Testing mvpart
2017-06-27
library(tidyverse)
#> Loading tidyverse: ggplot2
#> Loading tidyverse: tibble
#> Loading tidyverse: tidyr
#> Loading tidyverse: readr
#> Loading tidyverse: purrr
#> Loading tidyverse: dplyr
#> Conflicts with tidy packages ----------------------------------------------
#> filter(): dplyr, stats
#> lag(): dplyr, stats
library(mvpart)
# source("C:/Users/dora/Downloads/mvpart_1.1-1/mvpart/R/mvpart.R")Installation
The mvpart package is no longer active on CRAN but can be installed from the archives.
# install.packages("devtools")
devtools::install_github("cran/mvpart")Or download a realease from https://github.com/cran/mvpart/releases and install it with something like:
install.packages(
"C:/Users/dora/Desktop/mvpart-1.6-2.tar.gz",
repos = NULL, type = "source"
)Some people reported installation issues (https://goo.gl/oDjjz8).
Data
- Tutorial https://goo.gl/zDLdMi
- Data from Stuart Davies (https://goo.gl/NszXEi).
Overview data
2 files attached:
- KC3spp20 – is the species matrix of abundance in each of 600 quadrats
# See a few columns from the beginning, middle and end
KC3spp20 %>% dplyr::select(1:2, 250:252, 583:585)
#> # A tibble: 600 x 8
#> ACMEAC ACTEJA GARCPA GARCRO GARCS1 ZIZYAN ZIZYCA ZIZYXX
#> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 0 2 0 0 0 0 0 0
#> 2 0 2 0 0 0 0 0 0
#> 3 0 0 0 0 0 0 0 0
#> 4 0 0 0 0 0 0 0 0
#> 5 0 0 0 0 1 0 0 0
#> 6 0 0 0 0 0 0 0 0
#> 7 0 0 0 0 0 0 0 0
#> 8 0 3 0 0 0 0 0 0
#> 9 0 0 0 0 0 0 0 0
#> 10 0 0 0 0 0 0 0 0
#> # ... with 590 more rows
# For a cleaner output for interpretation, normalized tree spp data to a mean of
# 0 and standard deviation of 1 (https://goo.gl/zDLdMi).- kc.hab – a bunch of habitat variables for the same 600 quadrats
kc.hab %>% dplyr::glimpse()
#> Observations: 600
#> Variables: 17
#> $ RB_NO3 <dbl> 38.11836, 37.64459, 37.84317, 35.11749, 31.03317, 29....
#> $ RB_NH4 <dbl> 14.024729, 12.900971, 11.899093, 11.075753, 10.416937...
#> $ RB_PO4 <dbl> 0.2903664, 0.2953065, 0.3118399, 0.3349684, 0.3549544...
#> $ Al <dbl> 0.2653886, 0.2901095, 0.3443849, 0.4109257, 0.4416475...
#> $ pH_water <dbl> 5.706885, 5.682961, 5.605031, 5.584619, 5.643615, 5.6...
#> $ Na <dbl> 4.24959e-06, 3.92321e-06, 3.17810e-06, 1.05188e-06, 2...
#> $ Mn <dbl> 0.02355666, 0.02396195, 0.02802107, 0.02861206, 0.023...
#> $ Mg <dbl> 0.4802953, 0.4917929, 0.5061534, 0.5033838, 0.4756754...
#> $ K <dbl> 0.1665143, 0.1708170, 0.1873153, 0.1976758, 0.1931228...
#> $ Fe <dbl> 9.70257e-06, 1.16542e-05, 1.41739e-05, 1.74376e-05, 2...
#> $ Ca <dbl> 1.0141365, 1.0520401, 1.0804347, 0.9086867, 0.6853189...
#> $ BS <dbl> 0.8506632, 0.8397293, 0.8163130, 0.7863761, 0.7639926...
#> $ ECEC <dbl> 2.048155, 2.053425, 2.160907, 2.129814, 1.944862, 1.7...
#> $ Bray_P <dbl> 4.888940, 4.889003, 4.965516, 4.949387, 4.768834, 4.6...
#> $ meanelev <dbl> 152.6194, 149.7764, 147.2560, 145.9847, 142.9219, 137...
#> $ convex <dbl> 0.3956250, -0.5323750, -1.9500000, -0.1047500, 0.6016...
#> $ slope <dbl> 9.402307, 11.837283, 11.064807, 11.843923, 16.917556,...Results
In this section, we first explore one result in detail; then we’ll re-run the exact same model twice more and we’ll compare the results.
Build and run the model
abundance <- data.matrix(KC3spp20)
environmental_variables <- kc.hab
formula <- abundance ~ RB_NO3 + RB_NH4 + RB_PO4 + Al + pH_water + Na + Mn +
Mg + K + Fe + Ca + BS + ECEC + Bray_P + meanelev + convex + slope
# Set a new seed for random numbers to ensure results are reproducible
set.seed(1221)
# See `?mvpart()` for argument details
mvpart_run1 <- mvpart(
form = formula,
data = environmental_variables,
all.leaves = TRUE, # annotate all nodes
rsq = TRUE, # give "rsq" plot
pca = TRUE, # plot PCA of group means and add species and site information
wgt.ave.pca = TRUE # plot weighted averages acorss sites for species
)
#> rpart(formula = form, data = data)
#>
#> Variables actually used in tree construction:
#> [1] Bray_P BS Fe meanelev pH_water RB_PO4
#>
#> Root node error: 483776/600 = 806.29
#>
#> n= 600
#>
#> CP nsplit rel error xerror xstd
#> 1 0.083600 0 1.00000 1.00355 0.040850
#> 2 0.025010 2 0.83280 0.85445 0.035633
#> 3 0.021388 3 0.80779 0.85882 0.035499
#> 4 0.021148 4 0.78640 0.84342 0.035072
#> 5 0.021020 5 0.76525 0.84155 0.035058
#> 6 0.020172 6 0.74423 0.82880 0.034711
#> 7 0.014623 7 0.72406 0.80000 0.033189
#> May not be applicable for this method
Model details
Structure
str(mvpart_run1)
#> List of 13
#> $ frame :'data.frame': 15 obs. of 9 variables:
#> ..$ var : Factor w/ 18 levels "<leaf>","RB_NO3",..: 4 16 13 1 1 6 1 1 11 16 ...
#> ..$ n : int [1:15] 600 119 47 18 29 72 69 3 481 154 ...
#> ..$ wt : num [1:15] 600 119 47 18 29 72 69 3 481 154 ...
#> ..$ dev : num [1:15] 483776 128994 43170 12243 20580 ...
#> ..$ yval : num [1:15] 0.276 0.254 0.226 0.204 0.241 ...
#> ..$ complexity: num [1:15] 0.0836 0.02501 0.02139 0.0063 0.00854 ...
#> ..$ ncompete : num [1:15] 4 4 4 0 0 4 0 0 4 4 ...
#> ..$ nsurrogate: num [1:15] 0 0 0 0 0 0 0 0 0 0 ...
#> ..$ yval2 : num [1:15, 1:585] 0.0567 0 0 0 0 ...
#> $ where : int [1:600] 14 14 11 11 11 11 11 11 11 11 ...
#> $ call : language mvpart(form = formula, data = environmental_variables, all.leaves = TRUE, rsq = TRUE, pca = TRUE, wgt.ave.pca = TRUE)
#> $ terms :Classes 'terms', 'formula' language abundance ~ RB_NO3 + RB_NH4 + RB_PO4 + Al + pH_water + Na + Mn + Mg + K + Fe + Ca + BS + ECEC + Bray_P + meanelev + convex + slope
#> .. ..- attr(*, "variables")= language list(abundance, RB_NO3, RB_NH4, RB_PO4, Al, pH_water, Na, Mn, Mg, K, Fe, Ca, BS, ECEC, Bray_P, meanelev, convex, slope)
#> .. ..- attr(*, "factors")= int [1:18, 1:17] 0 1 0 0 0 0 0 0 0 0 ...
#> .. .. ..- attr(*, "dimnames")=List of 2
#> .. .. .. ..$ : chr [1:18] "abundance" "RB_NO3" "RB_NH4" "RB_PO4" ...
#> .. .. .. ..$ : chr [1:17] "RB_NO3" "RB_NH4" "RB_PO4" "Al" ...
#> .. ..- attr(*, "term.labels")= chr [1:17] "RB_NO3" "RB_NH4" "RB_PO4" "Al" ...
#> .. ..- attr(*, "order")= int [1:17] 1 1 1 1 1 1 1 1 1 1 ...
#> .. ..- attr(*, "intercept")= int 1
#> .. ..- attr(*, "response")= int 1
#> .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
#> .. ..- attr(*, "predvars")= language list(abundance, RB_NO3, RB_NH4, RB_PO4, Al, pH_water, Na, Mn, Mg, K, Fe, Ca, BS, ECEC, Bray_P, meanelev, convex, slope)
#> .. ..- attr(*, "dataClasses")= Named chr [1:18] "nmatrix.585" "numeric" "numeric" "numeric" ...
#> .. .. ..- attr(*, "names")= chr [1:18] "abundance" "RB_NO3" "RB_NH4" "RB_PO4" ...
#> $ cptable : num [1:7, 1:5] 0.0836 0.025 0.0214 0.0211 0.021 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:7] "1" "2" "3" "4" ...
#> .. ..$ : chr [1:5] "CP" "nsplit" "rel error" "xerror" ...
#> $ splits : num [1:35, 1:5] 600 600 600 600 600 119 119 119 119 119 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:35] "RB_PO4" "RB_NO3" "Fe" "Bray_P" ...
#> .. ..$ : chr [1:5] "count" "ncat" "improve" "index" ...
#> $ method : chr "mrt"
#> $ dissim : chr "euclidean"
#> $ parms : num 0
#> $ control :List of 9
#> ..$ minsplit : num 5
#> ..$ minbucket : num 2
#> ..$ cp : num 0.01
#> ..$ maxcompete : num 4
#> ..$ maxsurrogate : num 0
#> ..$ usesurrogate : num 2
#> ..$ surrogatestyle: num 0
#> ..$ maxdepth : num 30
#> ..$ xval : num 10
#> $ functions:List of 3
#> ..$ summary:function (yval, dev, wt, ylevel, digits)
#> ..$ text :function (yval, dev, wt, ylevel, digits, n, use.n)
#> ..$ bar :function (yval2)
#> $ y : int [1:600, 1:585] 0 0 0 0 0 0 0 0 0 0 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:600] "1" "2" "3" "4" ...
#> .. ..$ : chr [1:585] "ACMEAC" "ACTEJA" "ACTES1" "ACTIAN" ...
#> $ ordered : Named logi [1:17] FALSE FALSE FALSE FALSE FALSE FALSE ...
#> ..- attr(*, "names")= chr [1:17] "RB_NO3" "RB_NH4" "RB_PO4" "Al" ...
