多元回归模型过滤估计的点状图
Dot-and-whisker plots of filtered estimates for multiple regression models
我正在尝试绘制 4 种不同回归模型的置信区间的点须图。
数据可用here。
#first importing data
Q1<-read.table("~/Q1.txt", header=T)
# Optionally, read in data directly from figshare.
# Q1 <- read.table("https://ndownloader.figshare.com/files/13283882?private_link=ace5b44bc12394a7c46d", header=TRUE)
library(dplyr)
#splitting into female and male
female<-Q1 %>%
filter(sex=="F")
male<-Q1 %>%
filter(sex=="M")
library(lme4)
#Female models
#poisson regression
ab_f_LBS= lmer(LBS ~ ft + grid + (1|byear), data = subset(female))
#negative binomial regression
ab_f_surv= glmer.nb(age ~ ft + grid + (1|byear), data = subset(female), control=glmerControl(tol=1e-6,optimizer="bobyqa",optCtrl=list(maxfun=1e19)))
#Male models
#poisson regression
ab_m_LBS= lmer(LBS ~ ft + grid + (1|byear), data = subset(male))
#negative binomial regression
ab_m_surv= glmer.nb(age ~ ft + grid + (1|byear), data = subset(male), control=glmerControl(tol=1e-6,optimizer="bobyqa",optCtrl=list(maxfun=1e19)))
然后我只想绘制每个模型中的两个变量（ft2 和 gridSU）。
ab_f_LBS <- tidy(ab_f_LBS) %>% filter(!grepl('sd_Observation.Residual', term)) %>% filter(!grepl('byear', group))
ab_m_LBS <- tidy(ab_m_LBS) %>% filter(!grepl('sd_Observation.Residual', term)) %>% filter(!grepl('byear', group))
ab_f_surv <- tidy(ab_f_surv) %>% filter(!grepl('sd_Observation.Residual', term)) %>% filter(!grepl('byear', group))
ab_m_surv <- tidy(ab_m_surv) %>% filter(!grepl('sd_Observation.Residual', term)) %>% filter(!grepl('byear', group))
然后我准备制作点须图。
#required packages
library(dotwhisker)
library(broom)
dwplot(list(ab_f_LBS, ab_m_LBS, ab_f_surv, ab_m_surv),
vline = geom_vline(xintercept = 0, colour = "black", linetype = 2),
dodge_size=0.2,
style="dotwhisker") %>% # plot line at zero _behind_ coefs
relabel_predictors(c(ft2= "Immigrants",
gridSU = "Grid (SU)")) +
theme_classic() +
xlab("Coefficient estimate (+/- CI)") +
ylab("") +
scale_color_manual(values=c("#000000", "#666666", "#999999", "#CCCCCC"),
labels = c("Female LRS", "Male LRS", "Female survival", "Male survival"),
name = "First generation models") +
theme(axis.title=element_text(size=10),
axis.text.x = element_text(size=10),
axis.text.y = element_text(size=12, angle=90, hjust=.5),
legend.position = c(0.7, 0.8),
legend.justification = c(0, 0),
legend.title=element_text(size=12),
legend.text=element_text(size=10),
legend.key = element_rect(size = 0.1),
legend.key.size = unit(0.5, "cm"))
我遇到这个问题：
- 错误信息：
Error in psych::describe(x, ...) : unused arguments (conf.int = TRUE, conf.int = TRUE)。当我尝试只使用 1 个模型时（即 dwplot(ab_f_LBS) 它可以工作，但是一旦我添加另一个模型，我就会收到此错误消息。
如何在同一个点须图上绘制 4 个回归模型？
更新
traceback()的结果：
> traceback()
14: stop(gettextf("cannot coerce class \"%s\" to a data.frame", deparse(class(x))),
domain = NA)
13: as.data.frame.default(x)
12: as.data.frame(x)
11: tidy.default(x, conf.int = TRUE, ...)
10: broom::tidy(x, conf.int = TRUE, ...)
9: .f(.x[[i]], ...)
8: .Call(map_impl, environment(), ".x", ".f", "list")
7: map(.x, .f, ...)
6: purrr::map_dfr(x, .id = "model", function(x) {
broom::tidy(x, conf.int = TRUE, ...)
