I have a set of bivariate time series and want to resample them to the same length. I thought that it might be a good idea to use the mean length of all time series, since I wanted to avoid that some series will be shortened or extended too much. My first naive idea was using the resample method of the signal package, and resample each dimension of a series separately. I don't like this idea very much, since I have the fear that the alignment between the dimensions might suffer from the independent resampling, but I hoped this effect might be negligible.
My current problem is that the resampling of the data creates artifacts at the end and beginning of a series. It seems that the resample method assumes that the series starts from zero. Now I'm wondering if there is a more suitable resampling method which in the best case is readily available (in R) and maybe also supports bivariate time series.
Plot:
http://i.stack.imgur.com/3OwAt.png
Code example:
example <- c(-2014.1, -2014.1, -2014.1, -2014, -2014, -2013.9, -2013.9,
-2013.7, -2013.5, -2013.4, -2013.1, -2012.9, -2012.6, -2012.4,
-2012, -2011.7, -2011.4, -2011, -2010.5, -2010.1, -2009.5, -2009.1,
-2008.6, -2008, -2007.5, -2006.9, -2006.4, -2005.7, -2005.1,
-2004.4, -2003.7, -2003, -2002.4, -2001.5, -2000.7, -1999.9,
-1999.1, -1998.2, -1997.4, -1996.5, -1995.6, -1994.6, -1993.6,
-1992.6, -1991.7, -1990.7, -1989.7, -1988.9, -1987.9, -1986.9,
-1985.9, -1984.9, -1984, -1983, -1982.1, -1981.1, -1980.1, -1979.2,
-1978.2, -1977.2, -1976.4, -1975.4, -1974.4, -1973.4, -1972.4,
-1971.5, -1970.5, -1969.6, -1968.6, -1967.6, -1966.6, -1965.7,
-1964.7, -1963.9, -1962.9, -1961.9, -1960.9, -1959.9, -1958.9,
-1957.9, -1956.9, -1955.9, -1955, -1954, -1953, -1952.1, -1951.5,
-1951.1, -1950.6, -1950.1, -1949.6, -1949.2, -1948.7, -1948.2,
-1947.9, -1947.4, -1946.9, -1946.4, -1945.9, -1945.4, -1944.9,
-1944.4, -1943.9, -1943.4, -1943, -1942.5, -1942, -1941.5, -1941,
-1940.6, -1940.1, -1939.6, -1939.1, -1938.7, -1938.2, -1937.7,
-1937.2, -1936.7, -1936.2, -1935.7, -1935.4, -1934.9, -1934.4,
-1933.9, -1933.4, -1932.9, -1932.4, -1931.9, -1931.4, -1931,
-1930.5, -1930, -1929.5, -1929, -1928.6, -1928.1, -1927.6, -1927.1,
-1926.6, -1926.2, -1925.7, -1925.2, -1924.7, -1924.4, -1923.9,
-1923.4, -1922.9, -1922.4, -1921.9, -1921.4, -1920.9, -1920.4,
-1919.9, -1919.4, -1919, -1918.5, -1918, -1917.5, -1917, -1916.6,
-1916.1, -1915.6, -1915.1, -1914.6, -1914.2, -1913.7, -1913.2,
-1912.7, -1912.2, -1911.7, -1911.4, -1910.9, -1910.4, -1909.9,
-1909.4, -1908.9, -1908.4, -1907.9, -1907.5, -1906.9, -1906.5,
-1906, -1905.5, -1905, -1904.6, -1904, -1903.6, -1903.1, -1902.7,
-1902.2, -1901.7, -1901.2, -1900.7, -1900.4, -1899.9, -1899.4,
-1898.9, -1898.4, -1897.9, -1897.4, -1896.9, -1896.4, -1895.9,
-1895.2, -1895, -1894.5, -1894, -1893.6, -1893.1, -1892.6, -1892.1,
-1891.6, -1891.2, -1890.7, -1890.2, -1889.7, -1889.2, -1888.9,
-1888.4, -1887.9, -1887.4, -1886.9, -1886.4, -1885.6, -1885.4,
-1884.9, -1884.5, -1884, -1883.5, -1883, -1882.5, -1882.1, -1881.6,
-1881.1, -1880.6, -1880.2, -1879.7, -1879.2, -1878.7, -1878.4,
-1877.9, -1877.4, -1876.9, -1876.1, -1875.6, -1875.1, -1874.6,
-1874.1, -1873.6, -1873.2, -1872.7, -1872.2, -1871.7, -1871.4,
-1870.7, -1870.4, -1869.9, -1869.4, -1868.9, -1868.4, -1867.9,
-1867.4, -1867.2, -1866.4, -1866, -1865.5, -1865, -1864.5, -1864.1,
-1863.6, -1863.1, -1862.6, -1862.1, -1861.7, -1861.2, -1860.7,
-1860.2, -1859.7, -1859.4, -1858.9, -1858.4, -1857.9, -1857.4,
-1856.9, -1856.4, -1855.9, -1855.4, -1854.9, -1854.5, -1854,
-1853.5, -1853.1, -1852.6, -1852.1, -1851.6, -1851.1, -1850.7,
-1850.2, -1849.7, -1849.2)
plot(example, t="l", main="Original")
plot(resample(example,250,length(example)), t="l", main="Resampled")
Related
Through image recognition and segmentation I have already obtained an abstract representation of plants on a field (i. e. I exactly know all the coordinates of all plants in an image).
Now I want to detect the crop-rows in this abstract representation - and can't quite figure out how.
