lambda <- runif(10,min=0,max=3)
mean(lambda)
for (i in 1:10){
N <- rpois(i,mean(lambda))
mean(N)
plot(i,mean(N))
}
Hi all, this is a really simple R code block I have here. I am basically trying to create a plot where I can see how the mean of the poisson distribution is changing as I increase its iteration using the rpois function. I would like to post these values (mean(N)) all on the same graph so I can see the change but I am not quite sure how to do that.
I have been googling a lot and I came across qqplot or so but I just started using R few days ago and I have having a lot of trouble.
Any insights would be helpful
You can use the points function once a plot has been called:
lambda <- runif(10,min=0,max=3)
mean(lambda)
## First plot
N <- rpois(1,mean(lambda))
plot(1,mean(N), xlim = c(1,10))
## Subsequent points
for (i in 2:10){
N <- rpois(i,mean(lambda))
points(i,mean(N))
}
Related
I have a problem with plotting ECDF. I try to reverse the x axis value like 1-(the function).
Because I wanna have smaller in the beginning of the graph and decreasing like in my reference graph.
load("91-20.RData")
ts <- data.frame(dat91,dat92,dat93,dat94,dat95,dat96,dat97,
dat98,dat99,dat00,dat11,dat12,dat12,dat13,
dat14,dat15,dat16,dat17,dat18,dat19,dat20)
ts
tsclean <- na.omit(ts)
#--------------------------------------------------------
ggplot(tsclean, aes(tsclean$dat91)) +
stat_ecdf(geom = "step")
This graph what i have, but i wanna duplicate like the reference
load("91-20.RData")
ts <- data.frame(dat91,dat92,dat93,dat94,dat95,dat96,dat97,
dat98,dat99,dat00,dat11,dat12,dat12,dat13,
dat14,dat15,dat16,dat17,dat18,dat19,dat20)
ts
tsclean <- na.omit(ts)
I think the graph you're looking for is called an "exceedance" graph. A web search finds some resources; try a web search for "R exceedance graph".
EDIT: This is more suitable as a comment than an answer, but my web browser is being unhelpful at the moment; sorry for the distraction.
I was experimenting with the waffle package in r, and was trying to use a for loop to make multiple plots at once but was not able to get my code to work. I have a dataset with values for each year of renewables,and since it is over 40 years of data, was looking for a simple way to plot these with a for loop rather than manyally year by year. What am I doing wrong?
I have it from 1:16 as an experiment to see if it would work, although in reality I would do it for all the years in my dataset.
for(i in 1:16){
renperc<-islren$Value[i]
parts <- c(`Renewable`=(renperc), `Non-Renewable`=100-renperc)
waffle(parts, rows=10, size=1, colors=c("#00CC00", "#A9A9A9"),
title="Iceland Primary Energy Supply",
xlab=islren$TIME)
}
If I get your question correctly you want to plot all the 16 iterations in a same panel? You can parametrise your plot window to be divided into 16 smaller plots using par(mfrow = c(4,4)) (creating a 4 by 4 matrix and plotting into each cells recursively).
## Setting the graphical parameters
par(mfrow = c(4,4))
## Running the loop normally
for(i in 1:16){
renperc<-islren$Value[i]
parts <- c(`Renewable`=(renperc), `Non-Renewable`=100-renperc)
waffle(parts, rows=10, size=1, colors=c("#00CC00", "#A9A9A9"),
title="Iceland Primary Energy Supply",
xlab=islren$TIME)
}
If you need more plots (e.g. 40) you can increase the numbers in the graphical parameters (e.g. par(mfrow = c(6,7))) but that will create really tiny plots. One solution is to do it in multiple loops (for(i in 1:16); for(i in 17:32); etc.)
UPDATE: The code simply wasn't plotting anything when i tried putting in anything above one value (ex. 1:16) or a letter, both in terms of separate plots or many in one plot window (which I think perhaps waffle does not support in the same way as regular plots). In the end, I managed by making it into a function, although I'm still not sure why my original method wouldn't work if this did. See the code that worked below. I also tweaked it a bit, adding ggsave for example.
#function
waffling <- function(x){
renperc<-islren$Value[x]
parts <- c(`Renewable`=(renperc), `Non-Renewable`=100-renperc)
waffle(parts, rows=10, size=1, colors=c("#00CC00", "#A9A9A9"), title="",
xlab=islren$TIME[x])
ggsave(file=paste0("plot_", x,".png"))}
for(i in 1:57){
waffling(i)
}
There are similar questions on the website, but I could not find an answer to this seemingly very simple problem. I fit a mixture of two gaussians on the Old Faithful Dataset:
if(!require("mixtools")) { install.packages("mixtools"); require("mixtools") }
data_f <- faithful
plot(data_f$waiting, data_f$eruptions)
data_f.k2 = mvnormalmixEM(as.matrix(data_f), k=2, maxit=100, epsilon=0.01)
data_f.k2$mu # estimated mean coordinates for the 2 multivariate Gaussians
data_f.k2$sigma # estimated covariance matrix
I simply want to super-impose two ellipses for the two Gaussian components of the model described by the mean vectors data_f.k2$mu and the covariance matrices data_f.k2$sigma. To get something like:
For those interested, here is the MatLab solution that created the plot above.
If you are interested in the colors as well, you can use the posterior to get the appropriate groups. I did it with ggplot2, but first I show the colored solution using #Julian's code.