#> - attr(*, "class")= chr "rpart"Full summary
summary(mvpart_run1)
#> Call:
#> mvpart(form = formula, data = environmental_variables, all.leaves = TRUE,
#> rsq = TRUE, pca = TRUE, wgt.ave.pca = TRUE)
#> n= 600
#>
#> CP nsplit rel error xerror xstd
#> 1 0.08360031 0 1.0000000 1.0035530 0.04085045
#> 2 0.02501045 2 0.8327994 0.8544490 0.03563295
#> 3 0.02138814 3 0.8077889 0.8588180 0.03549908
#> 4 0.02114811 4 0.7864008 0.8434223 0.03507165
#> 5 0.02102028 5 0.7652527 0.8415517 0.03505806
#> 6 0.02017183 6 0.7442324 0.8288001 0.03471097
#> 7 0.01462291 7 0.7240606 0.7999995 0.03318905
#>
#> Node number 1: 600 observations, complexity param=0.08360031
#> Means=0.05667,0.205,0.01,0.1,0.03833,0.02333,0.001667,0.9433,0.035,0.2817,0.03667,0.6617,0.7117,0.006667,0.3183,0.065,0.2,0.3733,0.1367,0.085,0.02333,0.48,0.006667,0.2767,0.445,0.02833,0.1633,0.09333,0.125,0.001667,0.003333,0.09,0.6517,0.1217,0.005,0.57,0.08167,0.01667,0.003333,1.782,0.08333,0.015,1.227,0.4283,0.008333,1.035,0.1667,0.1633,0.005,0.01667,0.19,0.2133,0.02667,1.297,0.185,0.04833,0.01167,1.115,0.1617,0.005,0.3133,0.015,0.03167,0.015,0.01333,0.015,0.003333,0.02833,0.003333,0.055,0.02833,0.015,0.225,0.1583,1.608,0.2917,2.672,0.6367,0.1083,0.11,0.08333,0.1133,0.085,0.36,0.001667,1.738,0.01667,0.003333,1.578,0.001667,0.1483,0.1267,0.06333,0.1067,0.01333,1.068,0.001667,0.045,0.1083,0.2133,0.001667,0.3217,0.015,0.003333,0.03667,0.175,0.07,0.065,0.025,0.005,0.02,0.02,0.001667,0.001667,0.045,0.003333,0.005,0.006667,0.04,0.4867,0.7167,0.015,0.1267,0.035,0.008333,0.08833,0.001667,0.1983,0.001667,0.001667,0.06,0.01667,1.278,1.87,0.005,0.05833,0.05333,0.125,0.31,0.095,0.095,0.001667,0.3217,0.001667,0.09333,0.7717,3.778,0.008333,1.232,0.1367,0.02833,1.442,0.006667,0.003333,0.005,0.3317,0.01167,0.01167,0.005,0.7783,0.4267,0.3167,0.04167,0.305,0.1133,1.368,0.006667,0.06833,1.46,0.7,2.84,0.02167,0.3317,0.715,0.4267,0.585,0.001667,0.2617,0.2817,0.003333,0.1733,0.015,1.32,0.3067,0.03,0.3117,0.2367,0.001667,0.25,0.3183,0.001667,0.01167,0.7383,0.003333,0.006667,0.04333,0.33,0.04667,0.015,0.05167,0.02,0.001667,0.04667,0.01333,0.01667,0.03833,0.2233,0.145,0.2133,0.005,0.05667,0.2017,0.02667,0.1867,0.06667,0.001667,0.006667,0.001667,0.01667,0.001667,0.005,0.001667,0.001667,1.662,0.01833,0.001667,0.03667,0.001667,0.1367,0.2317,0.005,0.01,1.84,0.01,0.01,0.9917,0.01667,0.5083,0.003333,1.17,0.01,0.02833,0.79,0.1183,0.02167,0.3717,0.1183,0.01,0.4683,0.07833,0.001667,0.145,0.06667,0.006667,0.5967,0.025,0.03333,0.07333,0.03333,0.085,0.2217,1.607,0.665,0.06,0.4333,0.3483,0.06,0.165,0.23,3.403,0.005,0.1133,0.008333,0.145,0.09,0.07333,0.025,0.055,0.405,1.715,0.001667,0.07667,0.02167,0.01,0.001667,0.665,0.26,0.04333,0.1117,0.07167,0.64,0.006667,0.36,0.006667,1.398,0.9167,0.01,0.03,0.001667,0.2017,0.21,0.9017,0.003333,0.515,0.008333,0.005,0.01333,0.001667,0.01833,1.633,0.075,0.03,0.006667,0.01,0.03,0.04833,0.1783,2.328,0.01333,0.003333,0.2267,1.093,0.4933,0.1067,0.008333,0.04,0.01667,0.07833,0.06333,0.08667,0.03833,0.01833,0.035,0.003333,0.045,0.1333,0.2033,0.01333,0.005,0.01,0.001667,0.001667,0.2017,0.005,0.006667,0.065,0.025,0.015,0.001667,0.155,0.02,0.001667,0.18,0.055,0.02667,0.12,0.03167,0.01,0.03667,0.1533,0.003333,4.477,0.01,0.01,0.01167,0.1267,0.01167,0.605,0.165,0.2117,0.6067,0.015,0.015,0.001667,0.006667,0.01333,0.1583,0.1283,0.035,0.195,0.1683,0.02833,0.025,0.02667,0.006667,0.001667,0.2617,0.2783,0.2033,0.03,0.006667,0.08667,0.02167,0.57,0.2233,0.015,0.003333,0.4517,0.225,0.3517,0.005,0.01,0.2317,0.1333,0.001667,0.01333,0.51,0.155,0.055,0.3067,0.1183,0.06333,0.006667,0.03833,1.003,0.015,0.003333,0.645,0.001667,0.001667,0.025,0.001667,1.98,2.202,1.517,0.001667,0.4617,0.43,0.2733,0.8,0.006667,0.005,0.03167,0.001667,1.325,0.03833,0.225,0.03167,0.01333,0.01167,0.001667,0.7167,0.09167,0.02333,0.2733,0.3033,0.015,0.095,0.01833,0.003333,0.2733,0.1267,0.003333,0.04,0.5717,0.03333,0.015,0.9483,0.06333,0.01,0.2717,0.09667,0.045,0.585,0.035,0.7817,0.001667,0.1733,0.2967,0.02167,1.953,1.202,0.005,0.001667,0.045,0.04,0.006667,0.03,0.165,0.008333,0.001667,1.525,0.01667,0.105,0.06,0.001667,0.06,0.03667,0.1833,0.13,1.295,0.04667,0.4033,0.89,0.025,0.008333,0.01167,0.001667,0.2167,0.07667,0.735,0.003333,0.055,0.1133,0.001667,0.3183,0.2733,0.1133,0.17,1.725,0.3517,0.001667,0.1417,0.08667,0.5467,0.045,0.001667,0.02667,0.05833,0.02,0.006667,0.003333,14.25,0.345,0.415,0.003333,0.003333,0.025,0.001667,0.003333,0.005,0.01667,0.04,0.19,0.115,0.6167,0.03167,0.02667,0.055,0.385,0.08833,0.2283,0.2667,0.003333,0.4917,0.1217,1.088,0.003333,0.02333,0.08167,0.04833,0.025,0.025,0.055,0.01667,0.015,0.06167,0.005,0.14,0.02833,1.402,0.5933,0.3067,0.1333,0.52,0.001667,0.02167,0.01833,0.003333,0.05,0.385,1.265,0.16,0.04833,0.13,0.01667,0.4583,0.04333,0.9667,0.13,0.08833,0.06167,0.09833,0.001667,0.001667, Summed MSE=806.2928
#> left son=2 (119 obs) right son=3 (481 obs)
#> Primary splits:
#> RB_PO4 < 0.6196511 to the right, improve=0.08231885, (0 missing)
#> RB_NO3 < 31.53723 to the right, improve=0.06823403, (0 missing)
#> Fe < 1.355205e-05 to the right, improve=0.06765740, (0 missing)
#> Bray_P < 6.653596 to the right, improve=0.06374455, (0 missing)
#> BS < 0.9127396 to the right, improve=0.06325350, (0 missing)
#>
#> Node number 2: 119 observations, complexity param=0.02501045
#> Means=0,0.1008,0,0.008403,0.008403,0.008403,0.008403,0.3697,0.05882,0.1597,0.04202,0.3697,0.3193,0,0.07563,0.04202,0.5042,0.2437,0.1008,0.03361,0.02521,0.563,0,0,0.3529,0.04202,0.02521,0.1345,0.3361,0.008403,0.008403,0.008403,0.1933,0.2437,0.02521,0.8992,0.008403,0.01681,0,0.5042,0.008403,0,1.513,0.03361,0.02521,0.2437,0.1092,0.2101,0,0.008403,0.05042,0.04202,0.1092,0.4286,0.06723,0.03361,0,1.261,0.09244,0.008403,0.2521,0.02521,0.02521,0,0.01681,0.008403,0.008403,0.008403,0,0.008403,0.04202,0.02521,0.1597,0.05042,0.6639,0.6807,0.9664,0.1681,0.01681,0.05042,0.2185,0.06723,0.4286,0.1345,0,8.613,0,0,0.3109,0.008403,0.5462,0.5966,0.02521,0.05882,0.06723,1.126,0,0.008403,0.08403,0.1008,0,0.2773,0,0,0.1429,0.2857,0.03361,0.09244,0.05042,0,0,0.008403,0,0,0,0.01681,0,0,0.07563,0.6218,2.445,0,0.01681,0.008403,0.02521,0.3277,0.008403,0.05042,0,0,0.1345,0.07563,1.966,6.277,0,0,0,0.4622,0.03361,0.01681,0.07563,0.008403,1.538,0,0.3361,0.3613,1.866,0,0.4874,0.03361,0.008403,0.3782,0,0,0,0.1681,0.05882,0.008403,0.008403,0.09244,0.2101,0.1849,0.02521,0.2689,0.008403,0.3782,0,0.03361,0.3361,0.1261,1.101,0.008403,0.1597,0.3529,0.4874,0.1345,0,0.4286,0.05882,0,0.02521,0.01681,0.4874,0.4706,0,0.05882,0.09244,0,1.126,0.1261,0,0,1.034,0,0,0.06723,0.2353,0.01681,0.008403,0.03361,0,0,0.07563,0,0.008403,0.03361,0.395,0.2101,0.04202,0,0.2605,0.1345,0.01681,0.07563,0.01681,0.008403,0.008403,0.008403,0.08403,0.008403,0,0,0,5.353,0.09244,0,0.08403,0.008403,0.2605,1.059,0.02521,0.008403,4.815,0.05042,0,4.168,0.06723,2.143,0.01681,4.966,0.03361,0.1261,0.2773,0.07563,0.02521,0.3361,0.05882,0,0.1597,0.02521,0,0.06723,0,0,0.1261,0,0.01681,0.01681,0.09244,0.3277,0.437,0.2017,0.4622,0.02521,0.1597,0.2101,0.03361,0.01681,0.08403,0.7059,0,0.06723,0.01681,0.07563,0.008403,0.08403,0.05042,0.008403,0.1681,1.118,0,0.1176,0.1008,0.008403,0.008403,0.7395,0.521,0.008403,0.02521,0.01681,0.03361,0,0.03361,0,0.563,1.008,0,0,0,0.08403,0.04202,0.4118,0,0.1765,0,0,0.008403,0,0.03361,0.8571,0.02521,0.01681,0,0,0.03361,0.03361,0.437,5.555,0,0,0.1176,0.3193,0.1176,0.06723,0,0.1008,0,0,0.03361,0.06723,0,0.03361,0.06723,0.008403,0.1849,0.04202,0.1261,0,0.01681,0.008403,0,0,0.2353,0,0,0.05042,0.008403,0,0,0.6134,0.008403,0.008403,0.5042,0.1765,0.06723,0.09244,0.1429,0.05042,0,0.07563,0,6.294,0.01681,0,0.008403,0.008403,0,0.3697,0.1092,0.2521,0.1849,0.06723,0.03361,0,0,0.008403,0.008403,0.05882,0.04202,0.2185,0.1008,0.008403,0,0.008403,0,0,0.02521,0.2605,0.5126,0.1008,0.02521,0.01681,0,0.2185,0.06723,0.03361,0,0.2437,0.2773,0.2353,0,0.008403,0.958,0.03361,0,0,1.059,0.7311,0.2353,0.1933,0.05042,0.03361,0,0.02521,0.395,0.008403,0,0.1092,0.008403,0,0.02521,0,1.462,1.109,0.1765,0.008403,0.2773,2,0.1681,0.2185,0,0,0,0.008403,1.597,0.01681,0.3277,0.02521,0.008403,0.01681,0,0.3445,0.05042,0,0.1597,0.395,0.03361,0.05882,0.02521,0,0.2521,0.05882,0,0.01681,0.2605,0,0,0.5042,0.04202,0.008403,0.07563,0.1176,0.02521,0.2185,0.06723,0.1429,0,0.3277,0.563,0.008403,2.555,0.563,0,0,0.008403,0.02521,0,0.008403,0.2773,0.01681,0,0.5042,0.008403,0.008403,0.2269,0.008403,0.05882,0.01681,0,0.008403,0.8235,0.06723,0.3277,0.1933,0.008403,0,0,0,0.01681,0.01681,0.1345,0,0.008403,0.3613,0,0.8655,0.1345,0.07563,0.06723,4.361,0.1513,0.008403,0.04202,0.1092,0.06723,0.02521,0,0.008403,0.1008,0,0,0,5.924,0.06723,0.1092,0,0,0,0,0,0,0,0.05042,0.02521,0.02521,0.2353,0.008403,0,0,0.07563,0,0.1429,0.916,0,0.1597,0.03361,0.7059,0,0.008403,0,0.04202,0,0.008403,0.03361,0.01681,0,0.3025,0.008403,0.563,0.01681,2.235,0.2773,0.05042,0.07563,0.1933,0,0,0.01681,0,0.1176,0.2521,0.5798,0.04202,0.02521,0.03361,0,0.08403,0,0.6387,0.1261,0.008403,0.09244,0.02521,0,0, Summed MSE=1083.985
#> left son=4 (47 obs) right son=5 (72 obs)
#> Primary splits:
#> meanelev < 186.5991 to the left, improve=0.09379836, (0 missing)
#> slope < 20.52715 to the left, improve=0.08517653, (0 missing)
#> Fe < 4.3078e-06 to the left, improve=0.07877244, (0 missing)
