**R Psychologist » R**, and kindly contributed to R-bloggers)

A common way of illustrating the idea behind statistical power in null hypothesis significance testing, is by plotting the sampling distributions of the null hypothesis () and the alternative hypothesis (). Typically, these illustrations highlight the regions that correspond to making a type II error (), type I error () and correctly rejecting the null hypothesis (i.e. the test’s power; ).

In this post I will show how to create such “power plots” using R. Typically, I prefer to use `ggplot` for plotting, but tasks such as this is one of the few times were I think R’s base graphics have some merit—especially for creating black and white plots, since `ggplot` does not support using patterns. Thus, I will present code both for `ggplot` and base graphics.

Creating these plots is pretty straight forwards. You only need to be vaguely familiar with the mechanics of plotting polygons. For instance, say we want to plot a triangle with the following coordinates.

(2,3) /\ / \ / \ / \ (1,2) ------ (3,2)

Then we just specify x and y as vectors, like this:

# ggplot polygon example ggplot(data.frame(x=c(1,2,3),y=c(2,3,2)), aes(x,y)) + geom_polygon()

So, let us begin by creating the data for the two distributions and three polygons that we will need.

library(ggplot2) library(grid) # need for arrow() m1 <- 0 # mu H0 sd1 <- 1.5 # sigma H0 m2 <- 3.5 # mu HA sd2 <- 1.5 # sigma HA z_crit <- qnorm(1-(0.05/2), m1, sd1) # set length of tails min1 <- m1-sd1*4 max1 <- m1+sd1*4 min2 <- m2-sd2*4 max2 <- m2+sd2*4 # create x sequence x <- seq(min(min1,min2), max(max1, max2), .01) # generate normal dist #1 y1 <- dnorm(x, m1, sd1) # put in data frame df1 <- data.frame("x" = x, "y" = y1) # generate normal dist #2 y2 <- dnorm(x, m2, sd2) # put in data frame df2 <- data.frame("x" = x, "y" = y2) # Alpha polygon y.poly <- pmin(y1,y2) poly1 <- data.frame(x=x, y=y.poly) poly1 <- poly1[poly1$x >= z_crit, ] poly1<-rbind(poly1, c(z_crit, 0)) # add lower-left corner # Beta polygon poly2 <- df2 poly2 <- poly2[poly2$x <= z_crit,] poly2<-rbind(poly2, c(z_crit, 0)) # add lower-left corner # power polygon; 1-beta poly3 <- df2 poly3 <- poly3[poly3$x >= z_crit,] poly3 <-rbind(poly3, c(z_crit, 0)) # add lower-left corner # combine polygons. poly1$id <- 3 # alpha, give it the highest number to make it the top layer poly2$id <- 2 # beta poly3$id <- 1 # power; 1 - beta poly <- rbind(poly1, poly2, poly3) poly$id <- factor(poly$id, labels=c("power","beta","alpha"))

Now that we have all the data that we need, let us create the first plot using `ggplot`. The annotation is set manually, so it will be a bit tedious to change these plots.

# plot with ggplot2 ggplot(poly, aes(x,y, fill=id, group=id)) + geom_polygon(show_guide=F, alpha=I(8/10)) + # add line for treatment group geom_line(data=df1, aes(x,y, color="H0", group=NULL, fill=NULL), size=1.5, show_guide=F) + # add line for treatment group. These lines could be combined into one dataframe. geom_line(data=df2, aes(color="HA", group=NULL, fill=NULL),size=1.5, show_guide=F) + # add vlines for z_crit geom_vline(xintercept = z_crit, size=1, linetype="dashed") + # change colors scale_color_manual("Group", values= c("HA" = "#981e0b","H0" = "black")) + scale_fill_manual("test", values= c("alpha" = "#0d6374","beta" = "#be805e","power"="#7cecee")) + # beta arrow annotate("segment", x=0.1, y=0.045, xend=1.3, yend=0.01, arrow = arrow(length = unit(0.3, "cm")), size=1) + annotate("text", label="beta", x=0, y=0.05, parse=T, size=8) + # alpha arrow annotate("segment", x=4, y=0.043, xend=3.4, yend=0.01, arrow = arrow(length = unit(0.3, "cm")), size=1) + annotate("text", label="frac(alpha,2)", x=4.2, y=0.05, parse=T, size=8) + # power arrow annotate("segment", x=6, y=0.2, xend=4.5, yend=0.15, arrow = arrow(length = unit(0.3, "cm")), size=1) + annotate("text", label="1-beta", x=6.1, y=0.21, parse=T, size=8) + # H_0 title annotate("text", label="H[0]", x=m1, y=0.28, parse=T, size=8) + # H_a title annotate("text", label="H[a]", x=m2, y=0.28, parse=T, size=8) + ggtitle("Statistical Power Plots, Textbook-style") + # remove some elements theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(), panel.background = element_blank(), plot.background = element_rect(fill="#f9f0ea"), panel.border = element_blank(), axis.line = element_blank(), axis.text.x = element_blank(), axis.text.y = element_blank(), axis.ticks = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank(), plot.title = element_text(size=22)) ggsave("stat_power_ggplot.png", height=8, width=13, dpi=72)

Now, if we want a more “classical looking” black and white-plot, we need to use base graphics.

# example with base graphics png("stat_power_base.png", width=900, height=600, units="px") # save as png # reset plot.new() # set window size plot.window(xlim=range(x), ylim=c(-0.01,0.3)) # add polygons polygon(poly3, density=10) # 1-beta polygon(poly2, density=3, angle=-45, lty="dashed") # beta polygon(poly1, density=10, angle=0) # alpha # add h_a dist lines(df2,lwd=3) # add h_0 dist lines(df1,lwd=3) ### annotations # h_0 title text(m1, 0.3, expression(H[0]), cex=1.5) # h_a title text(m2, 0.3, expression(H[a]), cex=1.5) # beta annotation arrows(x0=-1, y0=0.045, x1=1, y1=0.01,lwd=2,length=0.15) text(-1.2, 0.045, expression(beta), cex=1.5) # beta annotation arrows(x0=4, y0=-0.01, x1=3.5, y1=0.01, lwd=2, length=0.15) text(x=4.1, y=-0.015, expression(alpha/2), cex=1.5) # 1-beta arrows(x0=6, y0=0.15, x1=5, y1=0.1, lwd=2,length=0.15) text(x=7, y=0.155, expression(paste(1-beta, " (\"power\")")), cex=1.5) # show z_crit; start of rejection region abline(v=z_crit) # add bottom line abline(h=0) title("Statistical Power") dev.off()

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