I am facing a probably pretty easy-to-solve issue: adding a log- curve to a scatter plot.
I have already created the corresponding model and now only need to add the respective curve/line.
The current model is as follows:
### DATA
SpStats_urbanform <- c (0.3702534,0.457769,0.3069843,0.3468263,0.420108,0.2548158,0.347664,0.4318018,0.3745645,0.3724192,0.4685135,0.2505839,0.1830535,0.3409849,0.1883303,0.4789871,0.3979671)
co2 <- c (6.263937,7.729964,8.39634,8.12979,6.397212,64.755192,7.330138,7.729964,11.058834,7.463414,7.196863,93.377393,27.854284,9.081405,73.483949,12.850917,12.74407)
### Plot initial plot
plot (log10 (1) ~ log10 (1), col = "white", xlab = "PUSHc values",
ylab = "Corrected GHG emissions [t/cap]", xlim =c(0,xaxes),
ylim =c(0,yaxes), axes =F)
axis(1, at=seq(0.05, xaxes, by=0.05), cex.axis=1.1)
axis(2, at=seq(0, yaxes, by=1), cex.axis=1.1 )
### FIT
fit_co2_urbanform <- lm (log10(co2) ~ log10(SpStats_urbanform))
### Add data points (used points() instead of simple plot() bc. of other code parts)
points (co2_cap~SpStats_urbanform, axes = F, cex =1.3)
Now, I've already all the fit_parameters and are still not able to construct the respective fit-curve for co2_cap (y-axis)~ SpStats_urbanform (x-axis)
Can anyone help me finalizing this little piece of code ?
First, if you want to plot in a log-log space, you have to specify it with argument log="xy":
plot (co2~SpStats_urbanform, log="xy")
Then if you want to add your regression line, then use abline:
abline(fit_co2_urbanform)
Edit: If you don't want to plot in a log-log scale then you'll have to translate your equation log10(y)=a*log10(x)+b into y=10^(a*log10(x)+b) and plot it with curve:
f <- coefficients(fit_co2_urbanform)
curve(10^(f[1]+f[2]*log10(x)),ylim=c(0,100))
points(SpStats_urbanform,co2)
Related
I am using the following code to plot the ROC curve after having run the logistic regression.
fit1 <- glm(formula=GB160M3~Behvscore, data=eflscr,family="binomial", na.action = na.exclude)
prob1=predict(fit1, type=c("response"))
eflscr$prob1 = prob1
library(pROC)
g1 <- roc(GB160M3~prob1, data=eflscr, plot=TRUE, grid=TRUE, print.auc=TRUE)
The ROC curves plotted look like this (see link below)
The x-axis scale does not fill the who chart.
How can I change the x axis to report 1 - specifically?
By default pROC sets asp = 1 to ensure the plot is square and both sensitivity and specificity are on the same scale. You can set it to NA or NULL to free the axis and fill the chart, but your ROC curve will be misshaped.
plot(g1, asp = NA)
Using par(pty="s") as suggested by Joe is probably a better approach
This is purely a labeling problem: note that the x axis goes decreasing from 1 to 0, which is exactly the same as plotting 1-specificity on an increasing axis. You can set the legacy.axes argument to TRUE to change the behavior if the default one bothers you.
plot(g1, legacy.axes = TRUE)
A good shortcut to getting a square plot is to run the following before plotting:
par(pty="s")
This forces the shape of the plot region to be square. Set the plotting region back to maximal by simply resetting the graphics device and clearing the plot.
dev.off()
As pointed out by #Calimo, there is the legacy.axes argument to reverse the x-axis and the label is also changed automatically. You can run ?plot.roc to see all the pROC plotting options.
