Quantile Quantile Plot Puns

My code looks like this:

qr_model <- rq(y ~ x, data, tau = c(0.1, 0.9))
qr_model_summary <- summary(qr_model, se = "boot")

Since I am using bootstrap standard errors, the standard errors are not replicable. How can I set the random seed to ensure replicability?

Hello!

I am doing a project at work to predict On time delivery percentage in a manufacturing process. I recently discovered Quantile Random Forest and I like the idea of it. I am thinking of using Quantile 0.5 as a point estimator and 0.1 and 0.9 quantile as prediction interval.

So far the results have been good but since I'm new to the real world project setting and new to quantile random forest, I was wondering is there something I should keep in mind while using this algorithm?

I read an article at Medium where they showed a use case of QRF at Instacart to predict On time delivery percentage but I was thinking why this algorithm is not so popular (maybe I just don't know about it).

What have your personal experience been using QRF?

Thank you!

Isn't quantile regression a good solution for estimating uncertainty in neural networks? I haven't seen much use of it, any reason why?

I’m learning some of the basics of GIS before starting the course in a couple of weeks and I was just wondering if anyone could provide some tips for this. Thank you!

Dear Statisticians,

I'm approaching the censored quantile regression for survival analysis, a very intrestring approach expecially in cancer research where some treatments (e.g. immunotherapy) have a delayed effect and the proportional hazards assumption of the Cox Model can't hold.

I know it can be a niche approach but for this I would like to have the opinions of those who worked on it.

Normally the censored quantile regression models the quantile of the logarithm of time to the event, pretty much like an AFT model, but know we are talking about the quantile of the log and not just the log of the time to the event, and again, pretty much like an AFT model the covariates have a linear relationship. Since we are modelling the "conditional quantile" we do not have to assume any distribution for the time to event and this is a strong point for this approach.

Immagine i have only one covariate "arm" with values: 1 = Treatment and 0 = Placebo. I want to ask you about the interpretation of the parameters of quantile regression for the log time of the event in a survival analysis with just this covariate.

Immagine i want to estimate the parameters for the 20-quantile:

The parameters i would see, would be an "intercept" parameter and "arm" parameter. For my interpretation, the parameter "arm" is the difference between the 20-quantile of log(time) for arm=1 and for the same quantile for arm=0. And as you can immagine, for me the intercept parameter is the quantile of log(time) for arm=0. So by exponentiating the "arm" parameter i can obtain the ratio between the 20-quantile of TIME for arm=1 and for arm=0.

I ask you first if this above is correct

Moving on i ask you: from the parameters i can obtain modelling quantile of log(time), is there a way to obtain something not in terms of ratio between the quantile of time, but in terms of difference between the quantile of time between arms?

Thank you for your support

Hi all! We are migrating our legacy metrics to prometheus now and we are a bit concerned about performance of summary quantiles in java library. As you might now, there is an option in prometheus to calculate quantiles on client side - as opposed to building histograms and calculating summaries on server side. For many reasons I don't want to discuss here the only option for us is to do client-side aggregations. We used micrometer initially and it worked as a charm. Unfortunately one of the teams reported they have issues with micrometer dependencies and they'd want us to use plain simpleclient. We ran some performance tests and summaries with quantiles performed 100x worse than micrometer percentiles! I tried to dig deeper into simpleclient's code and I see that quantile implementation there is worrying.

Do some of you by chance work in low-latency environment (we are processing millions events per second) and can suggest some alternative? Of course, metrics are not recorded on each event but we still want to reasonably sample them. At least we'd want to have comparable performance to micrometer.

I have a tibble and I used the quantile function to get the quantiles of some 4 different columns. How do I append those 4 quintiles into a tibble?

P1<-Quantile(t1$c1) P2<-Quantile(t1$c2) P3<-Quantile(t1$c3)

I wanna make a tibble that houses P1,P2 and P3

Hi I have a data frame that includes a date column. I want to keep the most recent rows that make up a certain percentage of the data (e.g., 10% or 25%). So essentially I need a way to get the 0.75 or 0.9 quantile of a sorted date column - but quantile() doesn't work with date objects. What would be the best way to do this?

The reason why I can't simply arrange the dataframe by date and find the cutoff manually is I want to apply this logic to dataframes where the data is grouped as well.