#> RB_NO3 < 26.13207 to the right, improve=0.07220717, (0 missing)
#> Al < 0.05823936 to the left, improve=0.07024895, (0 missing)
#>
#> Node number 3: 481 observations, complexity param=0.08360031
#> Means=0.07069,0.2308,0.01247,0.1227,0.04574,0.02703,0,1.085,0.02911,0.3119,0.03534,0.7339,0.8087,0.008316,0.3784,0.07069,0.1247,0.4054,0.1455,0.09771,0.02287,0.4595,0.008316,0.3451,0.4678,0.02495,0.1975,0.08316,0.07277,0,0.002079,0.1102,0.7651,0.09148,0,0.4886,0.09979,0.01663,0.004158,2.098,0.1019,0.01871,1.156,0.526,0.004158,1.231,0.1809,0.1518,0.006237,0.01871,0.2245,0.2557,0.006237,1.511,0.2141,0.05198,0.01455,1.079,0.1788,0.004158,0.3285,0.01247,0.03326,0.01871,0.01247,0.01663,0.002079,0.03326,0.004158,0.06653,0.02495,0.01247,0.2412,0.185,1.842,0.1954,3.094,0.7526,0.131,0.1247,0.0499,0.1247,0,0.4158,0.002079,0.03742,0.02079,0.004158,1.892,0,0.0499,0.0104,0.07277,0.1185,0,1.054,0.002079,0.05405,0.1143,0.2412,0.002079,0.3326,0.01871,0.004158,0.0104,0.1476,0.079,0.05821,0.01871,0.006237,0.02495,0.02287,0.002079,0.002079,0.05613,0,0.006237,0.008316,0.03119,0.4532,0.289,0.01871,0.1538,0.04158,0.004158,0.02911,0,0.2349,0.002079,0.002079,0.04158,0.002079,1.108,0.7796,0.006237,0.07277,0.06653,0.04158,0.3784,0.1143,0.09979,0,0.02079,0.002079,0.03326,0.8732,4.252,0.0104,1.416,0.1622,0.03326,1.705,0.008316,0.004158,0.006237,0.3721,0,0.01247,0.004158,0.948,0.4802,0.3493,0.04574,0.3139,0.1393,1.613,0.008316,0.07692,1.738,0.842,3.27,0.02495,0.3742,0.8046,0.4116,0.6965,0.002079,0.2204,0.3368,0.004158,0.21,0.01455,1.526,0.2661,0.03742,0.3742,0.2723,0.002079,0.03326,0.3659,0.002079,0.01455,0.6653,0.004158,0.008316,0.03742,0.3534,0.05405,0.01663,0.05613,0.02495,0.002079,0.0395,0.01663,0.01871,0.0395,0.1809,0.1289,0.2557,0.006237,0.006237,0.2183,0.02911,0.2141,0.079,0,0.006237,0,0,0,0.006237,0.002079,0.002079,0.7484,0,0.002079,0.02495,0,0.106,0.02703,0,0.0104,1.104,0,0.01247,0.2058,0.004158,0.104,0,0.2308,0.004158,0.004158,0.9168,0.1289,0.02079,0.3805,0.1331,0.01247,0.5447,0.09148,0.002079,0.1642,0.08316,0.008316,0.7131,0.03119,0.03742,0.08732,0.01871,0.02495,0.1684,1.954,0.7152,0.06861,0.501,0.3825,0.06653,0.2017,0.2661,4.071,0.006237,0.1247,0.006237,0.1622,0.1102,0.07069,0.01871,0.06653,0.4636,1.863,0.002079,0.06653,0.002079,0.0104,0,0.6466,0.1954,0.05198,0.1331,0.08524,0.79,0.008316,0.4407,0.008316,1.605,0.894,0.01247,0.03742,0.002079,0.2308,0.2516,1.023,0.004158,0.5988,0.0104,0.006237,0.01455,0.002079,0.01455,1.825,0.08732,0.03326,0.008316,0.01247,0.02911,0.05198,0.1143,1.53,0.01663,0.004158,0.2536,1.285,0.5863,0.1164,0.0104,0.02495,0.02079,0.09771,0.07069,0.09148,0.04782,0.01455,0.02703,0.002079,0.0104,0.1559,0.2225,0.01663,0.002079,0.0104,0.002079,0.002079,0.1933,0.006237,0.008316,0.06861,0.02911,0.01871,0.002079,0.04158,0.02287,0,0.09979,0.02495,0.01663,0.1268,0.004158,0,0.04574,0.1726,0.004158,4.027,0.008316,0.01247,0.01247,0.1559,0.01455,0.6632,0.1788,0.2017,0.711,0.002079,0.0104,0.002079,0.008316,0.01455,0.1954,0.1455,0.03326,0.1892,0.185,0.03326,0.03119,0.03119,0.008316,0.002079,0.3202,0.2827,0.1268,0.01247,0.002079,0.104,0.02703,0.657,0.262,0.0104,0.004158,0.5031,0.2121,0.3805,0.006237,0.0104,0.05198,0.158,0.002079,0.01663,0.3742,0.01247,0.0104,0.3347,0.1351,0.07069,0.008316,0.04158,1.154,0.01663,0.004158,0.7775,0,0.002079,0.02495,0.002079,2.108,2.472,1.848,0,0.5073,0.04158,0.2994,0.9439,0.008316,0.006237,0.0395,0,1.258,0.04366,0.1996,0.03326,0.01455,0.0104,0.002079,0.8087,0.1019,0.02911,0.3015,0.2807,0.0104,0.104,0.01663,0.004158,0.2786,0.1435,0.004158,0.04574,0.6486,0.04158,0.01871,1.058,0.06861,0.0104,0.3202,0.09148,0.0499,0.6757,0.02703,0.9397,0.002079,0.1351,0.2308,0.02495,1.805,1.36,0.006237,0.002079,0.05405,0.04366,0.008316,0.03534,0.1372,0.006237,0.002079,1.778,0.01871,0.1289,0.01871,0,0.06029,0.04158,0.2287,0.1601,1.412,0.04158,0.422,1.062,0.02911,0.0104,0.01455,0.002079,0.2661,0.09148,0.8836,0.004158,0.06653,0.05198,0.002079,0.183,0.3077,0.1227,0.1954,1.073,0.4012,0,0.1663,0.08108,0.6653,0.0499,0.002079,0.03119,0.04782,0.02495,0.008316,0.004158,16.31,0.4137,0.4906,0.004158,0.004158,0.03119,0.002079,0.004158,0.006237,0.02079,0.03742,0.2308,0.1372,0.711,0.03742,0.03326,0.06861,0.4615,0.1102,0.2495,0.106,0.004158,0.5738,0.1435,1.183,0.004158,0.02703,0.1019,0.0499,0.03119,0.02911,0.06029,0.01663,0.01871,0.002079,0.004158,0.03534,0.03119,1.195,0.6715,0.3701,0.1476,0.6008,0.002079,0.02703,0.01871,0.004158,0.03326,0.4179,1.435,0.1892,0.05405,0.1538,0.02079,0.5509,0.05405,1.048,0.131,0.1081,0.05405,0.1164,0.002079,0.002079, Summed MSE=654.7975
#> left son=6 (154 obs) right son=7 (327 obs)
#> Primary splits:
#> Fe < 1.36898e-05 to the right, improve=0.13037860, (0 missing)
#> meanelev < 296.7648 to the left, improve=0.09912164, (0 missing)
#> K < 0.2082182 to the left, improve=0.09470386, (0 missing)
#> RB_NO3 < 28.03992 to the right, improve=0.08659816, (0 missing)
#> BS < 0.7272487 to the right, improve=0.08015624, (0 missing)
#>
#> Node number 4: 47 observations, complexity param=0.02138814
#> Means=0,0.06383,0,0,0,0,0,0.4255,0.04255,0.1489,0.02128,0.234,0.1915,0,0.06383,0,0.2979,0.3617,0.04255,0.02128,0.06383,0.5319,0,0,0.3191,0,0,0.1277,0.1489,0,0,0,0.1064,0.4681,0,1.128,0.02128,0,0,1.277,0,0,0.5745,0.04255,0.02128,0.3404,0.1489,0.4255,0,0,0.04255,0.04255,0.1277,0.3404,0.04255,0.04255,0,1.213,0.1915,0.02128,0.2128,0,0.02128,0,0.04255,0.02128,0.02128,0.02128,0,0.02128,0.04255,0.04255,0.02128,0.02128,0.8936,0.2553,0.9787,0.04255,0,0.06383,0.08511,0.1489,0.04255,0.1702,0,17.89,0,0,0.1489,0,0.4255,0.04255,0.06383,0.06383,0.06383,1.404,0,0.02128,0.1064,0.02128,0,0.3404,0,0,0.1277,0.08511,0.04255,0,0.1064,0,0,0.02128,0,0,0,0,0,0,0.1277,0.3191,3.021,0,0.04255,0,0.02128,0.3191,0.02128,0,0,0,0.06383,0,0.4894,4.213,0,0,0,0.5106,0,0.02128,0.1064,0.02128,1.553,0,0.234,0.1489,0.5957,0,0.3617,0.06383,0,0.5106,0,0,0,0.4255,0.06383,0,0,0.04255,0.3617,0.1489,0.02128,0.3404,0.02128,0.08511,0,0,0.1489,0.2979,0.1064,0,0.1915,0.1277,0.1277,0.04255,0,0.2766,0.1277,0,0,0.02128,1.106,0.383,0,0,0,0,1.745,0.08511,0,0,0.5106,0,0,0.02128,0.2766,0.04255,0.02128,0,0,0,0,0,0.02128,0.04255,0.4468,0.1277,0.02128,0,0.1064,0.1702,0.02128,0.02128,0.02128,0.02128,0.02128,0,0.1489,0,0,0,0,2.319,0.1702,0,0.1064,0,0.383,2.191,0,0,3.702,0.02128,0,2.596,0,2.17,0,5.511,0.02128,0.08511,0.3404,0.06383,0.02128,0.08511,0.02128,0,0.02128,0.02128,0,0.06383,0,0,0.02128,0,0.02128,0.04255,0.1064,0.02128,0.2766,0.1915,0.3404,0.02128,0.02128,0.2128,0.06383,0.04255,0.1064,0.234,0,0.02128,0.04255,0.04255,0,0.06383,0,0.02128,0.1277,1.702,0,0.1064,0.1915,0.02128,0,0.2766,0.6383,0,0.04255,0,0.02128,0,0.02128,0,0.3191,0.8511,0,0,0,0.1064,0.04255,0.2128,0,0.08511,0,0,0,0,0.06383,0.2553,0,0.02128,0,0,0.06383,0.06383,0.5106,2.787,0,0,0.1064,0.2128,0.08511,0.06383,0,0.2128,0,0,0.08511,0.1702,0,0.08511,0.1702,0,0.2128,0.02128,0.1277,0,0.04255,0.02128,0,0,0.1915,0,0,0.08511,0,0,0,0.5532,0.02128,0,0.7872,0.06383,0.02128,0,0.1064,0.02128,0,0,0,1.723,0.02128,0,0.02128,0.02128,0,0.4043,0.06383,0.234,0.2128,0,0.02128,0,0,0,0.02128,0.02128,0,0.04255,0.1702,0,0,0,0,0,0.02128,0.06383,0.234,0.02128,0,0,0,0.234,0.02128,0.04255,0,0.1064,0.2766,0.383,0,0,1,0.04255,0,0,0.6383,1.17,0.06383,0.06383,0,0.04255,0,0,0.4255,0,0,0.06383,0,0,0.06383,0,1.936,1.128,0.1489,0.02128,0.4255,3.574,0.02128,0.08511,0,0,0,0,1.234,0,0,0,0,0,0,0.02128,0,0,0,0,0.06383,0.1489,0.04255,0,0.2553,0.04255,0,0.04255,0.1277,0,0,0.383,0.02128,0.02128,0.1064,0.2553,0.02128,0.04255,0.06383,0,0,0.5319,0.4468,0,2.702,0.7447,0,0,0.02128,0.04255,0,0.02128,0.5957,0,0,0.2979,0.02128,0.02128,0.383,0,0.04255,0.04255,0,0.02128,0.3191,0.08511,0.3617,0.04255,0.02128,0,0,0,0,0.02128,0.08511,0,0,0.6809,0,1.83,0.08511,0,0.02128,4.34,0,0,0.06383,0,0.02128,0.04255,0,0,0.1064,0,0,0,3.83,0,0.1489,0,0,0,0,0,0,0,0.1277,0,0.06383,0.2553,0.02128,0,0,0,0,0.02128,1.128,0,0.1277,0.08511,0.2553,0,0.02128,0,0,0,0.02128,0.04255,0,0,0.6596,0,0.6383,0,2.213,0.4043,0.06383,0.1064,0.1277,0,0,0,0,0.06383,0.1277,0.1915,0.1064,0.06383,0.02128,0,0.1064,0,0.1915,0.04255,0,0.08511,0.04255,0,0, Summed MSE=918.5188
#> left son=8 (18 obs) right son=9 (29 obs)
#> Primary splits:
#> BS < 0.9232842 to the right, improve=0.2396797, (0 missing)
#> Al < 0.1258257 to the left, improve=0.2389171, (0 missing)
#> Ca < 1.66205 to the right, improve=0.2276489, (0 missing)
#> meanelev < 171.9552 to the right, improve=0.2231174, (0 missing)
#> pH_water < 5.678727 to the right, improve=0.2197890, (0 missing)
#>
#> Node number 5: 72 observations, complexity param=0.02114811
