Linking yield with NLR PAV
Philipp Bayer
2020-09-22
Last updated: 2020-11-18
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Knit directory: R_gene_analysis/
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| File | Version | Author | Date | Message |
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| Rmd | 2009819 | Philipp Bayer | 2020-11-18 | fixed lme4 syntax |
| html | 22a48ac | Philipp Bayer | 2020-11-18 | Build site. |
| Rmd | 92e2580 | Philipp Bayer | 2020-11-18 | fixed lme4 syntax |
| html | 262a76f | Philipp Bayer | 2020-11-05 | Build site. |
| Rmd | 69e9c29 | Philipp Bayer | 2020-11-05 | wflow_publish(“analysis/yield_link.Rmd”) |
| html | 5ddfe2b | Philipp Bayer | 2020-11-05 | Build site. |
| Rmd | 25f0f54 | Philipp Bayer | 2020-11-05 | wflow_publish(“analysis/yield_link.Rmd”) |
| html | fa5c0ff | Philipp Bayer | 2020-11-04 | Build site. |
| Rmd | 2d9c3db | Philipp Bayer | 2020-11-04 | wflow_publish(c(“analysis/index.Rmd”, “analysis/yield_link.Rmd”)) |
| html | f34dd48 | Philipp Bayer | 2020-11-02 | Build site. |
| Rmd | be2f299 | Philipp Bayer | 2020-11-02 | wflow_publish(“analysis/yield_link.Rmd”) |
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| Rmd | 5166687 | Philipp Bayer | 2020-11-02 | wflow_publish(“analysis/yield_link.Rmd”) |
| Rmd | dae157b | Philipp Bayer | 2020-09-24 | Update of analysis |
| html | dae157b | Philipp Bayer | 2020-09-24 | Update of analysis |
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
library(tidyverse)-- Attaching packages ------------------------------------------------------------------------------------------------------------------- tidyverse 1.3.0 --v ggplot2 3.3.2 v purrr 0.3.4
v tibble 3.0.2 v dplyr 1.0.0
v tidyr 1.1.0 v stringr 1.4.0
v readr 1.3.1 v forcats 0.5.0-- Conflicts ---------------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()library(patchwork)
library(ggsci)
library(dabestr)Loading required package: magrittr
Attaching package: 'magrittr'The following object is masked from 'package:purrr':
set_namesThe following object is masked from 'package:tidyr':
extractlibrary(dabestr)
library(cowplot)
********************************************************Note: As of version 1.0.0, cowplot does not change the default ggplot2 theme anymore. To recover the previous behavior, execute:
theme_set(theme_cowplot())********************************************************
Attaching package: 'cowplot'The following object is masked from 'package:patchwork':
align_plotslibrary(ggsignif)
library(ggforce)
library(lme4)Loading required package: Matrix
Attaching package: 'Matrix'The following objects are masked from 'package:tidyr':
expand, pack, unpacklibrary(lmerTest)
Attaching package: 'lmerTest'The following object is masked from 'package:lme4':
lmerThe following object is masked from 'package:stats':
steplibrary(sjPlot)Learn more about sjPlot with 'browseVignettes("sjPlot")'.
Attaching package: 'sjPlot'The following objects are masked from 'package:cowplot':
plot_grid, save_plotlibrary(dotwhisker)
theme_set(theme_cowplot())Data loading
npg_col = pal_npg("nrc")(9)
col_list <- c(`Wild-type`=npg_col[8],
Landrace = npg_col[3],
`Old cultivar`=npg_col[2],
`Modern cultivar`=npg_col[4])
pav_table <- read_tsv('./data/soybean_pan_pav.matrix_gene.txt.gz')nbs <- read_tsv('./data/Lee.NBS.candidates.lst', col_names = c('Name', 'Class'))
nbs# A tibble: 486 x 2
Name Class
<chr> <chr>
1 UWASoyPan00953.t1 CN
2 GlymaLee.13G222900.1.p CN
3 GlymaLee.18G227000.1.p CN
4 GlymaLee.18G080600.1.p CN
5 GlymaLee.20G036200.1.p CN
6 UWASoyPan01876.t1 CN
7 UWASoyPan04211.t1 CN
8 GlymaLee.19G105400.1.p CN
9 GlymaLee.18G085100.1.p CN
10 GlymaLee.11G142600.1.p CN
# ... with 476 more rows# have to remove the .t1s
nbs$Name <- gsub('.t1','', nbs$Name)
nbs_pav_table <- pav_table %>% filter(Individual %in% nbs$Name)names <- c()
presences <- c()
for (i in seq_along(nbs_pav_table)){
if ( i == 1) next
thisind <- colnames(nbs_pav_table)[i]
pavs <- nbs_pav_table[[i]]
presents <- sum(pavs)
names <- c(names, thisind)
presences <- c(presences, presents)
}
nbs_res_tibb <- new_tibble(list(names = names, presences = presences))groups <- read_csv('./data/Table_of_cultivar_groups.csv')
groups <- groups %>%
mutate(`Group in violin table` = str_replace_all(`Group in violin table`, 'landrace', 'Landrace')) %>%
mutate(`Group in violin table` = str_replace_all(`Group in violin table`, 'Old_cultivar', 'Old cultivar')) %>%
mutate(`Group in violin table` = str_replace_all(`Group in violin table`, 'Modern_cultivar', 'Modern cultivar'))
groups$`Group in violin table` <-
factor(
groups$`Group in violin table`,
levels = c('Wild-type',
'Landrace',
'Old cultivar',
'Modern cultivar')
)
nbs_joined_groups <-
inner_join(nbs_res_tibb, groups, by = c('names' = 'Data-storage-ID'))Linking with yield
Can we link the trajectory of NLR genes with the trajectory of yield across the history of soybean breeding? let’s make a simple regression for now
Yield
yield <- read_tsv('./data/yield.txt')
yield_join <- inner_join(nbs_res_tibb, yield, by=c('names'='Line'))yield_join %>% ggplot(aes(x=presences, y=Yield)) + geom_hex() + geom_smooth() +
xlab('NLR gene count')
Protein
protein <- read_tsv('./data/protein_phenotype.txt')
