#' Data Simulation #' #' Simulate data with specific characteristics. #' #' @param n The number of observations to be generated. #' @param r A value or vector corresponding to the desired correlation coefficients. #' @param d A value or vector corresponding to the desired difference between the groups. #' @param mean A value or vector corresponding to the mean of the variables. #' @param sd A value or vector corresponding to the SD of the variables. #' @param names A character vector of desired variable names. #' @param ... Arguments passed to or from other methods. #' @examples #' #' # Correlation -------------------------------- #' data <- simulate_correlation(r = 0.5) #' plot(data\$V1, data\$V2) #' cor.test(data\$V1, data\$V2) #' summary(lm(V2 ~ V1, data = data)) #' #' # Specify mean and SD #' data <- simulate_correlation(r = 0.5, n = 50, mean = c(0, 1), sd = c(0.7, 1.7)) #' cor.test(data\$V1, data\$V2) #' round(c(mean(data\$V1), sd(data\$V1)), 1) #' round(c(mean(data\$V2), sd(data\$V2)), 1) #' summary(lm(V2 ~ V1, data = data)) #' #' # Generate multiple variables #' cor_matrix <- matrix(c( #' 1.0, 0.2, 0.4, #' 0.2, 1.0, 0.3, #' 0.4, 0.3, 1.0 #' ), #' nrow = 3 #' ) #' #' data <- simulate_correlation(r = cor_matrix, names = c("y", "x1", "x2")) #' cor(data) #' summary(lm(y ~ x1, data = data)) #' #' # t-test -------------------------------- #' data <- simulate_ttest(n = 30, d = 0.3) #' plot(data\$V1, data\$V0) #' round(c(mean(data\$V1), sd(data\$V1)), 1) #' diff(t.test(data\$V1 ~ data\$V0)\$estimate) #' summary(lm(V1 ~ V0, data = data)) #' summary(glm(V0 ~ V1, data = data, family = "binomial")) #' @export simulate_correlation <- function(n = 100, r = 0.5, mean = 0, sd = 1, names = NULL, ...) { if (!requireNamespace("MASS", quietly = TRUE)) { stop("Package 'MASS' required for this function to work. Please install it by running `install.packages('MASS')`.") } # Define matrix if (is.matrix(r)) { if (isSymmetric(r)) { if (any(r > 1)) { stop("'r' should only contain values between -1 and 1.") } else { sigma <- r } } else { stop("'r' should be a symetric matrix (relative to the diagonal).") } } else if (length(r) == 1) { if (abs(r) > 1) { stop("'r' should only contain values between -1 and 1.") } else { sigma <- matrix(c(1, r, r, 1), nrow = 2) } } else { stop("'r' should be a value (e.g., r = 0.5) or a square matrix.") } # Get data data <- MASS::mvrnorm( n = n, mu = rep_len(0, ncol(sigma)), # Means of variables Sigma = sigma, empirical = TRUE ) # Adjust scale if (any(sd != 1)) { data <- t(t(data) * rep_len(sd, ncol(sigma))) } # Adjust mean if (any(mean != 0)) { data <- t(t(data) + rep_len(mean, ncol(sigma))) } data <- as.data.frame(data) # Rename if (!is.null(names)) { if (length(names) == ncol(data)) { names(data) <- names } } data } #' @rdname simulate_correlation #' @export simulate_ttest <- function(n = 100, d = 0.5, names = NULL, ...) { x <- distribution_normal(n, 0, 1) # Continuous variables z <- 0 + d * x # Linear combination pr <- 1 / (1 + exp(-z)) # Pass it through an inverse logit function y <- distribution_binomial(n, 1, pr, random = 3) # Bernoulli response variable data <- data.frame(y = as.factor(y), x = x) names(data) <- paste0("V", 0:(ncol(data) - 1)) if (!is.null(names)) { if (length(names) == ncol(data)) { names(data) <- names } } data }