My problems are:
the rows in the images may be a bit rotated and not exactly in north/south orientation (angles may vary between -10° to +10°)
The number of crop-rows per image can vary per image and is not fixed - also it is unknown before processing has taken place
The rotation of the crop-rows may be slightly different in each processed image
I have hundreds of images / representations to be processed (so doing it by hand is obviously not really feasible :-) ), so I would need an algorithm that I can later e.g. put into a loop
Can you perhaps help me with at least a strategy (or code snippets) to do such a crop row detection? Idealy in the end for each crop row I would have the parameters of a linear equation (y=m*x+t), so that abline() can be used, but I am open for anything. In the end it could look like something like this (here purely for illustration purposes done by hand):
Underlying data of the images is here:
structure(c(5278.072, 2632.564, 393.34, 4057.704, 3805.599, 611.269,
1823.835, 3359.069, 3598.284, 5262.873, 2069.963, 1579.745, 4539.584,
3579.977, 4296.46, 1831.153, 2333.835, 1126.639, 152.948, 4030.205,
3368.738, 2066.733, 855.111, 2579.665, 3092.37, 1318.357, 1109.438,
3578.606, 375.756, 3796.788, 4520.064, 1807.36, 5001.773, 87.272,
4033.594, 836.708, 639.13, 3105.628, 1569.256, 2831.851, 826.444,
3557.598, 1078.643, 576.266, 4789.585, 3091.929, 5239.658, 1099.954,
1807.972, 2534.677, 4271.841, 5019.276, 2053.246, 1536.071, 3347.644,
4019.766, 3793.392, 5257.628, 604.323, 2561.307, 1792.665, 884.25,
109.456, 3066.108, 3750.833, 4511.819, 2815.08, 119.468, 4499.801,
2582.512, 2822.354, 3773.842, 1054.719, 4251.171, 4002.476, 2018.277,
1775.284, 4959.269, 2541.009, 4742.312, 2265.149, 3071.313, 1779.218,
3972.64, 2822.409, 5217.848, 1265.449, 1522.899, 3057.732, 5364.729,
346.341, 4226.012, 3287.299, 1767.18, 3991.963, 1811.498, 2785.251,
4488.214, 822.509, 2016.435, 3022.344, 2528.079, 4470.315, 3017.716,
572.771, 97.748, 5168.119, 4199.643, 2006.285, 3946.505, 2771.626,
3495.94, 1745.531, 3734.241, 3265.819, 4963.116, 1058.788, 300.408,
1252.845, 4453, 5411.107, 2768.93, 557.806, 2004.424, 2218.582,
4214.073, 4698.292, 5149.238, 4953.886, 1238.343, 3502.518, 2753.044,
5417.502, 1031.945, 2518.901, 1483.487, 4450.737, 2258.484, 289.261,
2987.945, 5156.371, 4171.407, 1995.901, 781.96, 3918.94, 1974.667,
316.758, 1470.993, 5160.868, 3237.828, 521.251, 787.228, 1039.416,
1202.261, 3456.837, 4148.167, 2200.492, 2720.912, 4915.451, 3902.744,
4435.419, 1209.418, 1471.057, 4641.269, 3913.51, 5412.672, 1953.878,
2220.277, 4911.249, 1006.368, 2974.173, 4410.827, 1688.391, 293.729,
1462.871, 4618.785, 5150.904, 2689.061, 1952.56, 5389.383, 2176.387,
995.073, 4125.245, 498.978, 5137.266, 5358.118, 1444.34, 1674.431,
2689.288, 2465.351, 4566.352, 765.125, 1196.984, 1687.859, 258.247,
1914.911, 4575.408, 3421.147, 495.879, 979.079, 1922.943, 4097.704,
737.439, 3410.562, 234.74, 2159.697, 471.983, 1418.991, 2440.575,
1942.708, 1162.525, 5312.409, 2162.656, 5059.814, 1411.412, 4558.905,
247.618, 4319.106, 3411.827, 1786.69, 1670.462, 1180.524, 1640.636,
4715.993, 3576.548, 3566.57, 3589.872, 3565.564, 3531.571, 3415.178,
3511.07, 3510.051, 3487.762, 3470.791, 3443.062, 3369.329, 3386.999,
3387.786, 3277.473, 3376.266, 3421.932, 3387.869, 3367.994, 3346.403,
3259.785, 3296.081, 3297.633, 3285.163, 3300.119, 2941.504, 3264.344,
3277.9, 3235.499, 3198.869, 3235.508, 3156.907, 3221.313, 3123.96,
3165.979, 3186.806, 3148.158, 3129.906, 3035.963, 2987.899, 3053.684,
3050.107, 3052.643, 3037.767, 3037.525, 2994.456, 3006.454, 2960.606,
2973.443, 2919.843, 2917.246, 2939.87, 2914.804, 2886.588, 2920.769,
2906.616, 2908.866, 2868.052, 2885.769, 2860.088, 2801.168, 2853.439,
2853.863, 2847.141, 2805.677, 2806.183, 2718.094, 2661.652, 2695.19,
2656.518, 2612.372, 2603.286, 2602.449, 2591.63, 2595.714, 2593.287,
2575.333, 2572.15, 2476.559, 2435.917, 2538.626, 2514.215, 2458.875,
2477.5, 2385.366, 2421.47, 2220.899, 2397.842, 2396.848, 2393.501,
2352.039, 2292.429, 2315.84, 2328.682, 2256.508, 2236.925, 2192.809,
2241.279, 2144.107, 2195.016, 2185.86, 2112.28, 2098.085, 2020.843,
1971.232, 1979.691, 1968.859, 1943.755, 1974.743, 1891.801, 1944.186,
1951.423, 1872.022, 1928.441, 1880.504, 1912.82, 1893.822, 1878.889,
1850.38, 1834.762, 1851.886, 1806.117, 1776.713, 1682.26, 1733.805,
1714.941, 1700.778, 1686.258, 1703.367, 1549.601, 1682.525, 1563.277,
1632.103, 1609.4, 1621.888, 1587.126, 1545.346, 1537.933, 1542.424,
1366.974, 1494.822, 1498.618, 1494.055, 1450.098, 1407.89, 1345.613,
1388.68, 1380.527, 1368.772, 1372.391, 1161.35, 1297.577, 1312.849,
1304.972, 1286.721, 1292.485, 1257.53, 1241.146, 1263.164, 1217.146,
1226.615, 993.046, 1166.837, 1112.254, 1072.249, 1117.723, 1061.758,
1098.207, 1084.597, 1059.916, 1059.685, 1063.814, 1054.735, 944.2,
982.653, 963.989, 969.55, 941.066, 907.014, 930.988, 776.849,
877.918, 889.259, 805.872, 831.361, 803.752, 786.654, 791.649,
814.271, 794.444, 776.833, 694.969, 664.718, 653.238, 661.703,
652.696, 655.997, 637.118, 539.101, 555.694, 491.482, 459.712,
453.73, 490.567, 391.441, 409.506, 319.697, 391.505, 390.46,
308.658, 310.59, 285.799, 268.86, 245.89, 195.933, 243.418, 214.203,
172.129, 173.754, 191.456, 194.795, 98.098, 99.4479999999999,
62.1419999999998), .Dim = c(224L, 2L))
Here is something that may help:
For each detected plant point, find the closest neighboring plant. Hopefully this finds a plant in the same crop row more often than not. If it's known a priori that images are roughly in north/south orientation, we should prefer looking more in the vertical direction to choose neighboring plants. One way to do that is to redefine "distance" for the nearest neighbor search as something anisotropic like
distance = 10 * (x0 - x1)² + (y0 - y1)²
Here is a plot of what this produces, making a line segment between each plant and its nearest neighbor:
It's not perfect, but could be a useful start. Most crop rows are lucky enough that a run of 4 or more plants are correctly chained together.
A thought on a possible strategy from here:
Identify the connected components, the "chains" of plants.
For each chain, regress a best fit line by least squares. Or better yet, use the RANSAC algorithm so that the fit robustly ignores a single stray plant in an otherwise colinear chain.
Again using the rough north/south orientation, consider the best fit line "valid" only if it's close enough to vertical. Supposing it is valid, find all plants that are close to the best fit line. If many plants are close, then the best fit line is likely a crop row.