#> Means=0,0.125,0,0.01389,0.01389,0.01389,0.01389,0.3333,0.06944,0.1667,0.05556,0.4583,0.4028,0,0.08333,0.06944,0.6389,0.1667,0.1389,0.04167,0,0.5833,0,0,0.375,0.06944,0.04167,0.1389,0.4583,0.01389,0.01389,0.01389,0.25,0.09722,0.04167,0.75,0,0.02778,0,0,0.01389,0,2.125,0.02778,0.02778,0.1806,0.08333,0.06944,0,0.01389,0.05556,0.04167,0.09722,0.4861,0.08333,0.02778,0,1.292,0.02778,0,0.2778,0.04167,0.02778,0,0,0,0,0,0,0,0.04167,0.01389,0.25,0.06944,0.5139,0.9583,0.9583,0.25,0.02778,0.04167,0.3056,0.01389,0.6806,0.1111,0,2.556,0,0,0.4167,0.01389,0.625,0.9583,0,0.05556,0.06944,0.9444,0,0,0.06944,0.1528,0,0.2361,0,0,0.1528,0.4167,0.02778,0.1528,0.01389,0,0,0,0,0,0,0.02778,0,0,0.04167,0.8194,2.069,0,0,0.01389,0.02778,0.3333,0,0.08333,0,0,0.1806,0.125,2.931,7.625,0,0,0,0.4306,0.05556,0.01389,0.05556,0,1.528,0,0.4028,0.5,2.694,0,0.5694,0.01389,0.01389,0.2917,0,0,0,0,0.05556,0.01389,0.01389,0.125,0.1111,0.2083,0.02778,0.2222,0,0.5694,0,0.05556,0.4583,0.01389,1.75,0.01389,0.1389,0.5,0.7222,0.1944,0,0.5278,0.01389,0,0.04167,0.01389,0.08333,0.5278,0,0.09722,0.1528,0,0.7222,0.1528,0,0,1.375,0,0,0.09722,0.2083,0,0,0.05556,0,0,0.125,0,0,0.02778,0.3611,0.2639,0.05556,0,0.3611,0.1111,0.01389,0.1111,0.01389,0,0,0.01389,0.04167,0.01389,0,0,0,7.333,0.04167,0,0.06944,0.01389,0.1806,0.3194,0.04167,0.01389,5.542,0.06944,0,5.194,0.1111,2.125,0.02778,4.611,0.04167,0.1528,0.2361,0.08333,0.02778,0.5,0.08333,0,0.25,0.02778,0,0.06944,0,0,0.1944,0,0.01389,0,0.08333,0.5278,0.5417,0.2083,0.5417,0.02778,0.25,0.2083,0.01389,0,0.06944,1.014,0,0.09722,0,0.09722,0.01389,0.09722,0.08333,0,0.1944,0.7361,0,0.125,0.04167,0,0.01389,1.042,0.4444,0.01389,0.01389,0.02778,0.04167,0,0.04167,0,0.7222,1.111,0,0,0,0.06944,0.04167,0.5417,0,0.2361,0,0,0.01389,0,0.01389,1.25,0.04167,0.01389,0,0,0.01389,0.01389,0.3889,7.361,0,0,0.125,0.3889,0.1389,0.06944,0,0.02778,0,0,0,0,0,0,0,0.01389,0.1667,0.05556,0.125,0,0,0,0,0,0.2639,0,0,0.02778,0.01389,0,0,0.6528,0,0.01389,0.3194,0.25,0.09722,0.1528,0.1667,0.06944,0,0.125,0,9.278,0.01389,0,0,0,0,0.3472,0.1389,0.2639,0.1667,0.1111,0.04167,0,0,0.01389,0,0.08333,0.06944,0.3333,0.05556,0.01389,0,0.01389,0,0,0.02778,0.3889,0.6944,0.1528,0.04167,0.02778,0,0.2083,0.09722,0.02778,0,0.3333,0.2778,0.1389,0,0.01389,0.9306,0.02778,0,0,1.333,0.4444,0.3472,0.2778,0.08333,0.02778,0,0.04167,0.375,0.01389,0,0.1389,0.01389,0,0,0,1.153,1.097,0.1944,0,0.1806,0.9722,0.2639,0.3056,0,0,0,0.01389,1.833,0.02778,0.5417,0.04167,0.01389,0.02778,0,0.5556,0.08333,0,0.2639,0.6528,0.01389,0,0.01389,0,0.25,0.06944,0,0,0.3472,0,0,0.5833,0.05556,0,0.05556,0.02778,0.02778,0.3333,0.06944,0.2361,0,0.1944,0.6389,0.01389,2.458,0.4444,0,0,0,0.01389,0,0,0.06944,0.02778,0,0.6389,0,0,0.125,0.01389,0.06944,0,0,0,1.153,0.05556,0.3056,0.2917,0,0,0,0,0.02778,0.01389,0.1667,0,0.01389,0.1528,0,0.2361,0.1667,0.125,0.09722,4.375,0.25,0.01389,0.02778,0.1806,0.09722,0.01389,0,0.01389,0.09722,0,0,0,7.292,0.1111,0.08333,0,0,0,0,0,0,0,0,0.04167,0,0.2222,0,0,0,0.125,0,0.2222,0.7778,0,0.1806,0,1,0,0,0,0.06944,0,0,0.02778,0.02778,0,0.06944,0.01389,0.5139,0.02778,2.25,0.1944,0.04167,0.05556,0.2361,0,0,0.02778,0,0.1528,0.3333,0.8333,0,0,0.04167,0,0.06944,0,0.9306,0.1806,0.01389,0.09722,0.01389,0,0, Summed MSE=1023.95
#> left son=10 (69 obs) right son=11 (3 obs)
#> Primary splits:
#> pH_water < 5.495651 to the right, improve=0.1387728, (0 missing)
#> Ca < 1.195579 to the right, improve=0.1387728, (0 missing)
#> BS < 0.8153253 to the right, improve=0.1387728, (0 missing)
#> meanelev < 303.5795 to the left, improve=0.1387728, (0 missing)
#> ECEC < 2.759437 to the right, improve=0.1218917, (0 missing)
#>
#> Node number 6: 154 observations, complexity param=0.02102028
#> Means=0.07792,0.3052,0.03896,0.2273,0.1039,0.07143,0,0.9545,0.01299,0.2662,0.006494,0.7013,0.5649,0.006494,0.5325,0.05195,0.08442,0.4481,0.1364,0,0.01948,0.3312,0.006494,0.5195,0.5844,0,0.3766,0.1234,0.05844,0,0.006494,0.3312,1.097,0.02597,0,1.162,0.07143,0.03247,0.006494,0.8506,0.2792,0.01948,0.9416,1.091,0.006494,1.325,0.1169,0.07792,0.01948,0.05844,0.3636,0.474,0,2.045,0.2597,0.06494,0.03247,1.455,0.4351,0.01299,0.4221,0.01299,0.06494,0.01948,0.01299,0.01299,0,0.05195,0.006494,0.1104,0.01948,0,0.2922,0.2143,2.721,0.2662,4.201,1.052,0.2532,0.05844,0.03247,0.1558,0,0.6558,0,0,0.01299,0.006494,2.292,0,0.03247,0,0.02597,0.3117,0,1.558,0,0.02597,0.05195,0.3636,0,0.2532,0.05844,0,0.01299,0.1688,0.0974,0.1039,0.01299,0,0.03247,0.01299,0,0.006494,0.1169,0,0.01299,0.02597,0.01299,0.2662,0.1623,0.01948,0.3312,0.03896,0,0.05195,0,0.3182,0.006494,0,0.03247,0.006494,0.3831,1.026,0.01948,0.2078,0.1429,0.05195,0.3182,0.1364,0.1299,0,0.01948,0,0.04545,0.5844,3.708,0.006494,1.74,0.3571,0.01948,1.455,0.006494,0,0.006494,0.2792,0,0.03247,0,1.682,0.9675,0.2338,0.1039,0.3766,0.1364,1.013,0.02597,0.06494,1.61,2.065,3.143,0,0.2987,0.4351,0.1169,0.8182,0.006494,0.3701,0.6169,0,0.474,0.04545,0.6234,0.2792,0.1169,0.3636,0.2987,0,0.01948,0.3442,0.006494,0.03247,0.4416,0,0,0.01948,0.3117,0.0974,0.006494,0.1039,0.04545,0,0.1039,0.01299,0.01948,0.09091,0.1623,0.2013,0.1818,0,0,0.1948,0.06494,0.2273,0.2013,0,0.006494,0,0,0,0,0,0.006494,1.091,0,0,0.07143,0,0.04545,0.01299,0,0.006494,1.175,0,0.01948,0.2727,0,0.1299,0,0.3442,0,0.006494,1.39,0.1299,0.02597,0.3506,0.1234,0.03247,0.6234,0.1429,0,0.1688,0.1688,0.01948,1.026,0.05195,0.09091,0.1558,0.03896,0.05195,0.2468,2.104,0.7532,0.02597,0.7143,0.1688,0.03896,0.4026,0.3961,3.857,0.01948,0.3506,0,0.1623,0.006494,0.02597,0.006494,0.1104,0.2597,1.506,0.006494,0.09091,0,0.01948,0,1.506,0.2662,0.02597,0.1948,0.1039,1.89,0,0.2273,0.006494,0.9221,1.117,0.03896,0.07143,0,0.1169,0.1623,0.8831,0.01299,0.7792,0.006494,0.01948,0.03896,0,0,1.357,0.1299,0.07792,0.01948,0.01948,0.006494,0.1039,0.03247,2.325,0.01948,0.006494,0.3312,0.9286,0.974,0.07792,0.01299,0.07143,0.01299,0.2208,0.1948,0.2143,0.02597,0.02597,0.02597,0,0.006494,0.1948,0.3182,0.03896,0.006494,0.006494,0,0.006494,0.2597,0.01948,0.006494,0.1299,0.01948,0.04545,0,0.08442,0.05844,0,0.2143,0.01299,0.04545,0.3052,0.006494,0,0.09091,0.09091,0.01299,0.539,0.006494,0,0,0.3896,0.02597,0.1494,0.1104,0.2662,0.6364,0.006494,0.006494,0,0.01299,0.03896,0.1883,0.1753,0.01299,0.1039,0.2532,0.04545,0.08442,0.07792,0.01299,0,0.4416,0.2987,0.1883,0,0,0.1688,0.05195,0.526,0.526,0,0,0.4286,0.1039,0.4416,0.006494,0.01948,0.01948,0.2662,0.006494,0.03896,0.5909,0.006494,0.02597,0.4805,0.09091,0.1039,0.02597,0.0974,1.104,0.01948,0.01299,1.26,0,0,0.06494,0,0.5455,0.987,2.429,0,0.3377,0.07143,0.1364,0.8831,0.006494,0.01948,0.08442,0,1.708,0.06494,0.3831,0.05195,0.006494,0,0,0.8117,0.1429,0.07143,0.6623,0.6299,0.03247,0.2662,0.006494,0,0.5974,0.08442,0.01299,0.1234,0.539,0.1234,0.03247,0.8896,0.06494,0.01948,0.3636,0.1169,0.04545,0.6818,0.006494,0.7403,0,0.1688,0.4675,0.04545,1.305,0.8571,0,0.006494,0.01299,0.1039,0.02597,0.09091,0.08442,0.01948,0,1.643,0.03896,0.2208,0.02597,0,0.03896,0.06494,0.5844,0.07792,0.8636,0.04545,0.5714,1.045,0.02597,0.01299,0.006494,0.006494,0.3247,0.06494,0.4351,0.01299,0.2013,0.01948,0,0.1104,0.3442,0.09091,0.3182,2.617,0.5649,0,0.1623,0.08442,0.9416,0.1234,0,0.04545,0.0974,0.03896,0.006494,0.01299,4.422,0.2662,0.4351,0.01299,0.01299,0.08442,0.006494,0,0.01948,0.06494,0.04545,0.3442,0.3961,0.8506,0.02597,0.1039,0.1429,0.7208,0.2468,0.1688,0.1039,0.01299,0.6039,0.3377,1.24,0.01299,0.05195,0.2273,0.06494,0.05195,0.03896,0.08442,0.02597,0.05844,0.006494,0.01299,0.03896,0.006494,1.429,0.2662,0.2013,0.1169,0.9416,0,0.08442,0.05195,0,0.04545,0.3506,2.013,0.2013,0.1299,0.1429,0.01299,0.2532,0.05844,0.8182,0.08442,0.0974,0,0.1429,0,0.006494, Summed MSE=571.1102
#> left son=12 (68 obs) right son=13 (86 obs)
#> Primary splits:
#> meanelev < 286.0175 to the left, improve=0.11562230, (0 missing)
#> slope < 17.99243 to the left, improve=0.09105075, (0 missing)
#> Na < 0.005221355 to the left, improve=0.08631254, (0 missing)
#> RB_PO4 < 0.3089932 to the right, improve=0.08218126, (0 missing)
#> Fe < 0.00019561 to the left, improve=0.07501699, (0 missing)
#>
#> Node number 7: 327 observations, complexity param=0.02017183
#> Means=0.06728,0.1957,0,0.07339,0.01835,0.006116,0,1.147,0.0367,0.3333,0.04893,0.7492,0.9235,0.009174,0.3058,0.07951,0.1437,0.3853,0.1498,0.1437,0.02446,0.5199,0.009174,0.263,0.4128,0.0367,0.1131,0.06422,0.07951,0,0,0.006116,0.6086,0.1223,0,0.1713,0.1131,0.009174,0.003058,2.685,0.01835,0.01835,1.257,0.2599,0.003058,1.187,0.211,0.1865,0,0,0.159,0.1529,0.009174,1.26,0.1927,0.04587,0.006116,0.9021,0.0581,0,0.2844,0.01223,0.01835,0.01835,0.01223,0.01835,0.003058,0.02446,0.003058,0.04587,0.02752,0.01835,0.2171,0.1713,1.428,0.1621,2.572,0.6116,0.07339,0.156,0.0581,0.1101,0,0.3028,0.003058,0.05505,0.02446,0.003058,1.703,0,0.0581,0.01529,0.0948,0.02752,0,0.8165,0.003058,0.06728,0.1437,0.1835,0.003058,0.37,0,0.006116,0.009174,0.1376,0.07034,0.0367,0.02141,0.009174,0.02141,0.02752,0.003058,0,0.02752,0,0.003058,0,0.03976,0.5413,0.3486,0.01835,0.07034,0.04281,0.006116,0.01835,0,0.1957,0,0.003058,0.04587,0,1.45,0.6636,0,0.009174,0.03058,0.0367,0.4067,0.104,0.08563,0,0.02141,0.003058,0.02752,1.009,4.508,0.01223,1.263,0.07034,0.03976,1.823,0.009174,0.006116,0.006116,0.4159,0,0.003058,0.006116,0.6024,0.2508,0.4037,0.01835,0.2844,0.1407,1.896,0,0.08257,1.798,0.2661,3.33,0.0367,0.4098,0.9786,0.5505,0.6391,0,0.1498,0.2049,0.006116,0.08563,0,1.951,0.2599,0,0.3792,0.2599,0.003058,0.03976,0.3761,0,0.006116,0.7706,0.006116,0.01223,0.04587,0.3731,0.03364,0.02141,0.03364,0.01529,0.003058,0.009174,0.01835,0.01835,0.01529,0.1896,0.0948,0.2905,0.009174,0.009174,0.2294,0.01223,0.208,0.02141,0,0.006116,0,0,0,0.009174,0.003058,0,0.5872,0,0.003058,0.003058,0,0.1346,0.03364,0,0.01223,1.07,0,0.009174,0.1743,0.006116,0.09174,0,0.1774,0.006116,0.003058,0.6942,0.1284,0.01835,0.3945,0.1376,0.003058,0.5076,0.06728,0.003058,0.1621,0.04281,0.003058,0.5657,0.02141,0.01223,0.05505,0.009174,0.01223,0.1315,1.884,0.6972,0.08869,0.4006,0.4832,0.07951,0.107,0.2049,4.171,0,0.01835,0.009174,0.1621,0.159,0.09174,0.02446,0.04587,0.5596,2.031,0,0.05505,0.003058,0.006116,0,0.2