R: groupedData changes model fit in nlme? - r

> dput(mydat)
structure(list(ID = c(31L, 35L, 115L, 48L, 36L, 73L, 111L, 51L,
113L, 20L, 16L, 51L, 59L, 79L, 107L, 90L, 60L, 72L, 21L, 28L,
104L, 65L, 63L, 132L, 99L, 52L, 93L, 57L, 87L, 83L, 57L, 69L,
110L, 12L, 78L, 125L, 80L, 80L, 126L, 74L, 48L, 135L, 7L, 5L,
66L, 51L, 136L, 46L, 3L, 80L, 130L, 6L, 129L, 63L, 88L, 49L,
60L, 71L, 42L, 89L, 106L, 128L, 114L, 82L, 103L, 8L, 67L, 130L,
118L, 130L, 48L, 13L, 51L, 100L, 85L, 21L, 87L, 67L, 39L, 8L,
18L, 29L, 74L, 103L, 98L, 135L, 88L, 10L, 93L, 128L, 2L, 90L,
12L, 10L, 66L, 52L, 25L, 128L, 123L, 75L, 13L, 3L, 37L, 85L,
53L, 13L, 10L, 76L, 93L, 68L, 40L, 36L, 29L, 109L, 96L, 120L,
4L, 75L, 81L, 119L, 45L, 11L, 77L, 136L, 33L, 17L, 15L, 126L,
99L, 45L, 26L, 37L, 42L, 2L, 105L, 98L, 62L, 42L, 27L, 124L,
47L, 85L, 115L, 122L, 120L, 100L, 136L, 62L, 99L, 99L, 78L, 71L,
93L, 118L, 28L, 103L, 43L, 64L, 56L, 124L, 128L, 103L, 82L, 138L,
110L, 60L, 49L, 12L, 92L, 89L, 123L, 44L, 108L, 71L, 3L, 26L,
125L, 6L, 113L, 117L, 97L, 6L, 17L, 91L, 109L, 126L, 32L, 90L,
114L, 66L, 104L, 12L, 1L, 98L, 76L, 60L, 23L, 69L, 84L, 111L),
Y = c(1.50403545875011, 0.786396740696073, 4.47452220273871,
4.38068147273783, 3.12839926871781, 3.71525102887885, 4.91099771631064,
5.6099549267089, 2.56348108539441, 3.19948091486236, 2.08635983067475,
1.0763458953491, 1.51606413703901, 5.24654043255577, 4.52424029343984,
4.20774205260695, 4.12910958622073, 0.633743240652555, 4.77971190302622,
3.93816934639032, 1.49484285995404, 1.56126305852856, 4.46548695066214,
1.0084930673158, 3.04727486738418, 3.35888620440587, 3.40432046722173,
3.76440032295639, 4.07871050532828, 4.19226071864204, 1.7160033436348,
1.03724192908934, 1.58238166258979, 3.68196445899468, 3.94299959336604,
4.1723985779393, 1.48656664161404, 2.82216807936802, 4.29307514012286,
1.56346766351964, 2.82672252016899, 2.88817949391832, 1.579432355962,
2.75587485567249, 4.52577018572453, 4.78804103487477, 3.76900787094377,
4.59964294342393, 1.20237162906479, 1.54913073517381, 2.36361197989214,
5.29470645462496, 4.37803432245733, 1.53760300072777, 4.68198252411321,
4.24868424001548, 2.70586371228392, 0.795680498261033, 2.86864443839483,
5.05097104595277, 1.75587485567249, 1.4190891773674, 4.60685410171667,
2.06818586174616, 0.965391506489986, 1.64857793561105, 5.11577022280303,
3.23527587668705, 3.70722941932729, 1.59578845118596, 3.48826861549546,
4.15706370038262, 0.487678450889512, 3.22814360759774, 4.77382300021727,
3.69583177282669, 4.62949114909487, 4.4704545944726, 2.69108149212297,
3.4379090355395, 4.8963222496021, 2.03342375548695, 4.28386634847347,
2.83569057149243, 1.69219063796734, 4.30362797638389, 3.03981055414835,
5.36239952864841, 1.58185218466613, 4.36789612311481, 2.85064623518307,
0.684841449866386, 4.95956127990689, 4.73626101314068, 3.74036268949424,
4.58490763903562, 2.04139268515822, 4.33829709023269, 4.43218332439469,
4.84305827754328, 1.81291335664286, 1.3818767641537, 1.23195309926451,
3.45651785780526, 3.28375338333253, 4.76814952267996, 4.21208101599211,
4.61628642071719, 3.52930199778798, 3.87926795682461, 0.152221483815728,
3.37621185028267, 1.05830921980416, 4.56712051193916, 4.01973923267471,
4.52527809662657, 3.55762748842683, 6.16255544574663, 3.3392526340327,
4.9800761268684, 4.67728750108277, 2.77305469336426, 4.37963179601937,
5.08042444249764, 4.41390299750444, 1.13552331082485, 4.83799224531426,
0.949064591865248, 1.67706978322635, 4.95286990226433, 1.25024436906661,
1.66401619974254, 5.82804345699154, 3.19728055812562, 4.28768978393608,
3.66913084737333, 1.61566819024883, 2.77959649125782, 1.46691039598072,
1.3041916833582, 2.99475694458763, 1.02667774806713, 3.63346845557959,
4.97170714505069, 4.92332693090275, 4.34570692127843, 1.48434160170634,
2.78175537465247, 4.30446898485417, 3.35621713421974, 2,
2.01703333929878, 2.71180722904119, 4.74145100905455, 3.49262072204319,
4.93047527477251, 4.47468237035325, 1.79239168949825, 4.8662518850263,
1.49607694983225, 1.60572117915046, 4.3945392313722, 2.07918124604762,
1.22862414376523, 1.68741633267633, 4.06740565843782, 4.09537859956006,
3.53617953213723, 3.67089495352021, 4.00436437110775, 2.10720996964787,
3.90167623132638, 6.60281435674245, 6.51005266486288, 4.60659630917929,
1.6845945840158, 4.9596613702735, 1.69603672384819, 2.68841982200271,
1.48366771663978, 1.5218420041367, 4.65083185753557, 1.83884909073726,
3.05766610390983, 3.61151088712666, 3.78290240597464, 2.01283722470517,
3.34084054981233, 1.27334439191564, 2.32837960343874, 4.10859884597357,
0.864516798738721, 1.22753724273698, 3.99100444033076, 0.752257491617398,
5.11358574735257, 4.13624480174614, 3.87128097285797, 3.99690551069567,
3.05766610390983), X = c(5.7, 6.3, 4.7, 17, 0.9, 0.6, 3,
4.1, 6.9, 2, 11.1, 3.7, 2, 12, 1, 3.4, 8.9, 12, 12, 0, 3.9,
3.9, 7.9, 17, 19, 1.7, 16, 13.9, 7, 9.9, 0.9, 0, 3.6, 17.3,
11.7, 5, 1.7, 5.6, 8.1, 11, 3.9, 16.3, 2.1, 19.7, 19.4, 2.7,
0.9, 2, 15.9, 15.9, 12.1, 16.6, 14, 7.1, 1.9, 1, 18.7, 0,
3.9, 0.9, 8.1, 11.9, 0, 4.1, 7, 2, 2.9, 9.7, 3.6, 3.4, 9.1,
8.1, 13.9, 2.4, 6.9, 11.1, 4.9, 0, 9.1, 18.9, 1.9, 12, 1.1,
2.9, 4, 7.7, 3.9, 1.7, 2.9, 2, 6.1, 2, 5, 10.7, 14.3, 2.9,
2.4, 17.9, 2, 0.9, 10, 2.9, 2.9, 1.9, 8.7, 1.9, 13.4, 0,
14, 0.9, 2.4, 2, 8.9, 1.9, 0, 9.9, 6.9, 3.7, 0, 15.1, 0,
12.1, 8.9, 10.1, 0.9, 11.7, 2.7, 11.9, 10.9, 9, 0.9, 0.9,
9.9, 17.9, 18.9, 15, 8, 15.7, 10.9, 9.7, 13.9, 3.9, 1.9,
1.9, 2, 3.1, 5.1, 0.7, 15.9, 7.9, 10.1, 6.1, 5, 4.9, 6.3,
2, 1.7, 2.9, 13.3, 2.1, 1, 3, 0.7, 7.9, 10, 4, 4, 4, 3.9,
1.9, 3.9, 4.9, 4.1, 11.9, 7, 13, 7.9, 7.1, 2.6, 8.7, 10.9,
6.1, 16.9, 2.7, 3.3, 0.3, 16.9, 0, 6, 16.3, 1.4, 9.1, 1.9,
3.9, 8, 6, 8, 11.9, 0, 0.9)), .Names = c("ID", "Y", "X"), row.names = c(NA,
-200L), class = "data.frame")
I have a simple data set with 3 variables, X, Y, and an subject ID.
> head(mydat)
ID Y X
1 31 1.5040355 5.7
2 35 0.7863967 6.3
3 115 4.4745222 4.7
4 48 4.3806815 17.0
5 36 3.1283993 0.9
6 73 3.7152510 0.6
I run the following nonlinear mixed model, and it runs just fine.
library(nlme)
model <- nlme(Y ~ a*exp(X) + b,
data = mydat,
fixed = list(a ~ 1, b ~ 1),
random = list(ID = pdDiag(list(a ~ 1, b ~ 1))),
start = list(fixed = c(a = 1, b = 1)))
Now I use the groupedData command on my data set. However, when I run the same exact analysis on the groupedData object, the model doesn't fit anymore.
mydat2 <- groupedData(Y ~ X | ID, data = mydat)
model2 <- nlme(Y ~ a*exp(X) + b,
data = mydat2,
fixed = list(a ~ 1, b ~ 1),
random = list(ID = pdDiag(list(a ~ 1, b ~ 1))),
start = list(fixed = c(a = 1, b = 1)))
Error in nlme.formula(Y ~ a * exp(X) + b, data = mydat2, fixed = list(a ~ :
step halving factor reduced below minimum in PNLS step
I do not understand why because the groupedData call should not have changed the contents of mydat. What went wrong?


mydat2 <- groupedData(Y ~ X | ID, data = mydat,order.groups=FALSE)
as the default for order.groups=TRUE (see the help for groupedData).