})
5: eval(lhs, parent, parent)
4: eval(lhs, parent, parent)
3: purrr::map_dfr(x, .id = "model", function(x) {
broom::tidy(x, conf.int = TRUE, ...)
}) %>% mutate(model = if_else(!is.na(suppressWarnings(as.numeric(model))),
paste("Model", model), model))
2: dw_tidy(x, by_2sd, ...)
1: dwplot(list(ab_f_LBS, ab_m_LBS, ab_f_surv, ab_m_surv), effects = "fixed",
by_2sd = FALSE)
这是我的会话信息：
> sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: OS X El Capitan 10.11.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_CA.UTF-8/en_CA.UTF-8/en_CA.UTF-8/C/en_CA.UTF-8/en_CA.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] dotwhisker_0.5.0 broom_0.5.0 broom.mixed_0.2.2
[4] glmmTMB_0.2.2.0 lme4_1.1-18-1 Matrix_1.2-14
[7] bindrcpp_0.2.2 forcats_0.3.0 stringr_1.3.1
[10] dplyr_0.7.6 purrr_0.2.5 readr_1.1.1
[13] tidyr_0.8.1 tibble_1.4.2 ggplot2_3.0.0
[16] tidyverse_1.2.1 lubridate_1.7.4 devtools_1.13.6
loaded via a namespace (and not attached):
[1] ggstance_0.3.1 tidyselect_0.2.5 TMB_1.7.14 reshape2_1.4.3
[5] splines_3.5.1 haven_1.1.2 lattice_0.20-35 colorspace_1.3-2
[9] rlang_0.2.2 pillar_1.3.0 nloptr_1.2.1 glue_1.3.0
[13] withr_2.1.2 modelr_0.1.2 readxl_1.1.0 bindr_0.1.1
[17] plyr_1.8.4 munsell_0.5.0 gtable_0.2.0 cellranger_1.1.0
[21] rvest_0.3.2 coda_0.19-2 memoise_1.1.0 Rcpp_0.12.19
[25] scales_1.0.0 backports_1.1.2 jsonlite_1.5 hms_0.4.2
[29] digest_0.6.18 stringi_1.2.4 grid_3.5.1 cli_1.0.1
[33] tools_3.5.1 magrittr_1.5 lazyeval_0.2.1 crayon_1.3.4
[37] pkgconfig_2.0.2 MASS_7.3-50 xml2_1.2.0 assertthat_0.2.0
[41] minqa_1.2.4 httr_1.3.1 rstudioapi_0.8 R6_2.3.0
[45] nlme_3.1-137 compiler_3.5.1
在 this vignette 的帮助下。如果您想使用 tidy 模型，您需要使用 model 变量创建一个 data.frame。
ab_f_LBS <- tidy(ab_f_LBS) %>%
filter(!grepl('sd_Observation.Residual', term)) %>%
filter(!grepl('byear', group)) %>%
mutate(model = "ab_f_LBS")
ab_m_LBS <- tidy(ab_m_LBS) %>%
filter(!grepl('sd_Observation.Residual', term)) %>%
filter(!grepl('byear', group)) %>%
mutate(model = "ab_m_LBS")
ab_f_surv <- tidy(ab_f_surv) %>%
filter(!grepl('sd_Observation.Residual', term)) %>%
filter(!grepl('byear', group)) %>%
mutate(model = "ab_f_surv")
ab_m_surv <- tidy(ab_m_surv) %>%
filter(!grepl('sd_Observation.Residual', term)) %>%
filter(!grepl('byear', group)) %>%
mutate(model = "ab_m_surv")
#required packages
library(dotwhisker)
library(broom)
tidy_mods <- bind_rows(ab_f_LBS, ab_m_LBS, ab_f_surv, ab_m_surv)
dwplot(tidy_mods,
vline = geom_vline(xintercept = 0, colour = "black", linetype = 2),
dodge_size=0.2,
style="dotwhisker") %>% # plot line at zero _behind_ coefs
relabel_predictors(c(ft2= "Immigrants",
gridSU = "Grid (SU)")) +
theme_classic() +
xlab("Coefficient estimate (+/- CI)") +
ylab("") +
scale_color_manual(values=c("#000000", "#666666", "#999999", "#CCCCCC"),
labels = c("Female LRS", "Male LRS", "Female survival", "Male survival"),
name = "First generation models") +
theme(axis.title=element_text(size=10),
axis.text.x = element_text(size=10),
axis.text.y = element_text(size=12, angle=90, hjust=.5),
legend.position = c(0.7, 0.8),
legend.justification = c(0, 0),
legend.title=element_text(size=12),
legend.text=element_text(size=10),
legend.key = element_rect(size = 0.1),
legend.key.size = unit(0.5, "cm"))
据我目前所见，引用小插图：
one can change the shape of the point estimate instead of using
different colors.
