510 |
445 |
225 |
| TBI |
0.818 |
-209 |
442 |
484 |
418 |
225 |
ggplot(index_coef_all8,
aes(
x = estimate,
y = fct_rev(fct_inorder(term)),
color = term
)) +
geom_point() +
geom_errorbarh(aes(
xmin = conf.low,
xmax = conf.high,
height = .2
)) +
facet_wrap( ~ index) +
geom_vline(xintercept = 0,
lty = 2,
col = 'gray60') +
ylab('') +
xlab('Coefficients') +
scale_color_discrete(guide = F) +
theme_javier()
## Warning: Removed 27 rows containing missing values (geom_errorbarh).
###residuals
index_residuals8 <-
augment(EG_models8, fit_index8)
ggplot(index_residuals8) +
geom_point(aes(x = .fitted, y = .resid), alpha = .3) +
facet_wrap(~ index, scale = 'free') +
geom_hline(yintercept = 0,
lty = 2,
col = 2)
ggplot(index_residuals8) +
stat_qq(aes(sample = .wtres), alpha = .3) +
facet_wrap(~ index, scale = 'free') +
geom_abline(
intercept = 0,
slope = 1,
lty = 2,
col = 2
)
library(MuMIn)
pseudo_rsq <-
EG_models8 %>%
ungroup() %>%
mutate(r_sq = map(fit_index8, r.squaredGLMM),
R_sq_dat = map(r_sq, data.frame)) %>%
unnest(R_sq_dat) %>%
rename(R_sq = .x..i..) %>%
mutate(Type = rep(c('Marginal', 'Conditional'), 9),
index = factor(index)) %>%
print()
## # A tibble: 18 × 3
## index R_sq Type
## <fctr> <dbl> <chr>
## 1 AMBI 0.1332 Marginal
## 2 AMBI 0.1682 Conditional
## 3 AMBI_S 0.2208 Marginal
## 4 AMBI_S 0.2656 Conditional
## 5 BENTIX 0.1201 Marginal
## 6 BENTIX 0.2530 Conditional
## 7 BQI 0.0915 Marginal
## 8 BQI 0.4975 Conditional
## 9 ITI 0.0931 Marginal
## 10 ITI 0.2598 Conditional
## 11 logN 0.1123 Marginal
## 12 logN 0.5950 Conditional
## 13 M_AMBI 0.1548 Marginal
## 14 M_AMBI 0.3774 Conditional
## 15 S 0.0896 Marginal
## 16 S 0.4821 Conditional
## 17 TBI 0.1048 Marginal
## 18 TBI 0.4532 Conditional
ggplot(pseudo_rsq) +
geom_col(aes(
x = fct_reorder(index, R_sq),
y = R_sq,
fill = Type
), position = 'stack') +
labs(y = 'Pseudo R - square', x = '') +
coord_flip() +
theme(legend.position = c(.9, .1))
###Model averaging######
options(na.action = "na.fail")
###AMBI_S#####
global_AMBI_S <-
lmer(
AMBI_S ~ (scale(metals) + scale(sqrtmud) + scale(sqrtTP)) * Type + (1|year) + (1|Region/estuary),
data = dat_mult,
weights = sqrt(n)
)
summary(global_AMBI_S)
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## AMBI_S ~ (scale(metals) + scale(sqrtmud) + scale(sqrtTP)) * Type +
## (1 | year) + (1 | Region/estuary)
## Data: dat_mult
## Weights: sqrt(n)
##
## REML criterion at convergence: 246
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.493 -0.587 -0.040 0.566 3.520
##
## Random effects:
## Groups Name Variance Std.Dev.
## estuary:Region (Intercept) 0.03021 0.1738
## year (Intercept) 0.00202 0.0449
## Region (Intercept) 0.00000 0.0000
## Residual 0.33823 0.5816
## Number of obs: 237, groups: estuary:Region, 24; year, 15; Region, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.88127 0.06351 12.40000 29.62 6.6e-13 ***
## scale(metals) 0.08024 0.05507 136.80000 1.46 0.147
## scale(sqrtmud) 0.36071 0.05252 115.60000 6.87 3.5e-10 ***
## scale(sqrtTP) -0.01558 0.06522 166.60000 -0.24 0.811
## Type8 0.14220 0.09392 14.10000 1.51 0.152
## scale(metals):Type8 0.00455 0.09309 184.80000 0.05 0.961
## scale(sqrtmud):Type8 -0.19354 0.07505 126.80000 -2.58 0.011 *
## scale(sqrtTP):Type8 0.02872 0.09119 178.80000 0.31 0.753
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(m) scl(s) sc(TP) Type8 scl(m):T8 scl(s):T8
