Topic: psych Goto Github
Some thing interesting about psych
Some thing interesting about psych
psych,The goal of psymetrics is to provide tools for extracting and visualizing psychometric model fit indices. It is compatible with models created using packages like lavaan, psych, and mirt.
User: brianmsm
psych,A Psych searcher
User: daih27
Home Page: https://daih27.github.io/psychphinder
psych,Repository contains source codes of a shiny web application that can be used to generate block randomized trials.
User: depthgr8
Home Page: https://depthgr8.wordpress.com/2017/03/30/block-randomized-trials/
psych,:necktie::blue_car::nose: Easily style YAML files using Psych, like Sequence/Mapping Flow style.
User: esotericpig
psych,Quid. The weird quiz game. Sometimes similar to Psych. /kwÉĒd/ means `what?`
User: littledivy
psych,A quick reference for how to run many models in R.
User: m-clark
Home Page: https://m-clark.github.io/R-models/
psych,Github repo for Nearley.JS Generator to create a random Psych Episode WIP
User: patrickmurphy
psych,A libyaml wrapper for Ruby
Organization: ruby
psych,My role in this group project was to perform regression analysis on quarterly financial data to predict a company's market capitalization. I used R to develop ordinary least squares (OLS), stepwise, ridge, lasso, relaxed lasso, and elastic net regression models. I first used stepwise and OLS regression to develop a model and examine its residual plots. The plot displaying the residuals against the predicted values indicated multiplicative errors. I, therefore, took the natural log transformation of the dependent variable. The resulting model's R2 was significantly, negatively impacted. After examining scatter plots between the log transformation of market capitalization and the independent variables, I discovered the independent variables also had to be transformed to produce a linear relationship. Using the log transformation of both the dependent and independent variables, I developed models using all the regression techniques mentioned to strike a balance between R2 and producing a parsimonious model. All the models produced similar results, with an R2 of around .80. Since OLS is easiest to explain, had similar residual plots, and the highest R2 of all the models, it was the best model developed.
User: tboudart
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