I am having a small issue making sense of a log-log plot. So if I create an x-y plot by the following:
xx <- exp(1:10)
yy <- exp(1:10)
plot(xx,yy)
The highest value is 22026.47. When I then plot it on as a log-log plot (this is purely a basic example), as per below
plot(xx,yy, log="yx")
the highest co-ordinate is over 5000. Can someone point me in the right direction to interpret this? For example how can I get the value by which 22026.47 is transformed.
I am not entirely sure what you are asking in regards to "the value by which 22026.47 is transformed". You can simply take the log of whatever value to get it, if that is what you are asking. Unsurprisingly:
log(22026.47)
#[1] 10
Anyway, perhaps some confusion stems from the fact that the log="xy" argument to plot plots your data on a log scale but with ticks marks and labels on the original scale. You say the highest coordinates is over 5000, but 22026.47 is over 5000 so that fits well. The two are just close on a log-scale; just as close as 2.72 and 7.39 corresponding to xx[1:2].
Compare your log-log plot with the result of
plot(log(xx), log(yy))
Here you are plotting the actual log-values of your data, and that is also reflected in your x-axis and y-axis labels.
Related
This is for research I am doing for my Masters Program in Public Health
I am graphing data against each other, a standard x,y type deal, over top of that I am plotting a predicted line. I get what I think to be the most funky looking point/boxplot looking thing ever with an x axis that is half filled out and I don't understand why as I do not call a boxplot function. When I call the plot function it is my understanding that only the points will plot.
The data I am plotting looks like this
TOTAL.LACE | DAYS.TO.FAILURE
9 | 15
16 | 7
... | ...
The range of the TOTAL.LACE is from 0 to 19 and DAYS.TO.FAILURE is 0 - 30
My code is as follows, maybe it is something before the plot but I don't think it is:
# To control the type of symbol we use we will use psymbol, it takes
# value 1 and 2
psymbol <- unique(FAILURE + 1)
# Build a test frame that will predict values of the lace score due to
# a patient being in a state of failure
test <- survreg(Surv(time = DAYS.TO.FAILURE, event = FAILURE) ~ TOTAL.LACE,
dist = "logistic")
pred <- predict(test, type="response") <-- produces numbers from about 14 to 23
summary(pred)
ord <- order(TOTAL.LACE)
tl_ord <- TOTAL.LACE[ord]
pred_ord <- pred[ord]
plot(TOTAL.LACE, DAYS.TO.FAILURE, pch=unique(psymbol)) <-- Produces goofy graph
lines(tl_ord, pred_ord) <-- this produces the line not boxplots
Here is the resulting picture
Not to sure how to proceed from here, this is an off shoot of another problem I had with the same data set at this link here I am not understanding why boxplots are being drawn, the reason being is I did not specifically call the boxplot() command so I don't know why they appeared along with point plots. When I issue the following command: plot(DAYS.TO.FAILURE, TOTAL.LACE) I only get points on the resulting plot like I expected, but when I change the order of what is plotted on x and y the boxplots show up, which to me is unexpected.
Here is a link to sample data that will hopefully help in reproducing the problem as pointed out by #Dwin et all Some Sample Data
Thank you,
Since you don't have a reproducible example, it is a little hard to provide an answer that deals with your situation. Here I generate some vaguely similar-looking data:
set.seed(4)
TOTAL.LACE <- rep(1:19, each=1000)
zero.prob <- rbinom(19000, size=1, prob=.01)
DAYS.TO.FAILURE <- rpois(19000, lambda=15)
DAYS.TO.FAILURE <- ifelse(zero.prob==1, DAYS.TO.FAILURE, 0)
And here is the plot:
First, the problem with some of the categories not being printed on the x-axis is because they don't fit. When you have so many categories, to make them all fit you have to display them in a smaller font. The code to do this is to use cex.axis and set the value <1 (you can read more about this here):
boxplot(DAYS.TO.FAILURE~TOTAL.LACE, cex.axis=.8)
As to the question of why your plot is "goofy" or "funky-looking", it is a bit hard to say, because those terms are rather nebulous. My guess is that you need to more clearly understand how boxplots work, and then understand what these plots are telling you about the distribution of your data. In a boxplot, the midline of the box is the 50th percentile of your data, while the bottom and top of the box are the 25th and 75th percentiles. Typically, the 'whiskers' will extend out to the furthest datapoint that is at most 1.5 times the inter-quartile range beyond the ends of the box. In your case, for the first 9 TOTAL.LACEs, more than 75% of your data are 0's, so there is no box and thus no whiskers are possible. Everything beyond the whisker limits is plotted as an individual point. I don't think your plots are "funky" (although I'll admit I have no idea what you mean by that), I think your data may be "funky" and your boxplots are representing the distributions of your data accurately according to the rules by which boxplots are constructed.