## scale(mtls) -0.038
## scl(sqrtmd) 0.048 0.010
## scl(sqrtTP) 0.018 -0.573 -0.459
## Type8 -0.650 0.026 -0.036 -0.011
## scl(mtl):T8 0.023 -0.581 -0.010 0.334 0.115
## scl(sqr):T8 -0.034 -0.008 -0.692 0.319 -0.034 -0.137
## scl(sTP):T8 -0.013 0.401 0.327 -0.710 -0.080 -0.561 -0.438
set_AMBI_S <- dredge(global_AMBI_S, rank = 'AICc') %>% print()
## Warning in dredge(global_AMBI_S, rank = "AICc"): comparing models fitted by
## REML
## Fixed term is "(Intercept)"
## Global model call: lme4::lmer(formula = AMBI_S ~ (scale(metals) + scale(sqrtmud) +
## scale(sqrtTP)) * Type + (1 | year) + (1 | Region/estuary),
## data = dat_mult, weights = sqrt(n))
## ---
## Model selection table
## (Int) scl(mtl) scl(sqr) scl(sTP) Typ scl(mtl):Typ scl(sqr):Typ
## 3 1.93 0.289
## 43 1.89 0.387 + +
## 11 1.87 0.284 +
## 4 1.93 0.0681 0.259
## 44 1.88 0.0801 0.352 + +
## 7 1.92 0.262 0.04221
## 12 1.86 0.0772 0.248 +
## 47 1.89 0.359 0.04617 + +
## 15 1.87 0.256 0.04292 +
## 60 1.88 0.0730 0.354 + + +
## 8 1.93 0.0658 0.258 0.00371
## 48 1.88 0.0797 0.352 0.00039 + +
## 28 1.87 0.1007 0.261 + +
## 16 1.86 0.0774 0.249 -0.00122 +
## 111 1.89 0.363 0.03902 + +
## 79 1.88 0.267 0.08800 +
## 112 1.88 0.0819 0.360 -0.01667 + +
## 64 1.88 0.0734 0.355 -0.00113 + + +
## 80 1.87 0.0722 0.259 0.04189 +
## 32 1.87 0.0987 0.259 0.00392 + +
## 128 1.88 0.0802 0.361 -0.01558 + + +
## 96 1.87 0.0809 0.261 0.03528 + +
## 5 1.93 0.20230
## 13 1.85 0.19740 +
## 6 1.93 0.0851 0.14810
## 14 1.85 0.0964 0.13530 +
## 10 1.84 0.2007 +
## 2 1.95 0.2003
## 77 1.86 0.21820 +
## 30 1.85 0.0922 0.13350 + +
## 78 1.85 0.0954 0.15240 +
## 26 1.84 0.1764 + +
## 94 1.85 0.0741 0.16690 + +
## 1 1.96
## 9 1.85 +
## scl(sTP):Typ df logLik AICc delta weight
## 3 6 -119 250 0.00 0.481
## 43 8 -117 252 1.76 0.199
## 11 7 -119 253 3.27 0.094
## 4 7 -119 253 3.57 0.081
## 44 9 -118 254 4.17 0.060
## 7 7 -120 255 5.63 0.029
## 12 8 -120 256 6.01 0.024
## 47 9 -119 257 7.17 0.013
## 15 8 -121 259 8.89 0.006
## 60 10 -119 259 9.59 0.004
## 8 8 -122 260 10.05 0.003
## 48 10 -120 260 10.70 0.002
## 28 9 -121 260 10.73 0.002
## 16 9 -122 262 12.56 0.001
## 111 + 10 -121 262 12.64 0.001
## 79 + 9 -122 263 12.83 0.001
## 112 + 11 -121 266 16.06 0.000
## 64 11 -121 266 16.11 0.000
## 80 + 10 -123 267 16.97 0.000
## 32 10 -123 267 17.24 0.000
## 128 + 12 -123 271 21.19 0.000
## 96 + 11 -124 272 22.02 0.000
## 5 6 -139 291 41.03 0.000
## 13 7 -139 293 42.98 0.000
## 6 7 -140 294 44.42 0.000
## 14 8 -139 295 45.51 0.000
## 10 7 -142 298 47.85 0.000
## 2 6 -143 298 48.33 0.000
## 77 + 8 -141 298 48.51 0.000
## 30 9 -141 301 51.13 0.000
## 78 + 9 -141 301 51.18 0.000
## 26 8 -143 303 52.89 0.000
## 94 + 10 -142 306 55.77 0.000
## 1 5 -155 320 70.45 0.000
## 9 6 -154 321 70.93 0.000
## Models ranked by AICc(x)
## Random terms (all models):
## '1 | year', '1 | Region/estuary'
top_AMBI_S <- subset(set_AMBI_S, delta < 10) %>% print()
