sjPlot plot_model not displaying interaction names
eimichae85 opened this issue · comments
Hi there,
I am running a generalised linear mixed model with a lot of factors and their interactions.
mod1<-glmer(cbind(branch_miner_Yes,branch_miner_No)~ Drought+Stratum+Year+Drought:Stratum+Drought:Year+Stratum:Year+(1|Tree.ID)+(1|Sample.ID), data=Herb1920, family=binomial(),na.action = na.exclude)
I want to visualise the model estimations with a forest plot. Using the following code:
`Eplot_Miner<-plot_model(mod1, vline.color = "gray", colors = c("black","black"),sort.est=F,show.values = TRUE, value.offset = .45,value.size = 6,title = "", dot.size=5.3, line.size =0.8 ,transform = NULL,order.terms = c(1,3, 2, 5,4,6))+
theme_classic()+
theme(axis.text=element_text(face="bold",size=18),
axis.title = element_text(color="black",face="bold",size=19),
axis.line = element_line(colour = 'black', size = 1),
axis.ticks = element_line(colour = "black", size = 1),
legend.text=element_text(size=14),
legend.title=element_text(size=15, face="bold"),
plot.title = element_text(color="black", size=18, face="bold"))
Eplot_Miner`
However, for some reason plot_model won't show the interaction terms on the y axis, it just says "conditional" for all of them (see picture).
I realized that the problem only apperars witha glmer but not with a glmmTMB model.
Do you have a small dataset to reproduce this issue, or any other (fake) example that reproduces this issue?
Yes.
Here is the complete R-code (and at the very end I also provide the data as dput())
Herb1920<-read.csv("dummy_data.csv", header=T, sep=";")
str(Herb1920)
#Necessary packages
library(tibble)
library (glmmTMB)
library(lme4)
library(DHARMa)#check for Overdispersion
library(tidyr)
library(ggplot2)
library(car)
library(xlsx)
library(FSA)
library(cowplot)
library(rlang)
library(sjstats)
library(sjPlot)
library(sjlabelled)
library(sjmisc)
library(lme4)
library(lmerTest)
library(emmeans)
library(effects)
library(insight)
library(dplyr)
library(vegan)
str(Herb1920)
#Faktoren definieren
Herb1920$Year<-factor(Herb1920$Year)
#relevel(Herb1920$Year,ref="2019")
Herb1920$Drought <-as.factor(Herb1920$Drought)
#relevel(Herb1920$Drought,ref="0")
Herb1920$Stratum <-as.factor(Herb1920$Stratum)
levels(Herb1920$Stratum) <- list(lower = "shade", upper = "sun")
#relevel(Herb1920$Stratum,ref="sun")
Herb1920$Tree.ID <-as.factor(Herb1920$Tree.ID)
mod<-glmer(cbind(branch_miner_Yes,branch_miner_No)~ Drought*Stratum+Drought*Year+Year*Stratum+(1|Tree.ID),
data=Herb1920, family=binomial(),na.action = na.exclude)
Eplot_Miner<-plot_model(mod, vline.color = "gray", colors = c("black","black"), sort.est=F,show.values = TRUE, value.offset = .45,value.size = 6,title = "", dot.size=5.3, line.size =0.8 ,transform = NULL,order.terms = c(1,3, 2, 5,4,6))+
theme_classic()+
theme(axis.text=element_text(face="bold",size=18),
axis.title = element_text(color="black",face="bold",size=19),
axis.line = element_line(colour = 'black', size = 1),
axis.ticks = element_line(colour = "black", size = 1),
legend.text=element_text(size=14),
legend.title=element_text(size=15, face="bold"),
plot.title = element_text(color="black", size=18, face="bold"))
Eplot_Miner
DATA
Herb1920<-structure(list(Drought = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), levels = c("no", "yes"), class = "factor"), Tree.ID = structure(c(1L,
1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L,
9L, 10L, 10L, 11L, 11L, 12L, 12L, 13L, 13L, 14L, 14L, 15L, 15L,
16L, 16L, 17L, 17L, 18L, 18L, 19L, 19L, 20L, 20L, 21L, 21L, 22L,