416,0.1621,0.06422,0.104,0.07645,0.2722,0.01223,0.5413,0.009174,1.927,0.789,0,0.02141,0.003058,0.2844,0.2936,1.089,0,0.5138,0.01223,0,0.003058,0.003058,0.02141,2.046,0.06728,0.01223,0.003058,0.009174,0.03976,0.02752,0.1529,1.156,0.01529,0.003058,0.2171,1.453,0.4037,0.1346,0.009174,0.003058,0.02446,0.03976,0.01223,0.03364,0.0581,0.009174,0.02752,0.003058,0.01223,0.1376,0.1774,0.006116,0,0.01223,0.003058,0,0.1621,0,0.009174,0.03976,0.03364,0.006116,0.003058,0.02141,0.006116,0,0.04587,0.03058,0.003058,0.04281,0.003058,0,0.02446,0.211,0,5.67,0.009174,0.01835,0.01835,0.04587,0.009174,0.9052,0.211,0.1713,0.7462,0,0.01223,0.003058,0.006116,0.003058,0.1988,0.1315,0.04281,0.2294,0.1529,0.02752,0.006116,0.009174,0.006116,0.003058,0.263,0.2752,0.09786,0.01835,0.003058,0.07339,0.01529,0.7187,0.1376,0.01529,0.006116,0.5382,0.263,0.3517,0.006116,0.006116,0.06728,0.107,0,0.006116,0.2722,0.01529,0.003058,0.2661,0.156,0.05505,0,0.01529,1.177,0.01529,0,0.5505,0,0.003058,0.006116,0.003058,2.844,3.171,1.575,0,0.5872,0.02752,0.3761,0.9725,0.009174,0,0.01835,0,1.046,0.03364,0.1131,0.02446,0.01835,0.01529,0.003058,0.8073,0.08257,0.009174,0.1315,0.1162,0,0.02752,0.02141,0.006116,0.1284,0.1713,0,0.009174,0.7003,0.003058,0.01223,1.138,0.07034,0.006116,0.2997,0.07951,0.05199,0.6728,0.0367,1.034,0.003058,0.1193,0.1193,0.01529,2.04,1.596,0.009174,0,0.07339,0.01529,0,0.009174,0.1621,0,0.003058,1.841,0.009174,0.08563,0.01529,0,0.07034,0.03058,0.06116,0.1988,1.67,0.03976,0.3517,1.07,0.03058,0.009174,0.01835,0,0.2385,0.104,1.095,0,0.003058,0.06728,0.003058,0.2171,0.2905,0.1376,0.1376,0.3456,0.3242,0,0.1682,0.07951,0.5352,0.01529,0.003058,0.02446,0.02446,0.01835,0.009174,0,21.91,0.4832,0.5168,0,0,0.006116,0,0.006116,0,0,0.03364,0.1774,0.01529,0.6453,0.04281,0,0.03364,0.3394,0.04587,0.2875,0.107,0,0.5596,0.05199,1.156,0,0.01529,0.04281,0.04281,0.02141,0.02446,0.04893,0.01223,0,0,0,0.03364,0.04281,1.086,0.8624,0.4495,0.1621,0.4404,0.003058,0,0.003058,0.006116,0.02752,0.4495,1.162,0.1835,0.01835,0.159,0.02446,0.6911,0.05199,1.156,0.1529,0.1131,0.07951,0.104,0.003058,0, Summed MSE=568.6327
#> left son=14 (204 obs) right son=15 (123 obs)
#> Primary splits:
#> Bray_P < 5.80845 to the left, improve=0.05248192, (0 missing)
#> meanelev < 231.0787 to the left, improve=0.04663039, (0 missing)
#> RB_NO3 < 30.46076 to the right, improve=0.03681987, (0 missing)
#> RB_NH4 < 5.242497 to the right, improve=0.03280300, (0 missing)
#> RB_PO4 < 0.5317823 to the left, improve=0.02986250, (0 missing)
#>
#> Node number 8: 18 observations
#> Means=0,0.1111,0,0,0,0,0,0.2222,0,0.1111,0.05556,0.1111,0.1111,0,0,0,0.1111,0.1667,0.05556,0,0,0.9444,0,0,0.2222,0,0,0.1111,0.1667,0,0,0,0.1111,0.1667,0,0.3889,0,0,0,1.778,0,0,0.3889,0,0,0.3333,0.1111,0.3889,0,0,0,0,0.05556,0,0,0,0,1.333,0.1111,0,0.05556,0,0,0,0.1111,0,0,0,0,0,0,0.05556,0,0.05556,0.3333,0.05556,0.4444,0.1111,0,0,0.05556,0,0,0.05556,0,35.39,0,0,0.05556,0,0.5556,0.05556,0,0,0.05556,0.9444,0,0,0.1667,0,0,0.1667,0,0,0.2778,0.1111,0,0,0,0,0,0.05556,0,0,0,0,0,0,0.05556,0.1111,5.444,0,0,0,0,0.3333,0,0,0,0,0.05556,0,0.6667,3.444,0,0,0,0.2778,0,0.05556,0,0,0.2778,0,0.2222,0,0.5,0,0.1111,0,0,0.2778,0,0,0,0.4444,0.1111,0,0,0.05556,0,0.2222,0.05556,0.1111,0,0.05556,0,0,0.1111,0,0.1667,0,0,0.05556,0.2222,0.05556,0,0.1111,0,0,0,0,0.1667,0.5556,0,0,0,0,1.333,0,0,0,0.8889,0,0,0.05556,0.1667,0,0,0,0,0,0,0,0.05556,0.05556,0.5,0.2222,0.05556,0,0.05556,0.05556,0,0,0,0,0.05556,0,0.1111,0,0,0,0,1.389,0.05556,0,0.1111,0,0.1667,2.278,0,0,2.889,0,0,2.111,0,2.111,0,6.111,0,0.1111,0.05556,0.05556,0,0.1667,0,0,0,0,0,0,0,0,0,0,0,0,0.05556,0,0.1667,0.05556,0.2778,0,0.05556,0.2778,0.1111,0,0,0.3333,0,0,0,0,0,0,0,0,0,0.9444,0,0.1111,0,0,0,0,0.1111,0,0.05556,0,0,0,0.05556,0,0.2222,0.4444,0,0,0,0.1111,0,0,0,0.05556,0,0,0,0,0,0.3333,0,0.05556,0,0,0,0.05556,0.4444,2.944,0,0,0.1111,0.1667,0.05556,0.05556,0,0.05556,0,0,0,0,0,0,0.1111,0,0.1667,0,0,0,0,0,0,0,0.1667,0,0,0.05556,0,0,0,0.4444,0,0,0.6667,0.1111,0.05556,0,0.1667,0,0,0,0,3.278,0,0,0,0,0,0.2778,0.1111,0.2222,0.1111,0,0,0,0,0,0.05556,0,0,0,0.05556,0,0,0,0,0,0,0.05556,0.2222,0,0,0,0,0.1667,0.05556,0,0,0,0.2222,0.3333,0,0,0.2778,0,0,0,0.4444,1.389,0,0,0,0,0,0,0.2778,0,0,0.05556,0,0,0,0,1.278,0.8333,0.05556,0.05556,0.2778,3.778,0,0.1667,0,0,0,0,1.056,0,0,0,0,0,0,0.05556,0,0,0,0,0,0,0,0,0.05556,0,0,0,0.1111,0,0,0.2222,0,0.05556,0.05556,0.2222,0.05556,0.1111,0,0,0,0.2222,0.1111,0,1.5,0.2222,0,0,0.05556,0,0,0,0.3333,0,0,0.1111,0,0,0.5,0,0.05556,0,0,0,0.3333,0.05556,0.1667,0,0.05556,0,0,0,0,0,0,0,0,0.05556,0,0.7222,0.1111,0,0,0.1667,0,0,0,0,0.05556,0,0,0,0,0,0,0,1.389,0,0.05556,0,0,0,0,0,0,0,0.05556,0,0,0.2222,0,0,0,0,0,0.05556,0.2222,0,0.1111,0,0.1667,0,0,0,0,0,0.05556,0,0,0,0.7222,0,0.2778,0,1.778,0.1667,0,0,0.2778,0,0,0,0,0,0.05556,0.05556,0,0,0,0,0.05556,0,0.3333,0,0,0.05556,0.1111,0,0, Summed MSE=680.1883
#>
#> Node number 9: 29 observations
#> Means=0,0.03448,0,0,0,0,0,0.5517,0.06897,0.1724,0,0.3103,0.2414,0,0.1034,0,0.4138,0.4828,0.03448,0.03448,0.1034,0.2759,0,0,0.3793,0,0,0.1379,0.1379,0,0,0,0.1034,0.6552,0,1.586,0.03448,0,0,0.9655,0,0,0.6897,0.06897,0.03448,0.3448,0.1724,0.4483,0,0,0.06897,0.06897,0.1724,0.5517,0.06897,0.06897,0,1.138,0.2414,0.03448,0.3103,0,0.03448,0,0,0.03448,0.03448,0.03448,0,0.03448,0.06897,0.03448,0.03448,0,1.241,0.3793,1.31,0,0,0.1034,0.1034,0.2414,0.06897,0.2414,0,7.034,0,0,0.2069,0,0.3448,0.03448,0.1034,0.1034,0.06897,1.69,0,0.03448,0.06897,0.03448,0,0.4483,0,0,0.03448,0.06897,0.06897,0,0.1724,0,0,0,0,0,0,0,0,0,0.1724,0.4483,1.517,0,0.06897,0,0.03448,0.3103,0.03448,0,0,0,0.06897,0,0.3793,4.69,0,0,0,0.6552,0,0,0.1724,0.03448,2.345,0,0.2414,0.2414,0.6552,0,0.5172,0.1034,0,0.6552,0,0,0,0.4138,0.03448,0,0,0.03448,0.5862,0.1034,0,0.4828,0.03448,0.1034,0,0,0.1724,0.4828,0.06897,0,0.3103,0.1724,0.06897,0.03448,0,0.3793,0.2069,0,0,0.03448,1.69,0.2759,0,0,0,0,2,0.1379,0,0,0.2759,0,0,0,0.3448,0.06897,0.03448,0,0,0,0,0,0,0.03448,0.4138,0.06897,0,0,0.1379,0.2414,0.03448,0.03448,0.03448,0.03448,0,0,0.1724,0,0,0,0,2.897,0.2414,0,0.1034,0,0.5172,2.138,0,0,4.207,0.03448,0,2.897,0,2.207,0,5.138,0.03448,0.06897,0.5172,0.06897,0.03448,0.03448,0.03448,0,0.03448,0.03448,0,0.1034,0,0,0.03448,0,0.03448,0.06897,0.1379,0.03448,0.3448,0.2759,0.3793,0.03448,0,0.1724,0.03448,0.06897,0.1724,0.1724,0,0.03448,0.06897,0.06897,0,0.1034,0,0.03448,0.2069,2.172,0,0.1034,0.3103,0.03448,0,0.4483,0.9655,0,0.03448,0,0.03448,0,0,0,0.3793,1.103,0,0,0,0.1034,0.06897,0.3448,0,0.1034,0,0,0,0,0.1034,0.2069,0,0,0,0,0.1034,0.06897,0.5517,2.69,0,0,0.1034,0.2414,0.1034,0.06897,0,0.3103,0,0,0.1379,0.2759,0,0.1379,0.2069,0,0.2414,0.03448,0.2069,0,0.06897,0.03448,0,0,0.2069,0,0,0.1034,0,0,0,0.6207,0.03448,0,0.8621,0.03448,0,0,0.06897,0.03448,0,0,0,0.7586,0.03448,0,0.03448,0.03448,0,0.4828,0.03448,0.2414,0.2759,0,0.03448,0,0,0,0,0.03448,0,0.06897,0.2414,0,0,0,0,0,0.03448,0.06897,0.2414,0.03448,0,0,0,0.2759,0,0.06897,0,0.1724,0.3103,0.4138,0,0,1.448,0.06897,0,0,0.7586,1.034,0.1034,0.1034,0,0.06897,0,0,0.5172,0,0,0.06897,0,0,0.1034,0,2.345,1.31,0.2069,0,0.5172,3.448,0.03448,0.03448,0,0,0,0,1.345,0,0,0,0,0,0,0,0,0,0,0,0.1034,0.2414,0.06897,0,0.3793,0.06897,0,0.06897,0.1379,0,0,0.4828,0.03448,0,0.1379,0.2759,0,0,0.1034,0,0,0.7241,0.6552,0,3.448,1.069,0,0,0,0.06897,0,0.03448,0.7586,0,0,0.4138,0.03448,0.03448,0.3103,0,0.03448,0.06897,0,0.03448,0.3103,0.1034,0.4828,0.06897,0,0,0,0,0,0.03448,0.1379,0,0,1.069,0,2.517,0.06897,0,0.03448,6.931,0,0,0.1034,0,0,0.06897,0,0,0.1724,0,0,0,5.345,0,0.2069,0,0,0,0,0,0,0,0.1724,0,0.1034,0.2759,0.03448,0,0,0,0,0,1.69,0,0.1379,0.1379,0.3103,0,0.03448,0,0,0,0,0.06897,0,0,0.6207,0,0.8621,0,2.483,0.5517,0.1034,0.1724,0.03448,0,0,0,0,0.1034,0.1724,0.2759,0.1724,0.1034,0.03448,0,0.1379,0,0.1034,0.06897,0,0.1034,0,0,0, Summed MSE=709.6528
#>
#> Node number 10: 69 observations
#> Means=0,0.1304,0,0.01449,0.01449,0,0.01449,0.3333,0.07246,0.1739,0.05797,0.4493,0.4058,0,0.08696,0.07246,0.6667,0.1594,0.1449,0.04348,0,0.6087,0,0,0.3623,0.07246,0.04348,0.08696,0.4638,0.01449,0.01449,0,0.1739,0.1014,0.04348,0.7536,0,0.02899,0,0,0.01449,0,2.174,0.02899,0.02899,0.1449,0.08696,0.07246,0,0.01449,0.05797,0.04348,0.1014,0.4493,0.07246,0.02899,0,1.217,0.02899,0,0.2899,0.04348,0.01449,0,0,0,0,0,0,0,0.04348,0.01449,0.2319,0.07246,0.5072,0.8841,0.942,0.2174,0.02899,0.04348,0.3188,0.01449,0.7101,0.1014,0,2.667,0,0,0.2609,0.01449,0.6522,1,0,0.02899,0.07246,0.9855,0,0,0.07246,0.1304,0,0.2464,0,0,0.1594,0.4203,0.02899,0.1594,0.01449,0,0,0,0,0,0,0.02899,0,0,0.04348,0.8406,2.159,0,0,0.01449,0.02899,0.3478,0,0.08696,0,0,0.1304,0.1304,2.826,7.942,0,0,0,0.4493,0.05797,0,0.05797,0,1.594,0,0.4203,0.5072,2.58,0,0.5362,0.01449,0.01449,0.2754,0,0,0,0,0.05797,0.01449,0.01449,0.1304,0.08696,0.2174,0.02899,0.2319,0,0.5652,0,0.05797,0.4348,0.01449,1.493,0.01449,0.1449,0.4783,0.7536,0.1884,0,0.5507,0.01449,0,0.04348,0.01449,0.08696,0.5362,0,0.08696,0.1449,0,0.7536,0.1449,0,0,1.42,0,0,0.1014,0.2174,0,0,0.04348,0,0,0.1304,0,0,0.02899,0.3768,0.1594,0.05797,0,0.3768,0.1159,0.01449,0.1159,0.01449,0,0,0.01449,0.04348,0.01449,0,0,0,5.391,0.04348,0,0.07246,0.01449,0.1884,0.3333,0.04348,0.01449,5.551,0.07246,0,5.159,0.1159,2.217,0.02899,4.797,0.04348,0.1449,0.2319,0.07246,0.02899,0.5217,0.07246,0,0.2029,0,0,0.05797,0,0,0.1159,0,0.01449,0,0.08696,0.5217,0.5652,0.1304,0.5507