[英]How to get years from a time series when the plot is displaying time series of a monthly freq (Rbeast )
我想從時間序列索引中提取年份（基礎時間序列是每月頻率）。 我想這樣做的原因是創建一個年度軸。
我正在使用 package Rbeast。
這是我對 tsp(NDVI_Chobe001.ts) [1] 2002.500 2020.417 12.000 的結果
##這里有更詳細的時間序列
time(NDVI_Chobe001.ts)
Jan Feb Mar Apr May Jun Jul Aug Sep
2002 2002.500 2002.583 2002.667
2003 2003.000 2003.083 2003.167 2003.250 2003.333 2003.417 2003.500 2003.583 2003.667
2004 2004.000 2004.083 2004.167 2004.250 2004.333 2004.417 2004.500 2004.583 2004.667
2005 2005.000 2005.083 2005.167 2005.250 2005.333 2005.417 2005.500 2005.583 2005.667
2006 2006.000 2006.083 2006.167 2006.250 2006.333 2006.417 2006.500 2006.583 2006.667
2007 2007.000 2007.083 2007.167 2007.250 2007.333 2007.417 2007.500 2007.583 2007.667
2008 2008.000 2008.083 2008.167 2008.250 2008.333 2008.417 2008.500 2008.583 2008.667
2009 2009.000 2009.083 2009.167 2009.250 2009.333 2009.417 2009.500 2009.583 2009.667
2010 2010.000 2010.083 2010.167 2010.250 2010.333 2010.417 2010.500 2010.583 2010.667
2011 2011.000 2011.083 2011.167 2011.250 2011.333 2011.417 2011.500 2011.583 2011.667
2012 2012.000 2012.083 2012.167 2012.250 2012.333 2012.417 2012.500 2012.583 2012.667
2013 2013.000 2013.083 2013.167 2013.250 2013.333 2013.417 2013.500 2013.583 2013.667
2014 2014.000 2014.083 2014.167 2014.250 2014.333 2014.417 2014.500 2014.583 2014.667
2015 2015.000 2015.083 2015.167 2015.250 2015.333 2015.417 2015.500 2015.583 2015.667
2016 2016.000 2016.083 2016.167 2016.250 2016.333 2016.417 2016.500 2016.583 2016.667
2017 2017.000 2017.083 2017.167 2017.250 2017.333 2017.417 2017.500 2017.583 2017.667
2018 2018.000 2018.083 2018.167 2018.250 2018.333 2018.417 2018.500 2018.583 2018.667
2019 2019.000 2019.083 2019.167 2019.250 2019.333 2019.417 2019.500 2019.583 2019.667
2020 2020.000 2020.083 2020.167 2020.250 2020.333 2020.417
Oct Nov Dec
2002 2002.750 2002.833 2002.917
2003 2003.750 2003.833 2003.917
2004 2004.750 2004.833 2004.917
2005 2005.750 2005.833 2005.917
2006 2006.750 2006.833 2006.917
2007 2007.750 2007.833 2007.917
2008 2008.750 2008.833 2008.917
2009 2009.750 2009.833 2009.917
2010 2010.750 2010.833 2010.917
2011 2011.750 2011.833 2011.917
2012 2012.750 2012.833 2012.917
2013 2013.750 2013.833 2013.917
2014 2014.750 2014.833 2014.917
2015 2015.750 2015.833 2015.917
2016 2016.750 2016.833 2016.917
2017 2017.750 2017.833 2017.917
2018 2018.750 2018.833 2018.917
2019 2019.750 2019.833 2019.917
2020
##這里是dput結果這也是時間序列的dput結果
dput(NDVI_Chobe001.ts)
structure(c(0.258672185, 0.237639041, 0.223988035, 0.22397988,
