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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
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1 answer
68 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
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105 views

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
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1 answer
70 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
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97 views

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
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0 answers
607 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
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1 answer
188 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
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0 answers
181 views

I am performing the quantile regression in R on a non linear model. I am getting the coefficients for the desired quantiles (tau = 0.05, 0.50, 0.95). All very nice, but running the code without ...
M B's user avatar
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1 answer
587 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
  • 518
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1 answer
685 views

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
549 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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124 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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41 views

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
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2 votes
1 answer
1k views

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
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1 vote
1 answer
356 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
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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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0 answers
180 views

I want to try adpat quantile regression to caret package. I wrote below code to adapt but I get the error as below: library(quantreg) quantregression <- list(type='Regression', library='quantreg',...
mesist's user avatar
  • 11
2 votes
1 answer
281 views

I'd' like to model the 25th, 50th and 75th quantile regression curves (q25, q50, q75) for 241 values of probability ('prob') depending on x0. For that purpose, I used the qgamV package as follows. ...
denis's user avatar
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1 vote
1 answer
381 views

I want to perform a time-to-event analysis using qgcomp package. I used qgcomp.cox.boot function and adjusted for confounding factors. However I have encountered some problems. library("ggplot2&...
Curry Qin's user avatar
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1 answer
467 views

I also posted this here. I'm trying to fit a quantile regression model to my input data. I would like to use sklearn, but I am getting a memory allocation error when I try to fit the model. The same ...
Archie's user avatar
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1 answer
203 views

When invoking boot.rq like this b_10 = boot.rq(x, y, tau = .1, bsmethod = "xy", cov = TRUE, R = reps, mofn = mofn) what does the B matrix (size R x p) in b_10 contain: bootstrapped ...
erised's user avatar
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Today, I was trying to implement a weighted bootstrap method in R and ran into a problem involving the 'weights' argument of the 'rq' function. Essentially, when putting it inside a function, it ...
Stéphane's user avatar
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1 answer
528 views

I am developing an iterative algorithm that uses quantile regression models at each iteration. For that I use the rq function from the quantreg package in R. So far it has worked fine. However, I have ...
Álvaro Méndez Civieta's user avatar
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1 vote
0 answers
656 views

Continuing from this question: Is it possible to use lqmm with a mira object? I have tried to get the random effects for the mixed models (lmm and lqmm), and it has been hard. library(lqmm) library(...
De La Cruz's user avatar
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2 votes
1 answer
536 views

I am using the package lqmm, to run a linear quantile mixed model on an imputed object of class mira from the package mice. I tried to make a reproducible example: library(lqmm) library(mice) summary(...
De La Cruz's user avatar
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1 answer
469 views

Following this question Bootstrapping CI for a quantile regression in R outside the quantreg framework, I would like to plot the confidence interval, obtained with the solution provided, on my ...
Recology's user avatar
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1 answer
174 views

I have an object, model1, resulting from a quantile regression. In model1 I have 3 columns and 99 rows with a step of 1 centile like this: > model1 tau intercept julian_day 1 0.01 17.25584 -...
Recology's user avatar
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1 answer
364 views

So I am trying to solve a problem where I have a dataset with 3 columns, "CustomerID", "Fast", and "Precise" where "CustomerID" is simply the number of the ...
kosky's user avatar
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1 answer
290 views

I could not find where the Manhattan distance of weights is calculated and multiplied with alpha (L1 reg. coefficient) in the Lasso Regression and the Quantile Regression source code of scikit-learn. ...
Berat Tuna Karlı's user avatar
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0 votes
0 answers
970 views

If I run the library for 'quantreg', I get a warning that backsolve is masked from base. Then I try to run a quantile regression and I get an error involving backsolve. How can I solve this? library(...
Lucasjansens's user avatar
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0 answers
120 views

I am currently exploring different probabilistic time series forecasting models for car sales data and have planned to evaluate the probabilistic forecasts with the metrics rho-risk as described on ...
Max Mikael's user avatar
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3 votes
1 answer
340 views

I am trying to plot the quantile regression lines for a set of data. I would like to extend the quantile regression lines from geom_quantile() in order to show how they forecast similar to using ...
joerminer's user avatar
  • 163
-1 votes
1 answer
82 views