protein_join <- left_join(nbs_res_tibb, protein, by=c('names'='Line')) %>% filter(!is.na(Protein))protein_join %>% ggplot(aes(x=presences, y=Protein)) + geom_hex() + geom_smooth() +
xlab('NLR gene count')
summary(lm(Protein ~ presences, data = protein_join))
Call:
lm(formula = Protein ~ presences, data = protein_join)
Residuals:
Min 1Q Median 3Q Max
-11.8479 -2.1274 -0.3336 1.9959 10.0949
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.98158 7.24125 -1.102 0.271
presences 0.11786 0.01624 7.258 8.07e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.106 on 960 degrees of freedom
Multiple R-squared: 0.05203, Adjusted R-squared: 0.05104
F-statistic: 52.69 on 1 and 960 DF, p-value: 8.075e-13Seed weight
Let’s look at seed weight:
seed_weight <- read_tsv('./data/Seed_weight_Phenotype.txt', col_names = c('names', 'wt'))
seed_join <- left_join(nbs_res_tibb, seed_weight) %>% filter(!is.na(wt))seed_join %>% filter(wt > 5) %>% ggplot(aes(x=presences, y=wt)) + geom_hex() + geom_smooth() +
ylab('Seed weight') +
xlab('NLR gene count')
summary(lm(wt ~ presences, data = seed_join))
Call:
lm(formula = wt ~ presences, data = seed_join)
Residuals:
Min 1Q Median 3Q Max
-12.2910 -2.8692 0.1462 2.7771 19.6962
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 91.40656 14.67990 6.227 8.28e-10 ***
presences -0.17636 0.03298 -5.348 1.21e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.714 on 690 degrees of freedom
Multiple R-squared: 0.0398, Adjusted R-squared: 0.0384
F-statistic: 28.6 on 1 and 690 DF, p-value: 1.213e-07Oil content
And now let’s look at the oil phenotype:
oil <- read_tsv('./data/oil_phenotype.txt')
oil_join <- left_join(nbs_res_tibb, oil, by=c('names'='Line')) %>% filter(!is.na(Oil))
oil_join# A tibble: 962 x 3
names presences Oil
<chr> <dbl> <dbl>
1 AB-01 445 17.6
2 AB-02 454 16.8
3 BR-24 455 20.6
4 ESS 454 20.9
5 For 448 21
6 HN001 448 23.6
7 HN002 444 18.5
8 HN003 446 17.5
9 HN004 442 18.9
10 HN005 440 15.5
# ... with 952 more rowsoil_join %>% ggplot(aes(x=presences, y=Oil)) + geom_hex() + geom_smooth() +
xlab('NLR gene count')
summary(lm(Oil ~ presences, data = oil_join))
Call:
lm(formula = Oil ~ presences, data = oil_join)
Residuals:
Min 1Q Median 3Q Max
-10.4376 -1.9081 0.4846 2.2401 9.0361
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 118.03941 7.31646 16.13 <2e-16 ***
presences -0.22591 0.01641 -13.77 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.139 on 960 degrees of freedom
Multiple R-squared: 0.1649, Adjusted R-squared: 0.1641
F-statistic: 189.6 on 1 and 960 DF, p-value: < 2.2e-16OK there are many, many outliers here. Clearly I’ll have to do something fancier - for example, using the first two PCs as covariates might get rid of some of those outliers.
Boxplots per group
Yield
nbs_joined_groups %>%
filter(!is.na(`Group in violin table`)) %>%
inner_join(yield, by=c('names'='Line')) %>%
ggplot(aes(x=`Group in violin table`, y=Yield, fill = `Group in violin table`)) +
geom_boxplot() +
scale_fill_manual(values = col_list) +
theme_minimal_hgrid() +
theme(axis.text.x = element_text(size=12),
axis.text.y = element_text(size=12)) +
geom_signif(comparisons = list(c('Old cultivar', 'Modern cultivar')),
map_signif_level = T) +
guides(fill=FALSE) +
ylab('Protein') +
xlab('Accession group')
And let’s check the dots:
nbs_joined_groups %>%
filter(!is.na(`Group in violin table`)) %>%
inner_join(yield_join, by = 'names') %>%
ggplot(aes(y=presences.x, x=Yield, color=`Group in violin table`)) +
geom_point() +
scale_color_manual(values = col_list) +
theme_minimal_hgrid() +
theme(axis.text.x = element_text(size=12),
axis.text.y = element_text(size=12)) +
ylab('NLR gene count')
nbs_joined_groups %>%
filter(!is.na(`Group in violin table`)) %>%
inner_join(yield_join, by = 'names') %>%
filter(`Group in violin table` != 'Landrace') %>%
ggplot(aes(x=presences.x, y=Yield, color=`Group in violin table`)) +
geom_point() +
scale_color_manual(values = col_list) +
theme_minimal_hgrid() +
geom_smooth() +
theme(axis.text.x = element_text(size=12),
axis.text.y = element_text(size=12)) +
xlab('NLR gene count')
## Protein
protein vs. the four groups:
nbs_joined_groups %>%
filter(!is.na(`Group in violin table`)) %>%
inner_join(protein, by=c('names'='Line')) %>%
ggplot(aes(x=`Group in violin table`, y=Protein, fill = `Group in violin table`)) +
geom_boxplot() +
scale_fill_manual(values = col_list) +
theme_minimal_hgrid() +
theme(axis.text.x = element_text(size=12),
axis.text.y = element_text(size=12)) +
geom_signif(comparisons = list(c('Wild-type', 'Landrace'),
c('Old cultivar', 'Modern cultivar')),
map_signif_level = T) +
guides(fill=FALSE) +
ylab('Protein') +
xlab('Accession group')
Seed weight
And seed weight:
nbs_joined_groups %>%
filter(!is.na(`Group in violin table`)) %>%
inner_join(seed_join) %>%
ggplot(aes(x=`Group in violin table`, y=wt, fill = `Group in violin table`)) +
geom_boxplot() +
scale_fill_manual(values = col_list) +
theme_minimal_hgrid() +
theme(axis.text.x = element_text(size=12),
axis.text.y = element_text(size=12)) +
geom_signif(comparisons = list(c('Wild-type', 'Landrace'),
c('Old cultivar', 'Modern cultivar')),
map_signif_level = T) +
guides(fill=FALSE) +
ylab('Seed weight') +
xlab('Accession group')
Wow, that’s breeding!