Good afternoon. I have a vector 'a' containing 16000 values. I get the descriptive statistics with the help of the following:
library(pastecs)
library(timeDate)
stat.desc(a)
skewness(a)
kurtosis(a)
Especially skewness=-0.5012, kurtosis=420.8073 (1)
Then I build a histogram of my empirical data:
hist(a, col="lightblue", breaks = 140, border="white", main="",
xlab="Value",xlim=c(-0.001,0.001))
After this I try to fit a theoretical distribution to my empirical data. I choose Variance-Gamma distribution and try to get its parameter estimates on my data:
library(VarianceGammma)
a_VG<-vgFit(a)
The parameter estimates are the following:
vgC=-11.7485, sigma=0.4446, theta=11.7193, nu=0.1186 (2)
Further, I create a sample from the Variance-Gamma distribution with the parameters from (2)
and build a histogram of created theoretical values:
VG<-rvg(length(a),vgC=-11.7485,sigma=0.4446,theta=11.7193,nu=0.1186)
hist(VG,breaks=140,col="orange",main="",xlab="Value")
Bu the second histogram differs absolutely from the first (empirical) histogram. Moreover, it is built on the basis of the parameters (2), which I got on the empirical data.
What's wrong with my code? How can I fix it?
P.S. When I type dput(a[abs(a) > 5e-4]) I get:
c(0.000801110480004752, 0.000588162271316861, 0.000555169128569233,
0.000502563410256229, 0.000854633994686438, 0.00593622112246628,
-0.000506168123513007, -0.000502909585836875, 0.000720924373137422,
0.00119141739181039, 0.000548159382141478, -0.000516511318695123,
-0.000744590777740584, 0.000595213912401249, 0.000514055190913965,
-0.000589061375421807, -0.00175392114572581, 0.000745548313668465,
-0.00075910234096277, -0.00059987613053103, 0.000583568488865538,
0.00426484136013094, 0.000610760059768012, 0.000575522836335551,
0.000823785810599276, 0.00181936036509178, -0.00073316272551871,
-0.00184238143420679, -0.000519146793923397, -0.00120324664043103,
-0.000882469414168696, -0.00148118339830283, 0.000929612782487155,
0.000565364610238817, 0.000578158613453894, 0.00060479145432879,
-0.00520576206828594, 0.000708404040882016, 0.00105224485893451,
0.000636486872540587, -0.00359655507585543, 0.000769164650506582,
0.000635701125126786, 0.000570489501935612, -0.000641260260277221,
0.000735092947873994, 0.000757195823062773, 0.000556002742616357,
-0.00207489740356159, -0.000553386431560554, 0.000511326871983186,
0.000504591469525195, -0.000749886905655472, -0.0013939718643865,
-0.000513742626250036, -0.00105021597423516, -0.00156667292147716,
0.000864563166150134, 0.00433724128055069, 0.00053855648931922,
-0.00150732363190365, 0.00052621785349416, 0.000987781100809215,
0.000560725818171903, 0.00176012436713435, -0.000594895431092368,
-0.000686229580335151, 0.00138682284509528, -0.000531964338888358,
-0.00179959148771403, 0.000574543871314503, -0.000686996216439084,
-0.000559043343629995, 0.00055881173674166, -0.000636332688477736,
-0.000623778186703561, -0.00173834148094443, -0.000567224129968125,
-0.00122578683434504, 0.00130960156515414, -0.000548203197176633,
-0.000522749285863711, -0.000820371086264871, 0.000756014225812507,
-0.000714081490558627, -0.000617600335221624, 0.000523639760748651,
-0.000578502663833191, 0.00107478825239227, 0.000612725356974764,
-0.00065509337422931, 0.000505887803587513, -0.000566716376848575,
0.000511727090058756, 0.000572807738912218, -0.000756026937699161,
0.000547948751494332, 0.000628323894238392, -0.000541350489317693,
-0.00133529454372372, -0.000590618859845904, -0.000700581963648972,
0.000735987224462775, 0.000528958898682319, 0.000838250041022448,
-0.000519084424130511, -0.00052258402856431, -0.000538130765869838,
-0.000631819887885854, 0.00054800880764283, 0.00266115500510899,
-0.000839092093771754, 0.000559253571783103, -0.000801028189803432,
-0.000608879021022801, -0.000538018076854385, -0.000689859734395171,
0.00329650346269972, 0.000765494493951024, -0.000689450477848297,
-0.000560199139975737, 0.00159082699266122, -0.00208548663121455,
-0.000598493596793759, 0.000563544422691464, 0.000626996183768824,
-0.000653166846808162, -0.000851350174739807, -0.00140687473245116,
-0.000887003220306326, -0.000765614651347946, -0.00100676206277761,
0.000724714394852555, 0.00108872127644233, -0.000678558537305918,
-0.000705087556212902, 0.000544828152248655, -0.000791700964308362,
0.000606125736727137, -0.00119335967326073, 0.00075413211796338,
0.000526038939010931, 0.00086543737231537, -0.000817788712950573,
-0.000584070926663571, 0.000619657281937691, 0.000680783312420274,
-0.000513831718574664, -0.00050972403875349, -0.00114542220685365,
-0.00070564389723593, -0.01057964950882, -0.000610357922434801,
0.000818264221596365, 0.000940825400308043, -0.000726555639413817,
-0.000591089505560305, 0.000564738888193972, -0.00068515060569041,
0.000668920238348747, -0.00110103375121717, -0.0015480433031172,
0.000663030855223568, 0.000500097431997304, -0.000600730311271391,
-0.000672397772962796, -0.000607852365856587, 0.000536711920570809,
0.000595055206488837, 0.000523123873687581, 0.000977280737528119,
0.000616410821629998, 0.000788593666889881, -0.000671642905915704,
0.000717328711735021, -0.000551853104219902, -0.000565153434708421,
-0.000802585212152707, 0.000536342062561701, 0.000682048510343591,
-0.000541902545439399, 0.000779676683974273, 0.000698841439971787,
0.000559313965908359, -0.00064986819016255, 0.000795421518319017,
0.00364973919549527, 0.000669658692276087, 0.00109045476974678,
0.000514411572742901, 0.000503832507211754, -0.000507376233564116,
0.001232871590787, 0.000561820312542594, -0.000501190337518054,
-0.000769036505996468, -0.000695537959007453, -0.000572065848166048,
-0.00167929926328192, 0.000597078186826749, 0.00710238430870014,
0.000745192112519888, -0.00116091022028009, -0.000791139281769659,
-0.00148898466632552, 0.000565144038962018, -0.000514019821833855,
-0.00148427996685285, -0.000822717245339888, -0.00062922111212238,
-0.000636011367371125, 0.00119640327632808, 0.000548455410294579,
0.000652678152560426, 0.000509244387833618, 0.000961872348987924,
0.000662064072514568, -0.00068116858054168, -0.000569930302445343,
0.00188358126928101, 0.00130560555273895, 0.000593470885775105,
0.00160093110088155, 0.000785262438315115, -0.000912313442922752,
0.000609996052359563, 0.000720137994393966, 0.000568163899000496,
0.00128685533068307, -0.000756787473447318, 0.000765932134255465,
0.00064884753100003, 0.000687571386270847, -0.000582094290400903,
-0.000693177295971736, -0.000601776208094762, 0.000503616387996786,
-0.000615095866544735, -0.000799593899689199, 0.000773750859128342,
-0.000522576090260074, 0.000503578107212022, -0.00104492224837571,
0.000547928732299141, 0.00310304337507183, 0.000893382870797765,
-0.000577792878910799, -0.000647710366578735, -0.00061992948706191,
0.000825702487162516, 0.000606579510524341, 0.000552792484727505,
0.000688600840895504, 0.000505093563534231, -0.000728420573667066,
-0.00157924525963438, -0.000603846616019865, -0.000521941317177976,
0.00150498158245682, -0.000584572670337735, 0.000713757870583365,
0.000524287801789924, 0.00107217649464886, 0.00213147531822244,
0.000566012832157625, -0.00069828890607937, 0.000641567963736378,
-0.000509531713644762, -0.000547564140049417, -0.00115275240244728,
0.000560465768010943, -0.000651807371497171, -0.00096487058986483,
0.000753687665266511, -0.000665599418910645, -0.000691278087025182,
-0.000578010050725553, -0.000685833148198256, 0.000698470819832764,
0.00102943368139208, -0.000725840586788706, 0.00125882415960632,
-0.000630791474954151, -0.000764813558678412, -0.000638539347184164,
0.000654486496518558, 0.000547453642294471, 0.000572020020495501,
-0.000605791001705214, 0.00660211658324172, 0.00114928683282756,
0.000985676480677711, -0.000694668292547718, -0.000528955637964401,
0.000647975568638159, 0.00116454536417443, 0.000506748841724303,
-0.000500925156604382, -0.000567015088082101, 0.00128711230206946,
0.000533633762033858, 0.00505991432758357, 0.000518058378462527,
-0.000592822519784875, 0.00177414999018666, 0.00059845426944527,
-0.000511614433724716, 0.0016614697907098, 0.000852196464322219,
0.00241689725305427, -0.000614317948913978, -0.000729717143318709,
-0.000612900648802039, -0.000727983564232204, -0.000694965869158182,
-0.000527752006066251, -0.000584233784708843, 0.000522097476268968,
0.000543092880677776, 0.000947121210698398, -0.00241810275096377,
0.00181893137435019, 0.000931873879297385, 0.000512116215015013,
0.000724985702444059, -0.000566713495050664, 0.000603953591362227
)
After fitting the data look like the following (empirical histogram-blue, theoretical histogram-orange):
The same when include freq=FALSE in hist
This will all be due to anomalous values in a not represented by the histogram you've shown. This could be the cause of both the very high kurtotsis, and the vgFit() algorithm failing to find a good fit.