## Global model call: lme4::lmer(formula = AMBI_S ~ (scale(metals) + scale(sqrtmud) +
## scale(sqrtTP)) * Type + (1 | year) + (1 | Region/estuary),
## data = dat_mult, weights = sqrt(n))
## ---
## Model selection table
## (Int) scl(mtl) scl(sqr) scl(sTP) Typ scl(mtl):Typ scl(sqr):Typ df
## 3 1.93 0.289 6
## 43 1.89 0.387 + + 8
## 11 1.87 0.284 + 7
## 4 1.93 0.0681 0.259 7
## 44 1.88 0.0801 0.352 + + 9
## 7 1.92 0.262 0.0422 7
## 12 1.86 0.0772 0.248 + 8
## 47 1.89 0.359 0.0462 + + 9
## 15 1.87 0.256 0.0429 + 8
## 60 1.88 0.0730 0.354 + + + 10
## logLik AICc delta weight
## 3 -119 250 0.00 0.486
## 43 -117 252 1.76 0.201
## 11 -119 253 3.27 0.095
## 4 -119 253 3.57 0.082
## 44 -118 254 4.17 0.060
## 7 -120 255 5.63 0.029
## 12 -120 256 6.01 0.024
## 47 -119 257 7.17 0.013
## 15 -121 259 8.89 0.006
## 60 -119 259 9.59 0.004
## Models ranked by AICc(x)
## Random terms (all models):
## '1 | year', '1 | Region/estuary'
ave_AMBI_S <- model.avg(top_AMBI_S)
summary(ave_AMBI_S)
##
## Call:
## model.avg(object = top_AMBI_S)
## Warning in if (!is.na(comcallstr)) {: the condition has length > 1 and only
## the first element will be used
## Component model call:
## ::(formula = AMBI_S ~ <10 unique rhs>, data = dat_mult, weights =
## sqrt(n))
## lme4(formula = AMBI_S ~ <10 unique rhs>, data = dat_mult, weights
## = sqrt(n))
## lmer(formula = AMBI_S ~ <10 unique rhs>, data = dat_mult, weights
## = sqrt(n))
##
## Component models:
## df logLik AICc delta weight
## 2 6 -119 250 0.00 0.49
## 246 8 -117 252 1.76 0.20
## 24 7 -119 253 3.27 0.09
## 12 7 -119 253 3.57 0.08
## 1246 9 -118 254 4.17 0.06
## 23 7 -120 255 5.63 0.03
## 124 8 -120 256 6.01 0.02
## 2346 9 -119 257 7.17 0.01
## 234 8 -121 259 8.89 0.01
## 12456 10 -119 259 9.59 0.00
##
## Term codes:
## scale(metals) scale(sqrtmud) scale(sqrtTP)
## 1 2 3
## Type scale(metals):Type scale(sqrtmud):Type
## 4 5 6
##
## Model-averaged coefficients:
## (full average)
## Estimate Std. Error Adjusted SE z value Pr(>|z|)
## (Intercept) 1.91e+00 5.97e-02 5.99e-02 31.86 <2e-16 ***
## scale(sqrtmud) 3.09e-01 5.67e-02 5.68e-02 5.44 <2e-16 ***
## Type8 5.13e-02 8.63e-02 8.65e-02 0.59 0.55
## scale(sqrtmud):Type8 -4.77e-02 8.26e-02 8.26e-02 0.58 0.56
## scale(metals) 1.26e-02 3.16e-02 3.17e-02 0.40 0.69
## scale(sqrtTP) 2.09e-03 1.24e-02 1.25e-02 0.17 0.87
## scale(metals):Type8 8.57e-05 5.02e-03 5.05e-03 0.02 0.99
##
## (conditional average)
## Estimate Std. Error Adjusted SE z value Pr(>|z|)
## (Intercept) 1.9089 0.0597 0.0599 31.86 <2e-16 ***
## scale(sqrtmud) 0.3090 0.0567 0.0568 5.44 <2e-16 ***
## Type8 0.1272 0.0937 0.0942 1.35 0.177
## scale(sqrtmud):Type8 -0.1708 0.0582 0.0585 2.92 0.004 **
## scale(metals) 0.0738 0.0369 0.0371 1.99 0.047 *
## scale(sqrtTP) 0.0434 0.0377 0.0379 1.15 0.252
## scale(metals):Type8 0.0213 0.0762 0.0766 0.28 0.781
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Relative variable importance:
## scale(sqrtmud) Type scale(sqrtmud):Type
## Importance: 1 0.4 0.28
## N containing models: 10 7 4
## scale(metals) scale(sqrtTP) scale(metals):Type
## Importance: 0.17 0.05 <0.01
## N containing models: 4 3 1
confint(ave_AMBI_S)
## 2.5 % 97.5 %
## (Intercept) 1.79147 2.0264
## scale(sqrtmud) 0.19762 0.4203
## Type8 -0.05748 0.3119
## scale(sqrtmud):Type8 -0.28554 -0.0561
## scale(metals) 0.00111 0.1464
## scale(sqrtTP) -0.03083 0.1176
## scale(metals):Type8 -0.12893 0.1715
####TBI####
global_TBI <-
lmer(
TBI ~ (scale(metals) + scale(sqrtmud) + scale(sqrtTP)) * Type + (1|year) + (1|Region/estuary),
data = dat_mult,
weights = sqrt(n)
)
set_TBI <- dredge(global_TBI, rank = 'AICc')
## Warning in dredge(global_TBI, rank = "AICc"): comparing models fitted by
## REML
## Fixed term is "(Intercept)"
top_TBI <- subset(set_TBI, delta < 10)