Related

Extrapolating data using interp linear interpolation for water quality data visualizations

Background: I'm working on creating data visualizations from water quality data. The code I have works great for the most part, but sometimes we have to drop data from the surface if it's too choppy to get good data, so we end up with data starting at 2m (sometimes 3m) depth. When we drop the values, the interpolation returns NA values at the surface, making the visualizations unusable for our purpose. See images for examples: Example of a good visualization Example of visualization with surface NAs
Example data snip and further explanation below code
The akima::interp function does not allow for extrapolation when using linear interpolation, and the non-linear option isn't appropriate for our data.
I'm looking for a workaround for this. My thoughts were either:
a line of code that does something like: "if minimum depth at station is greater than 1, copy data from minimum depth to all depths between 1 and the minimum" (disclaimer: I know this isn't appropriate for analysis, but for our purposes creating visualizations to show data trends we are okay with this)
something I'm missing within the function that will allow for extrapolation (or using a different linear interpolation function)
Here is the code I have so far (this is taken from within a larger function).
library(akima); library(dplyr)
library("data.table");library(tidyverse);library(naniar)
library(magrittr);library(janitor);library(lubridate);library(wql)
##Example data import
example.data<-structure(list(Station = c(2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8,
8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11,
11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12,
12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14,
14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15,
15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16,
16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17,
17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18,
18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18,
18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 20, 20, 20,
20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21,
21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22,
22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,
23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25, 25, 25,
25, 26, 26, 26, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27,
27, 27, 27, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 29,
29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29.5, 29.5, 29.5, 29.5,
29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 30, 30, 30, 30,
30, 30, 30, 30, 30, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,
32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33, 33,
33, 33, 34, 34, 649, 649, 649, 649, 649, 649, 649, 649, 649,
649, 657, 657, 657, 657, 657, 657, 657, 657, 657, 657), Distance.from.36 = c(127.7053,
127.7053, 127.7053, 127.7053, 127.7053, 127.7053, 127.7053, 127.7053,
127.7053, 127.7053, 125.3243, 125.3243, 125.3243, 125.3243, 125.3243,
125.3243, 125.3243, 125.3243, 125.3243, 125.3243, 119.8957, 119.8957,
119.8957, 119.8957, 119.8957, 119.8957, 119.8957, 119.8957, 119.8957,
119.8957, 119.8957, 119.8957, 119.8957, 119.8957, 119.8957, 119.8957,
115.6252, 115.6252, 115.6252, 115.6252, 115.6252, 115.6252, 115.6252,
115.6252, 115.6252, 115.6252, 115.6252, 115.6252, 110.8977, 110.8977,
110.8977, 110.8977, 110.8977, 110.8977, 110.8977, 110.8977, 110.8977,
110.8977, 104.981, 104.981, 104.981, 104.981, 104.981, 104.981,
104.981, 104.981, 104.981, 104.981, 104.981, 104.981, 104.981,
99.7699, 99.7699, 99.7699, 99.7699, 99.7699, 99.7699, 99.7699,
99.7699, 99.7699, 99.7699, 99.7699, 99.7699, 99.7699, 99.7699,
99.7699, 96.7889, 96.7889, 96.7889, 96.7889, 96.7889, 96.7889,
96.7889, 96.7889, 96.7889, 96.7889, 96.7889, 96.7889, 96.7889,
96.7889, 96.7889, 96.7889, 96.7889, 96.7889, 96.7889, 96.7889,
96.7889, 96.7889, 96.7889, 96.7889, 96.7889, 93.5951, 93.5951,
93.5951, 93.5951, 93.5951, 93.5951, 93.5951, 93.5951, 93.5951,
93.5951, 93.5951, 93.5951, 93.5951, 93.5951, 93.5951, 93.5951,
93.5951, 89.7672, 89.7672, 89.7672, 89.7672, 89.7672, 89.7672,
89.7672, 89.7672, 89.7672, 89.7672, 89.7672, 89.7672, 89.7672,
84.4458, 84.4458, 84.4458, 84.4458, 84.4458, 84.4458, 84.4458,
84.4458, 78.5444, 78.5444, 78.5444, 78.5444, 78.5444, 78.5444,
78.5444, 78.5444, 78.5444, 74.4288, 74.4288, 74.4288, 74.4288,
74.4288, 74.4288, 74.4288, 74.4288, 74.4288, 74.4288, 74.4288,
74.4288, 74.4288, 74.4288, 69.9895, 69.9895, 69.9895, 69.9895,
69.9895, 69.9895, 69.9895, 69.9895, 69.9895, 69.9895, 69.9895,
69.9895, 69.9895, 69.9895, 69.9895, 69.9895, 69.9895, 69.9895,
69.9895, 69.9895, 69.9895, 69.9895, 63.0794, 63.0794, 63.0794,
63.0794, 63.0794, 63.0794, 63.0794, 63.0794, 63.0794, 63.0794,
63.0794, 58.8909, 58.8909, 58.8909, 58.8909, 58.8909, 58.8909,
58.8909, 58.8909, 58.8909, 58.8909, 58.8909, 58.8909, 54.9481,
54.9481, 54.9481, 54.9481, 54.9481, 54.9481, 54.9481, 54.9481,
54.9481, 54.9481, 54.9481, 54.9481, 54.9481, 54.9481, 54.9481,
54.9481, 54.9481, 54.9481, 54.9481, 54.9481, 54.9481, 54.9481,
54.9481, 54.9481, 54.9481, 54.9481, 54.9481, 54.9481, 54.9481,
54.9481, 54.9481, 54.9481, 54.9481, 54.9481, 54.9481, 54.9481,
54.9481, 54.9481, 54.9481, 51.4501, 51.4501, 51.4501, 51.4501,
51.4501, 51.4501, 51.4501, 51.4501, 51.4501, 51.4501, 51.4501,
51.4501, 51.4501, 51.4501, 51.4501, 51.4501, 51.4501, 51.4501,
51.4501, 51.4501, 51.4501, 51.4501, 51.4501, 51.4501, 46.6266,
46.6266, 46.6266, 46.6266, 46.6266, 46.6266, 46.6266, 46.6266,
46.6266, 46.6266, 46.6266, 46.6266, 46.6266, 46.6266, 46.6266,
46.6266, 43.921, 43.921, 43.921, 43.921, 43.921, 43.921, 43.921,
43.921, 43.921, 43.921, 43.921, 43.921, 43.921, 43.921, 43.921,
43.921, 43.921, 39.6557, 39.6557, 39.6557, 39.6557, 39.6557,
39.6557, 39.6557, 39.6557, 39.6557, 39.6557, 39.6557, 39.6557,
39.6557, 37.0911, 37.0911, 37.0911, 37.0911, 37.0911, 37.0911,
37.0911, 37.0911, 32.8382, 32.8382, 32.8382, 32.8382, 32.8382,
32.8382, 28.756, 28.756, 28.756, 28.756, 28.756, 28.756, 28.756,
28.756, 26.1872, 26.1872, 26.1872, 26.1872, 26.1872, 26.1872,
26.1872, 26.1872, 26.1872, 26.1872, 23.5695, 23.5695, 23.5695,
23.5695, 23.5695, 23.5695, 23.5695, 23.5695, 23.5695, 23.5695,
23.5695, 23.5695, 20.2526, 20.2526, 20.2526, 20.2526, 20.2526,
20.2526, 20.2526, 20.2526, 20.2526, 20.2526, 20.2526, 17.564,
17.564, 17.564, 17.564, 17.564, 17.564, 17.564, 17.564, 17.564,
17.564, 17.564, 17.564, 14.7543, 14.7543, 14.7543, 14.7543, 14.7543,
14.7543, 14.7543, 14.7543, 14.7543, 14.7543, 10.6065, 10.6065,
10.6065, 10.6065, 10.6065, 10.6065, 10.6065, 10.6065, 10.6065,
10.6065, 8.1507, 8.1507, 8.1507, 8.1507, 8.1507, 8.1507, 8.1507,
8.1507, 6.8085, 6.8085, 6.8085, 6.8085, 6.8085, 6.8085, 6.8085,
6.8085, 6.8085, 6.8085, 3.529, 3.529, 132.9388, 132.9388, 132.9388,
132.9388, 132.9388, 132.9388, 132.9388, 132.9388, 132.9388, 132.9388,
147.2548, 147.2548, 147.2548, 147.2548, 147.2548, 147.2548, 147.2548,
147.2548, 147.2548, 147.2548), Depth = c(2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 21L, 22L, 23L, 24L, 25L, 26L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 2L, 3L, 4L, 5L, 6L, 7L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
3L, 4L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L), Calc.Chl = c(3.3, 3.2, 3.2, 3.2,
