quantile

I am attempting to create new variables using a function and lapply rather than working right in the data with loops. I used to use Stata and would have solved this problem with a method similar to that discussed here . Since naming variables programmatically is so difficult or at least awkward in R (and it seems you can't use indexing with assign ), I have left the naming process until after the lapply . I am then using a for loop to do the renaming prior to merging and again for the merging. Are there more efficient ways of doing this? How would I replace the loops? Should I be doing some

I want to compute a new column using the quantiles of another column (a continuous variable) incorporating the Sample Design of a complex survey. The idea is to create in the the data frame a new variable that indicates which quantile group each observation falls into Here is how I execute the idea without incorporating the sample design, so you can understand what I'm aiming for. # Load Data data(api) # Convert data to data.table format (mostly to increase speed of the process) apiclus1 <- as.data.table(apiclus1) # Create deciles variable apiclus1[, decile:=cut(api00, breaks=quantile(api00,

I am using Pandas to compute some financial risk analytics, including Value at Risk. In short, to compute Value at Risk (VaR), you take a time series of simulated portfolio changes in value, and then compute a specific tail percentile loss. For example, 95% VaR is the 5th percentile figure in that time series. I have my time series in a Pandas dataframe, and am currently using the pd.quantile() function to compute the percentile. My question is, typical market convention for VaR is use an exclusionary percentile (ie: 95% VaR is interpreted as: there is a 95% chance your portfolio will not

The quantile functions gives us the quantile of a given pandas series s , E.g. s.quantile(0.9) is 4.2 Is there the inverse function (i.e. cumulative distribution) which finds the value x such that s.quantile(x)=4 Thanks I had the same question as you did! I found an easy way of getting the inverse of quantile using scipy. #libs required from scipy import stats import pandas as pd import numpy as np #generate ramdom data with same seed (to be reproducible) np.random.seed(seed=1) df = pd.DataFrame(np.random.uniform(0,1,(10)), columns=['a']) #quantile function x = df.quantile(0.5)[0] #inverse of

Main question: Suppose you have a discrete, finite data set $d$. Then the command summary(d) returns the Min, 1st quartile, Median, mean, 3rd quartile, and max. My question is: what formula does R use to compute the 1st quartile? Background: My data set was: d=c(1,2,3,3,4,9) . summary(d) returns 2.25 as the first quartile. Now, one way to compute the first quartile is to choose a value q1 such that 25% of the data set is less than of equal to q1. Clearly, this is not what R is using. So, I was wondering, what formula does R use to compute the first quartile? Google searches on this topic have