,0.02899,0.2609,0.2174,0.01449,0,0.07246,1,0,0.1014,0,0.1014,0.01449,0.1014,0.08696,0,0.1884,0.7536,0,0.1304,0.04348,0,0.01449,1.058,0.4638,0.01449,0.01449,0.02899,0.04348,0,0.04348,0,0.6812,1.159,0,0,0,0.04348,0.02899,0.4203,0,0.2174,0,0,0.01449,0,0.01449,1.188,0.04348,0.01449,0,0,0.01449,0.01449,0.4058,6,0,0,0.1304,0.3188,0.1159,0.05797,0,0.02899,0,0,0,0,0,0,0,0.01449,0.1739,0.05797,0.1304,0,0,0,0,0,0.2754,0,0,0.02899,0.01449,0,0,0.6522,0,0.01449,0.3333,0.2609,0.1014,0.04348,0.1739,0.05797,0,0.08696,0,9.42,0.01449,0,0,0,0,0.3623,0.1449,0.2609,0.1449,0.1159,0.04348,0,0,0.01449,0,0.07246,0.07246,0.3333,0.05797,0.01449,0,0.01449,0,0,0.02899,0.3913,0.7246,0.1594,0.04348,0.02899,0,0.2029,0.07246,0.02899,0,0.3333,0.2899,0.1449,0,0.01449,0.971,0,0,0,1.333,0.4638,0.3623,0.2029,0.08696,0.02899,0,0.02899,0.3913,0.01449,0,0.1304,0.01449,0,0,0,1.174,1.116,0.1449,0,0.1884,1.014,0.2754,0.2754,0,0,0,0.01449,1.87,0.02899,0.5652,0.04348,0.01449,0.02899,0,0.5217,0.08696,0,0.1159,0.6232,0.01449,0,0.01449,0,0.2609,0.05797,0,0,0.3043,0,0,0.6087,0.05797,0,0.05797,0.02899,0.02899,0.3478,0.07246,0.2319,0,0.1884,0.6377,0.01449,2.551,0.4638,0,0,0,0.01449,0,0,0.07246,0.02899,0,0.5507,0,0,0.1304,0.01449,0.07246,0,0,0,1.174,0.04348,0.3188,0.2899,0,0,0,0,0.02899,0.01449,0.1739,0,0.01449,0.1594,0,0.2319,0.1594,0.1159,0.08696,4.551,0.2609,0.01449,0.02899,0.1449,0.08696,0.01449,0,0.01449,0.1014,0,0,0,7.304,0.1014,0.08696,0,0,0,0,0,0,0,0,0.01449,0,0.2319,0,0,0,0.1159,0,0.1739,0.8116,0,0.1594,0,1.014,0,0,0,0.07246,0,0,0.02899,0.02899,0,0.07246,0.01449,0.5362,0.02899,2.333,0.1884,0.04348,0.05797,0.1884,0,0,0.02899,0,0.1594,0.3478,0.8551,0,0,0.04348,0,0.07246,0,0.8116,0.1884,0.01449,0.1014,0.01449,0,0, Summed MSE=888.2915
#>
#> Node number 11: 3 observations
#> Means=0,0,0,0,0,0.3333,0,0.3333,0,0,0,0.6667,0.3333,0,0,0,0,0.3333,0,0,0,0,0,0,0.6667,0,0,1.333,0.3333,0,0,0.3333,2,0,0,0.6667,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1.333,0.3333,0,0,3,0,0,0,0,0.3333,0,0,0,0,0,0,0,0,0,0.6667,0,0.6667,2.667,1.333,1,0,0,0,0,0,0.3333,0,0,0,0,4,0,0,0,0,0.6667,0,0,0,0,0,0.6667,0,0,0,0,0,0.3333,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3333,0,0,0,0,0,0,0,0,0,0,1.333,0,5.333,0.3333,0,0,0,0,0,0.3333,0,0,0,0,0,0.3333,5.333,0,1.333,0,0,0.6667,0,0,0,0,0,0,0,0,0.6667,0,0,0,0,0.6667,0,0,1,0,7.667,0,0,1,0,0.3333,0,0,0,0,0,0,0,0.3333,0,0.3333,0.3333,0,0,0.3333,0,0,0.3333,0,0,0,0,0,0,0.3333,0,0,0,0,0,0,0,2.667,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,52,0,0,0,0,0,0,0,0,5.333,0,0,6,0,0,0,0.3333,0,0.3333,0.3333,0.3333,0,0,0.3333,0,1.333,0.6667,0,0.3333,0,0,2,0,0,0,0,0.6667,0,2,0.3333,0,0,0,0,0,0,1.333,0,0,0,0,0,0,0,0,0.3333,0.3333,0,0,0,0,0,0.6667,0,0,0,0,0,0,0,0,1.667,0,0,0,0,0.6667,0.3333,3.333,0,0.6667,0,0,0,0,0,2.667,0,0,0,0,0,0,0,38.67,0,0,0,2,0.6667,0.3333,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.6667,0,0,0,0,0,2.667,0,0.3333,0,1,0,6,0,0,0,0,0,0,0,0.3333,0.6667,0,0,0,0,0,0,0.3333,0,0.3333,0,0,0,0,0,0,0,0.3333,0,0,0,0,0,0.3333,0.6667,0,0,0.3333,0,0,0,0,0,0.6667,0,0,1.333,0,0,2,0,0,0,0.3333,0,0,0,0.3333,0,0,0,0,0.6667,0.6667,1.333,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1.333,0,0,3.667,1.333,0,0,0,0,0,0.3333,0,0,1.333,0,0,0,0,0,0,0,0,0,0,0.3333,0,0.3333,0.6667,0,0.3333,0,0,0,0,0,0,0,0,0,0,2.667,0,0,0,0,0,0,0,0,0.6667,0.3333,0,0.3333,0,0,0,0,0,0,0,0,0,0,0,0.3333,0.3333,0.3333,0.3333,0.3333,0,0,0,1,0.3333,0,0,0,0,0,0,0,7,0.3333,0,0,0,0,0,0,0,0,0,0.6667,0,0,0,0,0,0.3333,0,1.333,0,0,0.6667,0,0.6667,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3333,0.3333,0,0,1.333,0,0,0,0,0,0,0.3333,0,0,0,0,0,0,3.667,0,0,0,0,0,0, Summed MSE=733.7778
#>
#> Node number 12: 68 observations
#> Means=0.1029,0.6912,0,0.1176,0.02941,0.01471,0,1.324,0.02941,0.3676,0.01471,0.3676,0.4118,0,0.1029,0.01471,0.04412,0.05882,0.1176,0,0.04412,0.2059,0,1,0.5735,0,0,0.01471,0.04412,0,0.01471,0,0.4265,0.02941,0,2.324,0.1618,0.02941,0.01471,0.2794,0.4118,0,0.6324,0.7206,0,1.529,0.1324,0.1765,0.04412,0,0.2647,0.7794,0,1.162,0.1912,0.1471,0.07353,1,0.9853,0.02941,0.2206,0.01471,0.08824,0.02941,0.01471,0.02941,0,0.07353,0,0.25,0.01471,0,0.1618,0.2059,4.088,0.02941,3.912,0.1324,0.04412,0.1176,0,0.1912,0,0.6324,0,0,0.01471,0.01471,0.8235,0,0.01471,0,0.05882,0.3824,0,2.294,0,0.05882,0.01471,0.08824,0,0.2206,0,0,0,0.04412,0.1618,0.2206,0.02941,0,0.07353,0.02941,0,0.01471,0.04412,0,0.01471,0.02941,0.02941,0.1029,0.1912,0.04412,0.2059,0.07353,0,0.08824,0,0.02941,0,0,0.01471,0,0.01471,0.9412,0.04412,0.04412,0,0.08824,0.1176,0.25,0.2059,0,0.01471,0,0,0.1618,1.574,0,0.6324,0.2647,0.01471,1.471,0,0,0,0.6324,0,0.04412,0,0.1324,1.471,0.1029,0,0.8382,0.01471,0.6324,0,0.1029,0.9559,4.647,0.5588,0,0.4412,0.08824,0.1618,1,0,0.1471,1.368,0,0.01471,0.01471,1.397,0.1912,0,0.1176,0.04412,0,0.01471,0.3382,0.01471,0.02941,0.1029,0,0,0.01471,0.4853,0.1471,0.01471,0,0,0,0.04412,0.01471,0.04412,0.1324,0.1471,0.02941,0.1324,0,0,0.3382,0.07353,0.3529,0.4559,0,0.01471,0,0,0,0,0,0,0.2794,0,0,0,0,0.05882,0,0,0,0.2206,0,0,0.1765,0,0.1618,0,0,0,0,2.221,0.2206,0.05882,0.1618,0.1471,0.07353,0.1912,0.1471,0,0.07353,0.08824,0.02941,0.2059,0.1176,0.2059,0.2353,0,0,0.05882,0.8676,0.5294,0.01471,0.1029,0.3088,0.04412,0.6029,0.4706,1.118,0.01471,0.1176,0,0.08824,0.01471,0.02941,0.01471,0.25,0.3676,1.559,0,0.04412,0,0.04412,0,0.1765,0.5882,0.04412,0.2794,0.1176,1.265,0,0.3529,0.01471,0.4265,0.8676,0.02941,0,0,0.04412,0.2059,0.5147,0.02941,0.7059,0.01471,0,0.07353,0,0,0.3676,0.1618,0.04412,0.04412,0.01471,0.01471,0.01471,0.05882,0.3971,0,0.01471,0.07353,0.9265,0.3088,0.08824,0,0.1618,0.02941,0.04412,0.4265,0.4559,0,0.05882,0.02941,0,0.01471,0.3235,0.6029,0.07353,0.01471,0,0,0,0.01471,0,0,0.2941,0.02941,0.08824,0,0,0.02941,0,0.04412,0,0,0,0,0,0,0,0,0.1765,0.01471,0,0,0.3971,0.02941,0.2353,0.05882,0.1471,0.5441,0,0,0,0.02941,0.01471,0.3676,0.07353,0,0.02941,0.5147,0.07353,0.01471,0.01471,0.02941,0,0.07353,0.2059,0.04412,0,0,0.2647,0.01471,0.4559,0.07353,0,0,0.3088,0.1324,0.9853,0.01471,0.04412,0.02941,0.1176,0.01471,0.01471,0.2353,0,0.05882,0.2353,0,0.07353,0.04412,0,0.8529,0.01471,0,0.2059,0,0,0.05882,0,0.75,0.75,1.338,0,0.5588,0.1324,0.1029,0.6176,0.01471,0,0.1471,0,0.3824,0.01471,0.3971,0.01471,0.01471,0,0,0.2647,0,0.1618,0.02941,0.08824,0.07353,0.6029,0.01471,0,0.4853,0.04412,0,0.2647,0.2647,0.2794,0.05882,1.132,0.01471,0.02941,0.3382,0.2059,0.08824,0.1176,0.01471,0.1471,0,0.3382,0.3529,0,2.382,0.9559,0,0.01471,0,0.2059,0.05882,0.01471,0.1618,0.02941,0,0.3824,0.05882,0.07353,0.04412,0,0.05882,0.05882,1.324,0.1471,0.2059,0.04412,0.6912,0.4559,0.02941,0.02941,0.01471,0.01471,0.1324,0.01471,0.2647,0.02941,0,0.01471,0,0.2353,0.07353,0.05882,0.1471,3.074,0.3676,0,0.2941,0.01471,0.2353,0.02941,0,0.05882,0,0.02941,0.01471,0,7.132,0.02941,0.3235,0,0,0.01471,0.01471,0,0,0.07353,0.07353,0.25,0.8971,1.191,0.04412,0,0.02941,0.3676,0,0.1471,0.2059,0.02941,0.4412,0.75,0.2794,0.02941,0.02941,0.3971,0.01471,0.1176,0.07353,0.1765,0.05882,0.1176,0.01471,0,0.01471,0,2.044,0.6029,0.2206,0.04412,0.05882,0,0,0,0,0.04412,0.2206,0.5294,0.4559,0.2941,0.04412,0.02941,0.3824,0.05882,0.4559,0.1176,0.1324,0,0.1618,0,0, Summed MSE=339.354
#>
#> Node number 13: 86 observations
#> Means=0.05814,0,0.06977,0.314,0.1628,0.1163,0,0.6628,0,0.186,0,0.9651,0.686,0.01163,0.8721,0.0814,0.1163,0.7558,0.1512,0,0,0.4302,0.01163,0.1395,0.593,0,0.6744,0.2093,0.06977,0,0,0.593,1.628,0.02326,0,0.2442,0,0.03488,0,1.302,0.1744,0.03488,1.186,1.384,0.01163,1.163,0.1047,0,0,0.1047,0.4419,0.2326,0,2.744,0.314,0,0,1.814,0,0,0.5814,0.01163,0.04651,0.01163,0.01163,0,0,0.03488,0.01163,0,0.02326,0,0.3953,0.2209,1.64,0.4535,4.43,1.779,0.4186,0.01163,0.05814,0.1279,0,0.6744,0,0,0.01163,0,3.453,0,0.04651,0,0,0.2558,0,0.9767,0,0,0.0814,0.5814,0,0.2791,0.1047,0,0.02326,0.2674,0.04651,0.01163,0,0,0,0,0,0,0.1744,0,0.01163,0.02326,0,0.3953,0.1395,0,0.4302,0.01163,0,0.02326,0,0.5465,0.01163,0,0.04651,0.01163,0.6744,1.093,0,0.3372,0.2558,0.02326,0.4767,0.04651,0.06977,0,0.02326,0,0.0814,0.9186,5.395,0.01163,2.616,0.4302,0.02326,1.442,0.01163,0,0.01163,0,0,0.02326,0,2.907,0.5698,0.3372,0.186,0.01163,0.2326,1.314,0.04651,0.03488,2.128,0.02326,5.186,0,0.186,0.7093,0.0814,0.6744,0.01163,0.5465,0.02326,0,0.8372,0.06977,0.01163,0.3488,0.2093,0.5581,0.5,0,0.02326,0.3488,0,0.03488,0.7093,0,0,0.02326,0.1744,0.05814,0,0.186,0.0814,0,0.1512,0.01163,0,0.05814,0.1744,0.3372,0.2209,0,0,0.0814,0.05814,0.1279,0,0,0,0,0,0,0,0,0.01163,1.733,0,0,0.1279,0,0.03488,0.02326,0,0.01163,1.93,0,0.03488,0.3488,0,0.1047,0,0.6163,0,0.01163,0.7326,0.05814,0,0.5,0.1047,0,0.9651,0.1395,0,0.2442,0.2326,0.01163,1.674,0,0,0.09302,0.06977,0.09302,0.3953,3.081,0.9302,0.03488,1.198,0.05814,0.03488,0.2442,0.3372,6.023,0.02326,0.5349,0,0.2209,0,0.02326,0,0,0.1744,1.465,0.01163,0.1279,0,0,0,2.558,0.01163,0.01163,0.1279,0.09302,2.384,0,0.1279,0,1.314,1.314,0.04651,0.1279,0,0.1744,0.1279,1.174,0,0.8372,0,0.03488,0.01163,0,0,2.14,0.1047,0.1047,0,0.02326,0,0.1744,0.01163,3.849,0.03488,0,0.5349,0.9302,1.5,0.06977,0.02326,0,0,0.3605,0.01163,0.02326,0.04651,0,0.02326,0,0,0.09302,0.09302,0.01163,0,0.01163,0,0.01163,0.4535,0.03488,0.01163,0,0.01163,0.01163,0,0.1512,0.0814,0,0.3488,0.02326,0.0814,0.5465,0.01163,0,0.1628,0.1628,0.02326,0.8256,0,0,0,0.3837,0.02326,0.0814,0.1512,0.3605,0.7093,0.01163,0.01163,0,0,0.05814,0.04651,0.2558,0.02326,0.1628,0.04651,0.02326,0.1395,0.1279,0,0,0.7326,0.3721,0.3023,0,0,0.09302,0.0814,0.5814,0.8837,0,0,0.5233,0.0814,0.01163,0,0,0.01163,0.3837,0,0.05814,0.8721,0.01163,0,0.6744,0.1628,0.1279,0.01163,0.1744,1.302,0.02326,0.02326,2.093,0,0,0.06977,0,0.3837,1.174,3.291,0,0.1628,0.02326,0.1628,1.093,0,0.03488,0.03488,0,2.756,0.1047,0.3721,0.0814,0,0,0,1.244,0.2558,0,1.163,1.058,0,0,0,0,0.686,0.1163,0.02326,0.01163,0.7558,0,0.01163,0.6977,0.1047,0.01163,0.3837,0.04651,0.01163,1.128,0,1.209,0,0.03488,0.5581,0.0814,0.4535,0.7791,0,0,0.02326,0.02326,0,0.1512,0.02326,0.01163,0,2.64,0.02326,0.3372,0.01163,0,0.02326,0.06977,0,0.02326,1.384,0.04651,0.4767,1.512,0.02326,0,0,0,0.4767,0.1047,0.5698,0,0.3605,0.02326,0,0.01163,0.5581,0.1163,0.4535,2.256,0.7209,0,0.05814,0.1395,1.5,0.1977,0,0.03488,0.1744,0.04651,0,0.02326,2.279,0.4535,0.5233,0.02326,0.02326,0.1395,0,0,0.03488,0.05814,0.02326,0.4186,0,0.5814,0.01163,0.186,0.2326,1,0.4419,0.186,0.02326,0,0.7326,0.01163,2,0,0.06977,0.09302,0.1047,0,0.01163,0.01163,0,0.01163,0,0.02326,0.05814,0.01163,0.9419,0,0.186,0.1744,1.64,0,0.1512,0.09302,0,0.04651,0.4535,3.186,0,0,0.2209,0,0.1512,0.05814,1.105,0.05814,0.06977,0,0.1279,0,0.01163, Summed MSE=636.114