3.2, 3.2, 3.1, 3.1, 3.2, 3.2, 3.4, 3.4, 3.6, 3.6, 3.6, 3.7, 3.5,
3.4, 3.6, 3.6, 3.7, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 3.5, 3.4,
3.5, 3.5, 3.5, 3.5, 3.5, 3.4, 3.9, 3.9, 3.8, 3.9, 3.8, 3.8, 3.8,
3.8, 3.7, 3.7, 3.7, 3.6, 4.4, 4.4, 4.3, 4.4, 4.3, 4.1, 4.1, 4.1,
4, 4.1, 3.3, 3, 2.9, 2.9, 2.9, 2.8, 2.6, 2.8, 2.8, 2.7, 2.7,
2.7, 2.8, 3.1, 3.1, 2.8, 2.5, 2.5, 2.4, 2.2, 2.2, 2.1, 2, 1.7,
1.9, 2, 2.1, 2.1, 2.2, 2.2, 2, 2, 2, 1.9, 1.9, 1.9, 1.8, 1.7,
1.6, 1.6, 1.6, 1.8, 1.9, 1.9, 1.9, 1.9, 2, 1.9, 2, 2, 2, 2, 2,
1.2, 1.9, 1.9, 1.8, 1.7, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.7,
1.4, 1.3, 1.8, 2, 1.2, 1.7, 1.7, 1.6, 1.6, 1.5, 1.5, 1.4, 1.5,
1.7, 1.7, 1.7, 1.7, 2.9, 2.8, 1.8, 1.8, 1.8, 2.2, 2.1, 1.7, 4.5,
3.8, 2.8, 2.3, 2.8, 2.9, 2.6, 2.4, 2.9, 3.8, 3.6, 3.1, 3, 3.1,
3, 2.9, 2.6, 3, 3.1, 2.8, 2.6, 2.8, 2.3, 4, 3.8, 3.2, 2.8, 2.6,
2.5, 2.7, 2.6, 2.5, 2.5, 2.5, 2.6, 2.7, 2.7, 2.6, 2.7, 3, 3,
2.8, 2.8, 2.8, 2.8, 4.1, 4.3, 4.4, 4.3, 4.3, 4.4, 4.6, 4.8, 4.9,
4.8, 4.8, 3.6, 4, 4.2, 4.2, 4, 4.3, 4.9, 5.1, 5.5, 5.5, 5.3,
5.7, 4.1, 5, 4.7, 4.9, 4.9, 5.2, 5.2, 5.2, 5.2, 5, 4.9, 4.9,
5.1, 5.1, 5.3, 5.2, 5.5, 5.6, 5.7, 5.7, 5.3, 5.2, 5.2, 5.4, 5.6,
5.8, 5.8, 5.8, 5.8, 6, 6.1, 6, 6, 6, 5.9, 6, 5.8, 6.1, 6, 4.5,
5, 5.1, 4.5, 4.6, 4.5, 4.5, 4.4, 4.4, 4.5, 4.5, 4.5, 4.4, 4.6,
4.6, 4.7, 4.8, 4.7, 4.9, 4.8, 4.5, 4.9, 5.3, 5.6, 4.1, 4.1, 4.1,
4.2, 4.2, 4.4, 4.4, 4.3, 4.4, 4.4, 4.5, 4.8, 4.7, 4.7, 4.9, 5.1,
4.1, 3.9, 3.9, 3.9, 3.8, 3.8, 3.9, 4, 4.2, 4.3, 4.5, 4.6, 5,
5.1, 5.2, 5.3, 5.2, 6.2, 5.7, 4.5, 4.1, 4.1, 4.2, 4.2, 4.3, 4.3,
4.4, 4.4, 4.4, 4.6, 4.3, 4.2, 4.1, 4.2, 4.2, 4.2, 4.3, 4.3, 4.7,
4.6, 4.6, 4.6, 4.6, 4.6, 4.2, 4.2, 4.2, 4.1, 4.1, 4.3, 4.3, 4.3,
4.8, 4.6, 4.6, 4.6, 4.6, 4.5, 4.4, 4.3, 4.3, 4.4, 4.7, 4.6, 4.7,
4.6, 4.5, 4.4, 4.3, 4.2, 4.3, 4.3, 4.4, 4.5, 5.5, 5.4, 5.3, 5.1,
5, 4.9, 4.9, 5, 5, 5.1, 5, 7.1, 6.9, 6.9, 6.9, 6.8, 6.8, 6.7,
6.5, 6.4, 6.1, 5.5, 5.3, 7.3, 7.2, 7.3, 7.4, 7.1, 6.8, 6.6, 6.4,
6.2, 6, 7.3, 8.2, 8.8, 9.6, 10.4, 10.5, 10.8, 11.1, 11.3, 11.4,
6.7, 6.6, 6.8, 6.9, 6.9, 7.3, 7.6, 7.8, 8.2, 8.2, 8.2, 8.1, 8.1,
8.1, 8.1, 8.1, 8.1, 8.5, 8.3, 8.1, 3.1, 3.1, 3.1, 3.1, 3.2, 3.1,
3.1, 3.2, 3.2, 3.2, 2.1, 2.2, 2.2, 2.3, 2.4, 2.4, 2.2, 2.3, 2.2,
2.1), DO = c(91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L,
91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L,
91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 92L, 91L, 91L, 91L,
91L, 91L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 91L, 90L, 90L, 89L,
89L, 89L, 89L, 88L, 88L, 88L, 88L, 88L, 88L, 88L, 88L, 89L, 89L,
89L, 88L, 88L, 88L, 88L, 88L, 88L, 87L, 87L, 87L, 87L, 87L, 87L,
87L, 87L, 87L, 87L, 87L, 87L, 87L, 87L, 87L, 87L, 90L, 90L, 89L,
88L, 88L, 88L, 87L, 87L, 87L, 87L, 87L, 87L, 87L, 87L, 87L, 87L,
87L, 88L, 88L, 88L, 87L, 87L, 87L, 87L, 86L, 86L, 86L, 86L, 86L,
86L, 89L, 87L, 86L, 86L, 86L, 86L, 86L, 85L, 89L, 87L, 86L, 86L,
86L, 86L, 86L, 86L, 85L, 87L, 87L, 86L, 86L, 86L, 86L, 86L, 86L,
85L, 85L, 85L, 85L, 85L, 85L, 88L, 87L, 87L, 86L, 86L, 86L, 86L,
85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L,
85L, 85L, 87L, 87L, 86L, 86L, 86L, 85L, 85L, 85L, 85L, 85L, 85L,
84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 86L,
86L, 86L, 86L, 86L, 86L, 86L, 86L, 86L, 86L, 85L, 85L, 85L, 84L,
84L, 84L, 84L, 83L, 83L, 83L, 83L, 83L, 82L, 82L, 82L, 82L, 82L,
82L, 82L, 82L, 82L, 82L, 82L, 82L, 82L, 82L, 82L, 82L, 82L, 85L,
84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L,
84L, 84L, 84L, 84L, 84L, 83L, 83L, 83L, 83L, 83L, 85L, 85L, 84L,
84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L,
86L, 86L, 85L, 85L, 85L, 85L, 85L, 84L, 84L, 84L, 84L, 84L, 84L,
84L, 84L, 84L, 84L, 92L, 90L, 89L, 88L, 88L, 87L, 87L, 87L, 87L,
87L, 87L, 87L, 87L, 90L, 90L, 89L, 89L, 89L, 89L, 89L, 90L, 92L,
92L, 92L, 92L, 92L, 91L, 91L, 91L, 91L, 91L, 90L, 90L, 90L, 90L,
92L, 92L, 92L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 90L,
90L, 91L, 91L, 91L, 91L, 91L, 92L, 92L, 92L, 91L, 90L, 90L, 89L,
89L, 89L, 89L, 88L, 88L, 88L, 88L, 91L, 91L, 92L, 91L, 91L, 91L,
91L, 90L, 89L, 88L, 87L, 87L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 89L, 89L, 89L, 89L, 89L, 89L, 89L, 89L, 89L, 89L,
85L, 85L, 85L, 85L, 85L, 85L, 86L, 86L, 82L, 83L, 83L, 84L, 84L,
85L, 85L, 86L, 86L, 86L, 74L, 74L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L), Calc.SPM = c(35L, 38L, 37L, 39L, 42L, 42L, 45L, 48L, 46L,
46L, 46L, 44L, 46L, 52L, 50L, 53L, 59L, 67L, 65L, 65L, 41L, 41L,
41L, 40L, 42L, 41L, 41L, 45L, 46L, 47L, 46L, 47L, 47L, 48L, 48L,
49L, 47L, 47L, 46L, 48L, 48L, 51L, 55L, 55L, 65L, 66L, 70L, 72L,
55L, 55L, 60L, 60L, 62L, 68L, 71L, 69L, 77L, 72L, 47L, 54L, 60L,
63L, 68L, 74L, 94L, 94L, 106L, 123L, 120L, 127L, 130L, 36L, 33L,
31L, 34L, 34L, 41L, 55L, 68L, 87L, 114L, 168L, 204L, 226L, 240L,
262L, 42L, 44L, 54L, 58L, 52L, 52L, 51L, 50L, 58L, 68L, 90L,
118L, 156L, 174L, 180L, 187L, 180L, 190L, 189L, 187L, 187L, 185L,
184L, 183L, 192L, 28L, 28L, 28L, 29L, 30L, 31L, 31L, 35L, 40L,
41L, 45L, 48L, 53L, 78L, 169L, 186L, 195L, 25L, 25L, 25L, 27L,
29L, 32L, 32L, 41L, 65L, 70L, 67L, 66L, 69L, 27L, 29L, 74L, 102L,
148L, 141L, 149L, 217L, 24L, 25L, 33L, 88L, 118L, 133L, 170L,
223L, 225L, 27L, 32L, 36L, 41L, 45L, 52L, 66L, 106L, 110L, 106L,
113L, 136L, 149L, 185L, 23L, 23L, 23L, 23L, 24L, 28L, 33L, 40L,
60L, 72L, 76L, 84L, 90L, 92L, 91L, 91L, 90L, 89L, 86L, 85L, 84L,
86L, 15L, 14L, 15L, 14L, 20L, 29L, 32L, 34L, 34L, 37L, 45L, 14L,
14L, 15L, 16L, 17L, 40L, 47L, 53L, 67L, 70L, 85L, 100L, 11L,
11L, 11L, 12L, 12L, 12L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L,
13L, 14L, 16L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 17L, 18L, 19L,
18L, 20L, 22L, 23L, 24L, 27L, 30L, 28L, 26L, 25L, 24L, 25L, 13L,
14L, 16L, 16L, 17L, 17L, 19L, 20L, 21L, 21L, 21L, 22L, 23L, 23L,
23L, 23L, 21L, 20L, 23L, 22L, 25L, 36L, 39L, 41L, 30L, 34L, 40L,
47L, 53L, 56L, 62L, 64L, 67L, 70L, 88L, 93L, 87L, 89L, 98L, 109L,
17L, 19L, 19L, 21L, 22L, 23L, 28L, 38L, 50L, 63L, 70L, 100L,
118L, 117L, 128L, 135L, 124L, 22L, 19L, 18L, 26L, 42L, 51L, 63L,
65L, 70L, 68L, 70L, 79L, 92L, 31L, 31L, 30L, 31L, 35L, 38L, 42L,
44L, 47L, 45L, 48L, 50L, 51L, 50L, 22L, 23L, 23L, 23L, 31L, 54L,
58L, 56L, 25L, 25L, 25L, 27L, 27L, 25L, 24L, 26L, 30L, 34L, 23L,
23L, 23L, 24L, 24L, 22L, 21L, 20L, 20L, 20L, 21L, 24L, 23L, 25L,
26L, 26L, 26L, 28L, 31L, 36L, 34L, 36L, 34L, 24L, 26L, 28L, 28L,
32L, 32L, 32L, 32L, 31L, 32L, 34L, 37L, 27L, 27L, 29L, 29L, 27L,
27L, 27L, 25L, 25L, 23L, 35L, 35L, 35L, 35L, 39L, 40L, 40L, 43L,
45L, 43L, 67L, 72L, 75L, 82L, 96L, 137L, 142L, 105L, 245L, 264L,
267L, 279L, 248L, 224L, 180L, 137L, 104L, 98L, 293L, 294L, 33L,
35L, 36L, 36L, 35L, 34L, 38L, 38L, 38L, 39L, 27L, 27L, 27L, 28L,
28L, 28L, 28L, 28L, 28L, 29L), Salinity = c(0.06, 0.06, 0.06,
0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.08, 0.08, 0.08, 0.08,
0.08, 0.08, 0.07, 0.07, 0.07, 0.07, 0.08, 0.08, 0.08, 0.08, 0.08,
0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08,
0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16,
0.16, 0.66, 0.65, 0.66, 0.66, 0.66, 0.66, 0.66, 0.65, 0.65, 0.65,
2.97, 3.12, 3.15, 3.16, 3.17, 3.17, 3.16, 3.16, 3.2, 3.23, 3.23,
3.24, 3.24, 3.86, 4.2, 4.57, 4.68, 4.7, 5, 5.26, 5.5, 5.84, 6.05,
6.12, 6.13, 6.12, 6.12, 6.12, 6.01, 6.23, 6.78, 7.03, 7.16, 7.25,
7.35, 7.34, 7.74, 8.05, 8.25, 8.38, 8.54, 8.59, 8.59, 8.6, 8.58,
8.61, 8.61, 8.63, 8.63, 8.64, 8.63, 8.64, 8.64, 6.87, 6.88, 6.93,
7.37, 7.67, 7.89, 8.09, 8.34, 8.5, 8.54, 8.57, 8.67, 8.81, 9.28,
