Finds the trend for each season and each variable in a time series.

seasonTrend(x, plot = FALSE, type = c("slope", "relative"), pval = 0.05, ...)

Arguments

x

Time series vector, or time series matrix with column names

plot

Should the results be plotted?

type

Type of trend to be plotted, actual or relative to series median

pval

p-value for significance

...

Further options to pass to plotting function

Value

A data frame with the following fields:

series

series names

season

season number

sen.slope

Sen slope in original units per year

sen.slope.rel

Sen slope divided by median for that specific season and series

p

p-value for the trend according to the Mann-Kendall test.

missing

Proportion of slopes joining first and last fifths of the data that are missing

Details

The Mann-Kendall test is applied for each season and series (in the case of a matrix). The actual and relative Sen slope (actual divided by median for that specific season and series); the p-value for the trend; and the fraction of missing slopes involving the first and last fifths of the data are calculated (see mannKen).

If plot = TRUE, each season for each series is represented by a bar showing the trend. The fill colour indicates whether \(p < 0.05\) or not. If the fraction of missing slopes is 0.5 or more, the corresponding trends are omitted.

Parameters can be passed to the plotting function, in particular, to facet_wrap in ggplot2. The most useful parameters here are ncol (or nrow), which determines the number of columns (or rows) of plots, and scales, which can be set to "free_y" to allow the y-axis to change for each time series. Like all ggplot2 objects, the plot output can also be customized extensively by modifying and adding layers.