Oil content
And finally, Oil content:
nbs_joined_groups %>%
filter(!is.na(`Group in violin table`)) %>%
inner_join(oil_join, by = 'names') %>%
ggplot(aes(x=`Group in violin table`, y=Oil, fill = `Group in violin table`)) +
geom_boxplot() +
scale_fill_manual(values = col_list) +
theme_minimal_hgrid() +
theme(axis.text.x = element_text(size=12),
axis.text.y = element_text(size=12)) +
geom_signif(comparisons = list(c('Wild-type', 'Landrace'),
c('Old cultivar', 'Modern cultivar')),
map_signif_level = T) +
guides(fill=FALSE) +
ylab('Oil content') +
xlab('Accession group')
Oha, a single star. That’s p < 0.05!
Let’s redo the above hexplot, but also color the dots by group.
nbs_joined_groups %>%
filter(!is.na(`Group in violin table`)) %>%
inner_join(oil_join, by = 'names') %>%
ggplot(aes(x=presences.x, y=Oil, color=`Group in violin table`)) +
geom_point() +
scale_color_manual(values = col_list) +
theme_minimal_hgrid() +
theme(axis.text.x = element_text(size=12),
axis.text.y = element_text(size=12)) +
xlab('NLR gene count')
Oha, so it’s the wild-types that drag this out a lot.
Let’s remove them and see what it looks like:
nbs_joined_groups %>%
filter(!is.na(`Group in violin table`)) %>%
inner_join(oil_join, by = 'names') %>%
filter(`Group in violin table` %in% c('Old cultivar', 'Modern cultivar')) %>%
ggplot(aes(x=presences.x, y=Oil, color=`Group in violin table`)) +
geom_point() +
scale_color_manual(values = col_list) +
theme_minimal_hgrid() +
theme(axis.text.x = element_text(size=12),
axis.text.y = element_text(size=12)) +
xlab('NLR gene count') +
geom_smooth()
Let’s remove that one outlier:
nbs_joined_groups %>%
filter(!is.na(`Group in violin table`)) %>%
inner_join(oil_join, by = 'names') %>%
filter(`Group in violin table` %in% c('Old cultivar', 'Modern cultivar')) %>%
filter(Oil > 13) %>%
ggplot(aes(x=presences.x, y=Oil, color=`Group in violin table`)) +
geom_point() +
scale_color_manual(values = col_list) +
theme_minimal_hgrid() +
theme(axis.text.x = element_text(size=12),
axis.text.y = element_text(size=12)) +
xlab('NLR gene count') +
geom_smooth()
Does the above oil content boxplot become different if we exclude the one outlier? I’d bet so
nbs_joined_groups %>%
filter(!is.na(`Group in violin table`)) %>%
inner_join(oil_join, by = 'names') %>%
filter(names != 'USB-393') %>%
ggplot(aes(x=`Group in violin table`, y=Oil, fill = `Group in violin table`)) +
geom_boxplot() +
scale_fill_manual(values = col_list) +
theme_minimal_hgrid() +
theme(axis.text.x = element_text(size=12),
axis.text.y = element_text(size=12)) +
geom_signif(comparisons = list(c('Wild-type', 'Landrace'),
c('Old cultivar', 'Modern cultivar')),
map_signif_level = T) +
guides(fill=FALSE) +
ylab('Oil content') +
xlab('Accession group')
Nope, still significantly higher in modern cultivars!
Mixed modeling
Alright here’s my hypothesis: There’s a link between cultivar status (Old, Wild, Landrace, Modern), r-gene count, and yield, but it’s ‘hidden’ by country differences.
Great tutorial here: https://ourcodingclub.github.io/tutorials/mixed-models
So we’ll have to build some lme4 models!
Oil
nbs_joined_groups$presences2 <- scale(nbs_joined_groups$presences, center=T, scale=T)
hist(nbs_joined_groups$presences2)
oil_nbs_joined_groups <- nbs_joined_groups %>% inner_join(oil_join, by = 'names')
oil_nbs_joined_groups$Oil2 <- scale(oil_nbs_joined_groups$Oil, center=T, scale=T)basic.lm <- lm(Oil2 ~ presences2, data=oil_nbs_joined_groups)ggplot(oil_nbs_joined_groups, aes(x = presences2, y = Oil2)) +
geom_point() +
geom_smooth(method = "lm")
Hm looks messy, you can see two groups
plot(basic.lm, which = 1)
which is confirmed by the messy line
plot(basic.lm, which = 2)
and this garbage qqplot.
So let’s build an lmer model!
mixed.lmer <- lmer(Oil2 ~ presences2 + (1|`Group in violin table`), data=oil_nbs_joined_groups)
summary(mixed.lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Oil2 ~ presences2 + (1 | `Group in violin table`)
Data: oil_nbs_joined_groups
REML criterion at convergence: 1872.4
Scaled residuals:
Min 1Q Median 3Q Max
-4.5879 -0.5672 0.0869 0.6631 3.2111
Random effects:
Groups Name Variance Std.Dev.
Group in violin table (Intercept) 1.3349 1.1554
Residual 0.4075 0.6384
Number of obs: 951, groups: Group in violin table, 4
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.04360 0.57867 2.99844 -0.075 0.9447
presences2 -0.05350 0.02394 947.27006 -2.234 0.0257 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr)
presences2 -0.004So the Variance for Group in violin table is 1.3349, that means it’s 1.3349/(1.3349+0.4075) *100 = 76% of the variance is explained by the four groups!
plot(mixed.lmer)
qqnorm(resid(mixed.lmer))
qqline(resid(mixed.lmer))
These still look fairly bad - better than before, but the QQ plot still isn’t on the line.