10.01, 10.07, 10.09, 9.45, 9.5, 9.53, 9.74, 9.92, 10.11, 10.23,
10.94, 11.77, 11.87, 11.79, 11.77, 11.85, 13.1, 13.71, 14.52,
14.63, 14.98, 15.06, 15.06, 15.05, 16.77, 17.64, 18.82, 20.03,
20.27, 20.33, 20.48, 20.56, 20.57, 20.6, 21.19, 21.56, 21.66,
21.73, 21.78, 21.83, 21.88, 21.87, 21.87, 21.88, 21.89, 21.89,
21.89, 20.12, 20.13, 20.23, 20.31, 20.65, 21.24, 21.62, 21.87,
22.12, 22.18, 22.24, 22.34, 22.45, 22.53, 22.55, 22.53, 22.53,
22.52, 22.56, 22.57, 22.57, 22.56, 23.69, 25.05, 25.27, 25.53,
26.04, 26.32, 26.36, 26.38, 26.41, 26.46, 26.46, 25.11, 25.72,
25.97, 26.19, 26.59, 26.97, 27.05, 27.09, 27.15, 27.15, 27.22,
27.22, 27.36, 27.43, 27.48, 27.44, 27.33, 27.26, 27.27, 27.33,
27.37, 27.57, 27.68, 27.72, 27.79, 28.01, 28.19, 28.38, 28.47,
28.48, 28.48, 28.57, 28.66, 28.68, 28.8, 28.85, 28.92, 28.93,
28.95, 28.95, 28.95, 28.93, 28.93, 28.93, 28.91, 28.9, 28.91,
28.91, 28.92, 28.92, 28.92, 26.77, 26.95, 27.12, 27.26, 27.35,
27.33, 27.39, 27.41, 27.44, 27.46, 27.46, 27.47, 27.51, 27.58,
27.61, 27.62, 27.66, 27.71, 27.82, 27.73, 27.87, 27.94, 27.94,
27.94, 26.28, 26.32, 26.42, 26.48, 26.55, 26.59, 26.62, 26.62,
26.64, 26.65, 26.65, 26.65, 26.66, 26.66, 26.65, 26.65, 25.74,
25.9, 25.91, 26.16, 26.23, 26.25, 26.37, 26.45, 26.48, 26.49,
26.5, 26.52, 26.53, 26.53, 26.53, 26.53, 26.53, 24.72, 25, 25.13,
25.3, 25.43, 25.46, 25.49, 25.49, 25.5, 25.49, 25.49, 25.5, 25.5,
25.36, 25.36, 25.36, 25.36, 25.35, 25.35, 25.34, 25.34, 24.45,
24.45, 24.45, 24.45, 24.44, 24.44, 23.78, 23.78, 23.78, 23.78,
23.8, 23.81, 23.81, 23.81, 23.56, 23.56, 23.56, 23.58, 23.63,
23.68, 23.72, 23.73, 23.74, 23.74, 23.31, 23.31, 23.31, 23.33,
23.37, 23.47, 23.51, 23.58, 23.61, 23.61, 23.66, 23.74, 23.12,
23.16, 23.22, 23.26, 23.27, 23.27, 23.29, 23.33, 23.36, 23.38,
23.39, 23.05, 23.05, 23.06, 23.07, 23.08, 23.08, 23.09, 23.1,
23.12, 23.18, 23.25, 23.27, 22.63, 22.63, 22.7, 22.87, 22.89,
22.9, 22.93, 22.95, 22.98, 23.01, 22.33, 22.47, 22.51, 22.55,
22.58, 22.6, 22.62, 22.64, 22.68, 22.72, 21.29, 21.67, 21.67,
21.66, 21.68, 21.74, 21.9, 22.15, 20.73, 20.83, 20.91, 21.02,
21.1, 21.25, 21.39, 21.5, 21.68, 21.83, 20.46, 20.51, 0.06, 0.06,
0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06,
0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06), Temperature = c(19.14,
19.12, 19.11, 19.09, 19.08, 19.08, 19.06, 19.05, 19.06, 19.06,
19.1, 19.11, 19.09, 19.07, 19.07, 19.06, 19.06, 19.05, 19.05,
19.05, 19.32, 19.32, 19.33, 19.32, 19.32, 19.32, 19.32, 19.31,
19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.29, 19.71, 19.71,
19.72, 19.7, 19.7, 19.68, 19.65, 19.66, 19.62, 19.62, 19.62,
19.62, 19.78, 19.79, 19.76, 19.76, 19.74, 19.72, 19.72, 19.72,
19.71, 19.71, 19.63, 19.54, 19.5, 19.49, 19.48, 19.45, 19.39,
19.39, 19.35, 19.32, 19.33, 19.31, 19.3, 19.47, 19.21, 18.91,
18.86, 18.86, 18.81, 18.76, 18.72, 18.67, 18.64, 18.63, 18.63,
18.63, 18.63, 18.63, 18.93, 18.85, 18.7, 18.65, 18.64, 18.63,
18.62, 18.62, 18.56, 18.51, 18.48, 18.45, 18.42, 18.41, 18.41,
18.41, 18.41, 18.4, 18.41, 18.41, 18.41, 18.41, 18.41, 18.41,
18.41, 19.24, 19.21, 19.09, 18.62, 18.56, 18.5, 18.47, 18.45,
18.43, 18.44, 18.44, 18.44, 18.44, 18.41, 18.34, 18.34, 18.33,
18.72, 18.66, 18.65, 18.5, 18.39, 18.3, 18.28, 18.21, 18.13,
18.12, 18.13, 18.13, 18.12, 19.18, 18.47, 18.05, 18.02, 17.96,
17.95, 17.95, 17.95, 18.36, 17.55, 17.21, 16.85, 16.78, 16.76,
16.71, 16.69, 16.69, 16.84, 16.61, 16.51, 16.45, 16.42, 16.39,
16.37, 16.34, 16.34, 16.34, 16.34, 16.33, 16.33, 16.32, 17.11,
17.05, 16.88, 16.81, 16.69, 16.52, 16.4, 16.31, 16.21, 16.19,
16.17, 16.14, 16.12, 16.1, 16.1, 16.1, 16.1, 16.11, 16.11, 16.11,
16.11, 16.12, 16.32, 15.69, 15.54, 15.46, 15.2, 15.05, 15.02,
15.01, 14.99, 14.96, 14.96, 15.28, 15.16, 15.09, 15.04, 14.93,
14.77, 14.74, 14.72, 14.69, 14.69, 14.66, 14.65, 14.77, 14.74,
14.71, 14.73, 14.8, 14.83, 14.83, 14.8, 14.78, 14.66, 14.59,
14.57, 14.52, 14.39, 14.27, 14.15, 14.09, 14.09, 14.09, 14.03,
13.97, 13.96, 13.89, 13.86, 13.8, 13.79, 13.77, 13.77, 13.77,
13.78, 13.78, 13.78, 13.78, 13.78, 13.78, 13.78, 13.77, 13.77,
13.77, 14.97, 14.87, 14.77, 14.7, 14.65, 14.67, 14.65, 14.64,
14.63, 14.62, 14.62, 14.61, 14.59, 14.54, 14.52, 14.5, 14.49,
14.47, 14.4, 14.45, 14.38, 14.34, 14.34, 14.34, 15.6, 15.58,
15.53, 15.49, 15.46, 15.44, 15.43, 15.43, 15.41, 15.39, 15.38,
15.38, 15.38, 15.38, 15.39, 15.4, 15.69, 15.63, 15.63, 15.58,
15.58, 15.59, 15.57, 15.53, 15.5, 15.49, 15.48, 15.46, 15.45,
15.45, 15.44, 15.44, 15.45, 17.95, 17.59, 17.42, 17.15, 16.93,
16.87, 16.83, 16.81, 16.8, 16.81, 16.8, 16.79, 16.79, 17.2, 17.2,
17.2, 17.2, 17.21, 17.21, 17.22, 17.22, 18.16, 18.16, 18.16,
18.16, 18.16, 18.16, 18.88, 18.88, 18.88, 18.88, 18.87, 18.86,
18.86, 18.86, 19.34, 19.35, 19.34, 19.33, 19.26, 19.13, 19, 18.97,
18.96, 18.96, 19.76, 19.76, 19.77, 19.75, 19.68, 19.46, 19.37,
19.2, 19.14, 19.12, 19.03, 18.88, 20.13, 20.1, 19.98, 19.88,
19.86, 19.85, 19.81, 19.77, 19.73, 19.7, 19.68, 20.28, 20.27,
20.26, 20.25, 20.24, 20.23, 20.21, 20.18, 20.13, 19.96, 19.78,
19.76, 20.48, 20.48, 20.46, 20.31, 20.27, 20.25, 20.23, 20.2,
20.19, 20.17, 20.38, 20.36, 20.37, 20.36, 20.33, 20.31, 20.31,
20.31, 20.29, 20.26, 20.51, 20.43, 20.39, 20.37, 20.34, 20.22,
20.06, 19.91, 20.53, 20.49, 20.48, 20.46, 20.47, 20.48, 20.48,
20.48, 20.49, 20.48, 19.47, 19.43, 19.11, 19.1, 19.08, 19.09,
19.09, 19.09, 19.09, 19.1, 19.1, 19.11, 18.22, 18.22, 18.23,
18.22, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2)), class = "data.frame", row.names = c(NA,
-453L))
##Setting up data
example.data$Date<-'06/10/2005'
example.data$Date<-as.Date(example.data$Date,"%m/%d/%Y")
d_date<-example.data[example.data$Date=='2005-06-10',]
d_date<-drop_na(d_date,c(Calc.Chl,DO,Calc.SPM,Salinity,Temperature))
d_date$logDepth<-log10(d_date$Depth)
##add a line, if minimum depth #station > 1, copy minimum as all depths between 1 and min
#Interpolations
interp.chl <- interp(d_date$Distance.from.36, d_date$logDepth, d_date$Calc.Chl, nx = 1000, ny = 800,yo=seq(0,max(d_date$logDepth), length = 800))
interp.df.chl <- interp.chl %>% interp2xyz() %>% as.data.frame()
names(interp.df.chl) <- c("x", "y", "Chl")
interp.do <- interp(d_date$Distance.from.36, d_date$logDepth, d_date$DO, nx = 1000, ny = 800,yo=seq(0,max(d_date$logDepth), length = 800))
interp.df.do <- interp.do %>% interp2xyz() %>% as.data.frame()
names(interp.df.do) <- c("x", "y", "Oxy")
interp.spm <- interp(d_date$Distance.from.36, d_date$logDepth, d_date$Calc.SPM, nx = 1000, ny = 800,yo=seq(0,max(d_date$logDepth), length = 800))
interp.df.spm <- interp.spm %>% interp2xyz() %>% as.data.frame()
names(interp.df.spm) <- c("x", "y", "SPM")
interp.sal <- interp(d_date$Distance.from.36, d_date$logDepth, d_date$Salinity, nx = 1000, ny = 800,yo=seq(0,max(d_date$logDepth), length = 800))
interp.df.sal <- interp.sal %>% interp2xyz() %>% as.data.frame()
names(interp.df.sal) <- c("x", "y", "Sal")
interp.temp <- interp(d_date$Distance.from.36, d_date$logDepth, d_date$Temperature, nx = 1000, ny = 800,yo=seq(0,max(d_date$logDepth), length = 800))
interp.df.temp <- interp.temp %>% interp2xyz() %>% as.data.frame()
names(interp.df.temp) <- c("x", "y", "Temp")
For context, our data is organized by date/station/depth, where each date is sampled at many locations (stations), at depths in 1m increments. If we drop surface data, there is no 1m depth, and the first depth is 2. Here is a snip of data of two stations within one date that one minimum depth is 1m and the second is 2m.
TIA!

