My notes on using R
Shyh-Weir Tzang
2019/8/21
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library(ggplot2)
library(tidyverse)
library(reshape2)Simultaneously merge multiple data.frames in a list
- This is useful when merge many stock prices into one list object
https://stackoverflow.com/questions/8091303/simultaneously-merge-multiple-data-frames-in-a-list.
x <- tibble(i = c("a","b","c"), j = 1:3)
y <- tibble(i = c("b","c","d"), k = 4:6)
z <- tibble(i = c("c","d","a"), l = 7:9)
list(x, y, z) %>%
map_df(gather, key=key, value=value, -i) %>%
spread(key, value)## # A tibble: 4 x 4
## i j k l
## <chr> <int> <int> <int>
## 1 a 1 NA 9
## 2 b 2 4 NA
## 3 c 3 5 7
## 4 d NA 6 8Financial analysis with R
Unit root test in R
https://www.r-bloggers.com/unit-root-tests/ https://freakonometrics.hypotheses.org/12729
Applied econometrics with R
Tsay, An introduction to analysis of financial data with R
Simulation in R – Standford U
https://web.stanford.edu/class/bios221/labs/simulation/lab_3_simulation.html
- Generating Random Mixtures of Normal Data
sampa=rnorm(1000000,0,1)
sampb=rnorm(1500000,3,1)
combined = c(sampa, sampb)
plt = ggplot(data.frame(combined), aes(x=combined)) + stat_bin(binwidth=0.25, position="identity")
plt
a <- rnorm(1000, 0, 1)
b <- rnorm(1000, 5, 2)
c <- rnorm(1000, 3, 2)
d <- rnorm(1000, -2, 1)
d <- c(a, b, c, d)
df <- data.frame(d, id = as.factor(rep(c(1, 2, 3, 4), each = 1000)))
dim(df)## [1] 4000 2head(df)## d id
## 1 -0.42302901 1
## 2 -0.38196684 1
## 3 -1.85975094 1
## 4 1.23994382 1
## 5 0.73957802 1
## 6 -0.01873475 1ggplot(df) +
stat_density(aes(x = d, group = id), position = "stack", geom = "line", show.legend = F, color = "red") +
stat_density(aes(x = d, linetype = id), position = "identity", geom = "line")
Generating samples from a two-Gaussian mixture in r (code given in MATLAB)
- I’m trying to create (in r) the equivalent to the following MATLAB function that will generate n samples from a mixture of N(m1,(s1)^2) and N(m2, (s2)^2) with a fraction, alpha, from the first Gaussian.
gaussmix <- function(nsim,mean_1,mean_2,std_1,std_2,alpha){
U <- runif(nsim)
I <- as.numeric(U<alpha)
y <- I*rnorm(nsim,mean=mean_1,sd=std_1)+
(1-I)*rnorm(nsim,mean=mean_2,sd=std_2)
return(y)
}
z1 <- gaussmix(1000, 0, 0, 1, 4, 0.95)
ggplot(data.frame(z1), aes(z1)) +
geom_histogram(aes(y=..density..), binwidth = 0.1)+
stat_density(aes(z1), position = "stack", geom = "line", show.legend = F, color = "red")+
ggtitle("Plot of mixture normal distribution: \n
(1-X)*N (0, 1)+X*N (0, 16) with X being Bernoulli such that P (X=1)=0.05")+
xlab("x")+ylab("f(x)")
#
z1_standardized <- (z1-mean(z1))/sqrt(var(z1))
hist(z1_standardized,xlim=c(-10,10),ylim=c(0,500),
main="Histogram of 95% of N(0,1) and 5% of N(0,36)",
col="blue",xlab=" ")
#
ggplot(data.frame(z1_standardized), aes(z1_standardized)) +
geom_histogram(aes(y=..density..), binwidth = 0.1)+
stat_density(aes(z1_standardized), position = "stack", geom = "line", show.legend = F, color = "red")
plot(z1_standardized,type='l',
main="1000 samples from a mixture N(0,1) and N(0,36)",
col="blue",xlab="Samples",ylab="Mean",ylim=c(-10,10))
Generating random variables from a mixture of Normal distributions in r
- How can I sample from a mixture distribution, and in particular a mixture of Normal distributions in R? For example, if I wanted to sample from: 0.3×N(0,1)+0.5×N(10,1)+0.2×N(3,.1)
set.seed(8) # this makes the example reproducible
N = 1000 # this is how many data you want
probs = c(.3,.8) # these are *cumulative* probabilities; since they
# necessarily sum to 1, the last would be redundant
dists = runif(N) # here I'm generating random variates from a uniform
# to select the relevant distribution
# this is where the actual data are generated, it's just some if->then
# statements, followed by the normal distributions you were interested in
data = vector(length=N)
for(i in 1:N){
if(dists[i]<probs[1]){
data[i] = rnorm(1, mean=0, sd=1)
} else if(dists[i]<probs[2]){
data[i] = rnorm(1, mean=10, sd=1)
} else {
data[i] = rnorm(1, mean=3, sd=.1)
}
}
# here are a couple of ways of looking at the results
summary(data)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.2819 0.8443 3.1912 5.5346 10.0727 13.1630plot(density(data))