I want to plot several probability distributions side-ways (density on the x-axis, variable on y-axis). Each distribution will be associated with a different category, and I want them side-by-side so that I can compare between them. This is a bit like a box-plot but instead I want an theoretical probability distribution that I will specify giving parameters. So if they were all normal distributions, I would simply provide the mean and std deviation for each. Thanks.
do you mean something like this?
x <- seq(-10, 10, length=100)
normal.dist <- dnorm(x, 0, 2)
f.dist <- df(x, 3, 4)
t.dist <- dt(x, 3)
chi.dist <- dchisq(x,3)
par(mfrow=c(2,2))
plot(x, normal.dist, type='l', lty=1 )
plot(x, f.dist, type='l', lty=1, xlab="x value", col='blue')
plot(x, t.dist, type='l', lty=1, xlab="x value", col='red')
plot(x, chi.dist, type='l', lty=1, xlab="x value", col='green')
see also Roman Luštrik's very helpful link as well as the helfiles (e.g. ?dnorm).
Rotated axis
x <- seq(-10, 10, length=100)
normal.dist <- dnorm(x, 0, 1)
normal.dist2 <- dnorm(x, 0, 2)
normal.dist3 <- dnorm(x, 0, 3)
normal.dist4 <- dnorm(x, 0, 4)
par(mfrow=c(2,2))
plot(normal.dist, x, type='l', lty=1 )
plot(normal.dist2, x, type='l', lty=1, col='red' )
plot(normal.dist3, x, type='l', lty=1, col='green' )
plot(normal.dist4, x, type='l', lty=1, col='blue' )
You can set up a frame for plot display and specify how many plots you want to show in a frame using par(mfrow()), for example:
par(mfrow=c(2,2))
plot(first plot)
plot(second plot)
hist(third histogram)
boxplot(fourth boxplot)
See the following link for the full description:
http://www.statmethods.net/advgraphs/layout.html
Related
I'm trying to reproduce the plot of the image using this code in R:
N = 1:100
r = 1
K = 1
r1 = list(r*N*(1 - (N/K)))
plot(N, r1[[1]])
but negative values appear on the graph. What am I doing wrong or how can I graph the image?
Thanks in advance
You could use the curve function, which is designed for drawing function curves. In this way, you avoid the detour of generating values in advance.
For the basic curve you just need to code your varying variable N as x:
curve(expr=r*x*(1 - (x/K)), from=1, to=100)
To completely reproduce the plot, we open the R graphics toolbox a little further.
op <- par(mar=c(4, 8, 2, 5)) ## set margins
curve(r*x*(1 - (x/K)), 1, 100,
xlab="", ylab="", xaxt="n", yaxt="n",
axes=FALSE, xaxs="i", yaxs="i",
ylim=c(-8e3, 3e3), lwd=2)
axis(2, labels=FALSE, lwd.ticks=0)
abline(h=-5e3)
text(max(N), -5e3*1.05, "N", font=8, xpd=TRUE)
mtext("r", 2, .5, at=0, las=1, font=8)
mtext("Growth rate", 2, .5, at=2e3, las=1, font=6, cex=1.5)
## for the "K" tick and label in the plot, we need to solve the equation
## to get the intersect with our abitrary x axis at -5e3
f <- function(x, y) r*x*(1 - (x/K)) - y
x.val <- uniroot(f, y=-5e3, lower=0, upper=1000)$root
## and insert the solution as x.value
axis(1, x.val, labels=FALSE, pos=-5e3)
text(x.val, -5e3*1.1, "K", font=8, xpd=TRUE)
par(op) ## reset margins
Result
If you have a look at r1, you'll see that the data are plotted correctly. The values begin at zero and decrease.
If you simply wanted to shift the data for a quick visualization, you can add a scale factor:
#add a scale factor - all values positive
r2<-r1[[1]]+10000
plot(N, r2)
or
#add a scale factor - span y = 0
r3<-r1[[1]]+5000
plot(N, r3)
Add annotation to the plot:
abline(h=0, col="black") #add line at zero
text(65, -600, "K", cex=1.5, col="black") #add text
Given two variables, x and y, I run a dynlm regression on the variables and would like to plot the fitted model against one of the variables and the residual on the bottom showing how the actual data line differs from the predicting line. I've seen it done before and I've done it before, but for the life of me I can't remember how to do it or find anything that explains it.
This gets me into the ballpark where I have a model and two variables, but I can't get the type of graph I want.
library(dynlm)
x <- rnorm(100)
y <- rnorm(100)
model <- dynlm(x ~ y)
plot(x, type="l", col="red")
lines(y, type="l", col="blue")
I want to generate a graph that looks like this where you see the model and the real data overlaying each other and the residual plotted as a separate graph on the bottom showing how the real data and the model deviate.