R - How to correctly run the testRetest command in the psych package?

I have what should be a very simply issue, but I cannot figure what is goign on. I want to calculate the test retest reliabilty of a variable measured at time 1 time1 and time 2 time2 using the psych package. The command is telling me that object 'values' is not found. I have no idea what this means. Any help would be appreciated.
psych::testRetest(t1=df$time1, t2=df$time2)
df <- structure(list(time1 = c(2.11111111111111, 2.05555555555556,
4.44444444444444, 5.88888888888889, 5.16666666666667, 1.94444444444444,
4.11111111111111, 2.22222222222222, 1.61111111111111, 1.5, 3.33333333333333,
2.33333333333333, 3.66666666666667, 1.72222222222222, 4, 1.88888888888889,
1.66666666666667, 3.33333333333333, 2.38888888888889, 2.77777777777778,
2.38888888888889, 4.83333333333333, 1.88888888888889, 4, 2.61111111111111,
4.83333333333333, 1.27777777777778, 1.55555555555556, 1.72222222222222,
2.33333333333333, 2.16666666666667, 2.94444444444444, 2.11111111111111,
1.11111111111111, 3.05555555555556, 3.5, 2.05555555555556, 6.16666666666667,
2.33333333333333, 2.33333333333333, 2.77777777777778, 1.61111111111111,
4, 1.27777777777778, 1.27777777777778, 3.16666666666667, 5.77777777777778,
1.88888888888889, 1.94444444444444, 4.66666666666667, 3.5, 4.5,
4.16666666666667, 3.94444444444444, 3.16666666666667, 2.11111111111111,
2.44444444444444, 3.55555555555556, 2.94444444444444, 1.33333333333333,
3.22222222222222, 4.94444444444444, 2.44444444444444, 4.11111111111111,
2.38888888888889, 2.44444444444444, 3.5, 2.77777777777778, 4.77777777777778,
3.77777777777778, 2.22222222222222, 2.11111111111111, 3.16666666666667,
2.61111111111111, 2.16666666666667, 4.61111111111111, 4.83333333333333,
4.72222222222222, 2.5, 3.44444444444444, 3.77777777777778, 2.22222222222222,
3.88888888888889, 2.88888888888889, 2.16666666666667, 4.27777777777778,
2.77777777777778, 3.5, 2.72222222222222, 3.61111111111111, 4.83333333333333,
2.44444444444444, 4.27777777777778, 3.5, 4.72222222222222, 3.66666666666667,
3.33333333333333, 3.5, 2.11111111111111, 2.22222222222222, 3.44444444444444,
1.83333333333333, 1.94444444444444, 3.88888888888889, 4.33333333333333,
2.83333333333333, 1.05555555555556, 4.61111111111111, 4.16666666666667,
2.88888888888889, 2.11111111111111, 1.55555555555556, 2.5, 3.16666666666667,
2.5, 4.05555555555556, 3.55555555555556, 4.83333333333333, 5.05555555555556,
3.61111111111111, 2.27777777777778, 2.38888888888889, 3.55555555555556,
1.94444444444444, 2.66666666666667, 4.38888888888889), time2 = c(2.05555555555556,
2, 4.33333333333333, 5.55555555555556, 4.83333333333333, 1.94444444444444,
4.33333333333333, 2.16666666666667, 1.44444444444444, 1.61111111111111,
3.94444444444444, 1.83333333333333, 4.44444444444444, 1.5, 3.61111111111111,
2.22222222222222, 2.5, 3.38888888888889, 2.55555555555556, 2.77777777777778,
2.72222222222222, 5.55555555555556, 1.77777777777778, 3.77777777777778,
2.83333333333333, 5.33333333333333, 1.5, 1.44444444444444, 1.33333333333333,
2.44444444444444, 1.88888888888889, 3, 2, 1.33333333333333, 3.05555555555556,
3.33333333333333, 1.94444444444444, 5.72222222222222, 2.38888888888889,
2.61111111111111, 3.55555555555556, 1.94444444444444, 3.72222222222222,
1.38888888888889, 1.38888888888889, 2.83333333333333, 5.72222222222222,
1.83333333333333, 2.05555555555556, 4.5, 3.38888888888889, 4.94444444444444,
4.27777777777778, 4.38888888888889, 2.44444444444444, 2.16666666666667,
2.83333333333333, 3.72222222222222, 2.66666666666667, 1.16666666666667,
2.72222222222222, 5.22222222222222, 2.11111111111111, 4.38888888888889,
2.38888888888889, 4, 3.83333333333333, 2.22222222222222, 5.16666666666667,
4.11111111111111, 1.94444444444444, 2.22222222222222, 3.55555555555556,
2.88888888888889, 2.11111111111111, 4.61111111111111, 4.72222222222222,
4.61111111111111, 2.16666666666667, 3.61111111111111, 3.66666666666667,
2.94444444444444, 4.05555555555556, 3.27777777777778, 2.11111111111111,
4.38888888888889, 2.83333333333333, 2.55555555555556, 3.16666666666667,
3.33333333333333, 4.77777777777778, 2.55555555555556, 4.38888888888889,
2.94444444444444, 4.77777777777778, 3.77777777777778, 3.33333333333333,
3.55555555555556, 1.88888888888889, 2, 3.83333333333333, 2.22222222222222,
1.38888888888889, 3.33333333333333, 3.27777777777778, 2.83333333333333,
1.05555555555556, 4.61111111111111, 4.38888888888889, 2.94444444444444,
1.94444444444444, 1.88888888888889, 2.22222222222222, 3.16666666666667,
2.11111111111111, 5.5, 4.11111111111111, 4.83333333333333, 4.44444444444444,
3.88888888888889, 2.05555555555556, 2, 3.44444444444444, 2.16666666666667,
2.77777777777778, 4.72222222222222)), row.names = c(1L, 2L, 3L,
4L, 5L, 6L, 8L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 18L, 19L,
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L,
34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 43L, 44L, 45L, 46L, 47L,
48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 59L, 60L, 61L,
63L, 64L, 65L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L,
77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 86L, 88L, 89L, 90L, 91L,
93L, 94L, 95L, 96L, 98L, 99L, 100L, 104L, 105L, 106L, 107L, 108L,
109L, 110L, 111L, 112L, 114L, 116L, 117L, 118L, 119L, 120L, 121L,
122L, 123L, 125L, 127L, 128L, 130L, 132L, 133L, 134L, 139L, 141L,
144L, 146L, 147L, 149L, 150L, 154L, 156L, 157L, 158L, 163L, 167L
), class = "data.frame")