Type dput(a[abs(a) > 5e-4]) in the console and copy the output into your question. People then may be able to recreate aomething like the vector a without having to get all 16000 values and debug the vgFit issue.
Thanks for the extra data. There are some extreme values in there, but I don;t think those are what is causing the problem in vgFit. Fitting 4 parameters which can be almost any value is difficult, but you can help it along by rescaling your data to something typical. Try this:
b <- (a-mean(a))/sd(a)
vgf <- vgFit(b)
vgf$param
VG <- rvg(16000, param = vgf$param)
VG_rescaled <- VG*sd(a)+mean(a)
hist(VG_rescaled, breaks=140, col="orange", main="", xlab="Value")
and see if the two histograms are close enough now.
I'm trying to create a map with the spatstat package of R so that the sidebar shows the values of the third (preferably) or fourth column of my data frame and that the colors are also reflective of that third (or fourth column) chosen.
My script:
x=c(6.839887, 6.671494, 6.651083, 6.655289, 6.591903, 6.653641, 6.661709, 6.671664, 6.660044, 6.624659, 6.648162, 6.536877, 6.654134, 6.674678,6.618935, 6.677705, 6.643918, 6.644119, 6.670517, 6.583619, 6.649991, 6.647649, 6.656308, 6.645772, 6.648740, 6.643103, 6.652199, 6.666641,6.633400, 6.621282, 6.635427, 6.646127, 6.630862, 6.657919, 6.671616, 6.622935, 6.648225, 6.676911, 6.640234, 6.719334, 6.653202, 6.656747,6.724692, 6.639747, 6.630575, 6.657916, 6.618957, 6.640006, 6.645280, 6.614058, 6.576136, 6.631994, 6.617391, 6.782351, 6.620072, 6.661061,6.597216, 6.648755, 6.618436, 6.659507, 6.653993, 6.663255, 6.630893, 6.656322, 6.617265, 6.649022, 6.629346, 6.595224, 6.540263, 6.623435,6.652709, 6.608565, 6.618335, 6.645100, 6.790914, 6.643620, 6.462808, 6.680115, 6.716004, 6.668781, 6.765199, 6.674251, 6.647542, 6.724564,6.724556)
y=c(17.16749, 17.16727, 17.16678, 17.16673, 17.16813, 17.16663, 17.16652, 17.16636, 17.16629, 17.16856, 17.16521, 17.16519, 17.17002, 17.16465,17.17015, 17.16407, 17.16356, 17.17122, 17.16334, 17.17152, 17.16282, 17.16278, 17.16272, 17.17257, 17.16198, 17.17279, 17.16169, 17.16161,17.16146, 17.17352, 17.17389, 17.16076, 17.17420, 17.16046, 17.15917, 17.17571, 17.15895, 17.15881, 17.15860, 17.15827, 17.15797, 17.15776,17.17761, 17.15664, 17.15622, 17.15610, 17.15571, 17.15561, 17.15527,17.15514, 17.15494, 17.15447, 17.15438, 17.18041, 17.18053, 17.15402,17.18090, 17.15384, 17.18121, 17.15355, 17.15352, 17.15349, 17.18213,17.15242, 17.15201, 17.14978, 17.18591, 17.18688, 17.18707, 17.18761,17.14712, 17.18788, 17.18794, 17.14619, 17.18868, 17.14588, 17.14511,17.14471, 17.14440, 17.14430, 17.19116, 17.19140, 17.14222, 17.14123,17.33627)
z=c(32.23228,526.46061, -1300.03539, -376.04329, 139.67322,-913.24800, -526.46061, 354.55511, 483.48424, 161.16141, 182.64960, 419.0196, 75.20866, -225.62598, -1536.40546, -397.53148, -1106.64169, -440.50786, 118.18504,-290.09054, -1471.94089, 440.50786,-848.78343, -1385.98814, -676.87793, -1622.35821, -1450.45271,75.20866, -1557.89365, 161.16141, 376.04329, 354.55511, -32.23228,-1171.10626,-75.20866, 547.94880, -805.80706, 870.27162, -698.36612,-32.23228, -2331.46842, -182.64960, 75.20866, -719.85431,-1837.24009,913.24800, -1106.64169, 698.36612, 483.48424, -676.87793, -3019.09045, 891.75981, 1106.64169, 333.06692, -913.24800,333.06692, 934.73619, 354.55511, 75.20866, -891.75981, -247.11416, -1966.16922, 139.67322, -784.31887, -569.43699, -118.18504,-440.50786, 397.53148, -655.38974, 139.67322, 53.72047, -633.90155,-633.90155, 419.01967, -547.94880, 75.20866, 569.43699, 290.09054, -376.04329, 547.94880, 75.20866, -10.74409, 182.64960,-397.53148, -479.53833 )
w=c(96326.91, 96769.46, 95127.94, 95960.41, 96423.22, 95476.93, 95825.18,96615.67, 96731.03, 96442.47, 96461.73, 96673.36, 96365.44, 96095.53,94914.31, 95941.10, 95302.53, 95902.47, 96403.96, 96037.64, 94972.60,96692.58, 95535.03, 95050.29, 95689.84, 94836.56, 94992.03, 96365.44,94894.87, 96442.47, 96634.90, 96615.67, 96269.09, 95244.36, 96230.54,96788.68, 95573.74, 97076.62, 95670.50, 96269.09, 94193.69, 96134.12,96365.44, 95651.15, 94642.01, 97114.98, 95302.53, 96923.12, 96731.03,95689.84, 93567.91, 97095.80, 97287.46, 96596.43, 95476.93, 96596.43,97134.15, 96615.67, 96365.44, 95496.30, 96076.24, 94525.17, 96423.22,95593.10, 95786.52, 96191.98, 95902.47, 96654.13, 95709.18, 96423.22,96346.17, 95728.52, 95728.52, 96673.36, 95805.85, 96365.44, 96807.89,96557.96, 95960.41, 96788.68, 96365.44, 96288.37, 96461.73,95941.10, 99451.20)
shap.lo=data.frame(x,y,z,w)
library(spatstat)
shap.lo.win <- owin(range(shap.lo[,1]), range(shap.lo[,2]))
centroid.owin(shap.lo.win) ; area.owin(shap.lo.win)
shap.lo.ppp <- as.ppp(shap.lo[,c(1,2,3)], shap.lo.win) # making a ppp object
plot(density(shap.lo.ppp,0.02), col=topo.colors(25), main='', xlab='x',
ylab='y')
points(x, y)
the result is shown below
I would like to know why the sidebar shows different values than the ones shown in the third column of my data frame, that is, in addition to displaying no negative values, shows values much larger than those contained in the third column.
