**Update**

I followed the advice from Tim’s comment and changed the scaling in the sjPlotOdds-function to logarithmic scaling. The screenshots below showing the plotted glm’s have been updated.

**Summary**

In this posting I will show how to plot results from linear and logistic regression models (`lm`

and `glm`

) with ggplot. As in my previous postings on ggplot, the main idea is to have a highly customizable function for representing data. You can download all my scripts from my script page.

**The inspiration source**

My following two functions are based on an idea which I saw at the Sustainable Research Blog. Actually, this was a kind of starting point for me to get started with R and learn more about its data visualization facilities. After playing around some time with ggplot, I built my own function based on the script posted at Sustainable Research.

**Plotting odds ratios**

Plotting odds ratios gives you mainly two display styles: bars or plots (dots). First, let me show you the dot-style. Assuming you have a glm-object (in my examples, it’s called *logreg*) and have loaded the function `sjPlotOdds.R`

(see my script page for downloads), you can plot the results like this (*I have used* `oddsLabels=lab`

*, a vector with label-strings, which are used as axis-labels. If you leave out this parameter, the variable-names from the model will be taken.*):

sjp.glm(logreg, axisLabels.y=lab, gridBreaksAt=0.4)

In the above example, if you do not specifiy axis limits, the boundaries will be calculated according to the lowest and highest confidence interval, thus fitting the diagram to the highest possible “zoom”. The next example demonstrates this with bar charts:

sjp.glm(logreg, axisLabels.y=lab, type="bars", gridBreaksAt=0.4)

Both diagrams contain model summaries in the lower right corner. You can change many visual parameters, for instance hiding the summary, changing bar colors, changing border or background colors, line and bar size etc.

If you dislike the grid bars to become narrower with increasing odds ratio values, you can use the `transformTicks`

parameter, which uses exponential distances between the tick marks. This results in grid bars with (almost) equal distances. However, the tick values, of course, are accordingly set:

sjp.glm(logreg, axisLabels.y=lab, transformTicks=TRUE, gridBreaksAt=0.2, errorBarWidth=0, errorBarSize=1)

**Plotting betas and standardized betas of linear regressions**

Quite similar is my function `sjPlotLinreg.R`

which visualizes the results of linear regressions. Thus, it requires a lm-object.

sjp.lm(linreg, axisLimits=c(-0.5, 0.9), axisTitle.x="beta (blue) and std. beta (red)", sort="std", axisLabels.y=lab, axisLabelSize=1, breakLabelsAt=30)

As you can see, I have used `predictorLabelSize=1`

and `breakLabelsAt=30`

due to the long variable labels. By default, each label at the left axis would break into more lines, thus being narrower and worse to read. Then I used `sort="std"`

to sort the predictors according to their standardized beta values (default would be ordering according to the beta values).

sjp.lm(linreg, axisLabels.y=lab, axisLabelSize=1, breakLabelsAt=30, showStandardBeta=FALSE)

The `showStandardBeta=FALSE`

makes the red dots (standardized beta values) and their connecting line disappear.

sjp.lm(linreg, axisLabels.y=lab, axisLabelSize=1, breakLabelsAt=30, showValues=FALSE, showPValues=FALSE)

This last example shows how to hide the value labels inside the diagram, so you only have the dots for beta and standardized beta coefficients.

**Last remark**

In between I have also updated my other scripts. For instance, the sjPlotGroupFrequencies.R function can now also plot box plots or violin plots (see examples at the end of that posting). So make sure you have the latest version from my script page.

[...] Plotting lm and glm models with ggplot #rstats | Strenge Jacke! [...]

Odds ratios should be plotted on a log scale, not a linear scale as you have used. See the example on the Sustainable Research blog that inspired you – that’s the correct way to do it.

Thanks for the hint, Tim! I will change that the next days.

For anybody’s interest, I have found two links related to this issue:

http://aje.oxfordjournals.org/content/early/2011/06/29/aje.kwr156.long

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1127651/

Best wishes

Daniel

Good looking plots.

Daniel, great job. It worked perfectly for me — I’m using the sjPlotOdds function. But I have a doubt though. Is it possible to insert two or three models in the same graph — to compare the results — using the sjPlotOdds function?

I already though about that, too. When I have some time after Easter, I will try to figure out how to do this.

Cool. I’ll try to figure out a way to do it and will keep you posted. And, again, great job.