#>
#> Node number 14: 204 observations
#> Means=0.08824,0.1814,0,0.06863,0.02451,0.009804,0,1.044,0.02941,0.2451,0.04902,0.6176,0.6765,0.01471,0.3088,0.08333,0.1275,0.402,0.1176,0.1716,0,0.3676,0.009804,0.4216,0.4412,0.02941,0.09804,0.03431,0.05392,0,0,0.009804,0.6176,0.07353,0,0.2304,0.1765,0.009804,0.004902,1.5,0.02451,0.009804,1.167,0.3382,0,1.142,0.152,0.1667,0,0,0.2059,0.1912,0.01471,1.181,0.1127,0.03431,0.004902,0.8039,0.05882,0,0.2255,0.004902,0.009804,0.02941,0.01471,0.01961,0.004902,0.01961,0.004902,0.03922,0.01961,0.01471,0.1569,0.1422,1.392,0.1029,2.475,0.6471,0.1029,0.1471,0.04902,0.1324,0,0.2647,0,0.01961,0.03922,0.004902,1.843,0,0.02941,0.01961,0.1225,0.02941,0,0.6863,0.004902,0.04902,0.06373,0.1912,0.004902,0.3137,0,0.009804,0.004902,0.1275,0.09314,0.04412,0.03431,0.009804,0.02451,0.03922,0,0,0.02451,0,0.004902,0,0.02941,0.3088,0.1225,0.02451,0.1029,0.05882,0.004902,0.01471,0,0.1618,0,0,0.02451,0,0.01471,0.4118,0,0.009804,0.04902,0.03431,0.4412,0.1029,0.05392,0,0,0.004902,0,1.216,4.289,0.009804,1.083,0.07353,0.02941,1.858,0.004902,0.009804,0.004902,0.2745,0,0.004902,0.009804,0.5931,0.3333,0.3431,0.01471,0.1569,0.1716,2.005,0,0.1078,1.789,0.2892,3.299,0.05882,0.451,1.088,0.6176,0.6275,0,0.1863,0.2304,0.004902,0.004902,0,2.441,0.2255,0,0.4216,0.2647,0.004902,0.01961,0.3775,0,0.009804,0.4412,0.004902,0.01961,0.01961,0.3039,0.03922,0.01961,0.01961,0.01471,0,0.009804,0.004902,0.01961,0.009804,0.1716,0.06373,0.299,0.01471,0.009804,0.2108,0.009804,0.201,0.03431,0,0.004902,0,0,0,0.01471,0.004902,0,0.4167,0,0,0.004902,0,0.1127,0.01961,0,0.01471,0.3676,0,0.01471,0.06373,0.004902,0.03922,0,0.04412,0.009804,0,0.7353,0.1127,0.01471,0.3431,0.1569,0.004902,0.5049,0.06863,0,0.1471,0.05882,0,0.6569,0.02451,0.01961,0.08824,0.009804,0.004902,0.04902,1.956,0.5539,0.07843,0.4314,0.4706,0.1078,0.1176,0.1912,3.637,0,0.01471,0.01471,0.1324,0.2255,0.09314,0.03431,0.05882,0.4804,1.637,0,0.02941,0,0.004902,0,0.1471,0.2402,0.05882,0.1373,0.07353,0.3382,0.01471,0.4951,0.01471,1.98,0.8627,0,0.01961,0.004902,0.2745,0.348,0.8971,0,0.4265,0.01961,0,0.004902,0.004902,0.009804,1.838,0.05392,0.01471,0,0.009804,0.004902,0.02451,0.1078,0.7647,0.01961,0,0.1765,1.338,0.4118,0.1127,0.01471,0.004902,0.03922,0.02941,0.01961,0.04412,0.02451,0,0.01961,0.004902,0.009804,0.1127,0.1225,0.009804,0,0.009804,0.004902,0,0.1667,0,0.004902,0.04412,0.03922,0.009804,0.004902,0.01471,0.009804,0,0.009804,0.04412,0.004902,0.03922,0,0,0.01961,0.1373,0,4.436,0.01471,0.02941,0.009804,0.05882,0.009804,0.5833,0.1569,0.1275,0.7059,0,0.01961,0.004902,0.009804,0,0.2353,0.1422,0.02451,0.2402,0.1961,0.04412,0,0.009804,0.009804,0,0.2843,0.2892,0.05392,0.01471,0.004902,0.05882,0.01961,0.8725,0.1225,0.02451,0.009804,0.4559,0.25,0.3284,0.009804,0.004902,0.01961,0.1275,0,0,0.201,0.009804,0.004902,0.2941,0.1275,0.05392,0,0.01471,0.848,0.01961,0,0.4363,0,0.004902,0.009804,0,2.373,2.74,1.725,0,0.6618,0.009804,0.3039,1.108,0.01471,0,0.02941,0,0.6422,0.01961,0.1324,0.02451,0.01471,0.01961,0.004902,0.7696,0.04902,0.009804,0.06373,0.07843,0,0.04412,0.02451,0,0.1422,0.1961,0,0.01471,0.6716,0.004902,0.01961,0.9755,0.05392,0.004902,0.2843,0.04412,0.04412,0.4216,0.05882,1.221,0.004902,0.1127,0.08824,0.004902,1.471,1.623,0.004902,0,0.07353,0.009804,0,0,0.2549,0,0.004902,1.544,0.009804,0.08824,0.01471,0,0.06863,0.04412,0.09804,0.1569,1.294,0.02451,0.1863,1.191,0.01471,0.01471,0.02941,0,0.1765,0.1176,1.01,0,0,0.004902,0,0.1716,0.2108,0.1029,0.1078,0.2941,0.3333,0,0.2255,0.1225,0.6569,0.004902,0.004902,0.03431,0.03431,0.01471,0.01471,0,24.85,0.5049,0.3676,0,0,0.004902,0,0.009804,0,0,0.03431,0.1912,0.01961,0.5147,0.05882,0,0.004902,0.2794,0.03431,0.2598,0.03431,0,0.5294,0.06373,1.078,0,0.01471,0.05392,0.03922,0.03431,0.03922,0.04412,0.01471,0,0,0,0.02451,0.04902,0.7647,0.8824,0.4363,0.1471,0.3824,0.004902,0,0,0.009804,0.03431,0.4559,1.123,0.2402,0.02451,0.1373,0.03922,0.9657,0.06863,1.059,0.1569,0.1373,0.05392,0.1127,0,0, Summed MSE=480.8093
#>
#> Node number 15: 123 observations
#> Means=0.03252,0.2195,0,0.0813,0.00813,0,0,1.317,0.04878,0.4797,0.04878,0.9675,1.333,0,0.3008,0.07317,0.1707,0.3577,0.2033,0.09756,0.06504,0.7724,0.00813,0,0.3659,0.04878,0.1382,0.1138,0.122,0,0,0,0.5935,0.2033,0,0.07317,0.00813,0.00813,0,4.65,0.00813,0.03252,1.407,0.1301,0.00813,1.26,0.3089,0.2195,0,0,0.0813,0.08943,0,1.39,0.3252,0.06504,0.00813,1.065,0.05691,0,0.3821,0.02439,0.03252,0,0.00813,0.01626,0,0.03252,0,0.05691,0.04065,0.02439,0.3171,0.2195,1.488,0.2602,2.732,0.5528,0.02439,0.1707,0.07317,0.07317,0,0.3659,0.00813,0.1138,0,0,1.472,0,0.1057,0.00813,0.04878,0.02439,0,1.033,0,0.09756,0.2764,0.1707,0,0.4634,0,0,0.01626,0.1545,0.03252,0.02439,0,0.00813,0.01626,0.00813,0.00813,0,0.03252,0,0,0,0.05691,0.9268,0.7236,0.00813,0.01626,0.01626,0.00813,0.02439,0,0.252,0,0.00813,0.0813,0,3.829,1.081,0,0.00813,0,0.04065,0.3496,0.1057,0.1382,0,0.05691,0,0.07317,0.6667,4.87,0.01626,1.561,0.06504,0.05691,1.764,0.01626,0,0.00813,0.6504,0,0,0,0.6179,0.1138,0.5041,0.02439,0.4959,0.08943,1.715,0,0.04065,1.813,0.2276,3.382,0,0.3415,0.7967,0.439,0.6585,0,0.08943,0.1626,0.00813,0.2195,0,1.138,0.3171,0,0.3089,0.252,0,0.07317,0.374,0,0,1.317,0.00813,0,0.08943,0.4878,0.02439,0.02439,0.05691,0.01626,0.00813,0.00813,0.04065,0.01626,0.02439,0.2195,0.1463,0.2764,0,0.00813,0.2602,0.01626,0.2195,0,0,0.00813,0,0,0,0,0,0,0.8699,0,0.00813,0,0,0.1707,0.05691,0,0.00813,2.236,0,0,0.3577,0.00813,0.1789,0,0.3984,0,0.00813,0.626,0.1545,0.02439,0.4797,0.1057,0,0.5122,0.06504,0.00813,0.187,0.01626,0.00813,0.4146,0.01626,0,0,0.00813,0.02439,0.2683,1.764,0.935,0.1057,0.3496,0.5041,0.03252,0.08943,0.2276,5.057,0,0.02439,0,0.2114,0.04878,0.08943,0.00813,0.02439,0.6911,2.683,0,0.09756,0.00813,0.00813,0,0.3984,0.03252,0.07317,0.04878,0.0813,0.1626,0.00813,0.6179,0,1.837,0.6667,0,0.02439,0,0.3008,0.2033,1.407,0,0.6585,0,0,0,0,0.04065,2.39,0.08943,0.00813,0.00813,0.00813,0.09756,0.03252,0.2276,1.805,0.00813,0.00813,0.2846,1.642,0.3902,0.1707,0,0,0,0.05691,0,0.01626,0.1138,0.02439,0.04065,0,0.01626,0.1789,0.2683,0,0,0.01626,0,0,0.1545,0,0.01626,0.03252,0.02439,0,0,0.03252,0,0,0.1057,0.00813,0,0.04878,0.00813,0,0.03252,0.3333,0,7.715,0,0,0.03252,0.02439,0.00813,1.439,0.3008,0.2439,0.813,0,0,0,0,0.00813,0.1382,0.1138,0.07317,0.2114,0.0813,0,0.01626,0.00813,0,0.00813,0.2276,0.252,0.1707,0.02439,0,0.09756,0.00813,0.4634,0.1626,0,0,0.6748,0.2846,0.3902,0,0.00813,0.1463,0.07317,0,0.01626,0.3902,0.02439,0,0.2195,0.2033,0.05691,0,0.01626,1.724,0.00813,0,0.7398,0,0,0,0.00813,3.626,3.886,1.325,0,0.4634,0.05691,0.4959,0.748,0,0,0,0,1.715,0.05691,0.0813,0.02439,0.02439,0.00813,0,0.8699,0.1382,0.00813,0.2439,0.1789,0,0,0.01626,0.01626,0.1057,0.1301,0,0,0.748,0,0,1.407,0.09756,0.00813,0.3252,0.1382,0.06504,1.089,0,0.7236,0,0.1301,0.1707,0.03252,2.984,1.553,0.01626,0,0.07317,0.02439,0,0.02439,0.00813,0,0,2.333,0.00813,0.0813,0.01626,0,0.07317,0.00813,0,0.2683,2.293,0.06504,0.626,0.8699,0.05691,0,0,0,0.3415,0.0813,1.236,0,0.00813,0.1707,0.00813,0.2927,0.4228,0.1951,0.187,0.4309,0.3089,0,0.07317,0.00813,0.3333,0.03252,0,0.00813,0.00813,0.02439,0,0,17.03,0.4472,0.7642,0,0,0.00813,0,0,0,0,0.03252,0.1545,0.00813,0.8618,0.01626,0,0.0813,0.439,0.06504,0.3333,0.2276,0,0.6098,0.03252,1.285,0,0.01626,0.02439,0.04878,0,0,0.05691,0.00813,0,0,0,0.04878,0.03252,1.618,0.8293,0.4715,0.187,0.5366,0,0,0.00813,0,0.01626,0.439,1.228,0.08943,0.00813,0.1951,0,0.2358,0.02439,1.317,0.1463,0.07317,0.122,0.08943,0.00813,0, Summed MSE=634.9525Groupings
table(mvpart_run1$where)
#>
#> 4 5 7 8 11 12 14 15
#> 18 29 69 3 68 86 204 123Map
plot_where <- function(run) {
run <- get(run)
# Data
index <- 1:600
grouped_quadrats <- index %>%
index.to.gxgy(plotdim = c(600, 400)) %>%
as_tibble() %>%
mutate(
index = index,
group = as.factor(run$where)
)
# Plot
ggplot(grouped_quadrats, aes(gx, gy)) +
geom_raster(aes(fill = group)) +
geom_text(aes(label = index)) +
theme_minimal()
}
plot_where("mvpart_run1")
Evaluation
Compare multiple runs of the same model
Two runs of the same model are different because the algorithm uses random numbers.