22L, 23L, 23L, 24L, 24L, 25L, 25L, 26L, 26L, 27L, 27L, 28L, 28L,
29L, 29L, 30L, 30L, 31L, 31L, 32L, 32L, 33L, 33L, 34L, 34L, 35L,
35L, 36L, 36L, 37L, 37L, 38L, 38L, 39L, 39L, 40L, 40L, 41L, 41L,
42L, 42L, 43L, 43L, 44L, 44L, 45L, 45L, 46L, 46L, 47L, 47L, 48L,
48L, 1L, 1L, 2L, 2L, 3L, 3L, 10L, 10L, 11L, 11L, 12L, 12L, 13L,
13L, 14L, 14L, 15L, 15L, 16L, 16L, 17L, 17L, 18L, 18L, 19L, 19L,
21L, 21L, 22L, 22L, 23L, 23L, 24L, 24L, 25L, 25L, 26L, 26L, 27L,
27L, 28L, 28L, 29L, 29L, 30L, 30L, 31L, 31L, 32L, 32L, 33L, 33L,
37L, 37L, 38L, 38L, 39L, 39L, 43L, 43L, 44L, 44L, 45L, 45L, 46L,
46L, 47L, 47L, 48L, 48L), levels = c("102_6", "102_7", "102_8",
"105_1", "105_2", "105_4", "111_7", "111_8", "111_9", "113_2",
"113_4", "113_5", "114_7", "114_8", "114_9", "116_6", "116_7",
"116_9", "122_3", "122_4", "122_5", "132_3", "132_4", "132_5",
"242_2", "242_4", "242_5", "243_1", "243_2", "243_4", "245_1",
"245_2", "245_5", "246_1", "246_2", "246_3", "251_10", "251_8",
"251_9", "253_7", "253_8", "253_9", "254_6", "254_7", "254_8",
"267_10", "267_6", "267_8"), class = "factor"), Stratum = structure(c(1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L), levels = c("lower", "upper"), class = "factor"),
Year = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L), levels = c("1", "2"), class = "factor"),
Treatment = c("Control", "Control", "Control", "Control",
"Control", "Control", "Drought", "Drought", "Drought", "Drought",
"Drought", "Drought", "Control", "Control", "Control", "Control",
"Control", "Control", "Drought", "Drought", "Drought", "Drought",
"Drought", "Drought", "Control", "Control", "Control", "Control",
"Control", "Control", "Control", "Control", "Control", "Control",
"Control", "Control", "Drought", "Drought", "Drought", "Drought",
"Drought", "Drought", "Drought", "Drought", "Drought", "Drought",
"Drought", "Drought", "Drought", "Drought", "Drought", "Drought",
"Drought", "Drought", "Drought", "Drought", "Drought", "Drought",
"Drought", "Drought", "Drought", "Drought", "Drought", "Drought",
"Drought", "Drought", "Drought", "Drought", "Drought", "Drought",
"Drought", "Drought", "Control", "Control", "Control", "Control",
"Control", "Control", "Control", "Control", "Control", "Control",
"Control", "Control", "Control", "Control", "Control", "Control",
"Control", "Control", "Control", "Control", "Control", "Control",
"Control", "Control", "Control", "Control", "Control", "Control",
"Control", "Control", "Drought", "Drought", "Drought", "Drought",
"Drought", "Drought", "Control", "Control", "Control", "Control",
"Control", "Control", "Control", "Control", "Control", "Control",
"Control", "Control", "Drought", "Drought", "Drought", "Drought",
"Drought", "Drought", "Drought", "Drought", "Drought", "Drought",
"Drought", "Drought", "Drought", "Drought", "Drought", "Drought",
"Drought", "Drought", "Drought", "Drought", "Drought", "Drought",
"Drought", "Drought", "Drought", "Drought", "Drought", "Drought",
"Control", "Control", "Control", "Control", "Control", "Control",
"Control", "Control", "Control", "Control", "Control", "Control",
"Control", "Control", "Control", "Control", "Control", "Control"
), branch_miner_No = c(41L, 25L, 47L, 49L, 50L, 49L, 49L,
50L, 49L, 50L, 48L, 47L, 49L, 49L, 50L, 49L, 49L, 43L, 41L,
45L, 49L, 37L, 50L, 49L, 50L, 50L, 50L, 49L, 45L, 44L, 49L,
48L, 50L, 48L, 50L, 49L, 44L, 38L, 50L, 34L, 38L, 28L, 47L,
39L, 50L, 49L, 47L, 50L, 42L, 19L, 47L, 46L, 50L, 50L, 49L,
46L, 49L, 50L, 40L, 45L, 50L, 50L, 41L, 44L, 50L, 50L, 50L,
46L, 50L, 48L, 50L, 50L, 48L, 38L, 49L, 42L, 39L, 31L, 49L,