kable(top_TBI)
| 3 |
0.277 |
NA |
-0.030 |
NA |
NA |
NA |
NA |
NA |
6 |
272 |
-531 |
0.00 |
0.920 |
| 4 |
0.277 |
-0.012 |
-0.024 |
NA |
NA |
NA |
NA |
NA |
7 |
269 |
-524 |
7.19 |
0.025 |
| 11 |
0.274 |
NA |
-0.030 |
NA |
+ |
NA |
NA |
NA |
7 |
269 |
-524 |
7.32 |
0.024 |
| 7 |
0.277 |
NA |
-0.036 |
0.009 |
NA |
NA |
NA |
NA |
7 |
269 |
-523 |
8.79 |
0.011 |
| 2 |
0.273 |
-0.026 |
NA |
NA |
NA |
NA |
NA |
NA |
6 |
267 |
-522 |
8.98 |
0.010 |
| 8 |
0.276 |
-0.028 |
-0.034 |
0.025 |
NA |
NA |
NA |
NA |
8 |
269 |
-522 |
9.22 |
0.009 |
ave_TBI <- model.avg(top_TBI)
summary(ave_TBI)
##
## Call:
## model.avg(object = top_TBI)
## Warning in if (!is.na(comcallstr)) {: the condition has length > 1 and only
## the first element will be used
## Component model call:
## ::(formula = TBI ~ <6 unique rhs>, data = dat_mult, weights =
## sqrt(n))
## lme4(formula = TBI ~ <6 unique rhs>, data = dat_mult, weights =
## sqrt(n))
## lmer(formula = TBI ~ <6 unique rhs>, data = dat_mult, weights =
## sqrt(n))
##
## Component models:
## df logLik AICc delta weight
## 2 6 272 -531 0.00 0.92
## 12 7 269 -524 7.19 0.03
## 24 7 269 -524 7.32 0.02
## 23 7 269 -523 8.79 0.01
## 1 6 267 -522 8.98 0.01
## 123 8 269 -522 9.22 0.01
##
## Term codes:
## scale(metals) scale(sqrtmud) scale(sqrtTP) Type
## 1 2 3 4
##
## Model-averaged coefficients:
## (full average)
## Estimate Std. Error Adjusted SE z value Pr(>|z|)
## (Intercept) 0.276920 0.035453 0.035640 7.77 <2e-16 ***
## scale(sqrtmud) -0.029336 0.006611 0.006638 4.42 <2e-16 ***
## scale(metals) -0.000831 0.004403 0.004406 0.19 0.85
## Type8 0.000202 0.004646 0.004668 0.04 0.96
## scale(sqrtTP) 0.000329 0.002816 0.002819 0.12 0.91
##
## (conditional average)
## Estimate Std. Error Adjusted SE z value Pr(>|z|)
## (Intercept) 0.27692 0.03545 0.03564 7.77 <2e-16 ***
## scale(sqrtmud) -0.02964 0.00593 0.00595 4.98 <2e-16 ***
## scale(metals) -0.01857 0.01019 0.01021 1.82 0.069 .
## Type8 0.00855 0.02900 0.02915 0.29 0.769
## scale(sqrtTP) 0.01602 0.01161 0.01164 1.38 0.169
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Relative variable importance:
## scale(sqrtmud) scale(metals) Type scale(sqrtTP)
## Importance: 0.99 0.04 0.02 0.02
## N containing models: 5 3 1 2
confint(ave_TBI)
## 2.5 % 97.5 %
## (Intercept) 0.20707 0.34677
## scale(sqrtmud) -0.04131 -0.01797
## scale(metals) -0.03859 0.00145
## Type8 -0.04858 0.06568
## scale(sqrtTP) -0.00679 0.03883
###AMBI#####
global_AMBI <-
lmer(
AMBI ~ (scale(metals) + scale(sqrtmud) + scale(sqrtTP)) * Type++(1 |
year) + (1 | Region / estuary),
data = dat_mult,
weights = sqrt(n)
)
summary(global_AMBI)
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: AMBI ~ (scale(metals) + scale(sqrtmud) + scale(sqrtTP)) * Type +
## +(1 | year) + (1 | Region/estuary)
## Data: dat_mult
## Weights: sqrt(n)
##
## REML criterion at convergence: 414
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.044 -0.556 -0.073 0.475 3.555
##
## Random effects:
## Groups Name Variance Std.Dev.
## estuary:Region (Intercept) 0.048986 0.2213
## year (Intercept) 0.000888 0.0298
## Region (Intercept) 0.000000 0.0000
## Residual 0.719940 0.8485
## Number of obs: 237, groups: estuary:Region, 24; year, 15; Region, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.76606 0.08373 10.60000 21.09 5.3e-10 ***