Example
# Get ROC object
data(aSAH)
roc1 <- roc(aSAH$outcome, aSAH$s100b)
# Plot
par(pty="s")
plot(roc1, grid = TRUE, legacy.axes = TRUE)
# Reset graphics device and clear plot
dev.off()
I want to plot the histogram with real data and compare it with a theoretical normal distribution in one plot. But the scale looks different. Two plots have different scale
# you can generate some ramdom data on ystar which is realy data.
x<-seq(-4,4,length=200)
y<-dnorm(x,mean=0, sd=1)
plot(x,y, type = "l", lwd = 2, xlim = c(-3.5,3.5),ylim=c(0,0.7))
par(new = TRUE)
hist(ystar,xlim = c(-10,10),freq = FALSE,ylim=c(0,0.7),breaks = 50)
Desire output
Assuming that ystar is a vector, you should change this:
y<-dnorm(x,mean=0, sd=1)
To:
y<-dnorm(x,mean=mean(ystar), sd=sd(ystar))
This will produce a distribution function that approximately matches the histogram.
You should then be able to use the same x-limits for both the histogram and the theoretical distribution, which will eliminate the strange overlapping axis labels you have in your current version.
I try to draw line graph using R. The lines have been plotted, but the abline line doesn't show up.
M <- c(1.0,1.5,2.0,2.5,3)
y <- c(0.0466,0.0522,0.0608,0.0629,0.0660)
plot(M, y, type="l", col="red", xlab="sdr",
ylab="simulated type I error rate")
abline(h=c(0.025,0.075),col=4,lty=2)
This is my simple coding for the graph. Any ways to make the line pop out?
Try this instead:
M <- c(1.0,1.5,2.0,2.5,3)
y <- c(0.0466,0.0522,0.0608,0.0629,0.0660)
plot(M, y, type="l",col="red",xlab="sdr", ylim = c(0.025, 0.075),
ylab="simulated type I error rate")
abline(h=c(0.025,0.075),col=4,lty=2)
by using ylim.
I would refer you to read my answer for another post: curve() does not add curve to my plot when “add = TRUE” for more about setting ylim when plotting several objects on a graph.
This refers to my previous question.
how to plot multiple polygon plots in R?
How can i add lines (not segments) to existing plot(with polygon) which has diamond at its each terminal.
You can use lines for this:
#random plot
plot(1:10, xlim=c(0, 10), ylim=c(0,10))
#then plot line with x1,x2 and y1,y2 co-ordinates
#below x1=0 x2=5 y1=2 and y2=8
lines(c(0, 5), c(2, 8), type='l')
Make sure you add xlim and ylim in plot in order for lines to pick up the correct scale.
In R I'm able to overlap a normal curve to a density histogram:
Eventually I can convert the density histogram to a probability one:
a <- rnorm(1:100)
test <-hist(a, plot=FALSE)
test$counts=(test$counts/sum(test$counts))*100 # Probability
plot(test, ylab="Probability")
curve(dnorm(x, mean=mean(a), sd=sd(a)), add=TRUE)
But I cannot overlap the normal curve anymore since it goes off scale.
Any solution? Maybe a second Y-axis
Now the question is clear to me. Indeed a second y-axis seems to be the best choice for this as the two data sets have completely different scales.
In order to do this you could do:
set.seed(2)
a <- rnorm(1:100)
test <-hist(a, plot=FALSE)
test$counts=(test$counts/sum(test$counts))*100 # Probability
plot(test, ylab="Probability")
#start new graph
par(new=TRUE)
#instead of using curve just use plot and create the data your-self
#this way below is how curve works internally anyway
curve_data <- dnorm(seq(-2, 2, 0.01), mean=mean(a), sd=sd(a))
#plot the line with no axes or labels
plot(seq(-2, 2, 0.01), curve_data, axes=FALSE, xlab='', ylab='', type='l', col='red' )
#add these now with axis
axis(4, at=pretty(range(curve_data)))
Output:
At first you should save your rnorm data otherwise you get different data each time.
seed = rnorm(100)
Next go ahead with
hist(seed,probability = T)
curve(dnorm(x, mean=mean(na.omit(seed)), sd=sd(na.omit(seed))), add=TRUE)
Now you have the expected result. Histogram with density curve.
The y-axis isn't a "probability" as you have labeled it. It is count data. If you convert your histogram to probabilities, you shouldn't have a problem:
x <- rnorm(1000)
hist(x, freq= FALSE, ylab= "Probability")
curve(dnorm(x, mean=mean(x), sd=sd(x)), add=TRUE)