library(visreg) knitr::opts_knit$set(aliases=c(h = 'fig.height', w = 'fig.width')) knitr::opts_chunk$set(comment='#', collapse=TRUE, cache=FALSE, tidy=FALSE) knitr::knit_hooks$set(small.mar = function(before, options, envir) { if (before) par(mar = c(4, 4, .1, .1)) })
As noted in getting started, the default behavior of visreg
when constructing a conditional plot is to fill in the other variables with either the median (for continuous variables) or the most common category (for categorical variables). This can be modified using the cond
argument, which offers more explicit control over what to condition on. Note that this has no bearing on contrast plots (at least, in the absence of interactions), which do not depend on the other terms in the model.
The cond
argument must be provided as a named list. Each element of that list specifies the value for one of the terms in the model; any elements left unspecified are filled in with the median/most common category. For example, let's construct the a plot of wind vs. ozone, but condition on three different values for temperature: (1) a cold temperature of 50 degrees (2) the default median temperature of r median(airquality$Tem)
degrees and (3) a hot temperature of 100 degrees.
fit <- lm(Ozone ~ Solar.R + Wind + Temp, data=airquality) par(mfrow=c(1,3)) visreg(fit, "Wind", cond=list(Temp=50)) visreg(fit, "Wind") visreg(fit, "Wind", cond=list(Temp=100))
A few observations/remarks:
r round(with(airquality, cor(Temp, Wind)), 2)
), and thus, more cold windy days in the data set than cold calm days. The opposite phenomenon happens in the right plot, where the relative absence of hot windy days causes the confidence band to be wider for high winds than for low winds.Finally, recall that this model had three explanatory variables; in the above example, visreg
calculated the conditional response by filling in solar radiation with its median value, as it was not specified otherwise in the cond
argument.
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