Can R visualize the t.test or other hypothesis test results?

Question

I need to work with many hypothesis tests in R and present the results. Here is an example:

> library(MASS)
> h=na.omit(survey$Height)
> 
> pop.mean=mean(h)
> h.sample = sample(h,30)
> 
> t.test(h.sample,mu=pop.mean)

    One Sample t-test

data:  h.sample
t = -0.0083069, df = 29, p-value = 0.9934
alternative hypothesis: true mean is not equal to 172.3809
95 percent confidence interval:
 168.8718 175.8615
sample estimates:
mean of x 
 172.3667 

Is there any way that we visualize the t.test or other hypothesis test results?

Below is an example of what I am looking for:

Answer 1

There is a lot of things you can do. Here is just one where I draw a random sample from the standard normal distribution, then do a t-test, the plot the observed t and the t's needed to reject the null hypothesis that the mean is equal to 0.

N=20 #just chosen arbitrarily
samp=rnorm(N)
myTest=t.test(samp)
tcrit=qt(0.025, df=(N-1))

dum=seq(-3.5, 3.5, length=10^4)#For the plot

plot(dum, dt(dum, df=(N-1)), type='l', xlab='t', ylab='f(t)')
abline(v=myTest$statistic, lty=2)
abline(v=tcrit, col='red', lty=2)
abline(v=-tcrit, col='red', lty=2)

Of course your observed t will look different every time you re-run this code, which might make a good illustration if ran repeatedly.

Answer 2

There is also gginference package.

library(MASS)
h=na.omit(survey$Height)
pop.mean=mean(h)
h.sample = sample(h,30)
t.test(h.sample,mu=pop.mean)

library(gginference)
ggttest(t.test(h.sample,mu=pop.mean))

Answer 3

Here is one way. you can modify the plot to suit your needs:

library(ggplot2)
x <- seq(mean(h) - 4 * sd(h), mean(h) + 4 * sd(h), 0.01)
df <- data.frame(x = x, d = dnorm(x, mean(h), sd(h)))
ggplot(df, aes(x = x, y = d)) + geom_line() + theme_bw() + geom_vline(xintercept = c(mean(h) + 3 * sd(h), mean(h) - 3 * sd(h)), col = 'red') + xlim(120, 240)

If you don't like those vertical lines, you can try this instead:

ggplot(df, aes(x = x, y = d)) + geom_line() + theme_bw() + geom_segment(aes(x = mean(h) - 3 * sd(h), xend = mean(h) - 3 * sd(h), y = 0, yend = dnorm(mean(h) - 3 * sd(h), mean(h), sd(h)), col = 'red')) + geom_segment(aes(x = mean(h) + 3 * sd(h), xend = mean(h) + 3 * sd(h), y = 0, yend = dnorm(mean(h) + 3 * sd(h), mean(h), sd(h)), col = 'red')) + xlim(120, 240) + ylim(-0.001, 0.041)

Answer 4

Here is one way to visualize the results of many hypothesis tests using the estimated values and 95% confidence intervals. I have taken the idea directly from the TukeyHSD() plotting method, but implemented it with ggplot2. Unfortunately, there is no built-in plotting method for htest results in R.

library(MASS)
library(ggplot2)

h = na.omit(survey$Height)
pop.mean = mean(h)

n_reps = 20
sample_size = 30
res_list = list()

for (i in 1:n_reps) {
    h.sample = sample(h, sample_size)
    res_list[[i]] = t.test(h.sample, mu=pop.mean)
}

dat = data.frame(id=seq(length(res_list)),
                 estimate=sapply(res_list, function(x) x$estimate),
                 conf_int_lower=sapply(res_list, function(x) x$conf.int[1]),
                 conf_int_upper=sapply(res_list, function(x) x$conf.int[2]))

p = ggplot(data=dat, aes(x=estimate, y=id)) +
    geom_vline(xintercept=pop.mean, color="red", linetype=2) +
    geom_point(color="grey30") +
    geom_errorbarh(aes(xmin=conf_int_lower, xmax=conf_int_upper), 
                   color="grey30", height=0.4)

ggsave("CI_plot.png", plot=p, height=4, width=6, units="in", dpi=150)

Answer 5

I realize this is an old question but I recently created an R package on CRAN to solve this problem. The code below produces the desired graph:

library(MASS)
library(mcStats)
h=na.omit(survey$Height)

pop.mean=mean(h)
h.sample = sample(h,30)

showT.Test(h.sample,mu=pop.mean)

Answer 6

I think you're looking for

t <- t.test(h.sample,mu=pop.mean)
t$conf.int[2] # the t-statistic value (pink circle in your image)
t$p.value

use

str(t)

to see all available parameters.