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My sjPlot package for data visualization has just been updated on CRAN. I’ve added some features to existing function, which I want to introduce here.

## Plotting linear models

So far, plotting model assumptions of linear models or plotting slopes for each estimate of linear models were spread over several functions. Now, these plot types have been integrated into the `sjp.lm` function, where you can select the plot type with the `type` parameter. Furthermore, plotting standardized coefficients now also plot the related confidence intervals.

Detailed examples can be found here:
www.strengejacke.de/sjPlot/sjp.lm

## Plotting generalized linear models

Beside odds ratios, you now can also plot the predicted probabilities of the outcome for each predictor of generalized linear models. In case you have continuous variables, these kind of plots may be more intuitive than an odds ratio value.

Detailed examples can be found here:
www.strengejacke.de/sjPlot/sjp.glm

## Plotting (generalized) linear mixed effects models

The plotting function for creating plots of (generalized) linear mixed effects models (`sjp.lmer` and `sjp.glmer`) also got new plot types over the course of the last weeks.

For `sjp.lmer`, we have

• `re` (default) for estimates of random effects
• `fe` for estimates of fixed effects
• `fe.std` for standardized estimates of fixed effects
• `fe.cor` for correlation matrix of fixed effects
• `re.qq` for a QQ-plot of random effects (random effects quantiles against standard normal quantiles)
• `fe.ri` for fixed effects slopes depending on the random intercept.

and for `sjp.glmer`, we have

• `re` (default) for odds ratios of random effects
• `fe` for odds ratios of fixed effects
• `fe.cor` for correlation matrix of fixed effects
• `re.qq` for a QQ-plot of random effects (random effects quantiles against standard normal quantiles)
• `fe.pc` or `fe.prob` to plot probability curves (predicted probabilities) of all fixed effects coefficients. Use facet.grid to decide whether to plot each coefficient as separate plot or as integrated faceted plot.
• `ri.pc` or `ri.prob` to plot probability curves (predicted probabilities) of random intercept variances for all fixed effects coefficients. Use facet.grid to decide whether to plot each coefficient as separate plot or as integrated faceted plot.

Detailed examples can be found here:
www.strengejacke.de/sjPlot/sjp.lmer and www.strengejacke.de/sjPlot/sjp.glmer

## Plotting interaction terms of (generalized) linear (mixed effects) models

Another function, where new features were added, is `sjp.int` (formerly known as sjp.lm.int). This function is now kind of generic and can plot interactions of

• linar models (lm)
• generalized linar models (glm)
• linar mixed effects models (lme4::lmer)
• generalized linar mixed effects models (lme4::glmer)

For linear models (both normal and mixed effects), slopes of interaction terms are plotted. For generalized linear models, the predicted probabilities of the outcome towards the interaction terms is plotted.

Detailed examples can be found here:
www.strengejacke.de/sjPlot/sjp.int

## Plotting Likert scales

Finally, a comprehensive documentation for the `sjp.likert` function is finsihed, which can be found here:
www.strengejacke.de/sjPlot/sjp.likert

Tagged: data visualization, ggplot, R, rstats, sjPlot  To leave a comment for the author, please follow the link and comment on their blog: Strenge Jacke! » R.

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