Questions tagged [loess]

LOESS (or LOWESS) stands for locally weighted scatterplot smoothing. It is a form of local (k-nearest neighbor) kernel regression

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Loess line interpretation

I'm sorry for this noob question, but I'm following a practical to draw a plot of Boston Housing data set after using Gradient Boosting Machine to train the data, but I don't understand how to ...
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Predicition interval for LOESS smoothed data

Is the concept of a prediction interval for non-parameteric fits such as LOESS meaningful? I can't see why it wouldn't be - and the fact that it is implemented in R also implies it is. https://mclust-...
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How do I interpret or explain loess plot?

I used the following R code: ...
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Local regression with variables not in regression equation

I wanted to see if this approach could make sense. I am making a regression prediction model for house prices based on 2 numerical x variables. I also have location data (latitude and longitude) for ...
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Can this be considered as a trend?

I have this time series with a trend line plotted in R using the lowess function, f=0.2 (I couldn't use stl or decompose function because this data is measured annually). Although this seems to fit ...
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Can I use predictions from LOESS as a form of predictor transformation in logistic regression?

I am wondering if I can use the predictions from LOESS as a form of predictor transformation in logistic regression? For example, if one of the predictors is X, then can I use predict(loess(Y~X)) as a ...
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R comparing effect between two regression lines

I'm trying to make sense of how to do something here in R and how to think through a method to answer a business question. I have an original data frame that has information about advertising money ...
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R: "family" and "degree" specification in loess fitting

I can't understand the difference between the possible specifications of the family option in the loess command in R. This is ...
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Statistics on LOESS smoothing

I'm trying to make sense of a data set which contains thousands of independent measurements of intensity in the form of a scatterplot. These measurements are dependent on two major variables: ...
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Bootstrapping GAM LOESS models with Multiple Predictors [closed]

I have a data set with multiple predictors and am using a GAM model in conjunction with LOESS. I am trying to replicate this but include the bootstrap process as well. The following code is the ...
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STL decomposition of a daily time series (only business days)

I'm currently working with a daily (business days) time series which has a monthly seasonality and an overall positive trend over the last two years. I want to estimate the error component of the time ...
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Taking the mean of several data entries on same date to make one

I am analyzing the number of likes of several influencers over time and I want to plot it to see if there is a trend. Some influencers post several times on the same date, and then overall several ...
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What smoothing parameter makes sense for a LOESS calibration curve?

I am creating a calibration curve to asses the fit of a logistic regression. Does it make more sense to use the local or global optimum smoothing parameter for the LOESS line? The orange line uses ...
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How to Assess Linearity assumption of logit in logistic regression

In Applied Lineare Regression, (Hosmer, Lemeshow, Sturdivant 3rd ed.) Ch. 4, they present "Purposeful Selection." Part of step 5 is to assess the validity of the linearity assumption of the logit vs ...
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How is slope calculated in a calibration plot?

I am using logistic regression with white cell count and temperature as predictors and hospital admission>3 days as the outcome of interest. I'm using the rms package in R to assess calibration (curve ...
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Why so much difference in SE areas in these graphs

I am using ggplot with Python for showing regression/correlation. With method='lm' (means "linear model"), I get following graph: And with method='loess', I get following: The width of SE area is ...
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Should I use a confidence interval or a prediction interval around the LOESS fitted curve?

A Freakonometrics blog post shows how to use a LOESS regression of the residuals of a logistic model on the predicted values of the logistic model to assess the linearity of the predictors used in the ...
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What is the difference between Local Linear Regression (LLR) and Locally Estimated Scatterplot Smoothing (LOESS)?

I've looked into nonparametric regression packages in R and Python and came across two estimation methods that are relevant for my problem (i.e. replicating the semiparametric estimation in Carneiro, ...
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Loess preprocessing for robust quadratic regression?

It seems to me that we could fit a quadratic curve to the predictions of a LOESS curve in order to obtain a parametric model approximating the non-parametric LOESS model. Is this something that is ...
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What is represented by the y-axis in a loess smoothing curve?

I'm working on the Titanic data and I plotted local regression curves for a couple predictors. ...
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2 answers
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Rules of thumb for partial residual (component + residual) plots as diagnostics for linearity?

Here are the standard R diagnostic plots of a multiple linear regression model that includes an autoregressive term at lag-1 (i.e. AR(1)). I have logged & z-scored my input data. Ben Bolker says ...
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How to detect an increase in a loess model fitted value at the end

Sorry if the question is trivial, but I'm not finding a proper idea for this issue. I'd like to find if a series of fitted value of a loess is increasing in the end. I'm working with some data like ...
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Decomposing a time series with some zero values

There are many techniques to decompose a time series into trend, seasonal, and remainder components. I was wondering if these techniques can be applied without worry to time series which have some ...
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Machine Learning to filter similar loess curves from a large dataset

I think I have a good dataset to apply machine learning. I have a bunch of scatter plots and I generated a loess for each. Each loess is stored as a series of Y coordinates that correspond to X ...
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1 answer
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Difficulty plotting a loess smooth: what causes these problems?

I'm very new at this and I don't actually understand the differences between the plotting methods, but loess seems to be giving me the most informative graphs, ...
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STL function in R: is it possible to make it backward looking?