Is it possible to do this, that is, make the colors and the sidebar represent the third or fourth column of the data frame?
I thank the help of all you!
Let me quote directly from the help file for density.ppp:
This function is often misunderstood.
The result of density.ppp is not a spatial smoothing of the
marks or weights attached to the point pattern. To perform
spatial interpolation of values that were observed at the points
of a point pattern, use Smooth.ppp.
The result of density.ppp is not a probability density. It is
an estimate of the intensity function of the point process that
generated the point pattern data. Intensity is the expected
number of random points per unit area. The units of intensity are
“points per unit area”. Intensity is usually a function of
spatial location, and it is this function which is estimated by
density.ppp. The integral of the intensity function over a
spatial region gives the expected number of points falling in this
region.
So try Smooth.ppp (note the upper case S), and see if you can make that produce the results you expected.
We are trying to decompose a time series in R and we are able to do it but the results do not look right.
Here's the data with time stamps:
"_time","dummy_data"
"2017-02-05T00:00:00.000-0500","205.472222"
"2017-02-05T01:00:00.000-0500","169.443611"
"2017-02-05T02:00:00.000-0500","156.011944"
"2017-02-05T03:00:00.000-0500","177.169722"
"2017-02-05T04:00:00.000-0500","219.049167"
"2017-02-05T05:00:00.000-0500","215.821667"
"2017-02-05T06:00:00.000-0500","214.983333"
"2017-02-05T07:00:00.000-0500","246.723889"
"2017-02-05T08:00:00.000-0500","343.308889"
"2017-02-05T09:00:00.000-0500","473.200278"
"2017-02-05T10:00:00.000-0500","390.223333"
"2017-02-05T11:00:00.000-0500","373.974444"
"2017-02-05T12:00:00.000-0500","639.402500"
"2017-02-05T13:00:00.000-0500","512.378611"
"2017-02-05T14:00:00.000-0500","428.824444"
"2017-02-05T15:00:00.000-0500","477.611944"
"2017-02-05T16:00:00.000-0500","534.378611"
"2017-02-05T17:00:00.000-0500","716.046944"
"2017-02-05T18:00:00.000-0500","470.218056"
"2017-02-05T19:00:00.000-0500","411.426389"
"2017-02-05T20:00:00.000-0500","393.147222"
"2017-02-05T21:00:00.000-0500","368.726389"
"2017-02-05T22:00:00.000-0500","357.958056"
"2017-02-05T23:00:00.000-0500","379.284722"
"2017-02-06T00:00:00.000-0500","273.403056"
"2017-02-06T01:00:00.000-0500","269.764167"
"2017-02-06T02:00:00.000-0500","233.650000"
"2017-02-06T03:00:00.000-0500","227.802222"
"2017-02-06T04:00:00.000-0500","313.731389"
"2017-02-06T05:00:00.000-0500","327.502778"
"2017-02-06T06:00:00.000-0500","333.327500"
"2017-02-06T07:00:00.000-0500","393.542778"
"2017-02-06T08:00:00.000-0500","659.569167"
"2017-02-06T09:00:00.000-0500","770.509444"
"2017-02-06T10:00:00.000-0500","1037.536667"
"2017-02-06T11:00:00.000-0500","935.926667"
"2017-02-06T12:00:00.000-0500","791.124722"
"2017-02-06T13:00:00.000-0500","903.093889"
"2017-02-06T14:00:00.000-0500","802.309167"
"2017-02-06T15:00:00.000-0500","792.085000"
"2017-02-06T16:00:00.000-0500","728.378056"
"2017-02-06T17:00:00.000-0500","740.647222"
"2017-02-06T18:00:00.000-0500","737.986111"
"2017-02-06T19:00:00.000-0500","502.763056"
"2017-02-06T20:00:00.000-0500","475.400556"
"2017-02-06T21:00:00.000-0500","522.482500"
"2017-02-06T22:00:00.000-0500","410.664722"
"2017-02-06T23:00:00.000-0500","370.423611"
"2017-02-07T00:00:00.000-0500","278.871667"
"2017-02-07T01:00:00.000-0500","290.182500"
"2017-02-07T02:00:00.000-0500","220.329167"
"2017-02-07T03:00:00.000-0500","252.641944"
"2017-02-07T04:00:00.000-0500","355.666667"
"2017-02-07T05:00:00.000-0500","273.100833"
"2017-02-07T06:00:00.000-0500","315.117778"
"2017-02-07T07:00:00.000-0500","359.764167"
"2017-02-07T08:00:00.000-0500","691.946944"
"2017-02-07T09:00:00.000-0500","941.796667"
"2017-02-07T10:00:00.000-0500","859.472222"
"2017-02-07T11:00:00.000-0500","949.285278"
"2017-02-07T12:00:00.000-0500","790.790833"
"2017-02-07T13:00:00.000-0500","952.915000"
"2017-02-07T14:00:00.000-0500","1136.490278"
"2017-02-07T15:00:00.000-0500","1175.359444"
"2017-02-07T16:00:00.000-0500","859.848889"
"2017-02-07T17:00:00.000-0500","718.357778"
"2017-02-07T18:00:00.000-0500","698.445278"
"2017-02-07T19:00:00.000-0500","630.867778"
"2017-02-07T20:00:00.000-0500","485.426389"
"2017-02-07T21:00:00.000-0500","571.723056"
"2017-02-07T22:00:00.000-0500","458.161944"
"2017-02-07T23:00:00.000-0500","336.733333"
"2017-02-08T00:00:00.000-0500","344.001111"
"2017-02-08T01:00:00.000-0500","315.671111"
"2017-02-08T02:00:00.000-0500","285.107778"
"2017-02-08T03:00:00.000-0500","281.013611"
"2017-02-08T04:00:00.000-0500","260.244722"
"2017-02-08T05:00:00.000-0500","233.998056"
"2017-02-08T06:00:00.000-0500","260.235833"
"2017-02-08T07:00:00.000-0500","362.811944"
"2017-02-08T08:00:00.000-0500","633.142500"
"2017-02-08T09:00:00.000-0500","872.285278"
"2017-02-08T10:00:00.000-0500","938.051389"
"2017-02-08T11:00:00.000-0500","951.735278"
"2017-02-08T12:00:00.000-0500","967.694444"
"2017-02-08T13:00:00.000-0500","960.494167"
"2017-02-08T14:00:00.000-0500","1039.334444"