Let’s quickly check yield too
Yield
yield_nbs_joined_groups <- nbs_joined_groups %>% inner_join(yield_join, by = 'names')
yield_nbs_joined_groups$Yield2 <-scale(yield_nbs_joined_groups$Yield, center=T, scale=T)mixed.lmer <- lmer(Yield2 ~ presences2 + (1|`Group in violin table`), data=yield_nbs_joined_groups)
summary(mixed.lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield2 ~ presences2 + (1 | `Group in violin table`)
Data: yield_nbs_joined_groups
REML criterion at convergence: 2060.4
Scaled residuals:
Min 1Q Median 3Q Max
-3.1643 -0.6819 0.0316 0.6948 2.8002
Random effects:
Groups Name Variance Std.Dev.
Group in violin table (Intercept) 0.6466 0.8041
Residual 0.8600 0.9274
Number of obs: 761, groups: Group in violin table, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.23641 0.46910 1.98335 0.504 0.664692
presences2 -0.15364 0.04172 757.46580 -3.683 0.000247 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr)
presences2 0.025 Percentage explained by breeding group: 0.6466 / (0.6466+0.8600)*100 = 42%
plot(mixed.lmer)
qqnorm(resid(mixed.lmer))
qqline(resid(mixed.lmer))
:O
p-value of 0.000247 for the normalised presences while accounting for the breeding group, that’s beautiful.
ggplot(yield_nbs_joined_groups, aes(x = presences2, y = Yield2)) +
facet_wrap(~`Group in violin table`, nrow=2) + # a panel for each mountain range
geom_point(alpha = 0.5) +
theme_classic() +
geom_line(data = cbind(yield_nbs_joined_groups, pred = predict(mixed.lmer)), aes(y = pred), size = 1) +
theme_minimal_hgrid() +
theme(legend.position = "none") +
xlab('Scaled and centered NLR gene count') +
ylab('Scaled and centered yield')
Making the breeding group fixed
We have < 10 possible factors in the group, so making that fixed instead of random
# this doesn't work because you need at least one random effect
# mixed.lmer <- lmer(Yield2 ~ presences2 + `Group in violin table`, data=yield_nbs_joined_groups)Adding country
We should also add the country the plant is from as a random effect, that definitely has an influence too (perhaps a stronger one???)
Yield
country <- read_csv('./data/Cultivar_vs_country.csv')
names(country) <- c('names', 'PI-ID', 'Country')
yield_country_nbs_joined_groups <- yield_nbs_joined_groups %>% inner_join(country)mixed.lmer <- lmer(Yield2 ~ presences2 + (1|`Group in violin table`) + (1|Country), data=yield_country_nbs_joined_groups)
summary(mixed.lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield2 ~ presences2 + (1 | `Group in violin table`) + (1 | Country)
Data: yield_country_nbs_joined_groups
REML criterion at convergence: 1957
Scaled residuals:
Min 1Q Median 3Q Max
-3.09429 -0.56737 0.03072 0.65680 2.89981
Random effects:
Groups Name Variance Std.Dev.
Country (Intercept) 0.3807 0.6170
Group in violin table (Intercept) 0.4178 0.6464
Residual 0.7614 0.8726
Number of obs: 741, groups: Country, 40; Group in violin table, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.07150 0.40194 2.28533 0.178 0.87336
presences2 -0.11258 0.04116 726.98206 -2.735 0.00639 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr)
presences2 0.051 Nice! Yield is negatively correlated with the number of NLR genes when accounting for breeding group AND country
ggplot(yield_country_nbs_joined_groups, aes(x = presences2, y = Yield2, colour = Country)) +
facet_wrap(~`Group in violin table`, nrow=2) + # a panel for each mountain range
geom_point(alpha = 0.5) +
theme_classic() +
geom_line(data = cbind(yield_country_nbs_joined_groups, pred = predict(mixed.lmer)), aes(y = pred), size = 1) +
theme_minimal_hgrid() +
theme(legend.position = "none") +
xlab('Scaled and centered NLR gene count') +
ylab('Scaled and centered yield')
Some diagnostics:
plot(mixed.lmer)
qqnorm(resid(mixed.lmer))
qqline(resid(mixed.lmer))
Hm, the qqplot looks slightly worse than when I use maturity group alone, interesting!
BIG DISCLAIMER: Currently, I treat country and group not as nested variables, they’re independent. I think that is the way it should be in this case but I’m thinking.
Making the breeding group fixed
Since we have too few factors in the breeding groups we have to make that fixed, not random
mixed.lmer <- lmer(Yield2 ~ presences2 + `Group in violin table` + (1|Country), data=yield_country_nbs_joined_groups)
summary(mixed.lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield2 ~ presences2 + `Group in violin table` + (1 | Country)
Data: yield_country_nbs_joined_groups
REML criterion at convergence: 1951.8
Scaled residuals:
Min 1Q Median 3Q Max
-3.1864 -0.5700 0.0305 0.6525 2.8982
Random effects:
Groups Name Variance Std.Dev.
Country (Intercept) 0.3776 0.6145
Residual 0.7616 0.8727
Number of obs: 741, groups: Country, 40
Fixed effects:
Estimate Std. Error df t value
(Intercept) -0.15265 0.13488 33.06645 -1.132
presences2 -0.11149 0.04117 726.45969 -2.708
`Group in violin table`Old cultivar -0.29578 0.16164 734.95182 -1.830
`Group in violin table`Modern cultivar 1.00458 0.22335 360.40725 4.498
Pr(>|t|)
(Intercept) 0.26588
presences2 0.00693 **
`Group in violin table`Old cultivar 0.06768 .