0.28132144, 0.387710909, 0.556186453, 0.719580311, 0.443294248,
0.314433357, 0.292755672, 0.278023297, 0.246809774, 0.230477039,
0.228955071, 0.234966762, 0.396659718, 0.491330645, 0.716274496,
0.73417416, 0.576279481, 0.479403466, 0.423930923, 0.377550602,
0.313801774, 0.297335261, 0.27886131, 0.285054814, 0.323942137,
0.482912961, 0.544500134, 0.692623308, 0.512637643, 0.526592884,
0.432898755, 0.331892323, 0.294543398, 0.274633904, 0.247602217,
0.24889248, 0.268682448, 0.623132479, 0.732958587, 0.794572999,
0.697092229, 0.588587711, 0.389213056, 0.348485514, 0.282932917,
0.264831569, 0.254937301, 0.277647575, 0.342873991, 0.450392312,
0.631838679, 0.716638566, 0.508704542, 0.423229761, 0.324663042,
0.321186864, 0.295935102, 0.255618784, 0.257619908, 0.27254904,
0.270580191, 0.432220414, 0.585539863, 0.716581759, 0.593955107,
0.559614127, 0.388112159, 0.386833323, 0.323056858, 0.296201373,
0.299547175, 0.340499135, 0.487932489, 0.531619232, 0.755898976,
0.630418376, 0.624101053, 0.46624194, 0.407831925, 0.396696504,
0.348860597, 0.311130303, 0.301932775, 0.359536613, 0.389388896,
0.639178419, 0.693073002, 0.544238686, 0.608043068, 0.520223438,
0.489590537, 0.371915235, 0.322345492, 0.285747424, 0.262060636,
0.290601893, 0.272739968, 0.465184219, 0.597999142, 0.58280379,
0.498312536, 0.351555151, 0.313456794, 0.30176279, 0.272389062,
0.256200802, 0.257570355, 0.261360949, 0.280664516, 0.472871998,
0.635346196, 0.809469884, 0.708410897, 0.454619991, 0.374153554,
0.31216354, 0.277643235, 0.273912332, 0.279202729, 0.274954368,
0.3220379, 0.66542086, 0.786922135, 0.749038686, 0.494324261,
0.380828443, 0.31699487, 0.293321759, 0.275010923, 0.263032267,
0.254334753, 0.270928799, 0.319622211, 0.699834174, 0.733615599,
0.743950233, 0.701439492, 0.514223884, 0.414668945, 0.353937354,
0.304874948, 0.263697644, 0.25397833, 0.270116643, 0.310931558,
0.529621168, 0.769325391, 0.676328487, 0.561630209, 0.550257129,
0.408412794, 0.362071348, 0.294350827, 0.271100888, 0.26194204,
0.265314367, 0.311826075, 0.556399621, 0.586507353, 0.715697274,
0.71326184, 0.554275685, 0.423167172, 0.332214247, 0.316422341,
0.264796933, 0.253106015, 0.274547234, 0.274233387, 0.331729551,
0.568674796, 0.678751599, 0.651238604, 0.550351642, 0.50121297,
0.39598441, 0.373748125, 0.342521719, 0.308862893, 0.368914514,
0.346751371, 0.590197072, 0.550842239, 0.641805925, 0.73276961,
0.616976479, 0.501595155, 0.441702349, 0.420716154, 0.297677153,
0.302351869, 0.347572841, 0.347128221, 0.488411226, 0.739632011,