It seems that testRetest() in psych isn't well-suited for looking at single items; it's geared towards multiple items which form a scale (if you look at the documentation, the return values - apart from r12 - don't work for a single item).
If you're looking for a simple test-retest reliability, how about just using:
cor(df$time1, df$time2)
If you're looking at multiple items using testRetest(), you may also want to pivot_longer() your data (i.e. have a 'time' column which specifies the time point of the measurement):
df2 <- df %>%
mutate(id = row_number()) %>%
pivot_longer(time1:time2, names_prefix = "time", names_to = "time", values_to = "item1")
Your data will now look like this:
# A tibble: 6 x 3
id time item1
<int> <chr> <dbl>
1 1 1 2.11
2 1 2 2.06
3 2 1 2.06
4 2 2 2
5 3 1 4.44
6 3 2 4.33
If we run testRetest() on this, it won't work (noting that we need to coerce df2 back to a dataframe, as it doesn't like tibbles produced from dplyr):
testRetest(as.data.frame(df2), select = "item1")
# Error: grouping factors must have > 1 sampled level
But if we add another item (with random data), testRetest() will work:
df2 <- df2 %>%
mutate(item2 = sample(0:100, n(), replace = TRUE))
testRetest(as.data.frame(df2), select = c("item1", "item2"))
Results:
Test Retest reliability
Call: testRetest(t1 = as.data.frame(df2), select = c("item1",
"item2"))
Number of subjects = 126 Number of items = 2
Correlation of scale scores over time -0.11
Alpha reliability statistics for time 1 and time 2
raw G3 std G3 G6 av.r S/N se lower upper var.r
Time 1 0.01 0.06 0.03 0.03 0.07 0.03 0.07 1 0
Time 2 0.01 0.11 0.06 0.06 0.12 0.03 0.11 1 0

