###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 'tibble' was built under R version 3.3.3
## Warning: package 'tidyr' was built under R version 3.3.3
## Warning: package 'readr' was built under R version 3.3.3
## Warning: package 'purrr' was built under R version 3.3.3
## Warning: package 'dplyr' 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)
library(sjPlot)
## Warning: package 'sjPlot' was built under R version 3.3.3
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))
## Warning: package 'bindrcpp' was built under R version 3.3.3
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) %>%
mutate(
TBI = sqrt(TBI),
S = sqrt(S),
BENTIX = sqrt(max(BENTIX) - BENTIX) * -1,
AMBI = sqrt(max(AMBI) - AMBI),
AMBI_S = AMBI_S * -1,
Mud = scale(sqrtmud),
TP = scale(sqrtTP),
Metals = scale(metals)
)
dat_mult_centered <-
dat_mult %>%
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(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 ~
Mud +
Metals +
TP +
(1|Type) +
(0+Mud|Type) +
(0+TP|Type)+
(0+Metals|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(.) %>%
mutate(
term = factor(term),
term = fct_recode (
term,
Intercept = '(Intercept)',
Type = 'sd_(Intercept).Type.3',
`Mud x Type` = 'sd_Mud.Type.2',
`Metals x Type` = 'sd_Metals.Type',
`TP x Type` = 'sd_TP.Type.1',
`Estuary (Region)` = 'sd_(Intercept).estuary.Region',
Year = 'sd_(Intercept).year',
Region = 'sd_(Intercept).Region',
Residual = 'sd_Observation.Residual'
)
)
kable(index_coef_all8)
| AMBI |
Intercept |
-0.127 |
0.143 |
-0.883 |
-0.408 |
0.154 |
fixed |
| AMBI |
Mud |
-0.420 |
0.102 |
-4.136 |
-0.619 |
-0.221 |
fixed |
| AMBI |
Metals |
-0.170 |
0.105 |
-1.611 |
-0.376 |
0.037 |
fixed |
| AMBI |
TP |
0.025 |
0.108 |
0.230 |
-0.187 |
0.237 |
fixed |
| AMBI |
Estuary (Region) |
0.358 |
NA |
NA |
NA |
NA |
estuary.Region |
| AMBI |
Year |
0.096 |
NA |
NA |
NA |
NA |
year |
| AMBI |
Region |
0.000 |
NA |
NA |
NA |
NA |
Region |
| AMBI |
Metals x Type |
0.000 |
NA |
NA |
NA |
NA |
Type |
| AMBI |
TP x Type |
0.000 |
NA |
NA |
NA |
NA |
Type.1 |
| AMBI |
Mud x Type |
0.072 |
NA |
NA |
NA |
NA |
Type.2 |
| AMBI |
Type |
0.138 |
NA |
NA |
NA |
NA |
Type.3 |
| AMBI |
Residual |
1.419 |
NA |
NA |
NA |
NA |
Residual |
| AMBI_S |
Intercept |
-0.104 |
0.110 |
-0.940 |
-0.320 |
0.112 |
fixed |
| AMBI_S |
Mud |
-0.570 |
0.134 |
-4.247 |
-0.833 |
-0.307 |
fixed |
| AMBI_S |
Metals |
-0.158 |
0.094 |
-1.680 |
-0.341 |
0.026 |
fixed |
| AMBI_S |
TP |
-0.009 |
