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And graphs for both using cplot(m3, 'x2', what = 'predict') and cplot(m3, 'x2', what = 'effect'): The numbers i get from marginaleffects doesn't seems to match 'effect' clplot. And both instantaneous marginal effects (table and graph) doesn't seems to match predicted values rate of change. The number of characters that should be used to abbreviate the levels of the factor. Set to a large value for unabbreviated names. Polycol A vector of three colors indicating the colors of polygons when the difference is significant negative, insignificant, and significant positive, in that order. Defaults to c(‘gray80’, ‘white. Package ‘geneplotter’ November 18, 2020 Title Graphics related functions for Bioconductor Version 1.68.0 Author R. Gentleman, Biocore Description Functions for plotting genomic data.

This document describes how to plot estimates as forest plots (or dot whisker plots) of various regression models, using the plot_model() function. plot_model() is a generic plot-function, which accepts many model-objects, like lm, glm, lme, lmerMod etc.

• Dplot and cplot are functions for plotting lattice data. They are an alternative to base R's image function using ggplot2 instead. Dplot is used for discrete data and cplot for continuous data, they only differ in the fact that pixel values are treated as a factor in dplot, therefore, a discrete scale is used.
• In R, there is a special data type for ordinal data. This type is called ordered factors and is an extension of factors that you’re already familiar with. To create an ordered factor in R, you have two options: Use the factor function with the argument ordered=TRUE. Use the ordered function. Say you want to.

plot_model() allows to create various plot tyes, which can be defined via the type-argument. The default is type = 'fe', which means that fixed effects (model coefficients) are plotted. For mixed effects models, only fixed effects are plotted by default as well.

Fitting a logistic regression model

First, we fit a model that will be used in the following examples. The examples work in the same way for any other model as well.

Plotting estimates of generalized linear models

The simplest function call is just passing the model object as argument. By default, estimates are sorted in descending order, with the highest effect at the top.

The “neutral” line, i.e. the vertical intercept that indicates no effect (x-axis position 1 for most glm’s and position 0 for most linear models), is drawn slightly thicker than the other grid lines. You can change the line color with the vline.color-argument.

Sorting estimates

By default, the estimates are sorted in the same order as they were introduced into the model. Use sort.est = TRUE to sort estimates in descending order, from highest to lowest value.

Another way to sort estimates is to use the order.terms-argument. This is a numeric vector, indicating the order of estimates in the plot. In the summary, we see that “sex2” is the first term, followed by the three dependency-categories (position 2-4), the Barthel-Index (5) and two levels for intermediate and high level of education (6 and 7).

Now we want the educational levels (6 and 7) first, than gender (1), followed by dependency (2-4)and finally the Barthel-Index (5). Use this order as numeric vector for the order.terms-argument.

Estimates on the untransformed scale

By default, plot_model() automatically exponentiates coefficients, if appropriate (e.g. for models with log or logit link). You can explicitley prevent transformation by setting the transform-argument to NULL, or apply any transformation by using a character vector with the function name.

Showing value labels

By default, just the dots and error bars are plotted. Use show.values = TRUE to show the value labels with the estimates values, and use show.p = FALSE to suppress the asterisks that indicate the significance level of the p-values. Use value.offset to adjust the relative positioning of value labels to the dots and lines.

Labelling the plot

As seen in the above examples, by default, the plotting-functions of sjPlot retrieve value and variable labels if the data is labelled, using the sjlabelled-package. If the data is not labelled, the variable names are used. In such cases, use the arguments title, axis.labels and axis.title to annotate the plot title and axes. If you want variable names instead of labels, even for labelled data, use ' as argument-value, e.g. axis.labels = ', or set auto.label to FALSE.

Furthermore, plot_model() applies case-conversion to all labels by default, using the snakecase-package. This converts labels into human-readable versions. Use case = NULL to turn case-conversion off, or refer to the package-vignette of the snakecase-package for further options.

Pick or remove specific terms from plot

Use terms resp. rm.terms to select specific terms that should (not) be plotted.

