Odd's Ratio Plot Produced by the plot_model() function in the sjPlot package is too large for the plotting window in R

Question

Problem:

I have produced an odds ratio plot (below) from the data frame called FID (below) using the plot_model() function in the sjPlot package, but the figure is too large for the plotting window.

I have two models involving: (1) FID_Model_1 (see figure 1); and (2) Mixed_FID_Model_1 (see figure 2). The mixed model contains the lowest AIC value, and, on the surface, appears to contain the best goodness of fit. However, arguably, the FID_Model_1 uses fewer degrees of freedom, and the Odds ratio plot (figure 1) illustrates this model has meaningful predictor values.

Therefore, I have attempted to produce Odds' ratio plots for both of these models to explore these relationships further - FID_Model_1 and the Mixed_FID_Model_1.

                           df      AIC
         FID_Model_1       13 17643.63
         Mixed_FID_Model_1 22 12237.52

The idea is to show the relative importance of predictor variables in the mixed-effects glm() model called Mixed_FID_Model_1 (figure 2).

However, figure 2 is far too large to fit and into the plot window. I have tried resizing the plot window with different code syntax but I was unsuccessful.

If anyone is able to help, I would be deeply appreciative.

#####Open a plotter window
dev.new()

library(ggplot2)
library(sjmisc)
library(sjlabelled)
library(sjPlot)

##Plot the glm models


Mixed_FID_Model_1<-glm(FID~Year*Month, family=poisson, data=FID)

Model_FID_Model_1<-glm(FID~Year+Month, family=poisson, data=FID)



 ###Produce the Odds's Ratio Plots for models 1 and 2
    
    ##Open a plotting window
      dev.new()

##Model 1 - Blue_Model_2
plot_model(Blue_Model_2, 
           show.values = TRUE, value.offset = .3)


##Open a plotting window
dev.new()
##Model 2 - Mixed_Blue_Model_1
plot_model(Mixed_Blue_Model_1, 
           show.values = TRUE, value.offset = .3)

Odds Ratio Plot for figure 1

Odds Ratio Plot for figure 2

Data frame: FID

structure(list(FID = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 
29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 
45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 
61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 
77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 
93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 
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654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 
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732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 
745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 
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901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 
914, 915, 916, 917, 918), Year = c(2015, 2015, 2015, 2015, 2015, 
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2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
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2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017), 
    Month = 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, 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, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
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    4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 8L, 8L, 8L, 8L, 8L, 8L, 
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    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 
    12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
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    12L, 12L, 12L, 12L, 12L, 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, 
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    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
    9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
    9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 
    10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
    10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
    10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
    10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 
    12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
    12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
    12L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 
    9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
    9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
    10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
    10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
    10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 
    12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
    12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
    12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L
    ), .Label = c("January", "February", "March", "April", "May", 
    "June", "July", "August", "September", "October", "November", 
    "December"), class = "factor")), row.names = c(NA, 917L), class = "data.frame")

The blue point on the right hand side is clearly too large, effectively infinite in fact, to be meaningful. You probably have a pathological data situation. You should be looking carefully at your data with tabulations BEFORE jumping to regression based analytics. — IRTFM, Sep 27 '20 at 17:06
Thank you for your feedback, it was deeply appreciated. I already have tabulated my data, cleaned it up, and subsetted it! The problem is that I have a numerical independent numeric variable ‘FID’, two predictors that are factors:(1) Month - 12 levels; and (2) Monsoon season - 5 levels; and one column with 'Year - 3 levels' — Alice Hobbs, Sep 28 '20 at 02:05
I have experimented with different glm() models and a stepwise regression analysis revealed that year and month were the most relevant predictors. I added these predictors together as a mixed effects model and this is why I wanted to produce a plot to visualise both models. — Alice Hobbs, Sep 28 '20 at 02:10
I have edited this post and included the odds ratio plot for the model without mixed effects, and it appears that this is the best model between the two. — Alice Hobbs, Sep 28 '20 at 02:11
Another Stack Overflow commenter suggested I did a Bayesian analysis with MCMC for this type of data set. When I did a stepwise (backwards and forwards) analysis, Year and Month were the most important covariates. What is your opinion? — Alice Hobbs, Sep 28 '20 at 02:11
In both odd ratio figures, there are some meaningful results apart from the sections that you rightfully pointed out in the mixed model that have blue points that are effectively infinite. I feel confused now! Would it be possible to please take another look at this edited page, and I am open to any advice that you may offer. Thank you in advance if this is possible. — Alice Hobbs, Sep 28 '20 at 03:15
Look at the data (which you have since changed but remains garbage) before reaching for regression methods. Your outcome variable is essentially an integer sequence 1:918. It makes absolutely no sense to try to predict that data. — IRTFM, Sep 28 '20 at 06:34
I do see what you mean. The column FID are counts of whales, so would it be better to place the counts into the mean numberof individuals counted per month per year and then run the regression analysis? In your opinion, what would be the best type of analysis for this type of data? — Alice Hobbs, Sep 28 '20 at 08:39
The version of the data you offered has FID as a value that starts at 1 and increases in each period by exactly 1. It's clear that you think it is something meaningful but it's really just an index value. If your data was ever a series of counts then it has gotten corrupted, so the results are meaningless and not worthy of paying any effort at interpreting. Voting to close as not reproducible (although it is actually reproducible but junk.) — IRTFM, Sep 29 '20 at 00:28

Odd's Ratio Plot Produced by the plot_model() function in the sjPlot package is too large for the plotting window in R

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