I often find myself coming back to this answer I gave on Stack Overflow in 2014. It shows how to colour a plot based on an independent continuous variable using the base graphics… Continue reading →

Last week, in our mathematical statistics course, we’ve seen the law of large numbers (that was proven in the probability course), claiming that given a collection of i.i.d. random variables, with To visualize that convergence, we can use > m=100 > mean_samples=function(n=10){ + X=matrix(rnorm(n*m),nrow=m,ncol=n) + return(apply(X,1,mean)) + } > B=matrix(NA,100,20) > for(i in 1:20){ + B=mean_samples(i*10) + } > colnames(B)=as.character(seq(10,200,by=10)) > boxplot(B) It is...

Neural networks have always been one of the most fascinating machine learning model in my opinion, not only because of the fancy backpropagation algorithm, but also because of their complexity (think of deep learning with many hidden layers) and structure inspired by the brain. Neural networks have not always been popular, partly because they were,

Today I am giving away 10 sessions of free, online, one-on-one R help. My hope is to get a better understanding of how my readers use R, and the issues they face when working on their own projects. The sessions will be over the next two weeks, online and 30-60 minutes each. I just purchased Screenhero, The post

After you check the distribution of the data by ploting the histogram, the second thing to do is to look for outliers. Identifying the outliers is important becuase it might happen that an association you find in your analysis can be explained by the presence of outliers. The best tool to identify the outliers is

The quest for income microdata For a separate project, I've been looking for source data on income and wealth inequality. Not aggregate data like Gini coefficients or the percentage of income earned by the bottom 20% or top 1%, but the sources used to calculate those things. Because it's sensitve personal financial data either from surveys or tax...

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