0.773688839, 0.527578039, 0.531276077, 0.481584383, 0.450086834,
0.331415825, 0.298545112, 0.281891087, 0.301013691, 0.334150401,
0.537372378, 0.756594273, 0.778707894, 0.728867792, 0.634829201,
0.415475576, 0.353712963), .Tsp = c(2002.5, 2020.41666666667,
12), class = "ts")
#這里我明確指定選項參數 opt=list() #創建一個空列表到append個別model參數
opt$period=12 #Period of the cyclic/seasonal component of the modis time series
opt$startTime=2002.500000 ##specify start time
out=beast(NDVI_Chobe001.ts, opt)
p<-plot(out)
但是，plot 時間序列是每月頻率，請參見此處的示例[在此處輸入圖像描述][1]
##現在我從時間序列中提取年份以在繪圖時使用
time <- as.yearmon( time(NDVI_Chobe001.ts))
time<-format.Date(x,"%Y")
plot(out,axis(1,time, labels = TRUE))
但這不起作用，它仍然按月頻率顯示，我如何按年分配我的時間序列？
[1]: https://i.stack.imgur.com/VNxJC.jpg
我知道這個問題很老，我遲到的答復可能無關緊要。 無論如何，在舊版本的Rbeast中，如果給定ts輸入， beast function 僅使用其數據向量而不使用時間標簽信息（即 .tsp 屬性，例如start和frequency ）。 換句話說，時間信息必須額外提供給beast ； 如果不是，則將索引1:N用作默認時間。 在最新版本（ Rbeast v0.9.2 ）中，我稍微更改了 API； 現在beast可以處理ts對象的時間標簽了。
下面是使用您的樣本數據進行時間序列分解和變化點檢測的測試。
ndvi= c( 0.258,0.237,0.223,0.223,0.281,0.387,0.556,0.719,0.443,0.314,0.292,0.278,0.246,0.230,0.228,0.234,0.396,0.491,0.716, 0.734,0.576,0.479,0.423,0.377,0.313,0.297,0.278,0.285,0.323,0.482,0.544,0.692,0.512,0.526,0.432,0.331,0.294,0.274,
0.247,0.248,0.268,0.623,0.732,0.794,0.697,0.588,0.389,0.348,0.282,0.264,0.254,0.277,0.342,0.450,0.631,0.716,0.508, 0.423,0.324,0.321,0.295,0.255,0.257,0.272,0.270,0.432,0.585,0.716,0.593,0.559,0.388,0.386,0.323,0.296,0.299,0.340,
0.487,0.531,0.755,0.630,0.624,0.466,0.407,0.396,0.348,0.311,0.301,0.359,0.389,0.639,0.693,0.544,0.608,0.520,0.489, 0.371,0.322,0.285,0.262,0.290,0.272,0.465,0.597,0.582,0.498,0.351,0.313,0.301,0.272,0.256,0.257,0.261,0.280,0.472,
0.635,0.809,0.708,0.454,0.374,0.312,0.277,0.273,0.279,0.274,0.322,0.665,0.786,0.749,0.494,0.380,0.316,0.293,0.275, 0.263,0.254,0.270,0.319,0.699,0.733,0.743,0.701,0.514,0.414,0.353,0.304,0.263,0.253,0.270,0.310,0.529,0.769,0.676,
0.561,0.550,0.408,0.362,0.294,0.271,0.261,0.265,0.311,0.556,0.586,0.715,0.713,0.554,0.423,0.332,0.316,0.264,0.253, 0.274,0.274,0.331,0.568,0.678,0.651,0.550,0.501,0.395,0.373,0.342,0.308,0.368,0.346,0.590,0.550,0.641,0.732,0.616,
0.501,0.441,0.420,0.297,0.302,0.347,0.347,0.488,0.739,0.773,0.527,0.531,0.481,0.450,0.331,0.298,0.281,0.301,0.334, 0.537,0.756,0.778,0.728,0.634,0.415,0.353)
ndvi是一個數據向量； 從中創建一個ts object 作為beast的輸入：
ndvi_ts = ts(ndvi, start=2002.5, frequency = 12)
out = beast(ndvi_ts)
plot(out)
# By default, seasonality is fitted as a harmonic curve; use SVD-based bases instead
out = beast(ndvi_ts, season='svd')
plot(out)