33L, 38L, 49L, 48L, 48L, 49L, 49L, 48L, 50L, 45L, 37L, 28L,
25L, 45L, 45L, 39L, 35L, 38L, 43L, 46L, 34L, 49L, 33L, 40L,
47L, 47L, 39L, 46L, 31L, 47L, 40L, 47L, 45L, 47L, 42L, 48L,
47L, 39L, 25L, 37L, 46L, 38L, 42L, 44L, 48L, 47L, 46L, 48L,
49L, 38L, 44L, 39L, 31L, 41L, 42L, 44L, 18L, 23L, 48L, 26L,
26L, 28L, 32L, 47L, 46L, 49L, 33L, 47L, 38L, 35L, 17L, 39L,
30L, 44L, 42L, 47L, 36L, 8L, 33L, 32L, 37L, 33L, 38L, 32L,
45L, 47L, 41L), branch_miner_Yes = c(9L, 25L, 3L, 1L, 0L,
1L, 1L, 0L, 1L, 0L, 2L, 3L, 1L, 1L, 0L, 1L, 1L, 7L, 9L, 5L,
1L, 13L, 0L, 1L, 0L, 0L, 0L, 1L, 5L, 6L, 1L, 2L, 0L, 2L,
0L, 1L, 6L, 12L, 0L, 16L, 12L, 22L, 3L, 11L, 0L, 1L, 3L,
0L, 8L, 31L, 3L, 4L, 0L, 0L, 1L, 4L, 1L, 0L, 10L, 5L, 0L,
0L, 9L, 6L, 0L, 0L, 0L, 4L, 0L, 2L, 0L, 0L, 2L, 12L, 1L,
8L, 11L, 19L, 1L, 17L, 12L, 1L, 2L, 2L, 1L, 1L, 2L, 0L, 5L,
13L, 22L, 25L, 5L, 5L, 11L, 15L, 12L, 7L, 4L, 16L, 1L, 17L,
10L, 3L, 3L, 11L, 4L, 19L, 3L, 10L, 3L, 5L, 3L, 8L, 2L, 3L,
11L, 25L, 13L, 4L, 12L, 8L, 6L, 2L, 3L, 4L, 2L, 1L, 12L,
6L, 11L, 19L, 9L, 8L, 6L, 32L, 27L, 2L, 24L, 24L, 22L, 18L,
3L, 4L, 1L, 17L, 3L, 12L, 15L, 33L, 11L, 20L, 6L, 8L, 3L,
14L, 42L, 17L, 18L, 13L, 17L, 12L, 18L, 5L, 3L, 9L)), row.names = c(NA,
-166L), class = "data.frame")
I streamlined the code a bit. any idea why the problem with the "conditional" labelling exists?
I have a guess, but let me look into this later. I'm currently preparing the CRAN submission of parameters, which is internally used by sjPlot, so I can resolve this issue once I submitted parameters.
Should be fixed in parameters:
library(parameters)
library(lme4)
library(sjPlot)
library(ggplot2)
load("d:/mail/test.RData")
Eplot_Miner <- plot_model(mod, vline.color = "gray", colors = c("black", "black"), sort.est = F, show.values = TRUE, value.offset = .45, value.size = 6, title = "", dot.size = 5.3, line.size = 0.8, transform = NULL, order.terms = c(1, 3, 2, 5, 4, 6)) +
theme_classic() +
theme(
axis.text = element_text(face = "bold", size = 18),
axis.title = element_text(color = "black", face = "bold", size = 19),
axis.line = element_line(colour = "black", size = 1),
axis.ticks = element_line(colour = "black", size = 1),
legend.text = element_text(size = 14),
legend.title = element_text(size = 15, face = "bold"),
plot.title = element_text(color = "black", size = 18, face = "bold")
)
Eplot_Miner
Created on 2023-03-31 with reprex v2.0.2
Hi,
thank you for working on this.
Unfortunately I still have the same problem.
I updated my R-version (4.2.3) and all my packages: e.g. sJPlot(2.8.13) , parameters (0.20.2) etc.
Only two packages could not be updated: robustbase and cli but I guess that is not the problem.
I restarted R several times and only worked with the example that I provided here. Still I get the "conditional" labelling instead of the interaction names.
What to do?
The fixes are on github only, but both packages will be updated on CRAN the next days. If you don't want to wait, try:
remotes::install_github("strengejacke/sjPlot")
remotes::install_github("easystats/parameters")Ah apparently I was not patient enough:)
It works with the updated packages. Thanks a lot for fixing this issue so quickly!
Sorry I have another follow-up isssue that may be related to the package update.
I realized that the "vilne" ( "zero effect" line) is now much narrower now that it used to be. this is a problem because now 3/4 of my figures have a wide-zero effect line and 1/4 has a narrow line
(see exaples below):
For the love of my life I could not find a way to make this line wider. Is this somehow possible?
Thanks a lot!