Tag: regression

Feed: R-bloggers. Author: Method Matters. [This article was first published on Method Matters Blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. In this post, we will work through a simple example of Bayesian regression analysis with the rstanarm package in R. I’ve been reading Gelman, Hill and Vehtari’s recent book “Regression and Other Stories”, and this blog post is my attempt to apply some of the things I’ve learned. I’ve been absorbing ... Read More

Feed: R-bloggers. Author: Ronan Harrington. Introduction In this post, the BEA Infrastructure Investment data set from the #TidyTuesday project is used to illustrate variable transformation and the exploreR::masslm() function. The variable for gross infrastructure investment adjusted for inflation is transformed to make it less skewed. Using these transformed investment values, multiple linear models are then created to quickly see which variables in the data set have the largest impact on infrastructure investment. Setup Loading the R libraries and data set. # Loading libraries library(tidyverse) library(tidytuesdayR) library(exploreR) # Loading data tt <- tt_load("2021-08-10") Downloading file 1 of 3: `ipd.csv` Downloading file ... Read More

Feed: SAS Blogs. Author: Rick Wicklin. One of the benefits of using the SWEEP operator is that it enables you to "sweep in" columns (add effects to a model) in any order. This article shows that if you use the SWEEP operator, you can compute a SSCP matrix and use it repeatedly to estimate any linear regression model that uses those effects. This post relates to a previous post, in which I showed that you never need to use a permutation matrix to rearrange the order of rows and columns of a matrix. As an application, ... Read More

Feed: SAS Blogs. Author: Guohui Wu. In a previous post, I discussed spatial econometric modeling and how spatial regression models can be used to account for various types of spatial dependence in the data. In many data analyses, we often face the issue of not knowing the true model from which our data is generated. To resolve this model uncertainty issue, general practice is to perform model selection. This is done by fitting a set of candidate models and comparing their fit statistics using some criteria such as Akaike information criterion (AIC) and Schwarz information criterion (SBC). Among those candidate ... Read More

Feed: R-bloggers. Author: [R]eliability. In this post we will try to trick linear regression into thinking that a redundant variable is statistically significant. By redundant, I mean a candidate predictor variable that in reality is just noise (no effect on the outcome) but that we might include in an experiment because we don’t know if it is important or not. The trick is that we can set up the data generating process such that a redundant variable is highly correlated with the response. In such cases, the naive intuition is that the model would get “confused” about the importance of ... Read More

Feed: R-bloggers. Author: kjytay. TLDR: Pass the output of the isoreg function to as.stepfun to make an isotonic regression model into a black box object that takes in uncalibrated predictions and outputs calibrated ones. Isotonic regression is a method for obtaining a monotonic fit for 1-dimensional data. Let’s say we have data such that . (We assume no ties among the ‘s for simplicity.) Informally, isotonic regression looks for such that the ‘s approximate the ‘s well while being monotonically non-decreasing. (See this previous post for more technical details.) Isotonic regression can be performed easily in R with the stats ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. This article is part of a new series featuring problems with solution, to help you hone your machine learning and pattern recognition skills. Try to solve this problem by yourself first, before looking at the solution. Today's problem also has an intriguing mathematical appeal and solution: this allows you to check if your solution found using machine learning techniques, is correct or not. The level is for beginners.  The problem is as follows. Let X1, X2, X3 and so on be a sequence recursively defined by Xn+1 = Stdev(X1, ... Read More

Feed: R-bloggers. Author: finnstats. [This article was first published on Methods – finnstats, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Heteroscedasticity in Regression, one of the easiest ways to measure heteroscedasticity is while using the Breusch-Pagan Test. The test is mainly used to identify if heteroscedasticity is present in a regression analysis. This tutorial explains how to execute a Breusch-Pagan Test in R. pipe operator in R-Simplify Your Code with %>% » ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Aleksandras Sulzenko. Whenever we begin dealing with machine learning, we often turn to the simpler classification models. In fact, people outside of this sphere have mostly seen those models at work. After all, image recognition has become the poster child of machine learning. However, classification, while powerful, is limited. There are lots of tasks we would like to automate that are impossible to do on classification. A great example of that would be picking out some best candidates (according to historical data) from a set.   We are intending to implement ... Read More

Feed: R-bloggers. Author: R-bloggers &#124; A Random Walk. Tha aim of this post is to provide a working approach to perform piecewise constant or step function regression in Stan. To set up the regression problem, consider noisy observations (y_1, ldots, y_n in mathbb{R}) sampled from a standard signal plus i.i.d. Gaussian noise model of the form: [ begin{aligned} y_i & = f(x_i) + epsilon_i, quad i = 1,ldots, n \ epsilon_i & overset{text{iid}}{sim} N(0, sigma^2) end{aligned} ] with the independent variables (x_1,ldots, x_n in (0, 1]) assumed to be observed at regular (e.g. time) intervals. The function (f: (0,1] to mathbb{R}) ... Read More