set.seed(1234) # Set a new seed for random numbers
mvpart_run2 <- mvpart(form = formula, data = environmental_variables)
# Set a different seed and re-run the exact same model
set.seed(4321)
mvpart_run3 <- mvpart(form = formula, data = environmental_variables)
# compare all models run
all.equal(mvpart_run1, mvpart_run2)
#> [1] "Component \"call\": target, current do not match when deparsed"
#> [2] "Component \"cptable\": Mean relative difference: 0.01877428"
all.equal(mvpart_run1, mvpart_run3)
#> [1] "Component \"frame\": Attributes: < Component \"row.names\": Numeric: lengths (15, 13) differ >"
#> [2] "Component \"frame\": Component \"var\": Lengths: 15, 13"
#> [3] "Component \"frame\": Component \"var\": Lengths (15, 13) differ (string compare on first 13)"
#> [4] "Component \"frame\": Component \"var\": 1 string mismatch"
#> [5] "Component \"frame\": Component \"n\": Numeric: lengths (15, 13) differ"
#> [6] "Component \"frame\": Component \"wt\": Numeric: lengths (15, 13) differ"
#> [7] "Component \"frame\": Component \"dev\": Numeric: lengths (15, 13) differ"
#> [8] "Component \"frame\": Component \"yval\": Numeric: lengths (15, 13) differ"
#> [9] "Component \"frame\": Component \"complexity\": Numeric: lengths (15, 13) differ"
#> [10] "Component \"frame\": Component \"ncompete\": Numeric: lengths (15, 13) differ"
#> [11] "Component \"frame\": Component \"nsurrogate\": Numeric: lengths (15, 13) differ"
#> [12] "Component \"frame\": Component \"yval2\": Attributes: < Component \"dim\": Mean relative difference: 0.1333333 >"
#> [13] "Component \"frame\": Component \"yval2\": Numeric: lengths (8775, 7605) differ"
#> [14] "Component \"where\": Mean relative difference: 0.09572431"
#> [15] "Component \"call\": target, current do not match when deparsed"
#> [16] "Component \"cptable\": Attributes: < Component \"dim\": Mean relative difference: 0.1428571 >"
#> [17] "Component \"cptable\": Numeric: lengths (35, 30) differ"
#> [18] "Component \"splits\": Attributes: < Component \"dim\": Mean relative difference: 0.1428571 >"
#> [19] "Component \"splits\": Numeric: lengths (175, 150) differ"
all.equal(mvpart_run2, mvpart_run3)
#> [1] "Component \"frame\": Attributes: < Component \"row.names\": Numeric: lengths (15, 13) differ >"
#> [2] "Component \"frame\": Component \"var\": Lengths: 15, 13"
#> [3] "Component \"frame\": Component \"var\": Lengths (15, 13) differ (string compare on first 13)"
#> [4] "Component \"frame\": Component \"var\": 1 string mismatch"
#> [5] "Component \"frame\": Component \"n\": Numeric: lengths (15, 13) differ"
#> [6] "Component \"frame\": Component \"wt\": Numeric: lengths (15, 13) differ"
#> [7] "Component \"frame\": Component \"dev\": Numeric: lengths (15, 13) differ"
#> [8] "Component \"frame\": Component \"yval\": Numeric: lengths (15, 13) differ"
#> [9] "Component \"frame\": Component \"complexity\": Numeric: lengths (15, 13) differ"
#> [10] "Component \"frame\": Component \"ncompete\": Numeric: lengths (15, 13) differ"
#> [11] "Component \"frame\": Component \"nsurrogate\": Numeric: lengths (15, 13) differ"
#> [12] "Component \"frame\": Component \"yval2\": Attributes: < Component \"dim\": Mean relative difference: 0.1333333 >"
#> [13] "Component \"frame\": Component \"yval2\": Numeric: lengths (8775, 7605) differ"
#> [14] "Component \"where\": Mean relative difference: 0.09572431"
#> [15] "Component \"cptable\": Attributes: < Component \"dim\": Mean relative difference: 0.1428571 >"
#> [16] "Component \"cptable\": Numeric: lengths (35, 30) differ"
#> [17] "Component \"splits\": Attributes: < Component \"dim\": Mean relative difference: 0.1428571 >"
#> [18] "Component \"splits\": Numeric: lengths (175, 150) differ"Visualize grouping of three different runs
paste0("mvpart_run", 1:3) %>% purrr::map(plot_where)
#> [[1]]
#>
#> [[2]]
#>
#> [[3]]
mvpart() fails with scaled abundance data
The clustering actually is weird. It groups the top of the hill and the bottom
of the hill (red and green on the map) together in the second split. The
species are really different between these habitats, which suggests there is
an error.
The classification tree seems to be entirely driven by the soils, and the
species don’t carry much weight.
I wonder if the results would differ after scaling the species data (scale function in base package).These are data for each tree species normalized to a mean of 0 and standard deviation of 1, which creates a cleaner output for interpretation
# Standarize
abundance <- data.matrix(KC3spp20)
abund_scaled <- scale(abundance)
# Ad-hoc function to check that all columns have mean = 0 and sd = 1
are_all_columns_near <- function(.data, .near, .f) {
# Arguments:
# .data: Dataframe or matrix.
# .near: Scalar.
# .f: Summary function such as mean and sd to apply to all columns of data.
# Value:
# Returns TRUE if all columns are near .near; FALSE otherwise.
.data %>%
as_tibble() %>%
summarise_all(.f) %>%
purrr::map(dplyr::near, .near) %>%
as.logical() %>%
all()
}
abundance %>% are_all_columns_near(0, mean)
#> [1] FALSE
abund_scaled %>% are_all_columns_near(0, mean)
#> [1] TRUE
abundance %>% are_all_columns_near(1, sd)
#> [1] FALSE
abund_scaled %>% are_all_columns_near(1, sd)
#> [1] TRUE# Re-run analysis that resulted in mvpart_run1
formula_scaled <- abund_scaled ~ RB_NO3 + RB_NH4 + RB_PO4 + Al + pH_water +
Na + Mn + Mg + K + Fe + Ca + BS + ECEC + Bray_P + meanelev + convex + slope
set.seed(1221) # same seed as for mvpart_run1
# This one fails
mvpart_run1_scaled <- mvpart(
form = formula_scaled,
data = environmental_variables,
all.leaves = TRUE, # annotate all nodes
rsq = TRUE, # give "rsq" plot
pca = TRUE, # plot PCA of group means and add species and site information
wgt.ave.pca = TRUE # plot weighted averages acorss sites for species
)
#> rpart(formula = form, data = data)
#>
#> Variables actually used in tree construction:
#> [1] RB_PO4
#>
#> Root node error: 350415/600 = 584.03
#>
#> n= 600
#>
#> CP nsplit rel error xerror xstd
#> 1 0.019712 0 1.00000 1.0034 0.028632
#> 2 0.016878 1 0.98029 0.9885 0.028311
#> May not be applicable for this method
#> Error in rpart.pca(z, interact = interact.pca, wgt.ave = wgt.ave.pca): Only 2 terminal nodes -- PCA not done !!
The scaled data fails. Repeating mvpart_run1 to check it runs again:
abundance <- data.matrix(KC3spp20)
environmental_variables <- kc.hab
formula <- abundance ~ RB_NO3 + RB_NH4 + RB_PO4 + Al + pH_water + Na + Mn +
Mg + K + Fe + Ca + BS + ECEC + Bray_P + meanelev + convex + slope
# Set a new seed for random numbers to ensure results are reproducible
set.seed(1221)
# See `?mvpart()` for argument details
mvpart_run1 <- mvpart(
form = formula,
data = environmental_variables,
all.leaves = TRUE, # annotate all nodes
rsq = TRUE, # give "rsq" plot
pca = TRUE, # plot PCA of group means and add species and site information
wgt.ave.pca = TRUE # plot weighted averages acorss sites for species
)
#> rpart(formula = form, data = data)
#>
#> Variables actually used in tree construction:
#> [1] Bray_P BS Fe meanelev pH_water RB_PO4
#>
#> Root node error: 483776/600 = 806.29
#>
#> n= 600
#>
#> CP nsplit rel error xerror xstd
#> 1 0.083600 0 1.00000 1.00355 0.040850
#> 2 0.025010 2 0.83280 0.85445 0.035633
#> 3 0.021388 3 0.80779 0.85882 0.035499
#> 4 0.021148 4 0.78640 0.84342 0.035072
#> 5 0.021020 5 0.76525 0.84155 0.035058
#> 6 0.020172 6 0.74423 0.82880 0.034711
#> 7 0.014623 7 0.72406 0.80000 0.033189
#> May not be applicable for this method
Miscelaneas
Example: Path from root to leaf node in mvpart
I was recently asked by a R user about how one could extract the “rule” in a classification/regression tree. The requirement was to obtain the path traced from the root node to the leaf nodes and obtain all the paths or “rules”
path.rpart() function in the mvpart package provides this convenience
library(mvpart)
# Create a classification tree
ozone <- mvpart(Ozone ~ ., data = airquality)
print(ozone) # Gives you the various splits in the tree
#> n=116 (37 observations deleted due to missingness)
#>
#> node), split, n, deviance, yval
#> * denotes terminal node
#>
#> 1) root 116 125143.100 42.12931
#> 2) Temp< 82.5 79 42531.590 26.54430
#> 4) Wind>=6 77 14010.990 23.55844 *
#> 5) Wind< 6 2 1404.500 141.50000 *
#> 3) Temp>=82.5 37 22452.920 75.40541
#> 6) Temp< 87.5 20 12046.950 62.95000 *
#> 7) Temp>=87.5 17 3652.941 90.05882 *
# Issue the two commands below to see the graphical representation
plot(ozone)
text(ozone)
# To obtain the summary of the created tree
summary(ozone)
#> Call:
#> mvpart(form = Ozone ~ ., data = airquality)
#> n=116 (37 observations deleted due to missingness)
#>
#> CP nsplit rel error xerror xstd
#> 1 0.48071820 0 1.0000000 1.0074380 0.16730714
#> 2 0.21668088 1 0.5192818 0.6032604 0.19589580
#> 3 0.05396246 2 0.3026009 0.5451161 0.18139524
#> 4 0.03127077 3 0.2486385 0.3942332 0.08050562
#>
#> Node number 1: 116 observations, complexity param=0.4807182
#> mean=42.12931, MSE=1078.819
#> left son=2 (79 obs) right son=3 (37 obs)
#> Primary splits:
#> Temp < 82.5 to the left, improve=0.48071820, (0 missing)
#> Wind < 6.6 to the right, improve=0.40426690, (0 missing)
#> Solar.R < 153 to the left, improve=0.21080020, (5 missing)
#> Month < 6.5 to the left, improve=0.11595770, (0 missing)
#> Day < 24.5 to the left, improve=0.08216807, (0 missing)
#>
#> Node number 2: 79 observations, complexity param=0.2166809
#> mean=26.5443, MSE=538.3746
#> left son=4 (77 obs) right son=5 (2 obs)
#> Primary splits:
#> Wind < 6 to the right, improve=0.63755210, (0 missing)
#> Temp < 77.5 to the left, improve=0.22489660, (0 missing)
#> Day < 24.5 to the left, improve=0.13807170, (0 missing)
#> Solar.R < 153 to the left, improve=0.10449720, (2 missing)
#> Month < 8.5 to the right, improve=0.01924449, (0 missing)
#>
#> Node number 3: 37 observations, complexity param=0.05396246
#> mean=75.40541, MSE=606.8356
#> left son=6 (20 obs) right son=7 (17 obs)
#> Primary splits:
#> Temp < 87.5 to the left, improve=0.3007639, (0 missing)
#> Wind < 10.6 to the right, improve=0.2739298, (0 missing)
#> Solar.R < 131 to the left, improve=0.1608206, (3 missing)
#> Day < 1.5 to the right, improve=0.1513779, (0 missing)
#> Month < 6.5 to the left, improve=0.0392086, (0 missing)
#>
#> Node number 4: 77 observations
#> mean=23.55844, MSE=181.9609
#>
#> Node number 5: 2 observations
#> mean=141.5, MSE=702.25
#>
#> Node number 6: 20 observations
#> mean=62.95, MSE=602.3475
#>
#> Node number 7: 17 observations
#> mean=90.05882, MSE=214.8789
# To obtain the path to the leaf nodes
ozone$frame
#> var n wt dev yval complexity ncompete nsurrogate
#> 1 Temp 116 116 125143.060 42.12931 0.480718198 4 0
#> 2 Wind 79 79 42531.595 26.54430 0.216680876 4 0
#> 4 <leaf> 77 77 14010.987 23.55844 0.031270766 0 0
#> 5 <leaf> 2 2 1404.500 141.50000 0.010000000 0 0
#> 3 Temp 37 37 22452.919 75.40541 0.053962463 4 0
#> 6 <leaf> 20 20 12046.950 62.95000 0.025989987 0 0
#> 7 <leaf> 17 17 3652.941 90.05882 0.005931408 0 0
leafnodeRows <- grepl("leaf", ozone$frame$var)
nodevals <- as.numeric(rownames(ozone$frame)[leafnodeRows])
rules <- path.rpart(ozone, nodevals)
#>
#> node number: 4
#> root
#> Temp< 82.5
#> Wind>=6
#>
#> node number: 5
#> root
#> Temp< 82.5
#> Wind< 6
#>
#> node number: 6
#> root
#> Temp>=82.5
#> Temp< 87.5
#>
#> node number: 7
#> root
#> Temp>=82.5
#> Temp>=87.5
rulesdf <- do.call(
"rbind",
lapply(rules, function(x) paste(x, collapse = " -AND- "))
)
rulesdf <- data.frame(
nodeNumber = rownames(rulesdf),
rule = rulesdf[, 1],
stringsAsFactors = FALSE
)
rulesdf
#> nodeNumber rule
#> 4 4 root -AND- Temp< 82.5 -AND- Wind>=6
#> 5 5 root -AND- Temp< 82.5 -AND- Wind< 6
#> 6 6 root -AND- Temp>=82.5 -AND- Temp< 87.5
#> 7 7 root -AND- Temp>=82.5 -AND- Temp>=87.5Package rpart does not handle multi variate data
The rpart package seems like a good alternative because ?mvpart::mvpart() says it’s a wrapper of rpart(). However, mvpart::rpart() works with multivariate data, but rpart::rpart() does not.
mvpart::rpart(form = formula, data = environmental_variables) # passes
rpart::rpart(form = formula, data = environmental_variables) # failsrpart is active but mvpart is not. Maybe the authors can inform where else mvpart functions can be found.
Access rpart’s vignettes from R with:
browseVignettes("rpart")
# An Introduction to Recursive Partitioning Using the RPART Rutines (62 pages)
vignette("longintro")Learning more
- De’Ath, G. (2002). Multivariate regression trees: a new technique for modeling species–environment relationships. Ecology, 83(4), 1105-1117.