# If the input is a pure data vector, the time info has to be specified
# via 'start', 'deltat' (delta T), and 'freq'; otherwise, default times
# (i.e., 1:216) would be used.
out = beast( ndvi, start=2002.5, deltat=1/12, freq=12, season='svd')
plot(out)
beast output 是 class 'beast' 的LIST object，不能直接用ggplot2繪制。 上面的 plot function 不是 R 自己的 function 而是 Rbeast 對 S3 方法'plot.beast'的實現。 如果需要常規繪圖，可以提取 output 的各個變量以用於 R 的基礎 plot 或 ggplot2。 下面是一個例子：
out=beast(ndvi_ts, season='svd')
# out is a LIST variable; extract some elements
t = out$time # time
y = out$trend$Y # fitted trend compnt
yci = out$trend$CI[,,1] # estimated 95% CI interval for trend
cp = out$trend$cp # most probable changepoints in trend
ncp = sum(!is.nan(cp)) # number of changepoints given in cp
ymax=max(yci)
ymin=min(yci)
plot( x=c(min(t),max(t)), y=c(ymin,ymax), type='n', xlab='time', ylab='trend' )
lines(t,y)
polygon( x=c(t,rev(t)), y=c( yci[,1], rev(yci[,2]) ) , col=rgb(0,1,0,.2),border=NA )
yrange=par('usr')[3:4]
for (i in 1:ncp) {
lines( c(cp[i], cp[i] ), y=yrange, type='l', lty='dashed' )
}
如果需要多個子圖，一種方法是使用 R 的par(new=TRUE)選項，如下所示。
t = out$time # time
y = out$trend$Y # fitted trend compnt
yci = out$trend$CI[,,1] # estimated 95% CI interval for trend
cp = out$trend$cp # most probable changepoints in trend
ncp = sum(!is.nan(cp)) # number of changepoints given in cp
prob = out$trend$cpOccPr # probability of changepoints occurrence over time
ymax=max(yci)
ymin=min(yci)
plot.new()
# FIRST PLOT: Fine-tune the four margin numbers in plt to re-position
par(plt = c(0.15, 1-0.01, 1-0.01-.5, 1-0.01-.5+0.4),new=TRUE)
plot( x=c(min(t),max(t)), y=c(ymin,ymax), type='n', xaxt='n', xlab=NA, ylab='trend')
lines( t, y )
polygon( x=c( t, rev(t) ), y=c( yci[,1], rev(yci[,2]) ) , col=rgb(0,1,0,.2),border=NA )
yrange=par('usr')[3:4]
for (i in 1:ncp) {
lines( c(cp[i], cp[i] ), y=yrange, type='l', lty='dashed' )
}
# SECOND PLOT: Fine-tune the four margin numbers in plt to re-position
par(plt = c(0.15, 1-0.01, 1-0.01-.5-0.3, 1-0.01-.5-0.3+0.3),new=TRUE)
plot( x=t, y=prob, type='l', xlab='time', ylab='Prob' )
不確定這有多大用處。 無論如何，感謝您測試Rbeast 。 如果需要新功能，請告訴我。
我嘗試的是這樣的：
您的時間序列數據：
df <- structure(c(0.258672185, 0.237639041, 0.223988035, 0.22397988,
0.28132144, 0.387710909, 0.556186453, 0.719580311, 0.443294248,
0.314433357, 0.292755672, 0.278023297, 0.246809774, 0.230477039,
0.228955071, 0.234966762, 0.396659718, 0.491330645, 0.716274496,
0.73417416, 0.576279481, 0.479403466, 0.423930923, 0.377550602,
0.313801774, 0.297335261, 0.27886131, 0.285054814, 0.323942137,
0.482912961, 0.544500134, 0.692623308, 0.512637643, 0.526592884,
0.432898755, 0.331892323, 0.294543398, 0.274633904, 0.247602217,
0.24889248, 0.268682448, 0.623132479, 0.732958587, 0.794572999,
0.697092229, 0.588587711, 0.389213056, 0.348485514, 0.282932917,
0.264831569, 0.254937301, 0.277647575, 0.342873991, 0.450392312,
0.631838679, 0.716638566, 0.508704542, 0.423229761, 0.324663042,
0.321186864, 0.295935102, 0.255618784, 0.257619908, 0.27254904,
0.270580191, 0.432220414, 0.585539863, 0.716581759, 0.593955107,
0.559614127, 0.388112159, 0.386833323, 0.323056858, 0.296201373,