`Group in violin table`Modern cultivar 9.28e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) prsnc2 `Givt`Oc
presences2 0.119
`Grivtbl`Oc -0.112 -0.008
`Grivtbl`Mc -0.194 0.065 0.156 Non-normalised yield
Let’s see whether the ‘raw’ values perform the same.
mixed.lmer <- lmer(Yield ~ presences.x + (1|`Group in violin table`) + (1|Country), data=yield_country_nbs_joined_groups)
summary(mixed.lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield ~ presences.x + (1 | `Group in violin table`) + (1 | Country)
Data: yield_country_nbs_joined_groups
REML criterion at convergence: 1679.6
Scaled residuals:
Min 1Q Median 3Q Max
-3.09429 -0.56737 0.03072 0.65680 2.89981
Random effects:
Groups Name Variance Std.Dev.
Country (Intercept) 0.2602 0.5101
Group in violin table (Intercept) 0.2856 0.5345
Residual 0.5205 0.7215
Number of obs: 741, groups: Country, 40; Group in violin table, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 9.011013 2.481360 677.994843 3.631 0.000303 ***
presences.x -0.015192 0.005555 726.982171 -2.735 0.006389 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr)
presences.x -0.991Oh, lower p-values for the intercept
ggplot(yield_country_nbs_joined_groups, aes(x = presences.x, y = Yield, colour = Country)) +
facet_wrap(~`Group in violin table`, nrow=2) + # a panel for each mountain range
geom_point(alpha = 0.5) +
theme_classic() +
geom_line(data = cbind(yield_country_nbs_joined_groups, pred = predict(mixed.lmer)), aes(y = pred), size = 1) +
theme_minimal_hgrid() +
theme(legend.position = "none") +
xlab('Raw NLR gene count') +
ylab('Raw yield')
plot(mixed.lmer)
qqnorm(resid(mixed.lmer))
qqline(resid(mixed.lmer))
Making the breeding group fixed
mixed.lmer <- lmer(Yield ~ presences.x + `Group in violin table` + (1|Country), data=yield_country_nbs_joined_groups)
summary(mixed.lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield ~ presences.x + `Group in violin table` + (1 | Country)
Data: yield_country_nbs_joined_groups
REML criterion at convergence: 1675.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.1864 -0.5700 0.0305 0.6525 2.8982
Random effects:
Groups Name Variance Std.Dev.
Country (Intercept) 0.2581 0.5081
Residual 0.5206 0.7216
Number of obs: 741, groups: Country, 40
Fixed effects:
Estimate Std. Error df t value
(Intercept) 8.760056 2.465570 726.660933 3.553
presences.x -0.015045 0.005556 726.459065 -2.708
`Group in violin table`Old cultivar -0.244554 0.133648 734.951816 -1.830
`Group in violin table`Modern cultivar 0.830604 0.184672 360.407255 4.498
Pr(>|t|)
(Intercept) 0.000406 ***
presences.x 0.006929 **
`Group in violin table`Old cultivar 0.067680 .
`Group in violin table`Modern cultivar 9.28e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) prsnc. `Givt`Oc
presences.x -0.999
`Grivtbl`Oc 0.003 -0.008
`Grivtbl`Mc -0.074 0.065 0.156 Oh, lower p-values for the intercept
ggplot(yield_country_nbs_joined_groups, aes(x = presences.x, y = Yield, colour = Country)) +
facet_wrap(~`Group in violin table`, nrow=2) + # a panel for each mountain range
geom_point(alpha = 0.5) +
theme_classic() +
geom_line(data = cbind(yield_country_nbs_joined_groups, pred = predict(mixed.lmer)), aes(y = pred), size = 1) +
theme_minimal_hgrid() +
theme(legend.position = "none") +
xlab('Raw NLR gene count') +
ylab('Raw yield')
plot(mixed.lmer)
qqnorm(resid(mixed.lmer))
qqline(resid(mixed.lmer))
plot(resid(mixed.lmer))
These are the final numbers for the paper.
Plotting effect of each covariate
(re.effects <- plot_model(mixed.lmer, type = "re", show.values = TRUE))
#lmerTest breaks these other packages so I better unload it and reload only lme4
detach("package:lmerTest", unload=TRUE)
yield_country_nbs_joined_groups_renamed <- yield_country_nbs_joined_groups
names(yield_country_nbs_joined_groups_renamed) <- c('names', 'presences.x', 'PI-ID', 'Group', 'presences2', 'presences.y', 'Yield', 'Yield2', 'Country')
mixed.lmer <- lmer(Yield2 ~ presences2 + Group + (1|Country), data=yield_country_nbs_joined_groups_renamed)
dwplot(mixed.lmer,
vline = geom_vline(xintercept = 0, colour = "grey60", linetype = 2))
library(stargazer)
stargazer(mixed.lmer, type = "text",
digits = 3,
star.cutoffs = c(0.05, 0.01, 0.001),
digit.separator = "")
==================================================
Dependent variable:
-----------------------------
Yield2
--------------------------------------------------
presences2 -0.111**
(0.041)
GroupOld cultivar -0.296
(0.162)
GroupModern cultivar 1.005***
(0.223)
Constant -0.153
(0.135)
--------------------------------------------------
Observations 741
Log Likelihood -975.906
Akaike Inf. Crit. 1963.812
Bayesian Inf. Crit. 1991.460
==================================================
Note: *p<0.05; **p<0.01; ***p<0.001library(ggeffects)
ggpredict(mixed.lmer, terms = c("presences2", 'Group'), type = "re") %>%
plot() +
theme_minimal()
Let’s also plot that for non-normalised data
mixed.lmer <- lmer(Yield ~ presences.x + Group + (1|Country), data=yield_country_nbs_joined_groups_renamed)