所以我不确定形状和颜色的变化是否可以在不进一步挖掘的情况下轻松改变...
我有几个 comments/suggestions。 （tl;dr 是您可以大大简化您的 modeling/graphic-creating 流程...）
设置：
library(dplyr)
Q1 <- read.table("Q1.txt", header=TRUE)
library(lme4)
library(glmmTMB) ## use this for NB models
library(broom.mixed) ## CRAN version should be OK
library(dotwhisker) ## use devtools::install_github("fsolt/dotwhisker")
- 您标记为 "Poisson model" 的模型不是 -- 它是 线性 混合模型，并且参数不会与 NB 模型具有特别可比性
- 收到很多
glmer.nb的警告，改成glmmTMB
#Female models
#poisson regression
ab_f_LBS= glmer(LBS ~ ft + grid + (1|byear),
family=poisson, data = subset(Q1,sex=="F"))
#negative binomial regression
ab_f_surv = glmmTMB(age ~ ft + grid + (1|byear),
data = subset(Q1, sex=="F"),
family=nbinom2)
#Male models
#poisson regression
ab_m_LBS= update(ab_f_LBS, data=subset(Q1, sex=="M"))
ab_m_surv= update(ab_f_surv, data=subset(Q1, sex=="M"))
现在剧情：
dwplot(list(LBS_M=ab_m_LBS,LBS_F=ab_f_LBS,surv_m=ab_m_surv,surv_f=ab_f_surv),
effects="fixed",by_2sd=FALSE)+
geom_vline(xintercept=0,lty=2)
ggsave("dwplot1.png")
> sessionInfo()
R Under development (unstable) (2018-07-26 r75007)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.5 LTS
Matrix products: default
BLAS: /usr/local/lib/R/lib/libRblas.so
LAPACK: /usr/local/lib/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_CA.UTF8 LC_NUMERIC=C
[3] LC_TIME=en_CA.UTF8 LC_COLLATE=en_CA.UTF8
[5] LC_MONETARY=en_CA.UTF8 LC_MESSAGES=en_CA.UTF8
[7] LC_PAPER=en_CA.UTF8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_CA.UTF8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] bindrcpp_0.2.2 dotwhisker_0.5.0.9000 ggplot2_3.0.0
[4] broom.mixed_0.2.3 glmmTMB_0.2.2.0 lme4_1.1-18.9000
[7] Matrix_1.2-14 dplyr_0.7.6
loaded via a namespace (and not attached):
[1] Rcpp_0.12.19 pillar_1.3.0 compiler_3.6.0 nloptr_1.2.1
[5] plyr_1.8.4 TMB_1.7.14 bindr_0.1.1 tools_3.6.0
[9] digest_0.6.18 ggstance_0.3.1 tibble_1.4.2 nlme_3.1-137
[13] gtable_0.2.0 lattice_0.20-35 pkgconfig_2.0.2 rlang_0.2.2
[17] coda_0.19-2 withr_2.1.2 stringr_1.3.1 grid_3.6.0
[21] tidyselect_0.2.5 glue_1.3.0 R6_2.3.0 minqa_1.2.4
[25] purrr_0.2.5 tidyr_0.8.1 reshape2_1.4.3 magrittr_1.5
[29] backports_1.1.2 scales_1.0.0 MASS_7.3-50 splines_3.6.0
[33] assertthat_0.2.0 colorspace_1.3-2 labeling_0.3 stringi_1.2.4
[37] lazyeval_0.2.1 munsell_0.5.0 broom_0.5.0 crayon_1.3.4
我正在尝试绘制 4 种不同回归模型的置信区间的点须图。
数据可用here。
#first importing data
Q1<-read.table("~/Q1.txt", header=T)
