Qqnorm and Qqline in Ggplot2

qqnorm and qqline in ggplot2

The following code will give you the plot you want. The ggplot package doesn't seem to contain code for calculating the parameters of the qqline, so I don't know if it's possible to achieve such a plot in a (comprehensible) one-liner.

qqplot.data <- function (vec) # argument: vector of numbers
{
  # following four lines from base R's qqline()
  y <- quantile(vec[!is.na(vec)], c(0.25, 0.75))
  x <- qnorm(c(0.25, 0.75))
  slope <- diff(y)/diff(x)
  int <- y[1L] - slope * x[1L]

  d <- data.frame(resids = vec)

  ggplot(d, aes(sample = resids)) + stat_qq() + geom_abline(slope = slope, intercept = int)

}

qqline in ggplot2 with facets

You may try this:

library(plyr)

# create some data
set.seed(123)
df1 <- data.frame(vals = rnorm(1000, 10),
                  y = sample(LETTERS[1:3], 1000, replace = TRUE),
                  z = sample(letters[1:3], 1000, replace = TRUE))

# calculate the normal theoretical quantiles per group
df2 <- ddply(.data = df1, .variables = .(y, z), function(dat){
             q <- qqnorm(dat$vals, plot = FALSE)
             dat$xq <- q$x
             dat
}
)

# plot the sample values against the theoretical quantiles
ggplot(data = df2, aes(x = xq, y = vals)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  xlab("Theoretical") +
  ylab("Sample") +
  facet_grid(y ~ z)

Q-Q plot facet wrap with QQ line in R

I don't think there's any need for plyr or calling qqnorm youself. YOu can just do

ggplot(data = df1, aes(sample=vals)) +
  geom_qq() + 
  geom_qq_line(color="red") + 
  xlab("Theoretical") +
  ylab("Sample") +
  facet_grid(y ~ z)

What is the difference between 'stat_qq', 'geom_qq' and 'qqnorm' functions in R?

The qqnorm function comes with R, whereas stat_qq and geom_qq are functions of the ggplot2 package.

There's no difference in the statistical results. However, we have to enter different amounts of code to achieve similar (sober and publishable) visible results.

In base R we simply do:

qqnorm(y)
qqline(y, col=2)

In ggplot2 we type:

library(ggplot2)
ggplot(mapping=aes(sample=y)) +
  stat_qq() + 
  stat_qq_line(color=2) +
  labs(title="Normal Q-Q Plot") +    ## add title
  theme_bw() +                       ## remove gray background
  theme(panel.grid=element_blank())  ## remove grid

As for stat_qq and geom_qq, I can't see any difference in code between the two, they seem to be synonymous.

---

Data

set.seed(42)
y <- rt(200, df=5)

Identify points in QQ plot of ggplot2?

> order(stres, decreasing = TRUE)[1:2]
[1]  47 112

> stres[order(stres, decreasing = TRUE)[1:2]]
[1] 5.081862 4.275958

If you want to access the values qplot uses you can do the following:

plt <- print(qplot(sample=stres)+labs(title="QQ Plot/Studentized Residuals")+theme_bw())
plt[["data"]][[1]]

And you get the same result:

> sort(plt[["data"]][[1]]$sample, decreasing = TRUE)[1:2]
[1] 5.081862 4.275958

compare qqplot of a sample with a reference probability distribution in R

Here is a solution using ggplot2

ggplot(model, aes(sample = rstandard(model))) + 
  geom_qq() + 
  stat_qq_line(dparams=list(sd=sd(model.res)), color="red") +
  stat_qq_line()

The red line represents the qqline with your sample sd, the blackline a sd of 1.

You did not ask for that, but you could also add a smoothed qqplot:

data_model <- model
data_model$theo <- unlist(qqnorm(data_model$residuals)[1])

ggplot(data_model, aes(sample = rstandard(data_model))) + 
  geom_qq() + 
  stat_qq_line(dparams=list(sd=sd(model.res)), color="red") +
  geom_smooth(aes(x=data_model$theo, y=data_model$residuals), method = "loess")

---

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