I would like to smooth modeling input time series data using the stl function in R. Based on my understanding, the function performs a local regression using data ...
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Why does stl() decomposition require integer frequency?

I need to decompose and forecast weekly series with around 10 years of data. In this data leap years play an important role so I need the have non-integer frequency, frequency = (365.25/7) By reading ...
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2 answers
496 views

Expectation of Median of Absolute Random Variables

Let $X_1, X_2,..., X_n$ from $N(0,\sigma^2)$. What I want to get is not $E(median(|X|))$ , but $E(median(|X_1|,|X_2|,...,|X_n|))$ Reason why I need it is because I'm studying LOWESS, and in using ...
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Seasonal weights of a time series: stl function versus subtracting average values

Say that we are dealing with a time series of 25 years of daily measurements of a variable with seasonal cycles, e.g. it reaches higher values in winter than in summer. The objective is to compute ...
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How are the local polynomials in LOESS regression joined to make a smooth curve?

I am trying to understand LOESS regression. I've read the Wikipedia and Wolfram articles on it, and the R help for the loess() function. I think I understand now how the local polynomials are derived,...
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2 votes
0 answers
393 views

goodness of fit / likelihood of data given model for loess

Question How can I compute the likelihood of a set of data points given a LOESS fit (of the same and/or other data points)? Alternative Are there other (better?) ways to assess how 'good' a LOESS ...
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Could the equation of the curve provided by LOESS be obtained?

Considering the LOESS fitting done with the following R code: ...
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Use loess regression with many zero values

I have measuments of vegetation coverage on Y plotted against surface height (and hence flooding frequency) on X. The vegetation often has two herb layers, which are estimated seperately. If only one ...
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Should I capitalize GAM, MARS and LOESS?

I have to mention GAM (generalized additive models), MARS (multivariate adaptive regression splines), and LOESS in an academic paper. I think GAM and MARS are capitalized in most cases, but not sure ...
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Explanation of what Nate Silver said about loess

In a question I asked recently, I was told that it was a big "no-no" to extrapolate with loess. But, in Nate Silver's most recent article on FiveThirtyEight.com he discussed using loess for making ...
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Prediction Interval for Loess Forecast

I have consulted this question on the basis of prediction intervals for loess (How to calculate prediction intervals for LOESS?). However, I am unaware of whether it is proper or possible to use ...
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Confidence interval of first derivative of a loess smooth

I have plotted the evolution of parameter vs time. I would however prefer to plot the change in these parameter vs time. Here I have seen how to predict a confidence interval for a loess smooth and ...
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Weights in Loess

I have a good working knowledge of how the loess model works but am curious as to how weights work in conjunction with the model. Obviously, this method weights locally, but many statistical packages (...
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4 votes
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How does a LOESS model do its prediction?

I understand the theory behind LOESS, but how does it do prediction without coefficients? I'd like to use LOESS prediction, but need to be able to explain it.
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4 votes
2 answers
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Fitting a smoothed curve to a noisy data

I have a variable with sales data over time. It is very noisy at a disaggregate level but if you look at it as a whole, you can see a smoothing curve that follows a polynomial pattern. Is there a way ...
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Unexpected low values in LOESS regression

The following plot is generated using the scatterplot function from R's car package. The ...
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4 answers
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Non-linear terms in logistic regression?

I have a data set with binary response and several numerical and categorical predictors. I'm looking for ways to test if some of the numerical predictors are non-linear. I've read about lowess and ...
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1 answer
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Behaviour of neighbouring points in LOESS smoothing when data is not uniformly distributed

The purpose of loess is to create an 'average' value of the response for any $x$, by using points in the region of $x$ to create a local regression line. From what I understand, picking the ...
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Locally weighted regression vs. splines

What's the pros/cons of splines approaches compared to locally weighted regression approaches for the purposes of (a) scatter plot smoothing and (b) prediction? Obviously, in the case of prediction I ...
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3 votes
1 answer
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Calculating bias and variance in a LOESS fit

I have datasets that can form several different curvy patterns between the dependent and independent variables. The 'true' relationship likely depends on a large number of factors that aren't easily ...
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15 votes
1 answer
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GAM vs LOESS vs splines

Context: I want to draw a line in a scatterplot that doesn't appear parametric, therefore I am using geom_smooth() in ggplot in <...
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6 votes
0 answers
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When to choose GAM over LOESS? [duplicate]

I am smoothing the relationship between a binary variable y and a continuous variable x. For this I've looked into both GAM and ...
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1 answer
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Time series LOESS prediction performs better on aggregate data?

I'm using the stl function in R (https://stat.ethz.ch/R-manual/R-devel/library/stats/html/stl.html) that uses loess to decompose time series and make predictions. When the time unit is one month, the ...
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4 votes
0 answers
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Robustness Weights in LOESS behaving strangely

I've been playing around with writing my own LOESS module in Python (2 reasons: first, I wanted the practice, and second, the implementation in statsmodels doesn't ...
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2 votes
1 answer
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LOESS for subset of data

I want to analyze the performance of two classifiers. For that I have a dataset with 30000 observations that each have an independent variable interactions and a ...
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