Standardized estimates

For linear models, you can also plot standardized beta coefficients, using type = 'std' or type = 'std2'. These two options differ in the way how coefficients are standardized. type = 'std2' plots standardized beta values, however, standardization follows Gelman’s (2008) suggestion, rescaling the estimates by dividing them by two standard deviations instead of just one.

Bayesian models (fitted with Stan)

plot_model() also supports stan-models fitted with the rstanarm or brms packages. However, there are a few differences compared to the previous plot examples.

First, of course, there are no confidence intervals, but uncertainty intervals - high density intervals, to be precise.

Second, there’s not just one interval range, but an inner and outer probability. By default, the inner probability is fixed to .5 (50%), while the outer probability is specified via ci.lvl (which defaults to .89 (89%) for Bayesian models). However, you can also use the arguments prob.inner and prob.outer to define the intervals boundaries.

Third, the point estimate is by default the median, but can also be another value, like mean. This can be specified with the bpe-argument.

Tweaking plot appearance

There are several options to customize the plot appearance:

• The colors-argument either takes the name of a valid colorbrewer palette (see also the related vignette), 'bw' or 'gs' for black/white or greyscaled colors, or a string with a color name.
• value.offset and value.size adjust the positioning and size of value labels, if shown.
• dot.size and line.size change the size of dots and error bars.
• vline.color changes the neutral “intercept” line.
• width, alpha and scale are passed down to certain ggplot-geoms, like geom_errorbar() or geom_density_ridges().

Gelman A (2008) Scaling regression inputs by dividing by two standard deviations. Statistics in Medicine 27: 2865–2873.

• Scatter plots
• Histogram and density plots

The function qplot() [in ggplot2] is very similar to the basic plot() function from the R base package. It can be used to create and combine easily different types of plots. However, it remains less flexible than the function ggplot().

This chapter provides a brief introduction to qplot(), which stands for quick plot. Concerning the function ggplot(), many articles are available at the end of this web page for creating and customizing different plots using ggplot().

The data must be a data.frame (columns are variables and rows are observations).

The data set mtcars is used in the examples below:

mtcars : Motor Trend Car Road Tests.

Description: The data comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973 - 74 models).

Format: A data frame with 32 observations on 3 variables.

• [, 1] mpg Miles/(US) gallon
• [, 2] cyl Number of cylinders
• [, 3] wt Weight (lb/1000)

A simplified format of qplot() is :

• x : x values
• y : y values (optional)
• data : data frame to use (optional).
• geom : Character vector specifying geom to use. Defaults to “point” if x and y are specified, and “histogram” if only x is specified.
• xlim, ylim: x and y axis limits

Other arguments including main, xlab, ylab and log can be used also:

• main: Plot title
• xlab, ylab: x and y axis labels
• log: which variables to log transform. Allowed values are “x”, “y” or “xy”

Note that, the stat and position arguments to qplot() have been deprecated since ggplot2 version 2.0.0.

Basic scatter plots

The plot can be created using data from either numeric vectors or a data frame:

Scatter plots with smoothed line

The option smooth is used to add a smoothed line with its standard error:

To draw a regression line, read the following article: ggplot2 scatter plot

Smoothed line by groups

The argument color is used to tell R that we want to color the points by groups:

Change scatter plot colors

Points can be colored according to the values of a continuous or a discrete variable. The argument colour is used.

Note that you can also use the following R code to generate the second plot :

Change the shape and the size of points

Like color, the shape and the size of points can be controlled by a continuous or discrete variable.

Scatter plot with texts

The argument label is used to specify the texts to be used for each points:

PlantGrowth data set is used in the following example :

• geom = “boxplot”: draws a box plot
• geom = “dotplot”: draws a dot plot. The supplementary arguments stackdir = “center” and binaxis = “y” are required.
• geom = “violin”: draws a violin plot. The argument trim is set to FALSE

Change the color by groups:

The histogram and density plots are used to display the distribution of data.

Generate some data

The R code below generates some data containing the weights by sex (M for male; F for female):

Density plot

This analysis was performed using R (ver. 3.2.4) and ggplot2 (ver 2.1.0).

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