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Marco Zanotti
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Jan 9, 2024
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,84 @@ | ||
| # function to extract significance stars | ||
| extract_significance <- function(p_value) { | ||
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| res <- dplyr::case_when( | ||
| p_value < 0.001 ~ "***", | ||
| p_value >= 0.001 & p_value < 0.01 ~ "**", | ||
| p_value >= 0.01 & p_value < 0.05 ~ "*", | ||
| p_value >= 0.05 & p_value < 0.1 ~ ".", | ||
| TRUE ~ "" | ||
| ) | ||
| return(res) | ||
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| } | ||
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| # function to perform hypothesis testing | ||
| compute_hptests <- function(data, digits = 4) { | ||
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| # urca::ur.kpss() type = c("mu", "tau"), use.lag = NULL | ||
| # urca::ur.pp() type = c("Z-alpha", "Z-tau"), model = c("constant", "trend"), use.lag = NULL | ||
| # urca::ur.df() type = c("none", "drift", "trend"), lags = 1 | ||
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| x <- data$value | ||
| k <- trunc((length(x) - 1) ^ (1 / 3)) | ||
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| res <- tibble::tibble( | ||
| "Type" = c(rep("normality", 2), rep("autocorrelation", 2), rep("stationarity", 3)), | ||
| "Test" = c( | ||
| "Jarque-Bera", "Shapiro-Wilk", | ||
| "Box-Pierce", "Ljung-Box", | ||
| "Augmented Dickey-Fuller", "Phillips-Perron", "KPSS" | ||
| ), | ||
| "H0" = c( | ||
| "Normality", "Normality", | ||
| "No autocorrelation", "No autocorrelation", | ||
| "No stationarity", "No stationarity", "Stationarity" | ||
| ), | ||
| "Statistic" = rep(0, 7), | ||
| "P_value" = rep(0, 7), | ||
| "Signif" = rep("", 7) | ||
| ) | ||
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| # normality tests | ||
| tmp <- tseries::jarque.bera.test(x) | ||
| res[1, 4] <- unname(tmp$statistic) | ||
| res[1, 5] <- tmp$p.value | ||
| tmp <- stats::shapiro.test(x) | ||
| res[2, 4] <- unname(tmp$statistic) | ||
| res[2, 5] <- tmp$p.value | ||
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| # autocorrelation tests | ||
| tmp <- stats::Box.test(x, type = "Box-Pierce", lag = k) | ||
| res[3, 4] <- unname(tmp$statistic) | ||
| res[3, 5] <- tmp$p.value | ||
| tmp <- stats::Box.test(x, type = "Ljung-Box", lag = k) | ||
| res[4, 4] <- unname(tmp$statistic) | ||
| res[4, 5] <- tmp$p.value | ||
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| # stationarity | ||
| tmp <- suppressWarnings(tseries::adf.test(x, k = k)) | ||
| res[5, 4] <- unname(tmp$statistic) | ||
| res[5, 5] <- tmp$p.value | ||
| tmp <- suppressWarnings(tseries::pp.test(x)) | ||
| res[6, 4] <- unname(tmp$statistic) | ||
| res[6, 5] <- tmp$p.value | ||
| tmp <- suppressWarnings(tseries::kpss.test(x)) | ||
| res[7, 4] <- unname(tmp$statistic) | ||
| res[7, 5] <- tmp$p.value | ||
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| res <- res |> | ||
| dplyr::mutate(Signif = extract_significance(P_value)) |> | ||
| dplyr::mutate( | ||
| Statistic = round(Statistic, digits), | ||
| P_value = round(P_value, digits) | ||
| ) |> | ||
| # dplyr::mutate( | ||
| # result = paste0( | ||
| # round(statistic, digits), " ", signif, " \n", "(", round(p_value, digits), ")" | ||
| # ) | ||
| # ) |> | ||
| dplyr::select(-Type) | ||
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| return(res) | ||
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| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,97 @@ | ||
| # function to impute missing values | ||
| impute_data <- function(data, params, freq) { | ||
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| if (params$impute == FALSE) { | ||
| return(data) | ||
| } else { | ||
| n2f <- trunc(nrow(data) / freq) | ||
| p <- ifelse(n2f < 1, 1, 2) | ||
| data_impute <- data |> mutate(value = ts_impute_vec(value, period = p, lambda = "auto")) | ||
| return(data_impute) | ||
| } | ||
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| } | ||
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| # function to transform data | ||
| transform_data <- function(data, section, params, freq) { | ||
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| if (section == "viz_transf") { | ||
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| trf_prm <- getOption("tsf.dashboard.transfs") | ||
| if (!all(trf_prm %in% names(params))) { | ||
| stop(paste("Unknown transformations!")) | ||
| } | ||
| transf_params <- c( | ||
| params$log, params$boxcox, params$norm, | ||
| params$stand, params$diff, params$sdiff | ||
| ) |> as.logical() | ||
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| if (all(!transf_params)) { | ||
| return(data) | ||
| } else { | ||
| data_transf <- data | ||
| if (params$log) { # Log | ||
| data_transf <- data_transf |> mutate(value = log1p(value)) | ||
| } | ||
| if (params$boxcox) { # Box-Cox | ||
| data_transf <- data_transf |> mutate(value = box_cox_vec(value + 1, lambda = "auto")) | ||
| } | ||
| if (params$norm) { # Normalization | ||
| data_transf <- data_transf |> mutate(value = normalize_vec(value)) | ||
| } | ||
| if (params$stand) { # Standardization | ||
| data_transf <- data_transf |> mutate(value = standardize_vec(value)) | ||
| } | ||
| if (params$diff) { # Differencing | ||
| data_transf <- data_transf |> mutate(value = diff_vec(value, difference = 1)) |> drop_na() | ||
| } | ||
| if (params$sdiff) { # Seasonal differencing | ||
| data_transf <- data_transf |> mutate(value = diff_vec(value, difference = 1, lag = freq)) |> drop_na() | ||
| } | ||
| return(data_transf) | ||
| } | ||
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| } else if (section == "test_hp") { | ||
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| trf_prm <- getOption("tsf.dashboard.test_transfs") | ||
| if (!all(trf_prm %in% names(params))) { | ||
| stop(paste("Unknown transformations!")) | ||
| } | ||
| transf_params <- c( | ||
| params$test_log, params$test_diff, params$test_sdiff | ||
| ) |> as.logical() | ||
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| if (all(!transf_params)) { | ||
| return(data) | ||
| } else { | ||
| data_transf <- data | ||
| if (params$test_log) { # Log | ||
| data_transf <- data_transf |> mutate(value = log1p(value)) | ||
| } | ||
| if (params$test_diff) { # Differencing | ||
| data_transf <- data_transf |> mutate(value = diff_vec(value, difference = 1)) |> drop_na() | ||
| } | ||
| if (params$test_sdiff) { # Seasonal differencing | ||
| data_transf <- data_transf |> mutate(value = diff_vec(value, difference = 1, lag = freq)) |> drop_na() | ||
| } | ||
| return(data_transf) | ||
| } | ||
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| } else { | ||
| stop(paste("Unknown section", section)) | ||
| } | ||
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| } | ||
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| # function to clean data from anomalies | ||
| clean_data <- function(data, params) { | ||
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| if (params$clean == FALSE) { | ||
| return(data) | ||
| } else { | ||
| data_clean <- data |> mutate(value = ts_clean_vec(value)) | ||
| return(data_clean) | ||
| } | ||
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| } | ||
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