{"id":3430,"date":"2024-03-31T10:29:36","date_gmt":"2024-03-31T01:29:36","guid":{"rendered":"https:\/\/plaza.umin.ac.jp\/~OIO\/?p=3430"},"modified":"2024-04-01T07:53:36","modified_gmt":"2024-03-31T22:53:36","slug":"r-excess-mortality","status":"publish","type":"post","link":"https:\/\/plaza.umin.ac.jp\/~OIO\/?p=3430","title":{"rendered":"R excess mortality"},"content":{"rendered":"

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library(excessmort)\r\nlibrary(dplyr)\r\nlibrary(lubridate)\r\nlibrary(ggplot2)\r\n\r\n\r\n\r\n# A list with six ggplot objects: \u2018population\u2018 is a time series plot of the population. \u2018seasonal\u2018 is a\r\n# plot showing the estimated seasonal effect. \u2018trend\u2018 is a time series plot showing the estimated trend.\r\n# \u2018weekday\u2018 is a plot of the estimated weekday effects if they were estimated. \u2018expected\u2018is a time\r\n# series plot of the expected values. \u2018residual\u2018 is a time series plot of the residuals.\r\n\r\nflu_season <- seq(make_date(2017, 12, 16), make_date(2018, 1, 16), by = \"day\")\r\nexclude_dates <- c(flu_season, seq(make_date(2020, 1, 1), today(), by = \"day\"))\r\nres <- cdc_state_counts %>%\r\n  filter(state == \"Maryland\") %>%\r\n  compute_expected(exclude = exclude_dates,\r\n                   keep.components = TRUE)\r\n\r\np <- expected_diagnostic(expected = res, alpha = 0.50)\r\np$population\r\np$seasonal\r\np$trend\r\np$expected\r\np$residual\r\n<\/pre>\n
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  1. \u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u8aad\u307f\u8fbc\u307f<\/strong>:\n