This should do the trick:
library(dynlm)
set.seed(771104)
x <- 5 + seq(1, 10, len=100) + rnorm(100)
y <- x + rnorm(100)
model <- dynlm(x ~ y)
par(oma=c(1,1,1,2))
plotModel(x, model) # works with models which accept 'predict' and 'residuals'
and this is the code for plotModel,
plotModel = function(x, model) {
ymodel1 = range(x, fitted(model), na.rm=TRUE)
ymodel2 = c(2*ymodel1[1]-ymodel1[2], ymodel1[2])
yres1 = range(residuals(model), na.rm=TRUE)
yres2 = c(yres1[1], 2*yres1[2]-yres1[1])
plot(x, type="l", col="red", lwd=2, ylim=ymodel2, axes=FALSE,
ylab="", xlab="")
axis(1)
mtext("residuals", 1, adj=0.5, line=2.5)
axis(2, at=pretty(ymodel1))
mtext("observed/modeled", 2, adj=0.75, line=2.5)
lines(fitted(model), col="green", lwd=2)
par(new=TRUE)
plot(residuals(model), col="blue", type="l", ylim=yres2, axes=FALSE,
ylab="", xlab="")
axis(4, at=pretty(yres1))
mtext("residuals", 4, adj=0.25, line=2.5)
abline(h=quantile(residuals(model), probs=c(0.1,0.9)), lty=2, col="gray")
abline(h=0)
box()
}
what you're looking for is resid(model). Try this:
library(dynlm)
x <- 10+rnorm(100)
y <- 10+rnorm(100)
model <- dynlm(x ~ y)
plot(x, type="l", col="red", ylim=c(min(c(x,y,resid(model))), max(c(x,y,resid(model)))))
lines(y, type="l", col="green")
lines(resid(model), type="l", col="blue")
I am plotting two graphs on the same plot. Each one has a different ylim, and I would like to have the zeroes aligned in the middle of the plot.
This is my code:
# data
time <- seq(0.1, 10, by = 0.1)
det_rot <- runif(100, min=-100, max=100)
vel_mag <- runif(100, min=0, max=5)
# first plot
smoothingSpline = smooth.spline(time, det_rot, spar=0.20)
plot(time, det_rot,lwd=2,
ann=FALSE, las=2, pch="", ylim=c(-100,250)) # , pch=""
lines(smoothingSpline, lwd=2, col="red")
par(new=TRUE)
# second plot
smoothingSpline2 = smooth.spline(time, vel_mag, spar=0.20)
plot(time, vel_mag,
xaxt="n",yaxt="n",xlab="",ylab="",pch="", ylim=c(0,6))
lines(smoothingSpline2, lwd=2, col="blue",)
axis(4)
See the plot:
Simple fix: change ylims to c(-250, 250) and c(-6,6) respectively.
I have the following data and code in R:
x <- runif(1000, -9.99, 9.99)
mx <- mean(x)
stdevs_3 <- mx + c(-3, +3) * sd(x/5) # Statndard Deviation 3-sigma
And I plotted as line (alongwith 3 standard deviation and mean lines) in R:
plot(x, t="l", main="Plot of Data", ylab="X", xlab="")
abline(h=mx, col="red", lwd=2)
abline(h=stdevs_3, lwd=2, col="blue")
What I want to do:
Anywhere on the plot, whenever line is crossing 3 sigma thresholds (blue lines), above or below it, line should be in different color than black.
I tried this, but did not work:
plot(x, type="l", col= ifelse(x < stdevs_3[[1]],"red", "black"))
abline(h=mx, col="red", lwd=2)
abline(h=stdevs_3, lwd=2, col="blue")
Is there any other way?
This is what is requested, but it appears meaningless to me because of the arbitrary division of x by 5:
png( )
plot(NA, xlim=c(0,length(x)), ylim=range(x), main="Plot of Data", ylab="X", xlab="", )
stdevs_3 <- mx + c(-3, +3) * sd(x/5)
abline(h=mx, col="red", lwd=2)
abline(h=stdevs_3, lwd=2, col="blue")
segments( 0:999, head(x,-1), 1:1000, tail(x,-1) , col=c("black", "red")[
1+(abs(tail(x,-1)) > mx+3*sd(x/5))] )
dev.off()
I have two variables, x and y
x = runif(8, 0, runif(1, 1, 5))
y = x^2
that I want to plot. Note that the range of x (and hence y=x^2) is not always the same.
So, the command
plot(x, y, pch=19, col='red')
produces
However, I don't want the borders around the graph, so I use the bty='n' parameter for plot:
plot(x, y, pch=19, col='red', bty='n')
which produces
This is a bit unfortunate, imho, since I'd like the y-axis to go all the way up to 4 and the x-axis all the way to 2.
So, I ue the xaxp and yaxp parameters in the plot command:
plot(x, y, pch=19, col='red', bty='n',
xaxp=c(
floor (min(x)),
ceiling(max(x)),
5
),
yaxp=c(
floor (min(y)),
ceiling(max(y)),
5
)
)
which produces
This is a bit better, but it still doesn't show the full range. Also, I thought it nice that the default axis labaling uses steps that were like 1,2,3,4 or 0.5,1,1.5,2, not just some arbitrary fractions.
I guess R has some parameter or mechanism to plot the full range in the axis in a "humanly" fashion (0.5,1,1.5 ...) but I didn't find it. So, what could I try?
Try:
plot(x, y, pch=19, col='red', bty='n', xlim=c(min(x),max(x)),
ylim=c(min(y),max(y)), axes=FALSE)
axis(1, at=seq(floor(min(x)), ceiling(max(x)), 0.5))
axis(2, at=seq(floor(min(y)), ceiling(max(y)), 0.5))
Or if you'd prefer to hard-code those axis ranges:
axis(1, at=seq(0, 2, 0.5))
axis(2, at=seq(0, 4, 0.5))
Is that what you were after?