## scale(metals) 0.08404 0.07795 113.10000 1.08 0.283
## scale(sqrtmud) 0.35698 0.07404 90.90000 4.82 5.7e-06 ***
## scale(sqrtTP) -0.00983 0.09286 149.10000 -0.11 0.916
## Type8 0.27705 0.12559 12.70000 2.21 0.046 *
## scale(metals):Type8 0.02905 0.13300 166.00000 0.22 0.827
## scale(sqrtmud):Type8 -0.17800 0.10634 102.30000 -1.67 0.097 .
## scale(sqrtTP):Type8 -0.00299 0.13031 161.80000 -0.02 0.982
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(m) scl(s) sc(TP) Type8 scl(m):T8 scl(s):T8
## scale(mtls) -0.041
## scl(sqrtmd) 0.053 0.000
## scl(sqrtTP) 0.013 -0.563 -0.457
## Type8 -0.660 0.028 -0.037 -0.009
## scl(mtl):T8 0.025 -0.584 -0.001 0.329 0.119
## scl(sqr):T8 -0.037 0.000 -0.694 0.318 -0.037 -0.142
## scl(sTP):T8 -0.010 0.399 0.325 -0.711 -0.084 -0.563 -0.430
set_AMBI <- dredge(global_AMBI, rank = 'AICc') %>% print()
## Warning in dredge(global_AMBI, rank = "AICc"): comparing models fitted by
## REML
## Fixed term is "(Intercept)"
## Global model call: lme4::lmer(formula = AMBI ~ (scale(metals) + scale(sqrtmud) +
## scale(sqrtTP)) * Type + +(1 | year) + (1 | Region/estuary),
## data = dat_mult, weights = sqrt(n))
## ---
## Model selection table
## (Int) scl(mtl) scl(sqr) scl(sTP) Typ scl(mtl):Typ scl(sqr):Typ
## 3 1.87 0.302
## 11 1.75 0.291 +
## 43 1.77 0.382 + +
## 12 1.75 0.0812 0.255 +
## 4 1.88 0.0672 0.273
## 7 1.87 0.275 0.04263
## 15 1.75 0.263 0.04406 +
## 44 1.76 0.0881 0.348 + +
## 47 1.77 0.354 0.04802 + +
## 28 1.75 0.1036 0.263 + +
## 16 1.75 0.0870 0.259 -0.01080 +
## 79 1.76 0.271 0.09028 +
## 8 1.87 0.0663 0.272 0.00154
## 60 1.76 0.0790 0.351 + + +
## 48 1.76 0.0932 0.352 -0.00923 + +
## 111 1.77 0.355 0.04791 + +
## 80 1.75 0.0849 0.266 0.03501 +
## 32 1.75 0.1068 0.266 -0.00613 + +
## 64 1.77 0.0847 0.357 -0.01127 + + +
## 112 1.76 0.0938 0.356 -0.01637 + +
## 96 1.75 0.0845 0.267 0.03559 + +
## 128 1.77 0.0840 0.357 -0.00983 + + +
## 13 1.74 0.21090 +
## 5 1.87 0.21790
## 10 1.73 0.2135 +
## 14 1.73 0.0993 0.14660 +
## 6 1.88 0.0830 0.16510
## 77 1.74 0.23740 +
## 2 1.90 0.2125
## 26 1.73 0.1853 + +
## 30 1.73 0.0935 0.14430 + +
## 78 1.73 0.0989 0.17190 +
## 94 1.74 0.0669 0.19340 + +
## 9 1.74 +
## 1 1.90
## scl(sTP):Typ df logLik AICc delta weight
## 3 6 -203 418 0.00 0.373
## 11 7 -202 418 0.20 0.338
## 43 8 -202 421 2.44 0.110
## 12 8 -203 422 3.95 0.052
## 4 7 -204 423 4.56 0.038
## 7 7 -205 424 5.52 0.024
## 15 8 -204 424 5.73 0.021
## 44 9 -203 424 5.88 0.020
## 47 9 -204 426 7.83 0.007
## 28 9 -204 427 8.69 0.005
## 16 9 -205 428 9.79 0.003
## 79 + 9 -205 428 10.02 0.002
## 8 8 -206 429 10.32 0.002
## 60 10 -204 429 10.61 0.002
## 48 10 -204 430 11.69 0.001
## 111 + 10 -205 431 12.65 0.001
## 80 + 10 -206 432 14.21 0.000
## 32 10 -206 433 14.52 0.000
## 64 11 -206 435 16.41 0.000
## 112 + 11 -206 435 16.50 0.000
## 96 + 11 -207 437 18.64 0.000
## 128 + 12 -207 439 20.87 0.000
## 13 7 -213 441 23.05 0.000
## 5 6 -215 442 23.56 0.000
## 10 7 -215 445 26.36 0.000
## 14 8 -214 445 26.64 0.000
## 6 7 -216 446 27.80 0.000
## 77 + 8 -215 446 27.97 0.000
## 2 6 -217 447 28.86 0.000
## 26 8 -216 449 30.95 0.000
## 30 9 -216 450 31.61 0.000
## 78 + 9 -216 450 31.61 0.000
## 94 + 10 -216 454 35.50 0.000
## 9 6 -222 457 38.66 0.000
## 1 5 -224 458 40.16 0.000
## Models ranked by AICc(x)
## Random terms (all models):
## '1 | year', '1 | Region/estuary'
top_AMBI <- subset(set_AMBI, delta < 10) %>% print()