ggpredict(mixed.lmer, terms = c("presences.x", 'Group'), type = "re") %>%
plot() +
theme_minimal_hgrid() +
xlab('Raw NLR count') +
ylab('Raw yield')
# alright back to regular programming
library(lmerTest)
mixed.lmer <- lmer(Yield2 ~ presences2 + `Group in violin table` + (1|Country), data=yield_country_nbs_joined_groups)More complex models
If I add random slopes to either groups not much changes, I do get warnings indicating that there’s not much in the data:
mixed.lmer <- lmer(Yield2 ~ presences2 + (presences2|`Group in violin table`) + (1|Country), data=yield_country_nbs_joined_groups)
summary(mixed.lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield2 ~ presences2 + (presences2 | `Group in violin table`) +
(1 | Country)
Data: yield_country_nbs_joined_groups
REML criterion at convergence: 1954.8
Scaled residuals:
Min 1Q Median 3Q Max
-3.09789 -0.56422 0.04471 0.67067 2.88976
Random effects:
Groups Name Variance Std.Dev. Corr
Country (Intercept) 0.3920 0.6261
Group in violin table (Intercept) 0.3858 0.6211
presences2 0.0310 0.1761 0.30
Residual 0.7564 0.8697
Number of obs: 741, groups: Country, 40; Group in violin table, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.02964 0.38964 2.29148 0.076 0.945
presences2 -0.22515 0.12370 1.66243 -1.820 0.235
Correlation of Fixed Effects:
(Intr)
presences2 0.269 mixed.lmer <- lmer(Yield2 ~ presences2 + `Group in violin table` + (1 + presences2|Country), data=yield_country_nbs_joined_groups)
summary(mixed.lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield2 ~ presences2 + `Group in violin table` + (1 + presences2 |
Country)
Data: yield_country_nbs_joined_groups
REML criterion at convergence: 1951.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.1904 -0.5715 0.0314 0.6551 2.9003
Random effects:
Groups Name Variance Std.Dev. Corr
Country (Intercept) 0.397511 0.63048
presences2 0.002137 0.04623 1.00
Residual 0.761491 0.87263
Number of obs: 741, groups: Country, 40
Fixed effects:
Estimate Std. Error df t value
(Intercept) -0.1470 0.1387 28.1820 -1.060
presences2 -0.1162 0.0430 30.8335 -2.702
`Group in violin table`Old cultivar -0.3010 0.1615 733.1173 -1.863
`Group in violin table`Modern cultivar 1.0093 0.2192 166.8461 4.605
Pr(>|t|)
(Intercept) 0.2982
presences2 0.0111 *
`Group in violin table`Old cultivar 0.0628 .
`Group in violin table`Modern cultivar 8.15e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) prsnc2 `Givt`Oc
presences2 0.334
`Grivtbl`Oc -0.109 -0.017
`Grivtbl`Mc -0.188 0.044 0.158
convergence code: 0
boundary (singular) fit: see ?isSingularOh, a significant p-value, let’s plot plot that and compare with he previous plot:
ggplot(yield_country_nbs_joined_groups, aes(x = presences2, y = Yield2, colour = Country)) +
facet_wrap(~`Group in violin table`, nrow=2) + # a panel for each mountain range
geom_point(alpha = 0.5) +
theme_classic() +
geom_line(data = cbind(yield_country_nbs_joined_groups, pred = predict(mixed.lmer)), aes(y = pred), size = 1) +
theme_minimal_hgrid() +
theme(legend.position = "none") +
xlab('Scaled and centered NLR gene count') +
ylab('Scaled and centered yield')
Quite similar, mostly downwards trajectories for each country.
Let’s do that non-normalised:
mixed.lmer <- lmer(Yield ~ presences.x + `Group in violin table` + (1 + presences.x|Country), data=yield_country_nbs_joined_groups)
summary(mixed.lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield ~ presences.x + `Group in violin table` + (1 + presences.x |
Country)
Data: yield_country_nbs_joined_groups
REML criterion at convergence: 1675.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.1863 -0.5679 0.0294 0.6532 2.8982
Random effects:
Groups Name Variance Std.Dev. Corr
Country (Intercept) 5.191e-01 0.7204930
presences.x 2.402e-07 0.0004901 -0.98
Residual 5.206e-01 0.7215284
Number of obs: 741, groups: Country, 40
Fixed effects:
Estimate Std. Error df t value
(Intercept) 8.73673 2.46492 709.51652 3.544
presences.x -0.01499 0.00555 412.58876 -2.702
`Group in violin table`Old cultivar -0.24420 0.13365 733.16976 -1.827
`Group in violin table`Modern cultivar 0.83046 0.18493 218.59478 4.491
Pr(>|t|)
(Intercept) 0.000419 ***
presences.x 0.007186 **
`Group in violin table`Old cultivar 0.068096 .
`Group in violin table`Modern cultivar 1.15e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) prsnc. `Givt`Oc
presences.x -0.999
`Grivtbl`Oc 0.002 -0.007
`Grivtbl`Mc -0.076 0.067 0.156
convergence code: 0
unable to evaluate scaled gradient
Model failed to converge: degenerate Hessian with 2 negative eigenvaluesggplot(yield_country_nbs_joined_groups, aes(x = presences.x, y = Yield, colour = Country)) +
facet_wrap(~`Group in violin table`, nrow=2) + # a panel for each mountain range
geom_point(alpha = 0.5) +
theme_classic() +
geom_line(data = cbind(yield_country_nbs_joined_groups, pred = predict(mixed.lmer)), aes(y = pred), size = 1) +
theme_minimal_hgrid() +
theme(legend.position = "none") +
xlab('Raw NLR gene count') +
ylab('Raw yield')
Quite similar, mostly downwards trajectories for each country.