"2017-02-08T15:00:00.000-0500","808.363889"
"2017-02-08T16:00:00.000-0500","661.919722"
"2017-02-08T17:00:00.000-0500","604.166944"
"2017-02-08T18:00:00.000-0500","464.267500"
"2017-02-08T19:00:00.000-0500","425.867500"
"2017-02-08T20:00:00.000-0500","537.951111"
"2017-02-08T21:00:00.000-0500","528.523611"
"2017-02-08T22:00:00.000-0500","401.029444"
"2017-02-08T23:00:00.000-0500","396.677778"
"2017-02-09T00:00:00.000-0500","263.640833"
"2017-02-09T01:00:00.000-0500","233.905278"
"2017-02-09T02:00:00.000-0500","202.222778"
"2017-02-09T03:00:00.000-0500","194.326944"
"2017-02-09T04:00:00.000-0500","208.162222"
"2017-02-09T05:00:00.000-0500","257.111667"
"2017-02-09T06:00:00.000-0500","277.464444"
"2017-02-09T07:00:00.000-0500","403.655000"
"2017-02-09T08:00:00.000-0500","700.535278"
"2017-02-09T09:00:00.000-0500","888.195278"
"2017-02-09T10:00:00.000-0500","690.172778"
"2017-02-09T11:00:00.000-0500","822.210556"
"2017-02-09T12:00:00.000-0500","801.144167"
"2017-02-09T13:00:00.000-0500","983.958056"
"2017-02-09T14:00:00.000-0500","1003.791111"
"2017-02-09T15:00:00.000-0500","1085.289722"
"2017-02-09T16:00:00.000-0500","740.004167"
"2017-02-09T17:00:00.000-0500","616.664444"
"2017-02-09T18:00:00.000-0500","473.841389"
"2017-02-09T19:00:00.000-0500","570.906944"
"2017-02-09T20:00:00.000-0500","568.685000"
"2017-02-09T21:00:00.000-0500","547.024722"
"2017-02-09T22:00:00.000-0500","369.073889"
"2017-02-09T23:00:00.000-0500","431.275833"
"2017-02-10T00:00:00.000-0500","211.516667"
"2017-02-10T01:00:00.000-0500","169.348611"
"2017-02-10T02:00:00.000-0500","185.133611"
"2017-02-10T03:00:00.000-0500","173.875278"
"2017-02-10T04:00:00.000-0500","250.226667"
"2017-02-10T05:00:00.000-0500","237.783056"
"2017-02-10T06:00:00.000-0500","247.450000"
"2017-02-10T07:00:00.000-0500","375.930278"
"2017-02-10T08:00:00.000-0500","605.365000"
"2017-02-10T09:00:00.000-0500","647.407500"
"2017-02-10T10:00:00.000-0500","714.873889"
"2017-02-10T11:00:00.000-0500","767.262778"
"2017-02-10T12:00:00.000-0500","643.721389"
"2017-02-10T13:00:00.000-0500","653.969722"
"2017-02-10T14:00:00.000-0500","656.156111"
"2017-02-10T15:00:00.000-0500","763.260833"
"2017-02-10T16:00:00.000-0500","579.947778"
"2017-02-10T17:00:00.000-0500","734.142500"
"2017-02-10T18:00:00.000-0500","636.389722"
"2017-02-10T19:00:00.000-0500","360.105833"
"2017-02-10T20:00:00.000-0500","438.675278"
"2017-02-10T21:00:00.000-0500","420.990833"
"2017-02-10T22:00:00.000-0500","343.326111"
"2017-02-10T23:00:00.000-0500","293.787778"
"2017-02-11T00:00:00.000-0500","282.809444"
"2017-02-11T01:00:00.000-0500","249.653889"
"2017-02-11T02:00:00.000-0500","163.781111"
"2017-02-11T03:00:00.000-0500","245.236111"
"2017-02-11T04:00:00.000-0500","284.649444"
"2017-02-11T05:00:00.000-0500","337.534167"
"2017-02-11T06:00:00.000-0500","258.772222"
"2017-02-11T07:00:00.000-0500","322.805833"
"2017-02-11T08:00:00.000-0500","380.436667"
"2017-02-11T09:00:00.000-0500","523.488056"
"2017-02-11T10:00:00.000-0500","478.361111"
"2017-02-11T11:00:00.000-0500","422.119722"
"2017-02-11T12:00:00.000-0500","425.315000"
"2017-02-11T13:00:00.000-0500","465.892222"
"2017-02-11T14:00:00.000-0500","412.729167"
"2017-02-11T15:00:00.000-0500","493.920000"
"2017-02-11T16:00:00.000-0500","381.506667"
"2017-02-11T17:00:00.000-0500","458.291667"
"2017-02-11T18:00:00.000-0500","433.393611"
"2017-02-11T19:00:00.000-0500","415.926389"
"2017-02-11T20:00:00.000-0500","349.008056"
"2017-02-11T21:00:00.000-0500","353.280000"
"2017-02-11T22:00:00.000-0500","399.022778"
"2017-02-11T23:00:00.000-0500","316.480556"
"2017-02-12T00:00:00.000-0500","252.727222"
"2017-02-12T01:00:00.000-0500","274.278333"
"2017-02-12T02:00:00.000-0500","269.609444"
"2017-02-12T03:00:00.000-0500","221.645278"
"2017-02-12T04:00:00.000-0500","294.641389"
"2017-02-12T05:00:00.000-0500","347.502222"
"2017-02-12T06:00:00.000-0500","313.468333"
"2017-02-12T07:00:00.000-0500","330.429722"
"2017-02-12T08:00:00.000-0500","369.703333"
"2017-02-12T09:00:00.000-0500","518.679167"
"2017-02-12T10:00:00.000-0500","542.199167"
"2017-02-12T11:00:00.000-0500","475.780833"
"2017-02-12T12:00:00.000-0500","419.468056"
"2017-02-12T13:00:00.000-0500","447.899444"
"2017-02-12T14:00:00.000-0500","526.429722"
"2017-02-12T15:00:00.000-0500","517.046111"
"2017-02-12T16:00:00.000-0500","515.500833"
"2017-02-12T17:00:00.000-0500","631.053056"
"2017-02-12T18:00:00.000-0500","708.575000"
"2017-02-12T19:00:00.000-0500","453.120000"
"2017-02-12T20:00:00.000-0500","554.589444"
"2017-02-12T21:00:00.000-0500","453.551389"
"2017-02-12T22:00:00.000-0500","419.450556"
"2017-02-12T23:00:00.000-0500","328.177778"
"2017-02-13T00:00:00.000-0500","293.752500"
"2017-02-13T01:00:00.000-0500","246.943889"
"2017-02-13T02:00:00.000-0500","213.986944"
"2017-02-13T03:00:00.000-0500","219.086111"
"2017-02-13T04:00:00.000-0500","303.708611"
"2017-02-13T05:00:00.000-0500","353.969167"
"2017-02-13T06:00:00.000-0500","346.050833"
"2017-02-13T07:00:00.000-0500","470.292222"
"2017-02-13T08:00:00.000-0500","844.538056"
"2017-02-13T09:00:00.000-0500","1100.996667"