Hi, The DHARMA package seems to provide an unified way for diagnosing GL(M)M models. However, it seems to be used relatively infrequently, despite the idea of randomized quantile residuals being several decades old.

Is the idea just slow to catch up (or am I underestimating its popularity)? Or is there some problem/limitation I am missing?

Thanks for help.

Dear all,

I am slightly confused about this line of code which calculates quantiles:

ps <- ( seq(0,99) + 0.5 )/100

qs <- quantile(x, ps)

normalqs <- qnorm(ps, mean(x), popsd(x))

plot(normalqs,qs,xlab="Normal percentiles",ylab="Height percentiles")

abline(0,1) ##identity line

The questions I have are:

why not just use the qqnorm function?

what is the difference between the qnorm and qqnorm functions?

with the seq function in the first line, is the + 5 argument the increments to go up in?

Why is the firs line divided by 100?

Also, why is the seq code (ps) used for the prob argument of quantile?

Thanks in advance!

STL Fed

• The Survey of Consumer Finances, from the Board of Governors of the Federal Reserve System, tracks household balance sheets and other characteristics. Plotting the data allows us to compare the evolution of the net worth of households during and after the past three recessions (that is, not including the current recession). Here, households are divided into quantiles according to wealth: the top 1%, the next 9%, the next 40%, and the bottom 50%. These quantiles were affected differently, which can be explained by the asset composition of household wealth

• Figure 1 shows the gains in household net worth of all wealth quantiles during the eight-month recession from July 1990 to March 1991. By the time that recession ended, the top 1% and the bottom 50% of households had both gained slightly more than 10% in their net worth.

• Figure 3 covers the Great Recession of 2007-2009, which was longer than the previous two, lasting the 18 months from December 2007 to June 2009. Its impact on household net worth was most marked for the bottom 50% wealth quantile, whose net worth decreased as much as 42% during that recession.

• Differences in the asset composition of wealth across quantiles can explain these differences: The wealthiest households hold most of their wealth in the form of financial vehicles or stakes in businesses, while the least wealthy hold assets mostly in the form of housing and consumer durables.3 Thus, the dot-com stock bubble crash of 2001 and the bearish stock market between late 2007 and early 2009 reduced the net worth of the wealthiest households.

• Conversely, the real estate market collapse associated with the Great Recession most markedly impacted the net worth of the least wealthy. While average housing prices increased after the previous two recessions, they instead decreased by as much as 18% after 2007, with the largest reductions recorded among lower-tier homes. This was an unprecedented decline in U.S. home prices.

I'm trying to create an alerting rule for Traefik, so I am alerted if the latency to backends goes over a certain value.

My query is:

histogram_quantile(0.99, sum(rate(traefik_service_request_duration_seconds_bucket[5m])) by (service, method)) > 5

Which, if I'm understanding correctly, should trigger if the 99th percentile for the latency to backends goes over 10 seconds, but for some reason the histogram_quantile function isn't returning anything.

If I remove it, and run: sum(rate(traefik_service_request_duration_seconds_bucket[5m])) by (service, method), I get back entries for example:

{method="GET", service="minio-s3-internal-https-3c4897316bf71cea4328@kubernetescrd"} 1.1874999999999998

I'm not sure if I'm using the histogram_quantile incorrectly?

I've been using R on my desktop for a bit, just set it up on my laptop. Tried to knit a file I had written and successfully knitted on my PC, but get this error:

Error in UseMethod("quantile") : no applicable method for 'quantile' applied to an object of class "c('double', 'numeric')"

Any ideas on what the issue is or how to resolve the problem?

Thanks!

x = [1,3,7,11]

with interpolation = 'linear'

q1 = 2.5

q3 = 8

This optional parameter specifies the interpolation method to use when the desired quantile lies between two data points i < j:

• linear: i + (j - i) * fraction , where fraction is the fractional part of the index surrounded by i and j.

I don't understand what they mean by 'fraction is the fractional part of the index surrounded by i and j.'

I generate these data set

    data=rnorm(100)
    mu=mean(data)
    sd=sd(data)
    CI=c(mu-1.96*sd/sqrt(100),mu+1.96*sd/sqrt(100))
    quantile=quantile(data, probs=c(0.025, 0.975))

Ouput:

> CI
[1] -0.2759049  0.1245894
> quantile
     2.5%     97.5% 
-2.380301  1.693665 

why am I getting different value?