Statistics
sum
min
/max
length
mean
median
var
sample variancesd
(sample standard deviation)rafalib::popsd
(population standard deviation)cov(x, y)
covariancecor(x, y)
correlation (default Pearson)
Regression
lm(y ~ x)
Create a linear modelsummary
Summary the model
Generation
1:10
integer range from[1, 10]
seq(from = 1, to = 9, by = 2)
1 3 5 7 9rep(42, 10)
repeat 42 for 10 timesrep(1:3, times = 3)
1 2 3 1 2 3 1 2 3
Probability Distributions in R
Main: probability distributions in R
Overview
Type Distribution R Suffix Comments Continuous Normal -norm()
Lognormal -lnorm()
Normally distributed in a log scale Uniform -unif()
Discrete Binomial -binom()
Multinomial -multinom()
Similar to binomial but when there are more than 2 outcomes Poisson -pois()
Prefix:
Link to original
- PDF or PMF (
d-
) e.g.dnorm(x)
- CDF
( p-
) e.g.pnorm(x)
- the quantile function (
q-
)- The random deviate function (
-r
)
Monte-Carlo Methods
set.seed
set a seed for PRNGsample(x, size, replace = TRUE/FALSE, prob = NULL)
takes samples of the specified size with or without replacement.replicate
replicate an experimentn
times
Examples
Sample from 1-10 for 5 times without replacement
sample(x = 1:10, size = 5)
# 3 4 9 5 6
Randomly throw a die 5 times:
sample(1:6, 5, replace = T)
Do 1000 simulations of the sample sum of flipping a coin 10 times
simulation_result = replicate(1000, sum(sample(c(0, 1), 10, replace = T)))
Plot
See ggplot2