0.097 |
-0.095 |
-0.198 |
0.180 |
fixed |
| AMBI_S |
Estuary (Region) |
0.343 |
NA |
NA |
NA |
NA |
estuary.Region |
| AMBI_S |
Year |
0.085 |
NA |
NA |
NA |
NA |
year |
| AMBI_S |
Region |
0.000 |
NA |
NA |
NA |
NA |
Region |
| AMBI_S |
Metals x Type |
0.000 |
NA |
NA |
NA |
NA |
Type |
| AMBI_S |
TP x Type |
0.000 |
NA |
NA |
NA |
NA |
Type.1 |
| AMBI_S |
Mud x Type |
0.154 |
NA |
NA |
NA |
NA |
Type.2 |
| AMBI_S |
Type |
0.074 |
NA |
NA |
NA |
NA |
Type.3 |
| AMBI_S |
Residual |
1.251 |
NA |
NA |
NA |
NA |
Residual |
| BENTIX |
Intercept |
-0.163 |
0.176 |
-0.928 |
-0.508 |
0.181 |
fixed |
| BENTIX |
Mud |
-0.407 |
0.086 |
-4.703 |
-0.576 |
-0.237 |
fixed |
| BENTIX |
Metals |
-0.243 |
0.101 |
-2.406 |
-0.441 |
-0.045 |
fixed |
| BENTIX |
TP |
0.213 |
0.105 |
2.036 |
0.008 |
0.419 |
fixed |
| BENTIX |
Estuary (Region) |
0.567 |
NA |
NA |
NA |
NA |
estuary.Region |
| BENTIX |
Year |
0.000 |
NA |
NA |
NA |
NA |
year |
| BENTIX |
Region |
0.000 |
NA |
NA |
NA |
NA |
Region |
| BENTIX |
Metals x Type |
0.000 |
NA |
NA |
NA |
NA |
Type |
| BENTIX |
TP x Type |
0.000 |
NA |
NA |
NA |
NA |
Type.1 |
| BENTIX |
Mud x Type |
0.000 |
NA |
NA |
NA |
NA |
Type.2 |
| BENTIX |
Type |
0.161 |
NA |
NA |
NA |
NA |
Type.3 |
| BENTIX |
Residual |
1.283 |
NA |
NA |
NA |
NA |
Residual |
| BQI |
Intercept |
-0.039 |
0.285 |
-0.136 |
-0.598 |
0.520 |
fixed |
| BQI |
Mud |
-0.271 |
0.063 |
-4.286 |
-0.395 |
-0.147 |
fixed |
| BQI |
Metals |
-0.223 |
0.074 |
-3.004 |
-0.369 |
-0.078 |
fixed |
| BQI |
TP |
0.125 |
0.094 |
1.324 |
-0.060 |
0.309 |
fixed |
| BQI |
Estuary (Region) |
0.525 |
NA |
NA |
NA |
NA |
estuary.Region |
| BQI |
Year |
0.098 |
NA |
NA |
NA |
NA |
year |
| BQI |
Region |
0.506 |
NA |
NA |
NA |
NA |
Region |
| BQI |
Metals x Type |
0.000 |
NA |
NA |
NA |
NA |
Type |
| BQI |
TP x Type |
0.079 |
NA |
NA |
NA |
NA |
Type.1 |
| BQI |
Mud x Type |
0.000 |
NA |
NA |
NA |
NA |
Type.2 |
| BQI |
Type |
0.000 |
NA |
NA |
NA |
NA |
Type.3 |
| BQI |
Residual |
0.877 |
NA |
NA |
NA |
NA |
Residual |
| ITI |
Intercept |
-0.169 |
0.182 |
-0.930 |
-0.526 |
0.187 |
fixed |
| ITI |
Mud |
-0.329 |
0.085 |
-3.867 |
-0.496 |
-0.162 |
fixed |
| ITI |
Metals |
-0.095 |
0.101 |
-0.943 |
-0.294 |
0.103 |
fixed |
| ITI |
TP |
0.318 |
0.102 |
3.113 |
0.118 |
0.518 |
fixed |
| ITI |
Estuary (Region) |
0.550 |
NA |
NA |
NA |
NA |
estuary.Region |
| ITI |
Year |
0.217 |
NA |
NA |
NA |
NA |
year |
| ITI |
Region |
0.000 |
NA |
NA |
NA |
NA |
Region |