## Global model call: lme4::lmer(formula = AMBI ~ (scale(metals) + scale(sqrtmud) +
## scale(sqrtTP)) * Type + +(1 | year) + (1 | Region/estuary),
## data = dat_mult, weights = sqrt(n))
## ---
## Model selection table
## (Int) scl(mtl) scl(sqr) scl(sTP) Typ scl(mtl):Typ scl(sqr):Typ df
## 3 1.87 0.302 6
## 11 1.75 0.291 + 7
## 43 1.77 0.382 + + 8
## 12 1.75 0.0812 0.255 + 8
## 4 1.88 0.0672 0.273 7
## 7 1.87 0.275 0.0426 7
## 15 1.75 0.263 0.0441 + 8
## 44 1.76 0.0881 0.348 + + 9
## 47 1.77 0.354 0.0480 + + 9
## 28 1.75 0.1036 0.263 + + 9
## 16 1.75 0.0870 0.259 -0.0108 + 9
## logLik AICc delta weight
## 3 -203 418 0.00 0.376
## 11 -202 418 0.20 0.341
## 43 -202 421 2.44 0.111
## 12 -203 422 3.95 0.052
## 4 -204 423 4.56 0.039
## 7 -205 424 5.52 0.024
## 15 -204 424 5.73 0.021
## 44 -203 424 5.88 0.020
## 47 -204 426 7.83 0.007
## 28 -204 427 8.69 0.005
## 16 -205 428 9.79 0.003
## Models ranked by AICc(x)
## Random terms (all models):
## '1 | year', '1 | Region/estuary'
ave_AMBI <- model.avg(top_AMBI)
summary(ave_AMBI_S)
##
## Call:
## model.avg(object = top_AMBI_S)
## Warning in if (!is.na(comcallstr)) {: the condition has length > 1 and only
## the first element will be used
## Component model call:
## ::(formula = AMBI_S ~ <10 unique rhs>, data = dat_mult, weights =
## sqrt(n))
## lme4(formula = AMBI_S ~ <10 unique rhs>, data = dat_mult, weights
## = sqrt(n))
## lmer(formula = AMBI_S ~ <10 unique rhs>, data = dat_mult, weights
## = sqrt(n))
##
## Component models:
## df logLik AICc delta weight
## 2 6 -119 250 0.00 0.49
## 246 8 -117 252 1.76 0.20
## 24 7 -119 253 3.27 0.09
## 12 7 -119 253 3.57 0.08
## 1246 9 -118 254 4.17 0.06
## 23 7 -120 255 5.63 0.03
## 124 8 -120 256 6.01 0.02
## 2346 9 -119 257 7.17 0.01
## 234 8 -121 259 8.89 0.01
## 12456 10 -119 259 9.59 0.00
##
## Term codes:
## scale(metals) scale(sqrtmud) scale(sqrtTP)
## 1 2 3
## Type scale(metals):Type scale(sqrtmud):Type
## 4 5 6
##
## Model-averaged coefficients:
## (full average)
## Estimate Std. Error Adjusted SE z value Pr(>|z|)
## (Intercept) 1.91e+00 5.97e-02 5.99e-02 31.86 <2e-16 ***
## scale(sqrtmud) 3.09e-01 5.67e-02 5.68e-02 5.44 <2e-16 ***
## Type8 5.13e-02 8.63e-02 8.65e-02 0.59 0.55
## scale(sqrtmud):Type8 -4.77e-02 8.26e-02 8.26e-02 0.58 0.56
## scale(metals) 1.26e-02 3.16e-02 3.17e-02 0.40 0.69
## scale(sqrtTP) 2.09e-03 1.24e-02 1.25e-02 0.17 0.87
## scale(metals):Type8 8.57e-05 5.02e-03 5.05e-03 0.02 0.99
##
## (conditional average)
## Estimate Std. Error Adjusted SE z value Pr(>|z|)
## (Intercept) 1.9089 0.0597 0.0599 31.86 <2e-16 ***
## scale(sqrtmud) 0.3090 0.0567 0.0568 5.44 <2e-16 ***
## Type8 0.1272 0.0937 0.0942 1.35 0.177
## scale(sqrtmud):Type8 -0.1708 0.0582 0.0585 2.92 0.004 **
## scale(metals) 0.0738 0.0369 0.0371 1.99 0.047 *
## scale(sqrtTP) 0.0434 0.0377 0.0379 1.15 0.252
## scale(metals):Type8 0.0213 0.0762 0.0766 0.28 0.781
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Relative variable importance:
## scale(sqrtmud) Type scale(sqrtmud):Type
## Importance: 1 0.4 0.28
## N containing models: 10 7 4
## scale(metals) scale(sqrtTP) scale(metals):Type
## Importance: 0.17 0.05 <0.01
## N containing models: 4 3 1
confint(ave_AMBI_S)
## 2.5 % 97.5 %
## (Intercept) 1.79147 2.0264
## scale(sqrtmud) 0.19762 0.4203
## Type8 -0.05748 0.3119
## scale(sqrtmud):Type8 -0.28554 -0.0561
## scale(metals) 0.00111 0.1464
## scale(sqrtTP) -0.03083 0.1176
## scale(metals):Type8 -0.12893 0.1715
###BQI#####
global_BQI <-
lmer(
BQI ~ (scale(metals) + scale(sqrtmud) + scale(sqrtTP)) * Type + (1|year) + (1|Region/estuary),
data = dat_mult,
weights = sqrt(n)
)
summary(global_BQI)
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: BQI ~ (scale(metals) + scale(sqrtmud) + scale(sqrtTP)) * Type +
## (1 | year) + (1 | Region/estuary)
## Data: dat_mult
## Weights: sqrt(n)
##
## REML criterion at convergence: 688
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8073 -0.6246 0.0042 0.6746 2.2035
