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Tooling
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I'm looking for a good package to train a linear quantile regression model, i.e. $\hat y = \sum_{i=1}^n w_i \cdot X_i$. With $x_i$ are the input features, and $w_i$ are the bounded trainable weights. ...
student13's user avatar
  • 13
2 votes
1 answer
88 views

I am depicting the 14 day spei conditions leading up to and following wildfires. I'm looking for trends in variability or dips. In my study region, n = 399. As one could expect, there is a lot of ...
Ivey_Michaella's user avatar
  • 23
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1 answer
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I am running an analysis for quantile regression evaluated at the median. Below is the code that I have used. My Education level is a 4-level data, giving me 3 p-values. I would like to get an overall ...
HNSKD's user avatar
  • 1,654
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1 answer
78 views

(please tag 'expectreg' - don't have the rep) This framework and package seems to exist more or less in the shadows but I'm going to try my luck here. I'm trying to estimate distribution Y|X non-...
bikeactuary's user avatar
  • 527
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Given this dataset df=pd.DataFrame({'year':[2000,2000,2000,2000,2000,2001,2001,2001,2001,2001,2002,2002,2002,2002,2002],'metric':[2,3,4,5,6,12,13,14,15,16,22,23,24,25,26]}) running quantile ...
bagelanta's user avatar
  • 53
2 votes
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623 views

I am trying to implement Quantile loss for a regression problem based on the formula from this article (number 14 at the end of the article): Here is my implementation: import numpy as np def ...
nooshinha's user avatar
  • 268
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1 answer
192 views

I am using quantregForest to perform quantile regression. Since I already have a script which uses randomForest, at first I simply tried to replace all my calls to randomForest with quantregForest and ...
umbe1987's user avatar
  • 3,698
0 votes
1 answer
642 views

I have tried to implement quantile regression for the Boston dataset. library(MASS) data(Boston) attach(Boston) qr_res_0.9 <- rq(medv ~ lstat + rm + crim + dis, tau = 0.9,...
vp_050's user avatar
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1 answer
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i am doing a Quantile regression project. i have the R codes. i needed to install rqpd package for that. I find this code for installing the package : install.packages("rqpd", repos="...
Samira's user avatar
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1 answer
560 views

I continue to run into errors when run any form of quantile forest models with the prediction and quantile phases. I am following this example but with my own X and y. I have trained many a random ...
Tina B's user avatar
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1 vote
0 answers
130 views

I am currently working on a project where I need to estimate changes in physical fitness over time using segmented regression with quantile regression in R. The data I'm working with consists of ...
Antonio Martinko's user avatar
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1 answer
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I have two datasets. One with quantile estimates for multiple quantiles and the other with ols estimate. d1 <- structure( list( tau = c(0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, ...
chris jude's user avatar
  • 498
2 votes
1 answer
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Iv created two merged tables of quantiile regression as explained here https://yuzar-blog.netlify.app/posts/2022-12-01-quantileregression/ tbl_merge( tbls = list( tbl_regression(l) %>% ...
Hadar Klein's user avatar
  • 21
1 vote
1 answer
377 views

using R, I m performing a backtest on a time series by using quantile regression (quantreg::rq) on a number of features. These features are then selected based on a condition such as p-values <= 5%....
user12899748's user avatar
  • 11
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1 answer
128 views

Tl;dr - I'm trying to use the merge.data.table() function with row indexes and the suggestions given in the R documentation are not working. My data is roughly: library(data.table) library(quantreg) ...
Sarah Hirsch's user avatar
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