R nls() Initial Parameter Problem, nonlinear Regression

I get a error message:
Error in nlsModel(formula, mf, start, wts) :
singular gradient matrix at initial parameter estimates
when using the nls() function like
form_Q10_parabolic_SM <- as.formula(Lin_Flux..mymol.m.2.s.1. ~ (rRef<- 5.5354)*a*exp(b*Mean_Soil_Temp_V2..C.)*((-c*Soil_Moist_V3**2)+(d*Soil_Moist_V3)+e))
Q10_parabolic_SM <- nls(form_Q10_parabolic_SM, data = conB1_2015, start = list(a = 1, b = 0.11, c = 0.0001, d = 0.01, e = 0.1))
I got my initial parameters by using the preview() function of the nsltools library like this (same definition of the formula like above)
preview(form_Q10_parabolic_SM, data = conB1_2015, start = c(a = 1, b = 0.11, c = 0.0001, d = 0.01, e = 0.1), variable = 1)
Which gives me this output with the parameters a-e above:
This looks quite good by my eyes and I really don't know what to do at this point since the preview() works just fine.
Is my model too complex or overparameterized? Or did I just do something wrong with the nls function?
Any tips would be really appreciated!
> dput(head(conB1_2015, 30))
structure(list(X = c(13L, 68L, 69L, 70L, 71L, 72L, 73L, 74L,
75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L,
88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L), IV_Date = c("2015-01-14",
"2015-03-11", "2015-03-12", "2015-03-13", "2015-03-14", "2015-03-15",
"2015-03-16", "2015-03-17", "2015-03-18", "2015-03-19", "2015-03-20",
"2015-03-21", "2015-03-22", "2015-03-23", "2015-03-24", "2015-03-25",
"2015-03-26", "2015-03-27", "2015-03-28", "2015-03-29", "2015-03-30",
"2015-03-31", "2015-04-01", "2015-04-02", "2015-04-03", "2015-04-04",
"2015-04-05", "2015-04-06", "2015-04-07", "2015-04-08"), SMmean010.... = c(24.5341666666667,
23.4754166666667, 23.0585416666667, 22.830625, 22.7447916666667,
22.7729166666666, 22.7929166666667, 22.7354166666667, 22.6579166666667,
22.5935416666667, 22.5233333333333, 22.7641666666667, 23.6010416666667,
23.445625, 23.404375, 23.2845833333333, 23.0672916666667, 22.9347916666667,
22.8272916666667, 23.0316666666667, 23.988125, 25.5647916666667,
27.055, 27.7995833333333, 26.23125, 25.4658333333333, 25.0845833333333,
24.8175, 24.605, 24.4216666666667), Lin_Flux..mymol.m.2.s.1. = c(1.13,
2.146, 1.98708333333333, 1.88416666666667, 1.57083333333333,
1.93041666666667, 2.69875, 2.8075, 3.23272727272727, 2.35818181818182,
2.23833333333333, 1.84958333333333, 2.18695652173913, 2.16958333333333,
2.69791666666667, 3.025, 1.985, 1.88083333333333, 2.30416666666667,
2.775, 1.44458333333333, 1.78791666666667, 1.04863636363636,
1.03458333333333, 1.4725, 1.86833333333333, 1.71125, 1.79, 1.53166666666667,
1.97666666666667), Mean_Soil_Temp_V2..C. = c(4.739, 5.1864, 4.08408333333333,
3.61625, 3.68508333333333, 4.09925, 4.87079166666667, 5.64720833333333,
6.58433333333333, 5.05075, 4.93708333333333, 4.109, 3.2295, 3.537,
5.1395, 5.65270833333333, 5.931875, 5.61775, 5.88695833333333,
6.86308333333333, 5.61833333333333, 4.24566666666667, 3.05952173913043,
2.45716666666667, 3.6365, 3.68820833333333, 3.83766666666667,
4.3435, 4.8745, 6.29133333333333), Soil_Moist_V3 = c(25.603137,
21.98744709, 21.8053864833333, 21.6770563291667, 20.1319423708333,
19.9826592666667, 19.8279438958333, 20.1589541791667, 21.5796382,
21.5971315083333, 21.3742824541667, 21.8992939333333, 23.9737254583333,
23.4506886041667, 23.0956395708333, 22.574581225, 22.3561680833333,
21.3806269916667, 21.4045219791667, 21.5611478916667, 25.5090813166667,
28.6440265, 31.4434210347826, 31.9276734541667, 27.5706909333333,
25.1139413583333, 24.2945348333333, 24.0232171416667, 23.705631425,
22.8323341625), precip50..mm. = c(0.6, 0, 0, 0, 0.9, 1.3, 0,
0, 0, 0, 0, 6.6, 0, 0, 0, 0, 0.1, 0.2, 0.1, 6.1, 5, 17.6, 10.4,
6.6, 0, 0, 0, 0, 0, 0), RWI = c(0.6, 0.4, 0.2, 0.133333333333333,
0.9, 1.3, 1.3, 0.65, 0.433333333333333, 0.325, 0.26, 6.6, 6.6,
3.3, 2.2, 1.65, 0.1, 0.2, 0.1, 6.1, 5, 17.6, 10.4, 6.6, 6.6,
3.3, 2.2, 1.65, 1.32, 1.1)), na.action = structure(c(`1` = 1L,
`2` = 2L, `3` = 3L, `4` = 4L, `5` = 5L, `6` = 6L, `7` = 7L, `8` = 8L,
`9` = 9L, `10` = 10L, `11` = 11L, `12` = 12L, `13` = 13L, `15` = 15L,
`16` = 16L, `17` = 17L, `18` = 18L, `19` = 19L, `20` = 20L, `21` = 21L,
`22` = 22L, `23` = 23L, `24` = 24L, `25` = 25L, `26` = 26L, `27` = 27L,
`28` = 28L, `29` = 29L, `30` = 30L, `31` = 31L, `32` = 32L, `33` = 33L,
`34` = 34L, `35` = 35L, `36` = 36L, `37` = 37L, `38` = 38L, `39` = 39L,
`40` = 40L, `41` = 41L, `42` = 42L, `43` = 43L, `44` = 44L, `45` = 45L,
`46` = 46L, `47` = 47L, `48` = 48L, `49` = 49L, `50` = 50L, `51` = 51L,
`52` = 52L, `53` = 53L, `54` = 54L, `55` = 55L, `56` = 56L, `57` = 57L,
`58` = 58L, `59` = 59L, `60` = 60L, `61` = 61L, `62` = 62L, `63` = 63L,
`64` = 64L, `65` = 65L, `66` = 66L, `67` = 67L, `68` = 68L, `199` = 199L,
`218` = 218L, `219` = 219L, `220` = 220L, `221` = 221L, `222` = 222L,
`223` = 223L, `224` = 224L, `225` = 225L, `226` = 226L, `227` = 227L,
`228` = 228L, `229` = 229L, `230` = 230L, `231` = 231L, `232` = 232L,
`264` = 264L, `265` = 265L, `266` = 266L, `267` = 267L, `352` = 352L,
`353` = 353L, `354` = 354L, `355` = 355L, `356` = 356L, `357` = 357L,
`358` = 358L, `359` = 359L, `360` = 360L, `361` = 361L, `362` = 362L,
`363` = 363L, `364` = 364L, `365` = 365L, `366` = 366L), class = "omit"), row.names = c(14L,
69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L,
82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L,
95L, 96L, 97L), class = "data.frame")

The main problem is that the parameters are not uniquely identifiable. We can multiply a by an arbitrary number and divide c, d and e by that same number and we get the same model. Omit a.
Although it won't hurt the use of as.formula is redundant since it is already a formula.
Having an assignment within an nls formula is highly unusual. nls will think that Rref is a parameter and fail on that account. Remove the assignment.
If we make these changes then it does give an answer with the data in the updated version of the question.
form_Q10_parabolic_SM <- Lin_Flux..mymol.m.2.s.1. ~
exp(b*Mean_Soil_Temp_V2..C.) * ( (-c*Soil_Moist_V3**2) + (d*Soil_Moist_V3) + e)
Q10_parabolic_SM <- nls(form_Q10_parabolic_SM, data = conB1_2015,
start = list(b = 0.11, c = 0.0001, d = 0.01, e = 0.1))
giving:
> Q10_parabolic_SM
Nonlinear regression model
model: Lin_Flux..mymol.m.2.s.1. ~ exp(b * Mean_Soil_Temp_V2..C.) * ((-c * Soil_Moist_V3^2) + (d * Soil_Moist_V3) + e)
data: conB1_2015
b c d e
0.103062 -0.001564 -0.135531 3.528621
residual sum-of-squares: 3.979
Number of iterations to convergence: 6
Achieved convergence tolerance: 4.401e-06
plinear
Note that nls also has the plinear algorithm which has the advantage that only nonlinear parameters (in this case only b) need starting values. In that case the formula's RHS should be a matrix with the columns that multiply each linear parameter. It gives the same answer as above except the linear parameters are given names starting with .lin . Note that the plinear version converges in fewer iterations than the version using the default algorithm above. (Also it seems that the plinear version is not very sensitive to the starting value and even if we use b=1 as the starting value it converges.)
fo <- Lin_Flux..mymol.m.2.s.1. ~
cbind(-Soil_Moist_V3**2, Soil_Moist_V3, 1) * exp(b*Mean_Soil_Temp_V2..C.)
fm <- nls(fo, data = conB1_2015, start = list(b = 0.11), algorithm = "plinear")
giving:
> fm
Nonlinear regression model
model: Lin_Flux..mymol.m.2.s.1. ~ cbind(-Soil_Moist_V3^2, Soil_Moist_V3, 1) * exp(b * Mean_Soil_Temp_V2..C.)
data: conB1_2015
b .lin1 .lin.Soil_Moist_V3 .lin3
0.103062 -0.001564 -0.135528 3.528593
residual sum-of-squares: 3.979
Number of iterations to convergence: 3
Achieved convergence tolerance: 2.189e-06

Calculating Intraclasscorrelation coefficient - Error

I´m trying to calculate the intraclasscorrelation coefficient as a measure of agreement among raters. I have a dataset with x raters, and their average ratings.
I tried to calculate the ICC with the "irr" library. However, I keep getting error messages. I also seem to have problems with getting data in the right format. I first had two columns (1 with subject, 1 with rating). The package could not seem to handle that. Next, I tried to reshape the dataset from long to wide format. I tried to use spread and cast, however, none of these produces a useable dataset, that does not result in an error.
structure(list(subject = c(1L, 2L, 3L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L,
37L, 38L, 39L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L,
51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L,
64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L), Rating = c(4.38888888888889,
3.47368421052632, 4.35, 4.42105263157895, 4.5, 3.33333333333333,
4.73684210526316, 3.25, 4.33333333333333, 4.26315789473684, 3.5,
4.38888888888889, 2.42857142857143, 3.15, 5.11111111111111, 3.63157894736842,
3.35, 3.88888888888889, 4.15789473684211, 4, 3.44444444444444,
3.68421052631579, 3.95, 4.88888888888889, 4.42105263157895, 3.35,
3.94444444444444, 3.26315789473684, 4.05, 4.16666666666667, 4.42105263157895,
4.25, 3.94444444444444, 3.21052631578947, 4.2, 5.44444444444444,
3.78947368421053, 3.1, 5.02631578947368, 4.5, 3.33333333333333,
4, 4.1, 4.16666666666667, 4.89473684210526, 4.05, 4.33333333333333,
4.05263157894737, 3.05, 2.77777777777778, 4.47368421052632, 3.85,
3.77777777777778, 3.31578947368421, 2.95, 3.77777777777778, 3.36842105263158,
3.85, 4.66666666666667, 4, 4.55, 3.94444444444444, 4.31578947368421,
4.9, 4.27777777777778, 4.05263157894737, 3.95, 4.33333333333333,
4, 3.45)), row.names = c(1L, 4L, 7L, 10L, 13L, 16L, 19L, 22L,
25L, 28L, 31L, 34L, 37L, 40L, 43L, 46L, 49L, 52L, 55L, 58L, 61L,
64L, 67L, 70L, 73L, 76L, 79L, 82L, 85L, 88L, 91L, 94L, 97L, 100L,
103L, 106L, 109L, 112L, 115L, 118L, 121L, 124L, 127L, 130L, 133L,
136L, 139L, 142L, 145L, 148L, 151L, 154L, 157L, 160L, 163L, 166L,
169L, 172L, 175L, 178L, 181L, 184L, 187L, 190L, 193L, 196L, 199L,
202L, 205L, 208L), class = "data.frame")
library(tidyr)
library(reshape)
library(irr)
#check data
df7da3
str(df7da3)
#try to reshape data
df7da3_wide <- spread(df7da3, subject, Rating, fill = 0)
df7da3_widec <- cast(df7da3, ~ subject, mean, fill = 0, value = "Rating")
icc(ratings = df7da3_wide, model = "oneway", type = "consistency", unit = "average")
icc(ratings = df7da3_widec, model = "oneway", type = "consistency", unit = "average")
Both reshapes work without error. However, for both reshaped datasets, I get Warning messages and no result when trying to calculate the ICC.
1: In qf(1 - alpha/2, ns - 1, ns * (nr - 1)) : NaNs wurden erzeugt
2: In qf(1 - alpha/2, ns * (nr - 1), ns - 1) : NaNs wurden erzeugt (means generated)
Does anyone have any clue?
Thank you for your time!

