This is the one-time, user-operated setup that lets "Sign in with Google" work end-to-end (Decision #12: the AI never creates cloud resources). You do not need this to build or run the test suite — tests use a self-signed key and mocked Google endpoints. You need it only for a live browser demo.
Result you're after: a Client ID and Client secret for a new OAuth client, pasted into the right secrets files.
| # Code below demonstrates similar/same results from SEMinR and cSEM for given model | |
| # Needed changing default value of .disattenuate for cSEM | |
| # Response to: https://www.reddit.com/r/rstats/comments/1fy42vy/structural_equation_model_results_differ_when/ | |
| library(seminr) | |
| library(cSEM) | |
| nck <- corp_rep_data | |
| data <- MASS::mvrnorm(1000,mu = rep(0,37),Sigma =diag(1,37)) | |
| data <- cbind(mobi, corp_rep_data[1:250,3:17]) | |
| colnames(data) <- c(multi_items("A", 1:3), |
| # Unstandardizes construct scores of an estimated SEMinR model | |
| # (Based on rescale function of plspm) | |
| # usage: | |
| # my_model <- estimate_pls(...) | |
| # ustd <- unstandardize(my_model) | |
| unstandardize_scores <- function(pls_model) { | |
| construct_names <- seminr:::construct_names(pls_model$smMatrix) | |
| construct_names -> . | |
| sapply(., seminr:::items_of_construct, model=pls_model) -> . |
| library(seminr) | |
| comp1_items <- multi_items("IMAG", 1:5) | |
| comp2_items <- multi_items("CUEX", 1:3) | |
| comp3_items <- multi_items("CUSA", 1:3) | |
| mobi_mm <- constructs( | |
| # Three first-order composites | |
| composite("Comp1", comp1_items), | |
| composite("Comp2", comp2_items), |
| library(seminr) | |
| image_items <- multi_items("IMAG", 1:5) | |
| exp_items <- multi_items("CUEX", 1:3) | |
| items <- c(image_items, exp_items) | |
| almost_items <- items[-1] | |
| eca_mm <- constructs( |
| # plot_regr_rsq(points) plots points and regression | |
| # | |
| # You can supply the following parameter: | |
| # points - dataframe of x,y points to plot | |
| plot_regr_rsq <- function(points) { | |
| if (nrow(points) == 0) { | |
| plot(NA, xlim=c(-5,50), ylim=c(-5,50), xlab="x", ylab="y") | |
| return() | |
| } | |
| plot(points, xlim=c(-5,50), ylim=c(-5,50), pch=19, cex=2, col="gray") |
| # OOP | |
| module Scrambler | |
| def scramble | |
| json_output = to_json | |
| json_output.chars.sample(json_output.length).join | |
| end | |
| end | |
| class Student | |
| include Scrambler |
| docs <- c("OECD", "GDPR", "FTC") | |
| # Functions to create: | |
| pdf_to_df <- function(doc_name) { | |
| filename <- paste(doc_name, ".txt") | |
| #...return a dataframe | |
| data.frame() | |
| } | |
| create_bigrams <- function(doc_df) { data.frame() } |
| base_url = "http://api.githbub.com/v1" | |
| def commits_history | |
| repo_username = "leoli0320" | |
| repo_name = "my_fav_repo" | |
| api_endpoint = [base_url, repo_username, repo_name, 'commits'].join('/') | |
| req_params = {} |
| # Visualize the confidence intervals of samples drawn from a population | |
| # e.g., | |
| # visualize_sample_ci(sample_size=300, distr_func=rnorm, mean=50, sd=10) | |
| # visualize_sample_ci(sample_size=300, distr_func=runif, min=17, max=35) | |
| visualize_sample_ci <- function(num_samples = 100, sample_size = 100, | |
| pop_size=10000, distr_func=rnorm, ...) { | |
| # Simulate a large population | |
| population_data <- distr_func(pop_size, ...) | |
| pop_mean <- mean(population_data) | |
| pop_sd <- sd(population_data) |