| ITI |
Metals x Type |
0.000 |
NA |
NA |
NA |
NA |
Type |
| ITI |
TP x Type |
0.000 |
NA |
NA |
NA |
NA |
Type.1 |
| ITI |
Mud x Type |
0.000 |
NA |
NA |
NA |
NA |
Type.2 |
| ITI |
Type |
0.159 |
NA |
NA |
NA |
NA |
Type.3 |
| ITI |
Residual |
1.209 |
NA |
NA |
NA |
NA |
Residual |
| logN |
Intercept |
-0.313 |
0.574 |
-0.545 |
-1.438 |
0.812 |
fixed |
| logN |
Mud |
0.121 |
0.131 |
0.928 |
-0.135 |
0.378 |
fixed |
| logN |
Metals |
-0.103 |
0.080 |
-1.288 |
-0.259 |
0.054 |
fixed |
| logN |
TP |
0.090 |
0.157 |
0.571 |
-0.218 |
0.397 |
fixed |
| logN |
Estuary (Region) |
0.406 |
NA |
NA |
NA |
NA |
estuary.Region |
| logN |
Year |
0.175 |
NA |
NA |
NA |
NA |
year |
| logN |
Region |
0.916 |
NA |
NA |
NA |
NA |
Region |
| logN |
Metals x Type |
0.000 |
NA |
NA |
NA |
NA |
Type |
| logN |
TP x Type |
0.191 |
NA |
NA |
NA |
NA |
Type.1 |
| logN |
Mud x Type |
0.159 |
NA |
NA |
NA |
NA |
Type.2 |
| logN |
Type |
0.459 |
NA |
NA |
NA |
NA |
Type.3 |
| logN |
Residual |
0.930 |
NA |
NA |
NA |
NA |
Residual |
| M_AMBI |
Intercept |
-0.018 |
0.197 |
-0.089 |
-0.405 |
0.369 |
fixed |
| M_AMBI |
Mud |
-0.450 |
0.071 |
-6.302 |
-0.590 |
-0.310 |
fixed |
| M_AMBI |
Metals |
-0.154 |
0.084 |
-1.831 |
-0.318 |
0.011 |
fixed |
| M_AMBI |
TP |
0.080 |
0.109 |
0.732 |
-0.133 |
0.293 |
fixed |
| M_AMBI |
Estuary (Region) |
0.505 |
NA |
NA |
NA |
NA |
estuary.Region |
| M_AMBI |
Year |
0.102 |
NA |
NA |
NA |
NA |
year |
| M_AMBI |
Region |
0.303 |
NA |
NA |
NA |
NA |
Region |
| M_AMBI |
Metals x Type |
0.000 |
NA |
NA |
NA |
NA |
Type |
| M_AMBI |
TP x Type |
0.094 |
NA |
NA |
NA |
NA |
Type.1 |
| M_AMBI |
Mud x Type |
0.000 |
NA |
NA |
NA |
NA |
Type.2 |
| M_AMBI |
Type |
0.000 |
NA |
NA |
NA |
NA |
Type.3 |
| M_AMBI |
Residual |
1.015 |
NA |
NA |
NA |
NA |
Residual |
| S |
Intercept |
-0.048 |
0.256 |
-0.189 |
-0.549 |
0.453 |
fixed |
| S |
Mud |
-0.254 |
0.064 |
-3.980 |
-0.379 |
-0.129 |
fixed |
| S |
Metals |
-0.185 |
0.076 |
-2.446 |
-0.333 |
-0.037 |
fixed |
| S |
TP |
0.112 |
0.099 |
1.132 |
-0.082 |
0.307 |
fixed |
| S |
Estuary (Region) |
0.505 |
NA |
NA |
NA |
NA |
estuary.Region |
| S |
Year |
0.144 |
NA |
NA |
NA |
NA |
year |
| S |
Region |
0.441 |
NA |
NA |
NA |
NA |
Region |
| S |
Metals x Type |
0.000 |
NA |
NA |
NA |
NA |
Type |
| S |
TP x Type |
0.089 |
NA |
NA |
NA |
NA |
Type.1 |
| S |
Mud x Type |
0.000 |
NA |
NA |
NA |
NA |
Type.2 |
| S |
Type |
0.000 |
NA |
NA |
NA |
NA |
Type.3 |
| S |
Residual |
0.878 |