0.299547175, 0.340499135, 0.487932489, 0.531619232, 0.755898976,
0.630418376, 0.624101053, 0.46624194, 0.407831925, 0.396696504,
0.348860597, 0.311130303, 0.301932775, 0.359536613, 0.389388896,
0.639178419, 0.693073002, 0.544238686, 0.608043068, 0.520223438,
0.489590537, 0.371915235, 0.322345492, 0.285747424, 0.262060636,
0.290601893, 0.272739968, 0.465184219, 0.597999142, 0.58280379,
0.498312536, 0.351555151, 0.313456794, 0.30176279, 0.272389062,
0.256200802, 0.257570355, 0.261360949, 0.280664516, 0.472871998,
0.635346196, 0.809469884, 0.708410897, 0.454619991, 0.374153554,
0.31216354, 0.277643235, 0.273912332, 0.279202729, 0.274954368,
0.3220379, 0.66542086, 0.786922135, 0.749038686, 0.494324261,
0.380828443, 0.31699487, 0.293321759, 0.275010923, 0.263032267,
0.254334753, 0.270928799, 0.319622211, 0.699834174, 0.733615599,
0.743950233, 0.701439492, 0.514223884, 0.414668945, 0.353937354,
0.304874948, 0.263697644, 0.25397833, 0.270116643, 0.310931558,
0.529621168, 0.769325391, 0.676328487, 0.561630209, 0.550257129,
0.408412794, 0.362071348, 0.294350827, 0.271100888, 0.26194204,
0.265314367, 0.311826075, 0.556399621, 0.586507353, 0.715697274,
0.71326184, 0.554275685, 0.423167172, 0.332214247, 0.316422341,
0.264796933, 0.253106015, 0.274547234, 0.274233387, 0.331729551,
0.568674796, 0.678751599, 0.651238604, 0.550351642, 0.50121297,
0.39598441, 0.373748125, 0.342521719, 0.308862893, 0.368914514,
0.346751371, 0.590197072, 0.550842239, 0.641805925, 0.73276961,
0.616976479, 0.501595155, 0.441702349, 0.420716154, 0.297677153,
0.302351869, 0.347572841, 0.347128221, 0.488411226, 0.739632011,
0.773688839, 0.527578039, 0.531276077, 0.481584383, 0.450086834,
0.331415825, 0.298545112, 0.281891087, 0.301013691, 0.334150401,
0.537372378, 0.756594273, 0.778707894, 0.728867792, 0.634829201,
0.415475576, 0.353712963), .Tsp = c(2002.5, 2020.41666666667,
12), class = "ts")
創建具有適當日期的 dataframe：
# this will extract, convert and create a Date column from your data
df1 <- data.frame(as.matrix(df), date=time(df))
df1$year <- trunc(df1$date)
df1$month <- (df1$date - df1$year) * 12 + 1
df1$month <- as.integer(round(df1$month),0)
df1$Day <- 1
names(df1) <- c("Data", "Original_Date", "Year", "Month", "Day")
df1$Date <- paste(df1$Year, df1$Month, df1$Day, sep="-")
如果一切正常，您可以查看：
# check the structure
str(df1)
最后，plot吧：
# plot the data
library(ggplot2)
theme_set(theme_minimal())
ggplot(data = df1, aes(x = Date, y = Data)) + geom_line(color = "#00AFBB", size = 1)
plot(y=out$s, x=out$time, type='l') #The same as plot(out$s[,1]): plot the seasonal curve
plot(y=out$sProb, x=out$time, type='l') #Plot the probability of observing seasonal changepoints
plot(y=out$t, x=out$time, type='l') #The same as plot(out$t[,1]): plot the trend
plot(y=out$sProb, x=out$time, type='l') #Plot the probability of observing trend changepoints11
當基礎時間序列是每月頻率時，如何從時間序列索引中獲取年份？
[英]How to get years from a time series index when the underlying time series is of monthly freq?
如何繪制（時間序列圖）多年來（1880-2017年）1月至12月的每月溫度？
[英]How can Plot (time series plot) monthly temperatures from Jan to Dec for the years(1880-2017)?
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