See also

Author

Alan Jassby, James Cloern

Examples

x <- sfbayChla seasonTrend(x)
#> series season sen.slope sen.slope.rel p.value miss #> 1 s21 1 0.02582456 0.018476222 2.063109e-02 0.429 #> 2 s21 2 0.09055556 0.055622661 2.357831e-04 0.143 #> 3 s21 3 0.22425926 0.064271244 4.200806e-04 0.286 #> 4 s21 4 0.19305556 0.040038049 1.381407e-02 0.286 #> 5 s21 5 0.12873016 0.040815179 2.091911e-03 0.388 #> 6 s21 6 0.04429694 0.015143926 2.205212e-01 0.388 #> 7 s21 7 0.08115942 0.038816429 3.016211e-03 0.388 #> 8 s21 8 0.13115385 0.060824742 1.528304e-03 0.429 #> 9 s21 9 0.11846154 0.049549550 9.833619e-04 0.265 #> 10 s21 10 0.09744268 0.051095353 7.681235e-04 0.388 #> 11 s21 11 0.11195652 0.061538462 5.382187e-04 0.510 #> 12 s21 12 0.09467787 0.072380952 3.640470e-04 0.633 #> 13 s22 1 0.01877778 0.013027903 2.742615e-01 0.429 #> 14 s22 2 0.07281145 0.045542417 3.204204e-03 0.143 #> 15 s22 3 0.16533333 0.062686567 1.792358e-04 0.286 #> 16 s22 4 0.16833333 0.038329288 1.381407e-02 0.286 #> 17 s22 5 0.13421053 0.046189376 8.621461e-03 0.388 #> 18 s22 6 0.02816667 0.008680242 4.410008e-01 0.388 #> 19 s22 7 0.05916667 0.028563453 1.833157e-02 0.388 #> 20 s22 8 0.12100000 0.061118848 2.281250e-03 0.429 #> 21 s22 9 0.11171569 0.057075980 1.372855e-04 0.265 #> 22 s22 10 0.08262014 0.033116468 1.309395e-02 0.388 #> 23 s22 11 0.09712418 0.055902778 6.169274e-04 0.510 #> 24 s22 12 0.08714286 0.061860670 5.664976e-04 0.633 #> 25 s23 1 0.02490000 0.015808775 1.498717e-01 0.286 #> 26 s23 2 0.07477778 0.047722567 1.014759e-02 0.000 #> 27 s23 3 0.22634921 0.073819163 1.101182e-03 0.143 #> 28 s23 4 0.18045455 0.039831639 1.078891e-02 0.143 #> 29 s23 5 0.13411765 0.048451548 4.558221e-04 0.265 #> 30 s23 6 0.06923684 0.025048465 3.092915e-02 0.265 #> 31 s23 7 0.07990385 0.045241313 5.153545e-04 0.265 #> 32 s23 8 0.13000000 0.065897436 8.584157e-04 0.429 #> 33 s23 9 0.10689655 0.048969072 8.034765e-03 0.265 #> 34 s23 10 0.10946316 0.053039927 8.498215e-05 0.265 #> 35 s23 11 0.08671053 0.050238095 2.245618e-03 0.388 #> 36 s23 12 0.09000000 0.067661692 5.974040e-04 0.510 #> 37 s24 1 0.03722222 0.026737968 2.439877e-02 0.286 #> 38 s24 2 0.07224638 0.048728632 7.859583e-04 0.000 #> 39 s24 3 0.20340476 0.062720612 2.635687e-04 0.000 #> 40 s24 4 0.25310317 0.044190334 1.089705e-02 0.000 #> 41 s24 5 0.14689655 0.050260787 1.320966e-03 0.265 #> 42 s24 6 0.06000000 0.026364960 1.720610e-02 0.265 #> 43 s24 7 0.07375000 0.051000000 6.877945e-04 0.265 #> 44 s24 8 0.10450725 0.058492063 1.264730e-03 0.429 #> 45 s24 9 0.10784615 0.060579650 1.873869e-04 0.265 #> 46 s24 10 0.11428571 0.062893082 1.559275e-05 0.265 #> 47 s24 11 0.10453634 0.070852464 7.144313e-05 0.388 #> 48 s24 12 0.08200000 0.060606061 3.929482e-04 0.510 #> 49 s25 1 0.04253394 0.024442469 3.755059e-02 0.286 #> 50 s25 2 0.08107619 0.046081794 2.189389e-02 0.143 #> 51 s25 3 0.20888889 0.054457143 7.243571e-03 0.143 #> 52 s25 4 0.17304147 0.028059344 3.944737e-02 0.000 #> 53 s25 5 0.10125000 0.032518739 4.384618e-02 0.265 #> 54 s25 6 0.06224747 0.027655110 1.010486e-01 0.388 #> 55 s25 7 0.06842105 0.038847118 2.511973e-03 0.265 #> 56 s25 8 0.09133333 0.045138527 3.211097e-04 0.429 #> 57 s25 9 0.10859524 0.057490304 7.815038e-04 0.265 #> 58 s25 10 0.12047619 0.061641429 1.123478e-05 0.265 #> 59 s25 11 0.10363636 0.060857213 2.028286e-04 0.388 #> 60 s25 12 0.08500000 0.064615385 6.354214e-04 0.510 #> 61 s26 1 0.03000000 0.016216216 1.415092e-01 0.286 #> 62 s26 2 0.06121795 0.031590657 6.597205e-02 0.000 #> 63 s26 3 0.24561463 0.044749941 1.788872e-02 0.000 #> 64 s26 4 0.14691358 0.022686695 