"2017-02-13T10:00:00.000-0500","1271.799167"
"2017-02-13T11:00:00.000-0500","1447.440000"
"2017-02-13T12:00:00.000-0500","1034.428611"
"2017-02-13T13:00:00.000-0500","1076.172222"
"2017-02-13T14:00:00.000-0500","1087.887222"
"2017-02-13T15:00:00.000-0500","1259.043333"
"2017-02-13T16:00:00.000-0500","1140.602778"
"2017-02-13T17:00:00.000-0500","1148.073889"
"2017-02-13T18:00:00.000-0500","761.544722"
"2017-02-13T19:00:00.000-0500","568.453056"
"2017-02-13T20:00:00.000-0500","574.121667"
"2017-02-13T21:00:00.000-0500","458.542500"
"2017-02-13T22:00:00.000-0500","495.431389"
"2017-02-13T23:00:00.000-0500","404.716111"
"2017-02-14T00:00:00.000-0500","412.116667"
"2017-02-14T01:00:00.000-0500","310.014444"
"2017-02-14T02:00:00.000-0500","236.658333"
"2017-02-14T03:00:00.000-0500","222.509167"
"2017-02-14T04:00:00.000-0500","315.510556"
"2017-02-14T05:00:00.000-0500","369.872778"
"2017-02-14T06:00:00.000-0500","334.670000"
"2017-02-14T07:00:00.000-0500","461.795000"
"2017-02-14T08:00:00.000-0500","753.752222"
"2017-02-14T09:00:00.000-0500","923.558333"
"2017-02-14T10:00:00.000-0500","1107.344722"
"2017-02-14T11:00:00.000-0500","1194.949722"
"2017-02-14T12:00:00.000-0500","937.735833"
"2017-02-14T13:00:00.000-0500","1068.374722"
"2017-02-14T14:00:00.000-0500","1145.634167"
"2017-02-14T15:00:00.000-0500","988.993889"
"2017-02-14T16:00:00.000-0500","1067.995278"
"2017-02-14T17:00:00.000-0500","927.699722"
"2017-02-14T18:00:00.000-0500","739.886389"
"2017-02-14T19:00:00.000-0500","667.679722"
"2017-02-14T20:00:00.000-0500","604.938333"
"2017-02-14T21:00:00.000-0500","543.166667"
"2017-02-14T22:00:00.000-0500","442.916944"
"2017-02-14T23:00:00.000-0500","373.767778"
"2017-02-15T00:00:00.000-0500","337.545833"
"2017-02-15T01:00:00.000-0500","259.629167"
"2017-02-15T02:00:00.000-0500","186.775556"
"2017-02-15T03:00:00.000-0500","212.818611"
"2017-02-15T04:00:00.000-0500","274.319722"
"2017-02-15T05:00:00.000-0500","308.578056"
"2017-02-15T06:00:00.000-0500","316.756389"
"2017-02-15T07:00:00.000-0500","417.723611"
"2017-02-15T08:00:00.000-0500","841.546944"
"2017-02-15T09:00:00.000-0500","1067.051111"
"2017-02-15T10:00:00.000-0500","1109.526389"
"2017-02-15T11:00:00.000-0500","1087.352222"
"2017-02-15T12:00:00.000-0500","1045.260000"
"2017-02-15T13:00:00.000-0500","1285.351389"
"2017-02-15T14:00:00.000-0500","1064.570833"
"2017-02-15T15:00:00.000-0500","1067.725556"
"2017-02-15T16:00:00.000-0500","1055.146111"
"2017-02-15T17:00:00.000-0500","1014.010833"
"2017-02-15T18:00:00.000-0500","720.152500"
"2017-02-15T19:00:00.000-0500","732.987778"
"2017-02-15T20:00:00.000-0500","758.056111"
"2017-02-15T21:00:00.000-0500","799.688611"
"2017-02-15T22:00:00.000-0500","766.204722"
"2017-02-15T23:00:00.000-0500","722.300000"
"2017-02-16T00:00:00.000-0500","507.248333"
"2017-02-16T01:00:00.000-0500","424.247778"
"2017-02-16T02:00:00.000-0500","390.718056"
"2017-02-16T03:00:00.000-0500","364.996944"
"2017-02-16T04:00:00.000-0500","383.408889"
"2017-02-16T05:00:00.000-0500","414.637778"
"2017-02-16T06:00:00.000-0500","349.056667"
"2017-02-16T07:00:00.000-0500","542.903889"
"2017-02-16T08:00:00.000-0500","878.198056"
"2017-02-16T09:00:00.000-0500","1137.686389"
"2017-02-16T10:00:00.000-0500","1277.625278"
"2017-02-16T11:00:00.000-0500","1184.350278"
"2017-02-16T12:00:00.000-0500","923.879444"
"2017-02-16T13:00:00.000-0500","1066.437500"
"2017-02-16T14:00:00.000-0500","1290.296667"
"2017-02-16T15:00:00.000-0500","1026.999722"
"2017-02-16T16:00:00.000-0500","949.582500"
"2017-02-16T17:00:00.000-0500","857.506111"
"2017-02-16T18:00:00.000-0500","831.030278"
"2017-02-16T19:00:00.000-0500","720.440556"
"2017-02-16T20:00:00.000-0500","492.239167"
"2017-02-16T21:00:00.000-0500","508.844722"
"2017-02-16T22:00:00.000-0500","559.846667"
"2017-02-16T23:00:00.000-0500","332.631389"
"2017-02-17T00:00:00.000-0500","349.220833"
"2017-02-17T01:00:00.000-0500","237.833333"
"2017-02-17T02:00:00.000-0500","202.662778"
"2017-02-17T03:00:00.000-0500","232.843889"
"2017-02-17T04:00:00.000-0500","316.059167"
"2017-02-17T05:00:00.000-0500","338.066667"
"2017-02-17T06:00:00.000-0500","363.631389"
"2017-02-17T07:00:00.000-0500","460.921944"
"2017-02-17T08:00:00.000-0500","764.931111"
"2017-02-17T09:00:00.000-0500","931.416944"
"2017-02-17T10:00:00.000-0500","1032.328611"
"2017-02-17T11:00:00.000-0500","1247.801111"
"2017-02-17T12:00:00.000-0500","1159.197500"
"2017-02-17T13:00:00.000-0500","1282.267222"
"2017-02-17T14:00:00.000-0500","1183.746667"
"2017-02-17T15:00:00.000-0500","992.880278"
"2017-02-17T16:00:00.000-0500","791.148611"
"2017-02-17T17:00:00.000-0500","599.060278"
"2017-02-17T18:00:00.000-0500","487.915556"
"2017-02-17T19:00:00.000-0500","492.801111"
"2017-02-17T20:00:00.000-0500","413.024167"
"2017-02-17T21:00:00.000-0500","632.193056"
"2017-02-17T22:00:00.000-0500","493.108611"
"2017-02-17T23:00:00.000-0500","424.018889"
"2017-02-18T00:00:00.000-0500","384.328056"
"2017-02-18T01:00:00.000-0500","310.441944"
"2017-02-18T02:00:00.000-0500","205.145556"
"2017-02-18T03:00:00.000-0500","205.454167"
"2017-02-18T04:00:00.000-0500","253.227500"
