Publications by H. K. Tseng
Statistical Learning: Week 4
Fitting a linear model A description of variable names and measures can be seen here install.packages("ISLR") ## Installing package into 'C:/Users/hktse/Documents/R/win-library/3.6' ## (as 'lib' is unspecified) ## ## There is a binary version available but the source version is later: ## binary source needs_compilation ## ISLR 1.2 ...
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Statistical Learning: Week 3
Installing R and RStudio R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. RStudio is an integrated development environment for R, a programming language for statistical computing and graphics. You need to download base R in order to run RS...
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Statistical Learning - Week 16
U.S. Armament Cooperation Network at a glance: 1992-2017 Structural Topic Models (STM) A gentle introduction using international armament cooperation (IAC) data # load required packages and data library(stm) ## stm v1.3.6 successfully loaded. See ?stm for help. ## Papers, resources, and other materials at structuraltopicmodel.com data_sub <- r...
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Statistical Learning - Week 13
Stepwise Variable Selection # load required packages and data (Carseats data) library(olsrr) ## ## Attaching package: 'olsrr' ## The following object is masked from 'package:datasets': ## ## rivers library(ISLR) library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ...
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Statistical Learning - Week 11
## load required packages and data library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(ggplot2) library(tidyr) library(caret) ## Loading required package: lat...
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Support Hyperplane (SHP)
Understanding separating hyperplane (or support hyperplane) # generate random data # random seed set.seed(123) #set n = 500 data points. n <- 500 #Generate data frame with two uniformly distributed predictors lying between 0 and 1. df <- data.frame(x1 = runif(n), x2 = runif(n)) # create a variable y whose value is -...
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Note: LASSO, Ridge, and Penalty explained
LASSO vs. Ridge LASSO and Ridge regression are regression methods that perform \(variable\) \(selection\) and \(regularization\) to enhance the prediction accuracy and interpretability of the statistical model. In short, they do two things Variable selection: identify important variables in the data that explain major variation in the outcome v...
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Statistical Learning: Week-7
Ridge and LASSO regressions Fit ridge and LASSO regressions, interpret coefficients and visualize their variation across the range of \(\lambda\). # load required packages install.packages("glmnet") ## Installing package into 'C:/Users/hktse/Documents/R/win-library/3.6' ## (as 'lib' is unspecified) ## ## There is a binary version available ...
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Statistical Learning: Week 5
Binary outcome as a generalization of linear regression model with limited dependent variables Estimating a model of binary outcome using lm() function library(ISLR) data(Default) names(Default) ## [1] "default" "student" "balance" "income" ## convert the outcome variable "default" to numeric: Yes = 1, No = 0 Default$default <- ifelse(Default$...
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Statistical Learning: Week 3
Fitting a linear model A description of variable names and measures can be seen here install.packages("ISLR") ## Installing package into 'C:/Users/hktse/Documents/R/win-library/3.6' ## (as 'lib' is unspecified) ## ## There is a binary version available but the source version is later: ## binary source needs_compilation ## ISLR 1.2 ...
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