I'm relatively new to R and coding in general and I have been trying to replicate an example provided in this PDF (https://support.sas.com/resources/papers/proceedings17/SAS0525-2017.pdf) on quantile ...
Hermann Josef's user avatar
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1 vote
1 answer
578 views

I am using R to conduct a quantile regression with bootstrapped standard errors to test if one variable is higher than a second variable at the 5th, 50th, and 95th percentiles of the distributions. ...
Erin Morrissey's user avatar
  • 39
1 vote
2 answers
1k views

I am trying to manually pool results from quantile regression models run on multiply imputed data in R using mice. I make use of a bootstrapping procedure to get 95% CIs and P values of the model ...
Dion Groothof's user avatar
  • 1,476
4 votes
2 answers
3k views

First I used R implementation quantile regression, and after that I used Sklearn implementation with the same quantile (tau) and alpha=0.0 (regularization constant). I am getting the same formulas! I ...
Sapir Tubul's user avatar
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0 answers
238 views

Does anyone know how to fit a quadratic (or higher order) model on a continuous variable and do quantile regression on it in R? Additionally, how do you tell what level of tau fits the data better? ...
Nate's user avatar
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1 vote
1 answer
2k views

I have a dataframe df that is indexed by customer id. and includes: df=['Customer ID', 'Sales' ,'Product code' ,'Price']]: https://i.sstatic.net/vP8Gy.png I want to create a column Quantile, which ...
user13425814's user avatar
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0 votes
1 answer
2k views

I've been working with scikit-garden for around 2 months now, trying to train quantile regression forests (QRF), similarly to the method in this paper. The authors of the paper used R, but because my ...
ozmanda's user avatar
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2 votes
0 answers
370 views

I am looking for an R implementation of the quantile regression with fixed effects as proposed by Machado and Santos Silva (2019). Is it included in any package or is anyone currently working on it? I ...
glaucon's user avatar
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0 votes
0 answers
401 views

I am using statsmodels to train a linear quantile regression. I have different combinations of features that I need to try and train, but it is as if statsmodels only allows a certain amount of ...
andKaae's user avatar
  • 363
1 vote
2 answers
1k views

I am using rqpd package in R to have a quantile regression with fixed effects (quantreg package does not support quantile regressions with fixed effects) as follow: reg_q1 <- rqpd::rqpd(...
Reza's user avatar
  • 15
1 vote
1 answer
391 views

I am interested in training a random forest to learn some conditional quantile on some data {X, y} sampled independently from some distribution. That is, for some $$\alpha \in (0, 1)$$, a mapping $$\...
user3659451's user avatar
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0 votes
1 answer
89 views

By using the following code I am able to plot the results of my quantile regression model: quant_reg_all <- rq(y_quant ~ X_quant, tau = seq(0.05, 0.95, by = 0.05), data=df_lasso) quant_plot <- ...
Joehat's user avatar
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1 vote
2 answers
305 views

I am using quantreg package to predict new data based on training set. However, I noticed a discrepancy between predict.rq or predict and doing it manually. Here is an example: The quantile regression ...
tobinz 's user avatar
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0 votes
0 answers
642 views

Question: What factors may cause the prediction interval to have wider coverage than would be expected? Particularly with regard to quantile regression forests with the ranger package? Specific ...
Bryan Shalloway's user avatar
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0 votes
1 answer
164 views

Suppose I want to apply a simple quantile regression to a subset of columns in a dataframe, by all the factor values of a column. As an example, take mtcars. data(mtcars) cols <- c("mpg", ...
Jackdaw's user avatar
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0 votes
2 answers
284 views

I'm facing a problem that rq(y ~ x, tau = 0.50, method = "br") doesn't complete calculation. There is no error and warning. I traced code and found .Fortran() in rq.fit.br() does not finish ...
kabocha's user avatar
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0 votes
2 answers
1k views

The only package I know that does unconditional quantile regression in R is uqr. Unfortunately, it's been removed from CRAN. Even though I can still use it, its functionality is limited (e.g., does ...
Mangoaddict's user avatar
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0 votes
1 answer
668 views

Right now I'm trying to use statsmodels.formula.api's quantreg by putting in the formula and the dataframe by doing smf.quantreg('<independant values> ~ <dependant values>', df).fit(q=0.9) ...
Edward Kazuya Carlson's user avatar
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