"2017-02-18T05:00:00.000-0500","312.188889"
"2017-02-18T06:00:00.000-0500","321.928889"
"2017-02-18T07:00:00.000-0500","406.977778"
"2017-02-18T08:00:00.000-0500","452.448889"
"2017-02-18T09:00:00.000-0500","502.578333"
"2017-02-18T10:00:00.000-0500","489.765278"
"2017-02-18T11:00:00.000-0500","540.568333"
"2017-02-18T12:00:00.000-0500","510.270000"
"2017-02-18T13:00:00.000-0500","456.691944"
"2017-02-18T14:00:00.000-0500","494.579722"
"2017-02-18T15:00:00.000-0500","502.795556"
"2017-02-18T16:00:00.000-0500","429.950556"
"2017-02-18T17:00:00.000-0500","428.196667"
"2017-02-18T18:00:00.000-0500","478.975833"
"2017-02-18T19:00:00.000-0500","362.479167"
"2017-02-18T20:00:00.000-0500","366.515000"
"2017-02-18T21:00:00.000-0500","400.625833"
"2017-02-18T22:00:00.000-0500","374.313056"
"2017-02-18T23:00:00.000-0500","310.666667"
We use the following code to load and decompose this time series:
library("forecast")
data <- read.csv("data.csv")
x <- msts(data, seasonal.periods=c(24,168))
decom <- decompose(x[,2], "multiplicative")
plot(decom)
The decomposition looks like this:
The 'observed' and 'seasonal' components looks almost identical, the 'trend' doesn't seem right since it is rising up too sharply, and the 'random' component looks constant.
So we are not sure if we made a mistake in decomposing or loading this time series in R. Is there another way of decomposing this time series so that it makes more sense or is correct? Should we be loading the data another way so that the time stamps get properly assigned to the data values?
I am trying to calculate geometric standard deviation of each log normal distribution. In the below,for example, I have x data in first row, wich is bin size (from 10 to 1000), and corresponding five y data in the next rows.
10 10.9854 12.0679 13.2571 14.5635 15.9986 17.5751 19.307 21.2095 23.2995 25.5955 28.1177 30.8884 33.9322 37.2759 40.9492 44.9843 49.4171 54.2868 59.6362 65.5129 71.9686 79.0604 86.8511 95.4095 104.811 115.14 126.486 138.95 152.642 167.683 184.207 202.359 222.3 244.205 268.27 294.705 323.746 355.648 390.694 429.193 471.487 517.947 568.987 625.055 686.649 754.312 828.643 910.298 1000
0.0170496 0.0239502 0.0332355 0.0455609 0.0616994 0.0825406 0.109082 0.142408 0.18366 0.233988 0.294489 0.366137 0.449692 0.545614 0.653963 0.774317 0.905696 1.04651 1.19455 1.34698 1.50043 1.65109 1.79482 1.92739 2.04464 2.1427 2.21822 2.26854 2.29184 2.28729 2.25505 2.19628 2.11309 2.00838 1.8857 1.74903 1.60258 1.45057 1.29705 1.1457 0.999738 0.861783 0.733851 0.617327 0.513004 0.421137 0.341527 0.273605 0.216532 0.169284
0.564795 0.577687 0.5902 0.602296 0.61394 0.625095 0.635728 0.645804 0.655292 0.664162 0.672384 0.679932 0.686781 0.692908 0.698293 0.702918 0.706767 0.709826 0.712086 0.713539 0.71418 0.714007 0.71302 0.711223 0.708621 0.705224 0.701043 0.696093 0.69039 0.683953 0.676803 0.668965 0.660464 0.651327 0.641585 0.631268 0.620408 0.609041 0.5972 0.584922 0.572243 0.559201 0.545833 0.532178 0.518272 0.504155 0.489863 0.475433 0.460903 0.446307
1.88874 1.99575 2.1007 2.20265 2.30064 2.39374 2.48101 2.56155 2.63452 2.69913 2.75466 2.80051 2.83615 2.86117 2.87529 2.87836 2.87032 2.85127 2.82144 2.78116 2.7309 2.67121 2.60276 2.52629 2.44262 2.35262 2.2572 2.15731 2.0539 1.94791 1.84027 1.73188 1.62359 1.51621 1.41048 1.30706 1.20656 1.1095 1.01631 0.927365 0.842942 0.763252 0.688433 0.618555 0.553629 0.493609 0.4384 0.387865 0.341834 0.300104
0.190679 0.206669 0.223365 0.240726 0.258699 0.277225 0.296235 0.315651 0.335385 0.355342 0.375418 0.395503 0.41548 0.435228 0.454622 0.473532 0.491829 0.509384 0.526069 0.541758 0.556333 0.569679 0.58169 0.592269 0.60133 0.608797 0.614608 0.618714 0.62108 0.621687 0.620529 0.617615 0.612971 0.606636 0.598663 0.589119 0.578082 0.565642 0.5519 0.536964 0.52095 0.503979 0.486178 0.467675 0.4486 0.429082 0.409249 0.389225 0.36913 0.34908
1.63196 1.69464 1.75432 1.81053 1.8628 1.9107 1.95381 1.99177 2.02423 2.0509 2.07155 2.08598 2.09406 2.09572 2.09094 2.07977 2.06231 2.03872 2.00922 1.97406 1.93356 1.88808 1.83801 1.78377 1.72583 1.66463 1.60068 1.53447 1.46647 1.39719 1.32709 1.25664 1.18628 1.11642 1.04745 0.979718 0.913557 0.849249 0.787045 0.727157 0.669765 0.615008 0.562995 0.513798 0.467461 0.423997 0.383394 0.345615 0.310603 0.27828
So, I have five log normal distributions. (Actually, I have hundreds log normal distributions to be calculated.) Then, I want to calculate each geometric standard deviation, which does not seem to be implemented in r packages. R package ("psych") provides a tool to calculate geometric standard deviation, but not for such data I have..
Instead, it can be calculated following the equation below,
http://www.eng.utoledo.edu/~akumar/IAP1/lung/calculateDiameter.htm
But, I have no idea how to calculate using such equation in r. Hope someone help me to calculate a geometric standard deviation of each log normal distribution either using r package or calculating the equation. Thanks a lot for your help, in advance.
S
The easiest way is probably using:
exp(sd(log(x)))