# Optionally, read in data directly from figshare.
# Q1 <- read.table("https://ndownloader.figshare.com/files/13283882?private_link=ace5b44bc12394a7c46d", header=TRUE)
library(dplyr)
#splitting into female and male
female<-Q1 %>%
filter(sex=="F")
male<-Q1 %>%
filter(sex=="M")
library(lme4)
#Female models
#poisson regression
ab_f_LBS= lmer(LBS ~ ft + grid + (1|byear), data = subset(female))
#negative binomial regression
ab_f_surv= glmer.nb(age ~ ft + grid + (1|byear), data = subset(female), control=glmerControl(tol=1e-6,optimizer="bobyqa",optCtrl=list(maxfun=1e19)))
#Male models
#poisson regression
ab_m_LBS= lmer(LBS ~ ft + grid + (1|byear), data = subset(male))
#negative binomial regression
ab_m_surv= glmer.nb(age ~ ft + grid + (1|byear), data = subset(male), control=glmerControl(tol=1e-6,optimizer="bobyqa",optCtrl=list(maxfun=1e19)))
然后我只想绘制每个模型中的两个变量（ft2 和 gridSU）。
ab_f_LBS <- tidy(ab_f_LBS) %>% filter(!grepl('sd_Observation.Residual', term)) %>% filter(!grepl('byear', group))
ab_m_LBS <- tidy(ab_m_LBS) %>% filter(!grepl('sd_Observation.Residual', term)) %>% filter(!grepl('byear', group))
ab_f_surv <- tidy(ab_f_surv) %>% filter(!grepl('sd_Observation.Residual', term)) %>% filter(!grepl('byear', group))
ab_m_surv <- tidy(ab_m_surv) %>% filter(!grepl('sd_Observation.Residual', term)) %>% filter(!grepl('byear', group))
然后我准备制作点须图。
#required packages
library(dotwhisker)
library(broom)
dwplot(list(ab_f_LBS, ab_m_LBS, ab_f_surv, ab_m_surv),
vline = geom_vline(xintercept = 0, colour = "black", linetype = 2),
dodge_size=0.2,
style="dotwhisker") %>% # plot line at zero _behind_ coefs
relabel_predictors(c(ft2= "Immigrants",
gridSU = "Grid (SU)")) +
theme_classic() +
xlab("Coefficient estimate (+/- CI)") +
ylab("") +
scale_color_manual(values=c("#000000", "#666666", "#999999", "#CCCCCC"),
labels = c("Female LRS", "Male LRS", "Female survival", "Male survival"),
name = "First generation models") +
theme(axis.title=element_text(size=10),
axis.text.x = element_text(size=10),
axis.text.y = element_text(size=12, angle=90, hjust=.5),
legend.position = c(0.7, 0.8),
legend.justification = c(0, 0),
legend.title=element_text(size=12),
legend.text=element_text(size=10),
legend.key = element_rect(size = 0.1),
legend.key.size = unit(0.5, "cm"))
我遇到这个问题：
- 错误信息：
Error in psych::describe(x, ...) : unused arguments (conf.int = TRUE, conf.int = TRUE)。当我尝试只使用 1 个模型时（即dwplot(ab_f_LBS)它可以工作，但是一旦我添加另一个模型，我就会收到此错误消息。
如何在同一个点须图上绘制 4 个回归模型？
更新
traceback()的结果：
> traceback()
14: stop(gettextf("cannot coerce class \"%s\" to a data.frame", deparse(class(x))),
domain = NA)
13: as.data.frame.default(x)
12: as.data.frame(x)
11: tidy.default(x, conf.int = TRUE, ...)
10: broom::tidy(x, conf.int = TRUE, ...)
9: .f(.x[[i]], ...)
8: .Call(map_impl, environment(), ".x", ".f", "list")
7: map(.x, .f, ...)
6: purrr::map_dfr(x, .id = "model", function(x) {
broom::tidy(x, conf.int = TRUE, ...)