In the future (and I mean this politely), it will help you get more useful and faster answers if you can write questions that are more clearly specified, and contain a reproducible example.
Update: Thanks for providing more information. I gather by "funky" you mean that it is a boxplot, rather than a typical scatterplot. The thing to realize is that plot() is a generic function that will call different methods depending on what you pass to it. If you pass simple continuous data, it will produce a scatterplot, but if you pass continuous data and a factor, then it will produce a boxplot, even if you don't call boxplot explicitly. Consider:
plot(TOTAL.LACE, DAYS.TO.FAILURE)
plot(as.factor(TOTAL.LACE), DAYS.TO.FAILURE)
Evidently, you have converted DAYS.TO.FAILURE to a factor without meaning to. Presumably this was done in the pch=unique(psymbol) argument via the code psymbol <- unique(FAILURE + 1) above. Although I haven't had time to try this, I suspect eliminating that line of code and using pch=(FAILURE + 1) will accomplish your goals.
I try to overlay two histograms in the same plane but the option Probability=TRUE (relative frequencies) in hist() is not effective with the code below. It is a problem because the two samples have very different sizes (length(cl1)=9 and length(cl2)=339) and, with this script, I cannot vizualize differences between both histograms because each shows frequencies. How can I overlap two histograms with the same bin width, showing relative frequencies?
c1<-hist(dataList[["cl1"]],xlim=range(minx,maxx),breaks=seq(minx,maxx,pasx),col=rgb(1,0,0,1/4),main=paste(paramlab,"Group",groupnum,"cl1",sep=" "),xlab="",probability=TRUE)
c2<-hist(dataList[["cl2"]],xlim=range(minx,maxx),breaks=seq(minx,maxx,pasx),col=rgb(0,0,1,1/4),main=paste(paramlab,"Group",groupnum,"cl2",sep=" "),xlab="",probability=TRUE)
plot(c1, col=rgb(1,0,0,1/4), xlim=c(minx,maxx), main=paste(paramlab,"Group",groupnum,sep=" "),xlab="")# first histogram
plot(c2, col=rgb(0,0,1,1/4), xlim=c(minx,maxx), add=T)
cl1Col <- rgb(1,0,0,1/4)
cl2Col <- rgb(0,0,1,1/4)
legend('topright',c('Cl1','Cl2'),
fill = c(cl1Col , cl2Col ), bty = 'n',
border = NA)
Thanks in advance for your help!
When you call plot on an object of class histogram (like c1), it calls the S3 method for the histogram. Namely, plot.histogram. You can see the code for this function if you type graphics:::plot.histogram and you can see its help under ?plot.histogram. The help file for that function states:
freq logical; if TRUE, the histogram graphic is to present a
representation of frequencies, i.e, x$counts; if FALSE, relative
frequencies (probabilities), i.e., x$density, are plotted. The default
is true for equidistant breaks and false otherwise.
So, when plot renders a histogram it doesn't use the previously specified probability or freq arguments, it tries to figure it out for itself. The reason for this is obvious if you dig around inside c1, it contains all of the data necessarily for the plot, but does not specify how it should be rendered.
So, the solution is to reiterate the argument freq=FALSE when you run the plot functions. Notably, freq=FALSE works whereas probability=TRUE does not because plot.histogram does not have a probability option. So, your plot code will be:
plot(c1, col=rgb(1,0,0,1/4), xlim=c(minx,maxx), main=paste(paramlab,"Group",groupnum,sep=" "),xlab="",freq=FALSE)# first histogram
plot(c2, col=rgb(0,0,1,1/4), xlim=c(minx,maxx), add=T, freq=FALSE)
This all seems like a oversight/idiosyncratic decision (or lack thereof) on the part of the R devs. To their credit it is appropriately documented and is not "unexpected behavior" (although I certainly didn't expect it). I wonder where such oddness should be reported, if it should be reported at all.