1.315212e-01 0.000 #> 65 s26 5 0.12750000 0.037815126 6.726327e-03 0.265 #> 66 s26 6 0.07850000 0.031029222 3.488656e-02 0.265 #> 67 s26 7 0.09276471 0.044572126 4.108105e-04 0.265 #> 68 s26 8 0.10888889 0.051851852 1.639977e-03 0.429 #> 69 s26 9 0.11653846 0.053915392 9.480869e-03 0.265 #> 70 s26 10 0.11068182 0.058503497 1.470606e-04 0.265 #> 71 s26 11 0.08800000 0.056000244 3.845248e-03 0.388 #> 72 s26 12 0.07659420 0.054352544 5.208117e-03 0.510 #> 73 s27 1 0.05820175 0.035754535 7.235176e-03 0.286 #> 74 s27 2 0.07080000 0.037224259 5.103543e-02 0.000 #> 75 s27 3 0.32218661 0.069793023 3.333564e-03 0.000 #> 76 s27 4 0.22038194 0.025648900 4.608393e-02 0.000 #> 77 s27 5 0.09700000 0.025641026 1.312726e-02 0.265 #> 78 s27 6 0.08025000 0.029109006 6.330431e-02 0.265 #> 79 s27 7 0.09861111 0.063198784 4.136352e-05 0.265 #> 80 s27 8 0.12445833 0.060735736 2.574579e-03 0.429 #> 81 s27 9 0.11500000 0.056108597 1.751163e-05 0.265 #> 82 s27 10 0.12615385 0.074422583 1.403962e-05 0.265 #> 83 s27 11 0.10424242 0.072231140 1.544500e-05 0.388 #> 84 s27 12 0.08791667 0.070532915 5.138852e-04 0.510 #> 85 s28 1 0.03157895 0.019270833 9.052551e-02 0.286 #> 86 s28 2 0.07618820 0.041502114 2.080455e-02 0.000 #> 87 s28 3 0.36193667 0.056723751 1.089705e-02 0.000 #> 88 s28 4 0.10500000 0.011532738 4.145985e-01 0.000 #> 89 s28 5 0.10850000 0.033906250 2.856768e-02 0.265 #> 90 s28 6 0.09067873 0.037591036 2.554345e-02 0.265 #> 91 s28 7 0.09235714 0.052916667 4.402259e-05 0.388 #> 92 s28 8 0.12966667 0.058024691 5.128564e-03 0.429 #> 93 s28 9 0.11275362 0.044800664 3.120179e-04 0.265 #> 94 s28 10 0.12153846 0.055555556 3.993113e-03 0.510 #> 95 s28 11 0.08846154 0.057142857 4.074511e-03 0.388 #> 96 s28 12 0.08366667 0.061231244 1.018698e-02 0.510 #> 97 s29 1 0.05120690 0.029428105 2.642330e-02 0.286 #> 98 s29 2 0.07160819 0.035634921 8.572623e-03 0.000 #> 99 s29 3 0.26152899 0.033585203 5.363684e-02 0.000 #> 100 s29 4 0.12291667 0.013603409 2.117869e-01 0.000 #> 101 s29 5 0.11394231 0.034075957 4.466251e-02 0.265 #> 102 s29 6 0.07797980 0.035250115 2.805637e-02 0.265 #> 103 s29 7 0.10285714 0.059523810 2.533027e-04 0.265 #> 104 s29 8 0.11366667 0.050435855 8.884414e-03 0.429 #> 105 s29 9 0.15761905 0.067187500 8.758759e-05 0.265 #> 106 s29 10 0.13625000 0.070798425 3.567684e-05 0.265 #> 107 s29 11 0.10417391 0.069006919 6.443038e-05 0.388 #> 108 s29 12 0.08615385 0.065846154 2.106459e-03 0.510 #> 109 s30 1 0.07600000 0.048951049 5.340930e-04 0.286 #> 110 s30 2 0.08908120 0.039410244 3.643308e-03 0.000 #> 111 s30 3 0.41671569 0.061001413 9.925441e-03 0.000 #> 112 s30 4 0.11386111 0.012277565 4.362790e-01 0.000 #> 113 s30 5 0.11093333 0.035869565 6.879173e-02 0.265 #> 114 s30 6 0.12838235 0.056921943 8.392253e-04 0.265 #> 115 s30 7 0.13761905 0.065413534 2.533027e-04 0.265 #> 116 s30 8 0.14916667 0.058152517 1.895016e-03 0.429 #> 117 s30 9 0.16666667 0.082478632 2.111428e-05 0.265 #> 118 s30 10 0.14583333 0.076500000 2.130812e-04 0.265 #> 119 s30 11 0.12556722 0.081734007 6.445445e-05 0.388 #> 120 s30 12 0.07578947 0.050917095 3.336345e-03 0.510 #> 121 s31 1 0.08571429 0.052727273 4.074511e-03 0.429 #> 122 s31 2 0.07870370 0.036647248 6.063657e-02 0.143 #> 123 s31 3 0.35833333 0.042565187 1.007196e-01 0.286 #> 124 s31 4 0.14053333 0.014184634 4.198989e-01 0.286 #> 125 s31 5 0.16636364 0.044871795 3.033737e-02 0.388 #> 126 s31 6 0.13833333 0.047021944 1.649488e-02 0.388 #> 127 s31 7 0.15386667 0.062334061 1.514804e-03 0.592 #> 128 s31 8 0.19300000 0.054761905 2.304655e-02 0.429 #> 129 s31 9 0.24571429 0.086194478 3.589921e-04 0.510 #> 130 s31 