})
5: eval(lhs, parent, parent)
4: eval(lhs, parent, parent)
3: purrr::map_dfr(x, .id = "model", function(x) {
broom::tidy(x, conf.int = TRUE, ...)
}) %>% mutate(model = if_else(!is.na(suppressWarnings(as.numeric(model))),
paste("Model", model), model))
2: dw_tidy(x, by_2sd, ...)
1: dwplot(list(ab_f_LBS, ab_m_LBS, ab_f_surv, ab_m_surv), effects = "fixed",
by_2sd = FALSE)
这是我的会话信息：
> sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: OS X El Capitan 10.11.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_CA.UTF-8/en_CA.UTF-8/en_CA.UTF-8/C/en_CA.UTF-8/en_CA.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] dotwhisker_0.5.0 broom_0.5.0 broom.mixed_0.2.2
[4] glmmTMB_0.2.2.0 lme4_1.1-18-1 Matrix_1.2-14
[7] bindrcpp_0.2.2 forcats_0.3.0 stringr_1.3.1
[10] dplyr_0.7.6 purrr_0.2.5 readr_1.1.1
[13] tidyr_0.8.1 tibble_1.4.2 ggplot2_3.0.0
[16] tidyverse_1.2.1 lubridate_1.7.4 devtools_1.13.6
loaded via a namespace (and not attached):
[1] ggstance_0.3.1 tidyselect_0.2.5 TMB_1.7.14 reshape2_1.4.3
[5] splines_3.5.1 haven_1.1.2 lattice_0.20-35 colorspace_1.3-2
[9] rlang_0.2.2 pillar_1.3.0 nloptr_1.2.1 glue_1.3.0
[13] withr_2.1.2 modelr_0.1.2 readxl_1.1.0 bindr_0.1.1
[17] plyr_1.8.4 munsell_0.5.0 gtable_0.2.0 cellranger_1.1.0
[21] rvest_0.3.2 coda_0.19-2 memoise_1.1.0 Rcpp_0.12.19
[25] scales_1.0.0 backports_1.1.2 jsonlite_1.5 hms_0.4.2
[29] digest_0.6.18 stringi_1.2.4 grid_3.5.1 cli_1.0.1
[33] tools_3.5.1 magrittr_1.5 lazyeval_0.2.1 crayon_1.3.4
[37] pkgconfig_2.0.2 MASS_7.3-50 xml2_1.2.0 assertthat_0.2.0
[41] minqa_1.2.4 httr_1.3.1 rstudioapi_0.8 R6_2.3.0
[45] nlme_3.1-137 compiler_3.5.1
在 this vignette 的帮助下。如果您想使用 tidy 模型，您需要使用 model 变量创建一个 data.frame。
ab_f_LBS <- tidy(ab_f_LBS) %>%
filter(!grepl('sd_Observation.Residual', term)) %>%
filter(!grepl('byear', group)) %>%
mutate(model = "ab_f_LBS")
ab_m_LBS <- tidy(ab_m_LBS) %>%
filter(!grepl('sd_Observation.Residual', term)) %>%
filter(!grepl('byear', group)) %>%
mutate(model = "ab_m_LBS")
ab_f_surv <- tidy(ab_f_surv) %>%
filter(!grepl('sd_Observation.Residual', term)) %>%
filter(!grepl('byear', group)) %>%
mutate(model = "ab_f_surv")
ab_m_surv <- tidy(ab_m_surv) %>%
filter(!grepl('sd_Observation.Residual', term)) %>%
filter(!grepl('byear', group)) %>%
mutate(model = "ab_m_surv")
#required packages
library(dotwhisker)
library(broom)
tidy_mods <- bind_rows(ab_f_LBS, ab_m_LBS, ab_f_surv, ab_m_surv)
dwplot(tidy_mods,
vline = geom_vline(xintercept = 0, colour = "black", linetype = 2),
dodge_size=0.2,
style="dotwhisker") %>% # plot line at zero _behind_ coefs
relabel_predictors(c(ft2= "Immigrants",
gridSU = "Grid (SU)")) +
theme_classic() +
xlab("Coefficient estimate (+/- CI)") +
ylab("") +
scale_color_manual(values=c("#000000", "#666666", "#999999", "#CCCCCC"),
labels = c("Female LRS", "Male LRS", "Female survival", "Male survival"),
name = "First generation models") +
theme(axis.title=element_text(size=10),
axis.text.x = element_text(size=10),
axis.text.y = element_text(size=12, angle=90, hjust=.5),
legend.position = c(0.7, 0.8),
legend.justification = c(0, 0),
legend.title=element_text(size=12),
legend.text=element_text(size=10),
legend.key = element_rect(size = 0.1),
legend.key.size = unit(0.5, "cm"))
据我目前所见，引用小插图：
one can change the shape of the point estimate instead of using different colors.
