15 Review Sheet 2
barplot - This is the overall function for creating a bar graph. It has several functions that go along with it.
col - This is the same idea from the color argument used in a scatter plot, however, with bar plots you need to add a concatenation if you are looking to plot more than one bar (which you should!).
col=c("white","red","blue")
names.arg - This is similar to xlab,however, we are usually going to be referring to each bar by a different name so you will need to add a concatenation.
names.arg=c("Experimental","Control")
Data Generation
set.seed() - This function specifies a ‘seed’ of your choice. By choosing a ‘seed’ you are ensuring the ability of the data you produce to be replicable either for the use of a problem set, or for troubleshooting.
sample - This function allows you to create a sample from an existing vector. It has two arguments.
x - The vector that you will be sampling from. It is important that whatever you decide to sample from x cannot be larger than the length of x.
n - This specifies how many items you want to sample from x.
Sampling Example
sample(x, 10)
rnorm - This function returns whatever size you specify as a vector from the normal distribution.
N - This is the size of the vector that you would like.
mean - This is what you would like the mean of the data returned to be.
sd - This is what you would like the standard deviation of the data returned to be.
Example of rnorm rnorm(100, 120, 10)
library() - This function loads a package outside of the ones native to R. So far, the only package we will be using is BSDA.
BSDA
z.test() - This is the function you will use to perform a z test. It contains several arguments.
x - This will be your vector that continues the data you will be comparing to \(\mu\) and \(\sigma\).
sigma.x - This will be the population standard deviation, \(\sigma\), which will usually be given to you in a question.
mu - This will be the population mean, \(\mu\), which will usually be given to you in a question.
alternative - This argument specifies the directionality of the hypothesis you wish to test.
two.sided - This argument specifies that you wish to run a 2-tailed test.
lesser - This argument specified that you wish to run a 1-tailed test indicating that your sample is less than the population mean, \(\mu\).
greater - This argument specified that you wish to run a 1-tailed test indicating that your sample is greater than the population mean, \(\mu\).
Here is what a full z.test function could look like:
z.test(x, mu = 12, sigma.x = 3.12, alternative ="two.sided")
SIGN.test() - This is the function you will use to perform a sign test. It contains several arguments.
x - This will be your vector that continues the data that is the difference between the first group and second group.
md - This will rarely be given, but implied. The sign test will test against the \(H_0\), which states that no difference exists, so 0 should be the default value.
alternative - This argument specifies the directionality of the hypothesis you wish to test.
two.sided - This argument specifies that you wish to run a 2-tailed test.
lesser - This argument specified that you wish to run a 1-tailed test indicating that your sample is less than the population mean, \(\mu\).
greater - This argument specified that you wish to run a 1-tailed test indicating that your sample is greater than the population mean, \(\mu\).
Here is what a full sign test function could look like:
SIGN.test(x, md = 0, alternative = "two.sided")
T-Test: One-Sample, Paired, Independent
t.test - This function contains several arguments, the results, as well as the type of test that is run will be determined on which you specify.
x - This is a vector that you be comparing against the population mean, \(\mu\).
y1, y2 - These arguments specify two distinct vectors of the same length to be compared.
mu - This will be the population mean, \(\mu\), which will usually be given to you in a question.
alternative - This argument specifies the directionality of the hypothesis you wish to test.
two.sided - This argument specifies that you wish to run a 2-tailed test.
lesser - This argument specified that you wish to run a 1-tailed test indicating that your sample is less than the population mean, \(\mu\).
greater - This argument specified that you wish to run a 1-tailed test indicating that your sample is greater than the population mean, \(\mu\).
var.equal = TRUE - This argument specifies that the variances are assumed to be equal.
paired = TRUE - If specified, this will result in R assuming the sample(s) are to be paired.
Determining which Test will be run:
Single Sample T-Test
t.test(x, mu = 23, alternative ="lesser")
Paired Sample T-Test
t.test(y1, y2, paired = TRUE, var.equal= TRUE, alternative = "greater")
Independent T-Test
t.test(y1, y2, var.equal= TRUE, alternative = "two.sided")