10 0.14761905 0.059523810 2.138459e-03 0.388 #> 131 s31 11 0.14344828 0.069230769 6.387538e-03 0.388 #> 132 s31 12 0.09965517 0.060595694 7.608151e-03 0.633 #> 133 s32 1 0.08466667 0.043426295 1.400912e-02 0.286 #> 134 s32 2 0.11314103 0.041508857 3.904143e-02 0.143 #> 135 s32 3 0.30393939 0.038591740 1.083414e-01 0.286 #> 136 s32 4 0.15714286 0.013570195 4.118243e-01 0.286 #> 137 s32 5 0.14600000 0.041880342 6.409905e-02 0.388 #> 138 s32 6 0.15037037 0.052616279 1.061581e-03 0.388 #> 139 s32 7 0.17291667 0.065955683 6.783002e-04 0.388 #> 140 s32 8 0.17345455 0.050765625 1.612707e-02 0.429 #> 141 s32 9 0.26583333 0.089711663 2.365656e-05 0.510 #> 142 s32 10 0.17486928 0.075420875 4.497629e-05 0.388 #> 143 s32 11 0.17954545 0.089026915 2.737201e-04 0.388 #> 144 s32 12 0.11402299 0.056140351 5.000835e-03 0.633 #> 145 s33 1 0.13267559 0.064983234 8.706155e-03 0.714 #> 146 s33 2 0.12500000 0.047290640 1.391442e-01 0.286 #> 147 s33 3 0.41261905 0.061584932 7.299943e-02 0.286 #> 148 s33 4 0.24555556 0.026432292 1.866605e-01 0.571 #> 149 s33 5 0.17218750 0.041137124 1.242185e-02 0.510 #> 150 s33 6 0.12923077 0.035294118 4.776124e-02 0.633 #> 151 s33 7 0.15551267 0.054914309 7.392249e-02 0.694 #> 152 s33 8 0.29260057 0.071267073 4.674494e-02 0.714 #> 153 s33 9 0.28540000 0.090363812 8.706155e-03 0.633 #> 154 s33 10 0.19607143 0.059547052 1.951748e-02 0.633 #> 155 s33 11 0.21300000 0.087718232 7.487887e-03 0.633 #> 156 s33 12 0.09155172 0.025580770 1.147567e-01 0.755 #> 157 s34 1 0.12702381 0.056897759 3.433985e-02 0.714 #> 158 s34 2 0.13301149 0.047835498 5.614106e-02 0.286 #> 159 s34 3 0.35770022 0.044016892 2.723427e-01 0.286 #> 160 s34 4 0.26190179 0.022779532 1.941267e-01 0.714 #> 161 s34 5 0.21895833 0.054054054 1.571435e-02 0.510 #> 162 s34 6 0.12000000 0.031337162 1.075428e-01 0.633 #> 163 s34 7 0.17200000 0.055952381 1.293706e-02 0.633 #> 164 s34 8 0.24241379 0.052486188 6.904432e-02 0.714 #> 165 s34 9 0.20392857 0.057587310 3.399435e-03 0.633 #> 166 s34 10 0.16708333 0.045949360 9.455362e-03 0.633 #> 167 s34 11 0.22626263 0.094841270 6.143820e-04 0.633 #> 168 s34 12 0.18537500 0.089791131 2.398753e-02 0.796 #> 169 s35 1 0.14233516 0.054744294 6.410358e-02 0.878 #> 170 s35 2 0.07750000 0.024286275 4.297953e-01 0.286 #> 171 s35 3 0.21884259 0.028963648 4.419310e-01 0.429 #> 172 s35 4 0.20022222 0.018637926 3.811873e-01 0.714 #> 173 s35 5 0.12500000 0.033395176 1.658567e-01 0.673 #> 174 s35 6 0.09066667 0.027073733 8.676817e-02 0.816 #> 175 s35 7 0.10333333 0.033898990 6.410358e-02 0.694 #> 176 s35 8 0.16846154 0.053061224 8.676817e-02 0.714 #> 177 s35 9 0.35769231 0.075917160 3.114308e-03 0.633 #> 178 s35 10 0.29000000 0.086761406 8.123423e-03 0.694 #> 179 s35 11 0.23070707 0.095559846 3.191966e-03 0.633 #> 180 s35 12 0.13103448 0.062380952 1.560000e-01 0.796 #> 181 s36 1 0.15627160 0.061435374 1.027948e-02 0.714 #> 182 s36 2 0.11333333 0.046975547 1.255702e-01 0.286 #> 183 s36 3 0.35244444 0.040993102 2.157853e-01 0.286 #> 184 s36 4 0.34583333 0.036185221 1.941267e-01 0.714 #> 185 s36 5 0.10102823 0.035899364 2.016123e-01 0.673 #> 186 s36 6 0.10571429 0.030886624 9.123903e-02 0.755 #> 187 s36 7 0.09100000 0.028716892 1.763604e-01 0.694 #> 188 s36 8 0.19594203 0.052359457 1.081614e-01 0.714 #> 189 s36 9 0.27897727 0.069193483 1.649011e-03 0.633 #> 190 s36 10 0.28311741 0.075931996 4.479054e-03 0.694 #> 191 s36 11 0.26103896 0.099035285 1.276679e-04 0.633 #> 192 s36 12 0.22566667 0.082398303 1.734336e-02 0.796
seasonTrend(x, plot = TRUE, ncol = 4)
#> Warning: Removed 58 rows containing missing values (position_stack).