Setup 🔗
library(ggplot2)
library(gridExtra)
Distribution 🔗
size <- 100
vect <- seq(1, size, by=1)
m <- mean(vect)
std <- sqrt(var(vect))
distF <- data.frame(
indx = vect,
gaus = dnorm(vect, mean=m, sd=std),
pois = dpois(vect, vect),
unif = dunif(vect, min=1, max=size)
)
g1 <- ggplot(data=distF, aes(x=indx, y=gaus)) +
geom_point(shape=1) +
guides(legend.title=element_blank())
g2 <- ggplot(data=distF, aes(x=indx, y=pois)) +
geom_point(shape=1)
g3 <- ggplot(data=distF, aes(x=indx, y=unif)) +
geom_point(shape=1)
grid.arrange(g1, g2, g3, ncol=3)`
Random sampling 🔗
randF <- data.frame(
indx = vect,
gaus = rnorm(vect, mean=m, sd=std),
pois = rpois(vect, vect),
unif = runif(vect, min=1, max=size)
)
g1 <- ggplot(data=randF, aes(x=indx, y=gaus)) +
geom_point(shape=1)
g2 <- ggplot(data=randF, aes(x=indx, y=pois)) +
geom_point(shape=1)
g3 <- ggplot(data=randF, aes(x=indx, y=unif)) +
geom_point(shape=1)
grid.arrange(g1, g2, g3, ncol=3)
Random sampling with linear model 🔗
g1 <- ggplot(data=randF, aes(x=indx, y=gaus)) +
geom_point(shape=1) +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)
g2 <- ggplot(data=randF, aes(x=indx, y=pois)) +
geom_point(shape=1) +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)
g3 <- ggplot(data=randF, aes(x=indx, y=unif)) +
geom_point(shape=1) +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)
grid.arrange(g1, g2, g3, ncol=3)
Random sampling histogram 🔗
size <- 10000
vect <- seq(1, size, by=1)
m <- mean(vect)
std <- sqrt(var(vect))
randF <- data.frame(
indx = vect,
gaus = rnorm(vect, mean=m, sd=std),
pois = rpois(vect, vect),
unif = runif(vect, min=1, max=size)
)
binw <- size/9
g1 <- ggplot(data=randF, aes(x=gaus)) +
geom_histogram(binwidth=binw)
g2 <- ggplot(data=randF, aes(x=pois)) +
geom_histogram(binwidth=binw)
g3 <- ggplot(data=randF, aes(x=unif)) +
geom_histogram(binwidth=binw)
grid.arrange(g1, g2, g3, ncol=3)
Random sampling with density 🔗
binw <- size/9
g1 <- ggplot(data=randF, aes(x=gaus)) +
geom_histogram(aes(y = ..density..), binwidth=binw, alpha=.2, fill="red") +
geom_density(alpha=.7, col="blue")
g2 <- ggplot(data=randF, aes(x=pois)) +
geom_histogram(aes(y = ..density..), binwidth=binw, alpha=.2, fill="red") +
geom_density(alpha=.7, col="blue")
g3 <- ggplot(data=randF, aes(x=unif)) +
geom_histogram(aes(y = ..density..), binwidth=binw, alpha=.2, fill="red") +
geom_density(alpha=.7, col="blue")
grid.arrange(g1, g2, g3, ncol=3)
Poisson variations 🔗
size <- 100
vect <- seq(1, size, by=1)
poisF <- data.frame(
indx = vect,
p1 = rpois(vect, vect),
p2 = rpois(vect, rep(1, times=size)),
p3 = rpois(vect, vect^2)
)
g1 <- ggplot(data=poisF, aes(x=indx, y=p1)) +
geom_point(shape=1)
g2 <- ggplot(data=poisF, aes(x=indx, y=p2)) +
geom_point(shape=1)
g3 <- ggplot(data=poisF, aes(x=indx, y=p3)) +
geom_point(shape=1)
grid.arrange(g1, g2, g3, ncol=3)