And now both random slopes:
mixed.lmer <- lmer(Yield2 ~ presences2 + (presences2|`Group in violin table`) + (1 + presences2|Country), data=yield_country_nbs_joined_groups)
summary(mixed.lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield2 ~ presences2 + (presences2 | `Group in violin table`) +
(1 + presences2 | Country)
Data: yield_country_nbs_joined_groups
REML criterion at convergence: 1953.7
Scaled residuals:
Min 1Q Median 3Q Max
-3.11214 -0.56909 0.04459 0.66469 2.91084
Random effects:
Groups Name Variance Std.Dev. Corr
Country (Intercept) 0.42704 0.6535
presences2 0.01045 0.1022 0.81
Group in violin table (Intercept) 0.37523 0.6126
presences2 0.04201 0.2050 0.18
Residual 0.75392 0.8683
Number of obs: 741, groups: Country, 40; Group in violin table, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.03595 0.38772 2.35869 0.093 0.933
presences2 -0.23848 0.14300 1.96304 -1.668 0.240
Correlation of Fixed Effects:
(Intr)
presences2 0.231 ggplot(yield_country_nbs_joined_groups, aes(x = presences2, y = Yield2, colour = Country)) +
facet_wrap(~`Group in violin table`, nrow=2) + # a panel for each mountain range
geom_point(alpha = 0.5) +
theme_classic() +
geom_line(data = cbind(yield_country_nbs_joined_groups, pred = predict(mixed.lmer)), aes(y = pred), size = 1) +
theme_minimal_hgrid() +
theme(legend.position = "none") +
xlab('Scaled and centered NLR gene count') +
ylab('Scaled and centered yield')
Yeah, nah
Oil
oil_country_nbs_joined_groups <- oil_nbs_joined_groups %>% inner_join(country)
mixed.lmer <- lmer(Oil2 ~ presences2 + `Group in violin table` + (1|Country), data=oil_country_nbs_joined_groups)
summary(mixed.lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Oil2 ~ presences2 + `Group in violin table` + (1 | Country)
Data: oil_country_nbs_joined_groups
REML criterion at convergence: 1808.3
Scaled residuals:
Min 1Q Median 3Q Max
-4.5285 -0.5605 0.0983 0.6472 3.2209
Random effects:
Groups Name Variance Std.Dev.
Country (Intercept) 0.07703 0.2775
Residual 0.39128 0.6255
Number of obs: 929, groups: Country, 41
Fixed effects:
Estimate Std. Error df t value
(Intercept) -1.66466 0.09388 57.11719 -17.732
presences2 -0.03579 0.02416 917.65081 -1.481
`Group in violin table`Landrace 1.93042 0.06953 911.95172 27.763
`Group in violin table`Old cultivar 2.25628 0.11897 922.58914 18.965
`Group in violin table`Modern cultivar 2.46605 0.14998 209.92967 16.443
Pr(>|t|)
(Intercept) <2e-16 ***
presences2 0.139
`Group in violin table`Landrace <2e-16 ***
`Group in violin table`Old cultivar <2e-16 ***
`Group in violin table`Modern cultivar <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) prsnc2 `Givt` `Givt`Oc
presences2 -0.232
`Grpivtbl`L -0.644 0.434
`Grivtbl`Oc -0.405 0.249 0.509
`Grivtbl`Mc -0.447 0.246 0.432 0.338 No significance here.
Protein
protein_nbs_joined_groups <- nbs_joined_groups %>% inner_join(protein_join, by = 'names')
protein_nbs_joined_groups$Protein2 <- scale(protein_nbs_joined_groups$Protein, center=T, scale=T)
protein_country_nbs_joined_groups <- protein_nbs_joined_groups %>% inner_join(country)
mixed.lmer <- lmer(Protein2 ~ presences2 + `Group in violin table` + (1|Country), data=protein_country_nbs_joined_groups)
summary(mixed.lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Protein2 ~ presences2 + `Group in violin table` + (1 | Country)
Data: protein_country_nbs_joined_groups
REML criterion at convergence: 2472.7
Scaled residuals:
Min 1Q Median 3Q Max
-3.6001 -0.6747 -0.0423 0.6274 3.5103
Random effects:
Groups Name Variance Std.Dev.
Country (Intercept) 0.07042 0.2654
Residual 0.81212 0.9012
Number of obs: 929, groups: Country, 41
Fixed effects:
Estimate Std. Error df t value
(Intercept) 0.52736 0.11992 77.94050 4.397
presences2 0.04347 0.03466 923.71757 1.254
`Group in violin table`Landrace -0.72486 0.09980 923.95960 -7.263
`Group in violin table`Old cultivar -1.20226 0.17045 923.25626 -7.053
`Group in violin table`Modern cultivar -1.10372 0.20076 140.80066 -5.498
Pr(>|t|)
(Intercept) 3.43e-05 ***
presences2 0.21
`Group in violin table`Landrace 8.04e-13 ***
`Group in violin table`Old cultivar 3.42e-12 ***
`Group in violin table`Modern cultivar 1.76e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) prsnc2 `Givt` `Givt`Oc
presences2 -0.277
`Grpivtbl`L -0.712 0.438
`Grivtbl`Oc -0.445 0.253 0.511
`Grivtbl`Mc -0.507 0.270 0.463 0.340 No significance here.
Seed weight
seed_nbs_joined_groups <- nbs_joined_groups %>% inner_join(seed_join, by = 'names')
seed_nbs_joined_groups$wt2 <- scale(seed_nbs_joined_groups$wt, center=T, scale=T)
seed_country_nbs_joined_groups <- seed_nbs_joined_groups %>% inner_join(country)
mixed.lmer <- lmer(wt2 ~ presences2 + `Group in violin table` + (1|Country), data=seed_country_nbs_joined_groups)
summary(mixed.lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: wt2 ~ presences2 + `Group in violin table` + (1 | Country)
Data: seed_country_nbs_joined_groups
REML criterion at convergence: 1676
Scaled residuals:
Min 1Q Median 3Q Max
-2.9614 -0.6215 0.0110 0.5831 4.8138
Random effects:
Groups Name Variance Std.Dev.
Country (Intercept) 0.08566 0.2927
Residual 0.70082 0.8371
Number of obs: 664, groups: Country, 38
Fixed effects:
Estimate Std. Error df t value
(Intercept) -2.348857 0.183189 90.849260 -12.822
presences2 -0.007261 0.040531 656.652662 -0.179
`Group in violin table`Landrace 2.412123 0.166724 600.989922 14.468
`Group in violin table`Old cultivar 2.727120 0.224422 656.159635 12.152
`Group in violin table`Modern cultivar 2.782167 0.259555 67.478942 10.719
Pr(>|t|)
(Intercept) < 2e-16 ***
presences2 0.858
`Group in violin table`Landrace < 2e-16 ***
`Group in violin table`Old cultivar < 2e-16 ***
`Group in violin table`Modern cultivar 3.26e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) prsnc2 `Givt` `Givt`Oc
presences2 -0.267
`Grpivtbl`L -0.864 0.364
`Grivtbl`Oc -0.666 0.274 0.710
`Grivtbl`Mc -0.663 0.298 0.648 0.539 Again, no significance here.