##
## Random effects:
## Groups Name Variance Std.Dev.
## estuary:Region (Intercept) 0.6921 0.832
## year (Intercept) 0.0235 0.153
## Region (Intercept) 1.4180 1.191
## Residual 2.0693 1.438
## Number of obs: 237, groups: estuary:Region, 24; year, 15; Region, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.0245 0.6529 3.0000 7.70 0.0044 **
## scale(metals) -0.3404 0.1505 205.1000 -2.26 0.0247 *
## scale(sqrtmud) -0.3366 0.1468 210.7000 -2.29 0.0229 *
## scale(sqrtTP) -0.0379 0.1777 219.3000 -0.21 0.8311
## Type8 -0.4843 0.4177 20.4000 -1.16 0.2597
## scale(metals):Type8 -0.0332 0.2503 222.4000 -0.13 0.8947
## scale(sqrtmud):Type8 -0.2201 0.2084 226.8000 -1.06 0.2920
## scale(sqrtTP):Type8 0.4905 0.2459 218.0000 1.99 0.0473 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(m) scl(s) sc(TP) Type8 scl(m):T8 scl(s):T8
## scale(mtls) -0.024
## scl(sqrtmd) -0.001 0.052
## scl(sqrtTP) 0.021 -0.611 -0.463
## Type8 -0.248 0.021 -0.081 -0.015
## scl(mtl):T8 0.002 -0.574 -0.039 0.350 0.121
## scl(sqr):T8 -0.004 -0.038 -0.698 0.325 0.037 -0.106
## scl(sTP):T8 -0.005 0.423 0.337 -0.710 -0.081 -0.565 -0.469
set_BQI <- dredge(global_BQI, rank = 'AICc')
## Warning in dredge(global_BQI, rank = "AICc"): comparing models fitted by
## REML
## Fixed term is "(Intercept)"
top_BQI <- subset(set_BQI, delta < 10)
kable(top_BQI)
| 4 |
4.87 |
-0.252 |
-0.334 |
NA |
NA |
NA |
NA |
NA |
7 |
-346 |
706 |
0.00 |
0.227 |
| 12 |
5.01 |
-0.257 |
-0.327 |
NA |
+ |
NA |
NA |
NA |
8 |
-345 |
708 |
1.24 |
0.122 |
| 3 |
4.88 |
NA |
-0.453 |
NA |
NA |
NA |
NA |
NA |
6 |
-348 |
708 |
1.38 |
0.114 |
| 8 |
4.86 |
-0.378 |
-0.421 |
0.217 |
NA |
NA |
NA |
NA |
8 |
-346 |
708 |
1.50 |
0.107 |
| 80 |
5.02 |
-0.369 |
-0.449 |
0.040 |
+ |
NA |
NA |
+ |
10 |
-344 |
708 |
1.95 |
0.086 |
| 16 |
5.04 |
-0.392 |
-0.418 |
0.230 |
+ |
NA |
NA |
NA |
9 |
-345 |
709 |
2.43 |
0.067 |
| 28 |
5.01 |
-0.350 |
-0.369 |
NA |
+ |
+ |
NA |
NA |
9 |
-345 |
709 |
2.80 |
0.056 |
| 11 |
4.99 |
NA |
-0.447 |
NA |
+ |
NA |
NA |
NA |
7 |
-347 |
709 |
2.89 |
0.053 |
| 112 |
5.03 |
-0.352 |
-0.338 |
-0.030 |
+ |
NA |
+ |
+ |
11 |
-344 |
711 |
4.32 |
0.026 |
| 32 |
5.03 |
-0.463 |
-0.449 |
0.213 |
+ |
+ |
NA |
NA |
10 |
-345 |
711 |
4.49 |
0.024 |
| 44 |
5.01 |
-0.263 |
-0.387 |
NA |
+ |
NA |
+ |
NA |
9 |
-346 |
711 |
4.80 |
0.021 |
| 96 |
5.02 |
-0.348 |
-0.445 |
0.024 |
+ |
+ |
NA |
+ |
11 |
-344 |
711 |
5.03 |
0.018 |
| 48 |
5.03 |
-0.394 |
-0.467 |
0.226 |
+ |
NA |
+ |
NA |
10 |
-346 |
712 |
6.14 |
0.011 |
| 79 |
4.97 |
NA |
-0.475 |
-0.205 |
+ |
NA |
NA |
+ |
9 |
-347 |
713 |
6.19 |
0.010 |
| 7 |
4.88 |
NA |
-0.447 |
-0.008 |
NA |
NA |
NA |
NA |
7 |
-349 |
713 |
6.21 |
0.010 |
| 60 |
5.01 |
-0.353 |
-0.359 |
NA |
+ |
+ |
+ |
NA |
10 |
-346 |
713 |
6.50 |
0.009 |
| 43 |
4.99 |
NA |
-0.489 |
NA |
+ |
NA |
+ |
NA |
8 |
-348 |
713 |
6.67 |
0.008 |
| 2 |
4.81 |
-0.438 |
NA |
NA |
NA |
NA |
NA |
NA |
6 |
-350 |
713 |
6.99 |
0.007 |
| 128 |
5.02 |
-0.340 |
-0.337 |
-0.038 |
+ |
+ |
+ |
+ |
12 |
-344 |
714 |
7.45 |
0.005 |
| 10 |
5.00 |
-0.438 |
NA |
NA |
+ |
NA |
NA |
NA |
7 |
-350 |
714 |
7.67 |
0.005 |
| 111 |
4.98 |
NA |
-0.326 |
-0.283 |
+ |
NA |
+ |
+ |
10 |
-347 |
714 |
7.71 |
0.005 |
| 15 |
4.99 |
NA |
-0.443 |