I am trying to plot a set of data in R
x <- c(1,4,5,3,2,25)
my Y scale is fixed at 20 so that the last datapoint would effectively not be visible on the plot if i execute the following code
plot(x, ylim=c(0,20), type='l')
i wanted to show the range of the outlying datapoint by showing a smaller box above the plot, with an independent Y scale, representing only this last datapoint.
is there any package or way to approach this problem?
You may try axis.break (plotrix package) http://rss.acs.unt.edu/Rdoc/library/plotrix/html/axis.break.html, with which you can define the axis to break, the style, size and color of the break marker.
The potential disadvantage of this approach is that the trend perception might be fooled. Good luck!
I have two related problems.
Problem 1: I'm currently using the code below to generate a histogram overlayed with a density plot:
hist(x,prob=T,col="gray")
axis(side=1, at=seq(0,100, 20), labels=seq(0,100,20))
lines(density(x))
I've pasted the data (i.e. x above) here.
I have two issues with the code as it stands:
the last tick and label (100) of the x-axis does not appear on the histogram/plot. How can I put these on?
I'd like the y-axis to be of count or frequency rather than density, but I'd like to retain the density plot as an overlay on the histogram. How can I do this?
Problem 2: using a similar solution to problem 1, I now want to overlay three density plots (not histograms), again with frequency on the y-axis instead of density. The three data sets are at:
http://pastebin.com/z5X7yTLS
http://pastebin.com/Qg8mHg6D
http://pastebin.com/aqfC42fL
Here's your first 2 questions:
myhist <- hist(x,prob=FALSE,col="gray",xlim=c(0,100))
dens <- density(x)
axis(side=1, at=seq(0,100, 20), labels=seq(0,100,20))
lines(dens$x,dens$y*(1/sum(myhist$density))*length(x))
The histogram has a bin width of 5, which is also equal to 1/sum(myhist$density), whereas the density(x)$x are in small jumps, around .2 in your case (512 even steps). sum(density(x)$y) is some strange number definitely not 1, but that is because it goes in small steps, when divided by the x interval it is approximately 1: sum(density(x)$y)/(1/diff(density(x)$x)[1]) . You don't need to do this later because it's already matched up with its own odd x values. Scale 1) for the bin width of hist() and 2) for the frequency of x length(x), as DWin says. The last axis tick became visible after setting the xlim argument.
To do your problem 2, set up a plot with the correct dimensions (xlim and ylim), with type = "n", then draw 3 lines for the densities, scaled using something similar to the density line above. Think however about whether you want those semi continuous lines to reflect the heights of imaginary bars with bin width 5... You see how that might make the density lines exaggerate the counts at any particular point?
Although this is an aged thread, if anyone catches this. I would only think it is a 'good idea' to forego translating the y density to count scales based on what the user is attempting to do.
There are perfectly good reasons for using frequency as the y value. One idea in particular that comes to mind is that using counts for the y scale value can give an analyst a good idea about where to begin the 'data hunt' for stratifying heterogenous data, if a mixed distribution model cannot soundly or intuitively be applied.
In practice, overlaying a density estimate over the observed histogram can be very useful in data quality checks. For example, in the above, if I were looking at the above graphic as a single source of data with the assumption that it describes "1 thing" and I wish to model this as "1 thing", I have an issue. That is, I have heterogeneous data which may require some level of stratification. The density overlay then becomes a simple visual tool for detecting heterogeneity (apart from using log transformations to smooth between-interval variation), and a direction (locations of the mixed distributions) for stratifying the data.
I'd like to use ggplot2 density geometry using a log transformation for the x scale:
qplot(rating, data=movies, geom="density", log="x")
This, however, produces a chart with probabilities larger than 1. One solution that seems to work is to scale the dataset before calling qplot:
qplot(rating, data=transform(movies, rating=log(rating))
But then the x axis doesn't look nice. What is the correct way to handle this?
It seems that my question doesn't not, in fact, make sense. It seems that it is OK that probability densities are larger than one, as per [2]. What is important is that the integral over the entire space is equal to one [3].
This gives the right answer.
qplot(rating, y = ..scaled.., data=movies, geom="density", log="x")
stat_density produces new values, one of them is ..scaled.. which is the density scaled from 0 to 1.
HTH