# group data for coloring
data_f$group <- factor(apply(data_f.k2$posterior, 1, which.max))
# plotting
plot(data_f$eruptions, data_f$waiting, col = data_f$group)
for (i in 1: length(data_f.k2$mu)) ellipse(data_f.k2$mu[[i]],data_f.k2$sigma[[i]], col=i)
And for my version using ggplot2.
# needs ggplot2 package
require("ggplot2")
# ellipsis data
ell <- cbind(data.frame(group=factor(rep(1:length(data_f.k2$mu), each=250))),
do.call(rbind, mapply(ellipse, data_f.k2$mu, data_f.k2$sigma,
npoints=250, SIMPLIFY=FALSE)))
# plotting command
p <- ggplot(data_f, aes(color=group)) +
geom_point(aes(waiting, eruptions)) +
geom_path(data=ell, aes(x=`2`, y=`1`)) +
theme_bw(base_size=16)
print(p)
You can use the ellipse-function from package mixtools. The initial problem was that this function swaps x and y from your plot. I'll try to figure this out and update the answe. (I'll leave the colors to somebody else...)
plot( data_f$eruptions,data_f$waiting)
for (i in 1: length(data_f.k2$mu)) ellipse(data_f.k2$mu[[i]],data_f.k2$sigma[[i]])
Using mixtools internal plotting function:
plot.mixEM(data_f.k2, whichplots=2)
I have to write own function to draw the density function of binomial distribution and hence draw
appropriate graph when n = 20 and p = 0.1,0.2,...,0.9. Also i need to comments on the graphs.
I tried this ;
graph <- function(n,p){
x <- dbinom(0:n,size=n,prob=p)
return(barplot(x,names.arg=0:n))
}
graph(20,0.1)
graph(20,0.2)
graph(20,0.3)
graph(20,0.4)
graph(20,0.5)
graph(20,0.6)
graph(20,0.7)
graph(20,0.8)
graph(20,0.9)
#OR
graph(20,scan())
My first question : is there any way so that i don't need to write down the line graph(20,p) several times except using scan()?
My second question :
I want to see the graph in one device or want to hit ENTER to see the next graph. I wrote
par(mfcol=c(2,5))
graph(20,0.1)
graph(20,0.2)
graph(20,0.3)
graph(20,0.4)
graph(20,0.5)
graph(20,0.6)
graph(20,0.7)
graph(20,0.8)
graph(20,0.9)
but the graph is too tiny. How can i present the graphs nicely with giving head line n=20 and p=the value which i used to draw the graph?[though it can be done by writing mtext() after calling the function graphbut doing so i have to write a similar line few times. So i want to do this including in function graph. ]
My last question :
About comment. The graphs are showing that as the probability of success ,p is increasing the graph is tending to right, that is , the graph is right skewed.
Is there any way to comment on the graph using program?
Here a job of mapply since you loop over 2 variables.
graph <- function(n,p){
x <- dbinom(0:n,size=n,prob=p)
barplot(x,names.arg=0:n,
main=sprintf(paste('bin. dist. ',n,p,sep=':')))
}
par(mfcol=c(2,5))
mapply(graph,20,seq(0.1,1,0.1))
Plotting base graphics is one of the times you often want to use a for loop. The reason is because most of the plotting functions return an object invisibly, but you're not interested in these; all you want is the side-effect of plotting. A loop ignores the returned obects, whereas the *apply family will waste effort collecting and returning them.
par(mfrow=c(2, 5))
for(p in seq(0.1, 1, len=10))
{
x <- dbinom(0:20, size=20, p=p)
barplot(x, names.arg=0:20, space=0)
}
I have a problem with the function lines.
this is what I have written so far:
model.ew<-lm(Empl~Wage)
summary(model.ew)
plot(Empl,Wage)
mean<-1:500
lw<-1:500
up<-1:500
for(i in 1:500){
mean[i]<-predict(model.ew,data.frame(Wage=i*100),interval="confidence",level=0.90)[1]
lw[i]<-predict(model.ew,data.frame(Wage=i*100),interval="confidence",level=0.90)[2]
up[i]<-predict(model.ew,data.frame(Wage=i*100),interval="confidence",level=0.90)[3]
}
plot(Wage,Empl)
lines(mean,type="l",col="red")
lines(up,type="l",col="blue")
lines(lw,type="l",col="blue")
my problem i s that no line appears on my plot and I cannot figure out why.
Can somebody help me?
You really need to read some introductory manuals for R. Go to this page, and select one that illustrates using R for linear regression: http://cran.r-project.org/other-docs.html
First we need to make some data:
set.seed(42)
Wage <- rnorm(100, 50)
Empl <- Wage + rnorm(100, 0)
Now we run your regression and plot the lines:
model.ew <- lm(Empl~Wage)
summary(model.ew)
plot(Empl~Wage) # Note. You had the axes flipped here
Your first problem was that you flipped the axes. The dependent variable (Empl) goes on the vertical axis. That is the main reason you didn't get any lines on the plot. To get the prediction lines requires no loops at all and only a single plot call using matlines():
xval <- seq(min(Wage), max(Wage), length.out=101)
conf <- predict(model.ew, data.frame(Wage=xval),
interval="confidence", level=.90)
matlines(xval, conf, col=c("red", "blue", "blue"))
That's all there is to it.