所以我不确定形状和颜色的变化是否可以在不进一步挖掘的情况下轻松改变...
我有几个 comments/suggestions。 （tl;dr 是您可以大大简化您的 modeling/graphic-creating 流程...）
设置：
library(dplyr)
Q1 <- read.table("Q1.txt", header=TRUE)
library(lme4)
library(glmmTMB) ## use this for NB models
library(broom.mixed) ## CRAN version should be OK
library(dotwhisker) ## use devtools::install_github("fsolt/dotwhisker")
- 您标记为 "Poisson model" 的模型不是 -- 它是 线性 混合模型，并且参数不会与 NB 模型具有特别可比性
- 收到很多
glmer.nb的警告，改成glmmTMB
#Female models
#poisson regression
ab_f_LBS= glmer(LBS ~ ft + grid + (1|byear),
family=poisson, data = subset(Q1,sex=="F"))
#negative binomial regression
ab_f_surv = glmmTMB(age ~ ft + grid + (1|byear),
data = subset(Q1, sex=="F"),
family=nbinom2)
#Male models
#poisson regression
ab_m_LBS= update(ab_f_LBS, data=subset(Q1, sex=="M"))
ab_m_surv= update(ab_f_surv, data=subset(Q1, sex=="M"))
现在剧情：
dwplot(list(LBS_M=ab_m_LBS,LBS_F=ab_f_LBS,surv_m=ab_m_surv,surv_f=ab_f_surv),
effects="fixed",by_2sd=FALSE)+
geom_vline(xintercept=0,lty=2)
ggsave("dwplot1.png")
> sessionInfo()
R Under development (unstable) (2018-07-26 r75007)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.5 LTS
Matrix products: default
BLAS: /usr/local/lib/R/lib/libRblas.so
LAPACK: /usr/local/lib/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_CA.UTF8 LC_NUMERIC=C
[3] LC_TIME=en_CA.UTF8 LC_COLLATE=en_CA.UTF8
[5] LC_MONETARY=en_CA.UTF8 LC_MESSAGES=en_CA.UTF8
[7] LC_PAPER=en_CA.UTF8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_CA.UTF8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] bindrcpp_0.2.2 dotwhisker_0.5.0.9000 ggplot2_3.0.0
[4] broom.mixed_0.2.3 glmmTMB_0.2.2.0 lme4_1.1-18.9000
[7] Matrix_1.2-14 dplyr_0.7.6
loaded via a namespace (and not attached):
[1] Rcpp_0.12.19 pillar_1.3.0 compiler_3.6.0 nloptr_1.2.1
[5] plyr_1.8.4 TMB_1.7.14 bindr_0.1.1 tools_3.6.0
[9] digest_0.6.18 ggstance_0.3.1 tibble_1.4.2 nlme_3.1-137
[13] gtable_0.2.0 lattice_0.20-35 pkgconfig_2.0.2 rlang_0.2.2
[17] coda_0.19-2 withr_2.1.2 stringr_1.3.1 grid_3.6.0
[21] tidyselect_0.2.5 glue_1.3.0 R6_2.3.0 minqa_1.2.4
[25] purrr_0.2.5 tidyr_0.8.1 reshape2_1.4.3 magrittr_1.5
[29] backports_1.1.2 scales_1.0.0 MASS_7.3-50 splines_3.6.0
[33] assertthat_0.2.0 colorspace_1.3-2 labeling_0.3 stringi_1.2.4
[37] lazyeval_0.2.1 munsell_0.5.0 broom_0.5.0 crayon_1.3.4