Publications by Tarid Wongvorachan
Polytomous IRT_Practice
In this practice, we will be using the science data set, which is a survey of the Consumer Protection and Perceptions of Science and Technology (1992). The data has seven items in total, Comfort: Science and technology are making our lives healthier, easier and more comfortable. Environment: Scientific and technological research cannot play an ...
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Correlation and Regression with Tidydata
Learning objective: Analyze the results of correlation tests and simple regression models for many data sets at once. While the broom package in tidymodel is useful for summarizing the result of a single analysis in a consistent format, it is really designed for high-throughput applications, where you must combine results from multiple analyses. ...
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Tidymodel_Model Evaluation
setwd("D:/Class Materials & Work/Summer 2020 practice/3_Tidymodel_Model evaluation") getwd() ## [1] "D:/Class Materials & Work/Summer 2020 practice/3_Tidymodel_Model evaluation" library(tidymodels) # for the rsample package, along with the rest of tidymodels ## -- Attaching packages ---------------------------------------------------------------...
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Unidimensional EFA_Practice
Exploratory Factor Analysis is a statistical technique in social science to explain the variance between several measured variables as a smaller set of latent variables. EFA is often used to consolidate survey data by revealing the groupings (factors) that underly individual questions. An EFA provides information on each item’s relationship to ...
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Chi Square Test of Independence
setwd("D:/Class Materials & Work/Summer 2020 practice/Chi Square Test of Independence") getwd() ## [1] "D:/Class Materials & Work/Summer 2020 practice/Chi Square Test of Independence" #Hypothesis #Null:the variables are independent, there is no relationship between the two categorical variables. Knowing the value of one variable does not help t...
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Tidymodel_Preprocessing
setwd("D:/Class Materials & Work/Summer 2020 practice/2_Tidymodel_Preprocess your data") getwd() ## [1] "D:/Class Materials & Work/Summer 2020 practice/2_Tidymodel_Preprocess your data" #Loading packages require(tidymodels) ## Loading required package: tidymodels ## -- Attaching packages ---------------------------------------------------------...
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Tidymodel_A predictive model case study
setwd("D:/Class Materials & Work/Summer 2020 practice/5_Tidymodel_Case study") getwd() ## [1] "D:/Class Materials & Work/Summer 2020 practice/5_Tidymodel_Case study" In this final case study, we will use all of the previous practices, tidymodel 1-4, as a foundation to build a predictive model from beginning to end with data on hotel stays. We ha...
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Hypothesis Testing using Resampling and Tidy data
Learning objective: Perform common hypothesis tests for statistical inference using flexible functions. The tidymodel package infer is able to perform statistical inference that coheres with the tidyverse framework. The package is centered around 4 main verbs, supplemented with many utilities to visualize and extract value from their outputs. Reg...
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Tidymodel_Parameter Tuning
setwd("D:/Class Materials & Work/Summer 2020 practice/4_Tidymodel_Parameter tuning") getwd() ## [1] "D:/Class Materials & Work/Summer 2020 practice/4_Tidymodel_Parameter tuning" In building a model, hyperparameters such as the number of predictors that are sampled at splits in a tree-based model (mtry) or the learning rate in a boosted tree mode...
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Tidymodel_Model building
setwd("D:/Class Materials & Work/Summer 2020 practice/1_Tidymodel_Model_building") We will specify and train models Loading required packages library(tidymodels) # for the parsnip package, along with the rest of tidymodels ## -- Attaching packages -------------------------------------------------------------------------------- tidymodels 0.1.0 ...
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