Histogram stata
We will learn more about frequency density in a bit. sum(d$frequency_density * d$class_width) # 1 When multiplied by the class width, the product will always sum upto 1. Rel_freq <- frequency / length(mtcars$mpg)ĭ <- ame(frequency = frequency, class_width = class_width, relative_frequency = rel_freq, frequency_density = freq_density)ĭ # frequency class_width relative_frequency frequency_density Stata for Unix(GUI) users should use the name() option if there is more than one graph displayed to ensure that the correct graph is exported (see Technical note for Stata for Unix(GUI) users).
Relative Frequency = Frequency / Total Observations h <- hist(mtcars$mpg, breaks = c(10, 18, 24, 30, 35)) Type graph export lename.sufx in the Command window.
#Histogram stata code#
Thanks to Maxim Massenkoff for submitting the additional code and figure.Frequency Density = Relative Frequency / Class Width (histogram write if female=0, start(30) width(5) color(green%30)), /// Twoway (histogram write if female=1, start(30) width(5) color(red%30)) /// So 100% opacity is the same as the original histogram. All three tasks are easily done in Stata with the following sequence of commands: reg y2 x predict y2hat predict error2, resid hist error2, bin(50) sum y2 y2hat error2. The higher the opacity, the less transparent the histogram will become. Then plot the residuals using Statas histogram command, and summarize all of the variables. The option color(red%30) makes the female histogram red with 30 percent opacity and color(green%30) makes the male histogram green with 30 percent opacity. Transparency is specified as a color modifier. Now the we can see that females have more density to the right of the graph while the males have more density towards the left side.Īn even better method is to add transparency, which became available as of Stata 15. A separate window with the histogram displayed will be opened. Stata assumes you are working with continuous data Very simple syntax: hist varname Put a comma after your varname and start adding options bin(): change the number of bars that the graph displays normal: overlay normal curve addlabels: add actual values to bars Histogram options. In the dialogue box that opens, choose a variable from the drop-down menu in the ‘Data’ section, and press ‘Ok’. To obtain a histogram for a categorical variable using the Stata menus. (histogram write if female=0, start(30) width(5) ///įcolor(none) lcolor(black)), legend(order(1 "Female" 2 "Male" )) To create histogram in Stata, click on the ‘Graphics’ option in the menu bar and choose ‘Histogram’ from the dropdown. Stata handout), and that you have read in the set of data that you want to. The lcolor(black) option sets the line color to black.
The option fcolor(none) sets the fill color to none while So, let’s render the male histogram as transparent rectangles withīlack outlines. That’s a bit better, but parts of the male histogram still block out our view of theįemale histogram. (histogram write if female=0, start(30) width(5)), /// Twoway (histogram write if female=1, start(30) width(5) color(green)) /// Option, which sets both the fill color and line color to green. Let’sĬhange the female histogram color to green. Both histograms were rendered in the same color. (histogram write if female=0, start(30) width(5)) For plotting histogram-like displays, kernel- density estimates and plots based on distribution functions or quantile functions, a large variety of choices is. Twoway (histogram write if female=1, start(30) width(5)) /// Let’s load the hsbdemoĭataset and overlay histograms for males and female for the variable write. However you can create frequency weights that will be multiples of the probability weights and agree in precision to any desired accuracy. The problem with sampling weights is that they can be non-integral. This sounds like it should be pretty easy. The histogram, kdensity, and cumul commands all take frequency weights, which must be integers.