比如我想得到3年和6年的生存概率为结局的列线图,我就可以写出如下代码: plot(nomogram(f, fun=list(function(x) surv(3, x), function(x) surv(6, x)), funlabel=c("age 3 Survival Probability", "age 6 Survival Probability")) 上面的代码中f是cph对象,fun中给定的就是将线性预测值转换成生存概率...
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In the previous part of this article, I have shown you many differenttypes of plots. However, there are plenty of programming tricks for the modification of plots in general. In the following, you will find a list of tutorials that explain such general modifications of plots in R. ggplot2 ...
As you write code, you'll encounter errors. Don't worry about them; try to understand them and find solutions. Remember, programming is all about solving problems, and errors are part of the process. How to Run R? 1. Run R in your browser. ...
Example plot(1:10, pch=25, cex=2) Result: Try it Yourself » The values of the pch parameter ranges from 0 to 25, which means that we can choose up to 26 different types of point shapes:❮ Previous Next ❯ Track your progress - it's free! Log in Sign Up ...
4.2.3 Scatterplot with Legend 4.2.4 Boxplot with Reordered and Formatted Axes 4.2.5 Barplot with Error Bars 😦 4.2.6 Limitations of Base Graphics 4.1 Simulation 4.1.1 Generating Random Numbers Generally, every probability distribution function is associated with four R corresponding functions, whic...
Change R base plot line types Simple format of R lines functions: plot(x, y, type = "l", lty = 1). Create the main R base plot frame. lines(x, y, type = "l", lty = 1). Add lines onto the plot. Key options: x,y: variables to be used for the x and y axes, respect...
Over 1400 graphs with reproducible code divided in 8 big categories and over 50 chart types, in addition of tools to choose and create colors and color palettes
The alluvial plot and Sankey diagram are both forms of visualization for general flow diagrams. These plot types are designed to show the change in magnitude of some quantity as it flows between states. Although the difference between alluvial plot and sankey diagram is always discussed online, li...
We can display it with a scatter plot so that we can see each county as a dot, but seperate the dots by the state they belong to. We’ll set colors = "PuOr" to specify a diverging color scale:df %>% plot_ly( x = ~state, y = ~percbelowpoverty, type = "scatter", color = ...