NA |
NA |
NA |
NA |
Residual |
| TBI |
Intercept |
-0.070 |
0.259 |
-0.269 |
-0.578 |
0.439 |
fixed |
| TBI |
Mud |
-0.279 |
0.060 |
-4.627 |
-0.397 |
-0.161 |
fixed |
| TBI |
Metals |
-0.248 |
0.072 |
-3.453 |
-0.388 |
-0.107 |
fixed |
| TBI |
TP |
0.197 |
0.084 |
2.328 |
0.031 |
0.362 |
fixed |
| TBI |
Estuary (Region) |
0.413 |
NA |
NA |
NA |
NA |
estuary.Region |
| TBI |
Year |
0.191 |
NA |
NA |
NA |
NA |
year |
| TBI |
Region |
0.464 |
NA |
NA |
NA |
NA |
Region |
| TBI |
Metals x Type |
0.000 |
NA |
NA |
NA |
NA |
Type |
| TBI |
TP x Type |
0.063 |
NA |
NA |
NA |
NA |
Type.1 |
| TBI |
Mud x Type |
0.000 |
NA |
NA |
NA |
NA |
Type.2 |
| TBI |
Type |
0.000 |
NA |
NA |
NA |
NA |
Type.3 |
| TBI |
Residual |
0.825 |
NA |
NA |
NA |
NA |
Residual |
##Model summary####
index_Summ8 <-
glance(EG_models8, fit_index8)
kable(index_Summ8)
| AMBI |
1.419 |
-321 |
666 |
708 |
642 |
225 |
| AMBI_S |
1.251 |
-293 |
609 |
651 |
585 |
225 |
| BENTIX |
1.283 |
-304 |
632 |
674 |
608 |
225 |
| BQI |
0.877 |
-226 |
475 |
517 |
451 |
225 |
| ITI |
1.209 |
-295 |
614 |
656 |
590 |
225 |
| logN |
0.930 |
-243 |
510 |
551 |
486 |
225 |
| M_AMBI |
1.015 |
-255 |
534 |
576 |
510 |
225 |
| S |
0.878 |
-226 |
477 |
519 |
453 |
225 |
| TBI |
0.825 |
-212 |
448 |
489 |
424 |
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 72 rows containing missing values (geom_errorbarh).
###residuals
index_residuals8 <-
augment(EG_models8, fit_index8)
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
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 x 3
## index R_sq Type
## <fctr> <dbl> <chr>
## 1 AMBI 0.10479 Marginal
## 2 AMBI 0.17120 Conditional
## 3 AMBI_S 0.20408 Marginal
## 4 AMBI_S 0.27534 Conditional
## 5 BENTIX 0.08920 Marginal
## 6 BENTIX 0.24812 Conditional
## 7 BQI 0.08258 Marginal
## 8 BQI 0.46386 Conditional
## 9 ITI 0.04902 Marginal
## 10 ITI 0.24316 Conditional
## 11 logN 0.00886 Marginal
## 12 logN 0.60496 Conditional
## 13 M_AMBI 0.14730 Marginal
## 14 M_AMBI 0.37065 Conditional
## 15 S 0.07050 Marginal
## 16 S 0.42625 Conditional
## 17 TBI 0.09270 Marginal
## 18 TBI 0.44175 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))
####Random effects###
ran_ef <-
EG_models8 %>%
ungroup() %>%
mutate(ran_ef = map(fit_index8,~sjp.lmer(.x, type = "re", show.ci = F, sort.est = "(Intercept)")),
ran_ef1 = map(fit_index8,~sjp.lmer(.x, type = "ri.slope")))
## Plotting random effects...
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