-0.006 |
+ |
NA |
NA |
NA |
8 |
-349 |
714 |
7.74 |
0.005 |
| 64 |
5.03 |
-0.467 |
-0.437 |
0.213 |
+ |
+ |
+ |
NA |
11 |
-346 |
715 |
8.21 |
0.004 |
ave_BQI <- model.avg(top_BQI)
summary(ave_BQI)
##
## Call:
## model.avg(object = top_BQI)
## Warning in if (!is.na(comcallstr)) {: the condition has length > 1 and only
## the first element will be used
## Component model call:
## ::(formula = BQI ~ <23 unique rhs>, data = dat_mult, weights =
## sqrt(n))
## lme4(formula = BQI ~ <23 unique rhs>, data = dat_mult, weights =
## sqrt(n))
## lmer(formula = BQI ~ <23 unique rhs>, data = dat_mult, weights =
## sqrt(n))
##
## Component models:
## df logLik AICc delta weight
## 12 7 -346 706 0.00 0.23
## 124 8 -345 708 1.24 0.12
## 2 6 -348 708 1.38 0.11
## 123 8 -346 708 1.50 0.11
## 12347 10 -344 708 1.95 0.09
## 1234 9 -345 709 2.43 0.07
## 1245 9 -345 709 2.80 0.06
## 24 7 -347 709 2.89 0.05
## 123467 11 -344 711 4.32 0.03
## 12345 10 -345 711 4.49 0.02
## 1246 9 -346 711 4.80 0.02
## 123457 11 -344 711 5.03 0.02
## 12346 10 -346 712 6.14 0.01
## 2347 9 -347 713 6.19 0.01
## 23 7 -349 713 6.21 0.01
## 12456 10 -346 713 6.50 0.01
## 246 8 -348 713 6.67 0.01
## 1 6 -350 713 6.99 0.01
## 1234567 12 -344 714 7.45 0.01
## 14 7 -350 714 7.67 0.00
## 23467 10 -347 714 7.71 0.00
## 234 8 -349 714 7.74 0.00
## 123456 11 -346 715 8.21 0.00
##
## Term codes:
## scale(metals) scale(sqrtmud) scale(sqrtTP)
## 1 2 3
## Type scale(metals):Type scale(sqrtmud):Type
## 4 5 6
## scale(sqrtTP):Type
## 7
##
## Model-averaged coefficients:
## (full average)
## Estimate Std. Error Adjusted SE z value Pr(>|z|)
## (Intercept) 4.94596 0.60385 0.60701 8.15 <2e-16 ***
## scale(metals) -0.25478 0.17397 0.17428 1.46 0.144
## scale(sqrtmud) -0.38797 0.11916 0.11959 3.24 0.001 ***
## Type8 -0.21270 0.37030 0.37169 0.57 0.567
## scale(sqrtTP) 0.04638 0.12829 0.12859 0.36 0.718
## scale(sqrtTP):Type8 0.05693 0.15382 0.15399 0.37 0.712
## scale(metals):Type8 0.02276 0.10192 0.10216 0.22 0.824
## scale(sqrtmud):Type8 -0.00508 0.07295 0.07317 0.07 0.945
##
## (conditional average)
## Estimate Std. Error Adjusted SE z value Pr(>|z|)
## (Intercept) 4.9460 0.6038 0.6070 8.15 <2e-16 ***
## scale(metals) -0.3206 0.1304 0.1309 2.45 0.014 *
## scale(sqrtmud) -0.3926 0.1120 0.1125 3.49 <2e-16 ***
## Type8 -0.3975 0.4276 0.4298 0.92 0.355
## scale(sqrtTP) 0.1225 0.1848 0.1853 0.66 0.509
## scale(sqrtTP):Type8 0.3775 0.1894 0.1903 1.98 0.047 *
## scale(metals):Type8 0.1958 0.2355 0.2364 0.83 0.407
## scale(sqrtmud):Type8 -0.0576 0.2394 0.2401 0.24 0.810
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Relative variable importance:
## scale(sqrtmud) scale(metals) Type scale(sqrtTP)
## Importance: 0.99 0.79 0.54 0.38
## N containing models: 21 16 18 13
## scale(sqrtTP):Type scale(metals):Type
## Importance: 0.15 0.12
## N containing models: 6 6
## scale(sqrtmud):Type
## Importance: 0.09
## N containing models: 8
confint(ave_BQI)
## 2.5 % 97.5 %
## (Intercept) 3.75625 6.1357
## scale(metals) -0.57707 -0.0641
## scale(sqrtmud) -0.61309 -0.1721
## Type8 -1.23986 0.4449
## scale(sqrtTP) -0.24076 0.4857
## scale(sqrtTP):Type8 0.00442 0.7505
## scale(metals):Type8 -0.26753 0.6592
## scale(sqrtmud):Type8 -0.52828 0.4130