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rggplot2bandwidthkernel-density

Set different bandwidths in ggplot2 facet_grid plotting

Suppose I have a data set called "data", and is generated through:

library(reshape2)  # Reshape data, needed in command "melt"
library(ggplot2)   # apply ggplot

density <-rep (0.05, each=800)

tau <-rep (0.05, each=800)

# define two different models: network and non-network 
model <-rep(1:2, each=400, times=1)


## Create data and factors for the plot
df <- melt(rnorm(800, -3, 0.5))

data <- as.data.frame(cbind(density, tau, model, df$value))

data$density <- factor(data$density,levels=0.05,
                       labels=c("Density=0.05")) 

data$tau <- factor(data$tau,levels=0.05,
                   labels=c("tau=0.05"))

data$model<- factor(data$model,levels=c(1,2),
                    labels=c("Yes",
                             "No")) 

ggplot(data=data, aes(x=V4, shape=model, colour=model, lty=model)) + 

  stat_density(adjust=1, geom="line",position="identity") +

  facet_grid(tau~density, scale="free") +

  geom_vline(xintercept=-3, lty="dashed") +

  ggtitle("Kernel Density") +

  xlab("Data") +

  ylab("Kernel Density") +

  theme(plot.title=element_text(face="bold", size=17),  # change fond size of title
        axis.text.x= element_text(size=14),
        axis.text.y= element_text(size=14),
        legend.title=element_text(size=14),
        legend.text =element_text(size=12),
        strip.text.x=element_text(size=14),             # change fond size of x_axis
        strip.text.y=element_text(size=14))             # change fond size of y_axis

Looking at the data, variable V4 is separated into two subsets by the model (Yes [1:400] and No [401:800]), and the kernel density is plotted without change the original bandwidth since adjust=1.

What I want to do is: for the Yes model, the bandwidth changes to 10 times of the original, but for the No model, the bandwidth keeps unchanged. Can I do something like letting the adjust=c(10, 1)? I know how to realize this by plot()+lines(), but I want to do this in ggplot() for further analysis.

Solution

  • I wouldn't recommend this, since it creates a very misleading plot, but you can do it with two calls to stat_density(...).

    ggplot(data=data, aes(x=V4, shape=model, colour=model, lty=model)) + 
  stat_density(data=data[data$model=="Yes",], adjust=10, 
               geom="line",position="identity") +
  stat_density(data=data[data$model=="No",], adjust=1, 
               geom="line",position="identity") +
  facet_grid(tau~density, scale="free") +
  geom_vline(xintercept=-3, lty="dashed") +
  ggtitle("Kernel Density") +
  xlab("Data") +
  ylab("Kernel Density") +
  theme(plot.title=element_text(face="bold", size=17),  
        axis.text.x= element_text(size=14),
        axis.text.y= element_text(size=14),
        legend.title=element_text(size=14),
        legend.text =element_text(size=12),
        strip.text.x=element_text(size=14),             
        strip.text.y=element_text(size=14))