The final yield model
This is the final yield model for the paper
mixed.lmer <- lmer(Yield ~ presences.x + `Group in violin table` + (1|Country), data=yield_country_nbs_joined_groups)
summary(mixed.lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield ~ presences.x + `Group in violin table` + (1 | Country)
Data: yield_country_nbs_joined_groups
REML criterion at convergence: 1675.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.1864 -0.5700 0.0305 0.6525 2.8982
Random effects:
Groups Name Variance Std.Dev.
Country (Intercept) 0.2581 0.5081
Residual 0.5206 0.7216
Number of obs: 741, groups: Country, 40
Fixed effects:
Estimate Std. Error df t value
(Intercept) 8.760056 2.465570 726.660933 3.553
presences.x -0.015045 0.005556 726.459065 -2.708
`Group in violin table`Old cultivar -0.244554 0.133648 734.951816 -1.830
`Group in violin table`Modern cultivar 0.830604 0.184672 360.407255 4.498
Pr(>|t|)
(Intercept) 0.000406 ***
presences.x 0.006929 **
`Group in violin table`Old cultivar 0.067680 .
`Group in violin table`Modern cultivar 9.28e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) prsnc. `Givt`Oc
presences.x -0.999
`Grivtbl`Oc 0.003 -0.008
`Grivtbl`Mc -0.074 0.065 0.156 ggplot(yield_country_nbs_joined_groups, aes(x = presences.x, y = Yield, colour = Country)) +
facet_wrap(~`Group in violin table`, nrow=2) + # a panel for each mountain range
geom_point(alpha = 0.5) +
theme_classic() +
geom_line(data = cbind(yield_country_nbs_joined_groups, pred = predict(mixed.lmer)), aes(y = pred), size = 1) +
theme_minimal_hgrid() +
theme(legend.position = "none") +
xlab('Raw NLR gene count') +
ylab('Raw yield')
plot(mixed.lmer)
qqnorm(resid(mixed.lmer))
qqline(resid(mixed.lmer))
plot(resid(mixed.lmer))
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17134)
Matrix products: default
locale:
[1] LC_COLLATE=English_Australia.1252 LC_CTYPE=English_Australia.1252
[3] LC_MONETARY=English_Australia.1252 LC_NUMERIC=C
[5] LC_TIME=English_Australia.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lmerTest_3.1-2 ggeffects_0.16.0 stargazer_5.2.2
[4] dotwhisker_0.5.0 sjPlot_2.8.6 lme4_1.1-21
[7] Matrix_1.2-18 ggforce_0.3.1 ggsignif_0.6.0
[10] cowplot_1.0.0 dabestr_0.3.0 magrittr_1.5
[13] ggsci_2.9 patchwork_1.0.0 forcats_0.5.0
[16] stringr_1.4.0 dplyr_1.0.0 purrr_0.3.4
[19] readr_1.3.1 tidyr_1.1.0 tibble_3.0.2
[22] ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2.9000
loaded via a namespace (and not attached):
[1] TH.data_1.0-10 minqa_1.2.4 colorspace_1.4-1
[4] ellipsis_0.3.1 sjlabelled_1.1.7 rprojroot_1.3-2
[7] estimability_1.3 ggstance_0.3.4 parameters_0.9.0
[10] fs_1.5.0.9000 rstudioapi_0.11 glmmTMB_1.0.2.1
[13] hexbin_1.28.1 farver_2.0.3 fansi_0.4.1
[16] mvtnorm_1.1-1 lubridate_1.7.9 xml2_1.3.2
[19] codetools_0.2-16 splines_3.6.3 knitr_1.29
[22] sjmisc_2.8.5 polyclip_1.10-0 jsonlite_1.7.1
[25] nloptr_1.2.1 broom_0.5.6 dbplyr_1.4.4
[28] effectsize_0.3.0 compiler_3.6.3 httr_1.4.2
[31] sjstats_0.18.0 emmeans_1.4.5 backports_1.1.10
[34] assertthat_0.2.1 cli_2.0.2 later_1.1.0.1
[37] tweenr_1.0.1 htmltools_0.5.0 tools_3.6.3
[40] coda_0.19-3 gtable_0.3.0 glue_1.4.2
[43] Rcpp_1.0.5 cellranger_1.1.0 vctrs_0.3.1
[46] nlme_3.1-148 insight_0.10.0 xfun_0.17
[49] ps_1.3.4 rvest_0.3.5 lifecycle_0.2.0
[52] getPass_0.2-2 MASS_7.3-51.6 zoo_1.8-8
[55] scales_1.1.1 hms_0.5.3 promises_1.1.1
[58] sandwich_2.5-1 RColorBrewer_1.1-2 TMB_1.7.16
[61] yaml_2.2.1 stringi_1.5.3 bayestestR_0.7.5
[64] boot_1.3-25 rlang_0.4.7 pkgconfig_2.0.3
[67] evaluate_0.14 lattice_0.20-41 labeling_0.3
[70] processx_3.4.4 tidyselect_1.1.0 plyr_1.8.6
[73] R6_2.4.1 generics_0.0.2 multcomp_1.4-13
[76] DBI_1.1.0 mgcv_1.8-31 pillar_1.4.4
[79] haven_2.3.1 whisker_0.4 withr_2.2.0
[82] survival_3.2-3 performance_0.5.1 modelr_0.1.8
[85] crayon_1.3.4 utf8_1.1.4 rmarkdown_2.3
[88] grid_3.6.3 readxl_1.3.1 blob_1.2.1
[91] callr_3.4.4 git2r_0.27.1 reprex_0.3.0
[94] digest_0.6.25 xtable_1.8-4 httpuv_1.5.4
[97] numDeriv_2016.8-1.1 munsell_0.5.0