Calling function inside with-statement gives error variable not found in function scope

I am preparing a bootstrapped estimation of a mean prediction error on a multiple imputed dataset. My function seems to be unable to find the dependent variable in scope. Is there some way to circumvent that?
Multiple imputation runs smoothly, but the specific problem seems to be that the line
mod.nb.train <- with(data = data.mi.train, exp = glm.nb(f))
cannot find the variable CG.tot:
Error in eval(expr, envir, enclos) : object 'CG.tot' not found
However, if I state the formula as a string:
glm.nb(formula=CG.tot~Fibrinogen)
it works...
Minimal running example:
library(mice)
library(MASS)
#compute the mean prediction error on a dataframe with missing data
predicterr <- function(f, data, indices){
if(!(class(f)=="formula")){stop("'f' must be of the 'formula' type")}
if(!(class(data)=="data.frame")){stop("'data' must be of the 'data.frame' type")}
#recompute random sampling & multiple imputation
data.test <- data[sample(nrow(data), 15),]
data.train <- data[setdiff(rownames(data), rownames(data.test)),]
data.mi.train <- mice(data.train)
data.mi.test <- mice(data.test)
#recompute model
mod.nb.train <- with(data = data.mi.train, exp = glm.nb(f))
coeffs <- summary(pool(mod.nb.train))[,"est"]
#compute prediction error on each dataset row
errvec <- apply(complete(data.mi.test, include = F, action = "long")[,c(names(coeffs)[-1], as.character(f)[2])],
1, function(x){
return(exp(sum(x[1:length(x)-1]*coeffs[-1], coeffs[1]))-x[length(x)])
})
return(mean(errvec))
}
predicterr(CG.tot~Fibrinogen, d.mi)
Dataset (a little long, but that's for the imputation...):
d <- structure(list(Hb = c(7.5, 12.9, 12.9, 10.2, 10.5, 11.2, 12.7,
9.3, 11.7, 13.4, 151, 10.9, 5.9, 12.8, 10.2, 15.3, 13.8, 9.6,
7.6, 12.2, 11.1, 13.6, 8.9, 7.2, 7.8, 8.7, 10.3, 14, 8.8, 7.5
), Hct = c(23, 39.8, 39.4, 31.6, 32.5, 34.4, 39, 28, 35.9, 41.2,
43.8, 33.7, 18.6, 37.7, 31.7, 44, 87.3, 29.4, 23.6, 37.7, 34.3,
39.8, 27.4, 22.6, 24.2, 29.1, 31.8, 43.1, 27.3, 23.3), EXTEM.CT = c(51L,
60L, 45L, 115L, 55L, 48L, 49L, 106L, 56L, 68L, 61L, 53L, 69L,
44L, 58L, 126L, 47L, 68L, 49L, 68L, 51L, 84L, 63L, 66L, 51L,
108L, 63L, 51L, 53L, 63L), EXTEM.CFT = c(133L, 162L, 175L, 216L,
101L, 60L, 140L, 248L, 137L, 203L, 113L, 199L, 316L, 90L, 224L,
235L, 133L, 46L, 308L, 300L, 119L, 420L, 44L, 207L, 91L, 69L,
96L, 130L, 153L, 99L), EXTEM.MCF = c(59L, 55L, 50L, 46L, 64L,
72L, 52L, 46L, 50L, 50L, 60L, 40L, 40L, 56L, 46L, 47L, 52L, 67L,
40L, 35L, 83L, 30L, 82L, 47L, 61L, 76L, 63L, 51L, 58L, 58L),
INTEM.CT = c(NA, 158L, 154L, 240L, 141L, 141L, 143L, 122L,
104L, 193L, 183L, 186L, 182L, 172L, 192L, 149L, 133L, 162L,
238L, 158L, 144L, 144L, 162L, 213L, 139L, 157L, 104L, 376L,
140L, 192L), INTEM.CFT = c(NA, 91L, 119L, 165L, 97L, 51L,
118L, 190L, 84L, 90L, 82L, 114L, 226L, 90L, 89L, 209L, NA,
64L, 203L, 222L, 64L, 104L, 43L, 170L, 66L, 50L, 61L, 332L,
70L, 66L), INTEM.MCF = c(NA, 57L, 48L, 48L, 74L, 70L, 49L,
50L, 50L, 55L, 58L, 49L, 40L, 57L, 48L, 46L, 64L, 68L, 44L,
39L, 64L, 54L, 80L, 51L, 64L, 78L, 68L, 54L, 62L, 61L), FIBTEM.CT = c(50L,
62L, 101L, 123L, 58L, 49L, 49L, 74L, 77L, 117L, 61L, 54L,
79L, 41L, 69L, 189L, 49L, 67L, 55L, 56L, 57L, 59L, 56L, 62L,
57L, 65L, 51L, 58L, 68L, 67L), FIBTEM.CFT = c(NA, NA, NA,
NA, NA, 94L, NA, NA, NA, NA, NA, 615L, NA, 56L, NA, NA, NA,
79L, NA, NA, 625L, NA, 75L, NA, 892L, NA, NA, NA, NA, 1206L
), FIBTEM.MCF = c(9L, 9L, NA, 5L, 10L, 21L, 11L, 4L, 6L,
3L, 16L, 7L, 6L, 31L, NA, 4L, NA, 35L, 11L, 10L, 42L, NA,
28L, 13L, 22L, 28L, 8L, 7L, 9L, 21L), INR = c(1.14, 1, 1,
1.33, 1.01, 1.07, 1.06, 1.43, 1.22, 1.12, 1.18, 1.54, NA,
1.3, 1.13, 1.05, 1.09, 1.11, 1.49, 1.22, 1.33, 1.04, NA,
1.87, 1.67, 1, 1, 1.07, 1.12, 1.88), PTT = c(30, 28.4, 22.1,
37.8, 25.6, 28.9, 27.2, 32.7, 27.2, 28.9, 27.3, 69.9, 132,
31.9, 26.5, NA, 28.9, 44.3, 50.8, 36.6, NA, 23.5, 30, 70.6,
41.2, 30.1, 25.7, 26.7, 26, 41.9), Platelets = c(150, 193,
343, 138, 284, 216, 141, 291, 142, 230, 254, 126, NA, 249,
153, 308, 253, 66, 30, 41, 293, 208, 545, 141, 136, 256,
249, 305, 327, 112), Fibrinogen = c(1.3, NA, NA, 0.9, 2.1,
3.4, 2.3, 1.1, 1.5, 1.1, 1.8, 0.8, NA, 2.3, 2.4, NA, 2.2,
7.4, 1.8, 1.7, NA, 2.6, 7.1, 0.6, 1.2, NA, 1.1, 2.5, 1.7,
2), CG.tot = c(3L, 2L, 3L, 11L, 12L, 0L, 1L, 10L, 4L, 4L,
5L, 0L, 12L, 11L, 3L, 9L, 5L, 0L, 4L, 0L, 0L, 3L, 0L, 21L,
2L, 1L, 1L, 1L, 2L, 3L)), .Names = c("Hb", "Hct", "EXTEM.CT",
"EXTEM.CFT", "EXTEM.MCF", "INTEM.CT", "INTEM.CFT", "INTEM.MCF",
"FIBTEM.CT", "FIBTEM.CFT", "FIBTEM.MCF", "INR", "PTT", "Platelets",
"Fibrinogen", "CG.tot"), row.names = c(50L, 38L, 54L, 82L, 86L,
4L, 24L, 78L, 59L, 58L, 72L, 16L, 85L, 81L, 45L, 77L, 70L, 6L,
63L, 7L, 11L, 53L, 13L, 93L, 36L, 30L, 18L, 19L, 40L, 43L), class = "data.frame")

You're missing one parameter in glm.nb:
mod.nb.train <- with(data = data.mi.train, exp = glm.nb(f, environment()))
and it works.

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