Hi! Great work! Can you provide your GLM code? I have some troubles with my model. Iam using family = binomial with logit link. Thank you

The data set is not yet public, but you can look at the questionnaires we used to collect the data:

http://www.uke.de/extern/eurofamcare/deli.php

http://www.uke.de/extern/eurofamcare/documents/deliverables/cat_uk.pdf

You can easily find the related questions because the variable names follow the same numbering / order.

Hi SJ,

Just saw your code and has been trying to use it on my data. The x-axis limits is however giving me problems. If I use the format : axisLimits=c(0.8, 10.0),

gridBreaksAt=0.2). The x- axis seem not to be equally spaced and shrinks towards the tail end of the x-axis scale. Your graph however looked good and equally spaced.

My odd ratios are 1.15, 2.37, 3.7 and 6.54.

I loaded your code and used plotted the graph like this:

plotOdds(glm1,

oddsLabels=NULL,

axisLimits=c(0.8, 10.0),

gridBreaksAt=0.2)

Kindly advice me on what to do.

Hi, this is because in the first attempt I used the wrong scaling (scale_y_continuous) for plottings odds ratios. Since we have a logistic regression (and not linear), the axis shoud also have log-scaling (see Tim’s comment here: http://strengejacke.wordpress.com/2013/03/22/plotting-lm-and-glm-models-with-ggplot-rstats/#comment-405). I already updated the screenshots, maybe you have to reload this page?

Kind regards

Daniel

[...] ← Plotting lm and glm models with ggplot #rstats [...]

I’ve tried the script and it seems like not graph the reference (intercept) factor/level. Is it correct?

Yes, the intercept is not plotted in both functions. However, in the model summary of the sjPlotLinreg, you find the intercept value as “y=…”. If this is not the correct notation for the intercept, please let me know and I’ll fix that.

I think the graph should have all factors/categories, like in “effects” library.

In my experience, the intercept value may deviate a lot from the beta coefficient values, which means you may have a large gap on the x axis between intercept and beta values (for instance, intercept of, let’s say -5, betas ranging from 2 to 3). To avoid this, I decided to not plot the intercept but provide this value in the plot annotation. Perhaps I should make it optional to include the intercept in the graph.

Daniel, it would be great to have an option to plot reference factor.

I’ve update the script and it’s now possible to plot the intercept for the glm (sjPlotOdds). It’s not implemented for the lm-plotting-function yet, but will come soon.

I think your code is good especially relating to studies in epidemiology. I just used the odd ratio plots in a presentation today. Great!! keep it up.

Thank you for your comment! Good to know my scripts are useful to someone. :-)

I’ve just updated some of my scripts and will upload them on my page in the course of this week, hopefully. Some bug fixes for the frequency-plotting-functions, and standardization of parameter names for all functions.

[...] linear model: sjPlotLinreg.R Plotting (generalized) linear models have also already been described in a posting, so I will keep it short here and just give a running [...]

Perhaps I’m being dense, but I’m unable to get this script to work.

I try

But I get a few errors. The function “halfnorm” is missing, which does not appear to come from a CRAN listed package. I am also not getting the package “faraway”. Finally, there doesn’t appear to be a

function to call.

I’m probably missing a simple step or there is something slightly different about my environment that wasn’t accounted for…

Sorry, I should have updated my blog posts. The function name has changed, so the function call is now: sip.glm(…). See this posting for more examples.

To plot the model assumptions, you need following two packages:

car, faraway

Best wishes

Daniel

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Thanks for this great script, it is very helpful. I was wondering if there is a way to avoid reordering the variables by the size of the coefficients. I want to compare the same predictors for different outcome variables and so it would be helpful if they were presented in the same order. I have been playing around with the script but unfortunately it has been unsuccessful. Thanks for yours or anyones help!

Hi Rich,

I’m planning to enhance the script so you can plot several fitted models in one plot, each outcome variable (or each model) represented by a certain color. This may take some time, so I probably will first supply an update that has a “sort” parameter. The sjPlotLinreg-function already has such a parameter, but only offers ordering according to beta or standardized beta values, so I would update this function as well.

Ok, update is online, sorting should work now. Use “sortOdds”. Please refere to my script page for the updated script and make sure to read the change log first!

This is perfect, thanks so much!

[…] asked whether it would be possible to compare multiple (generalized) linear models in one graph (see comment). While it is already possible to compare multiple models as table output, I now managed to build a […]

[…] asked whether it would be possible to compare multiple (generalized) linear models in one graph (see comment). While it is already possible to compare multiple models as table output, I now managed to build a […]

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