Environmental Typologies based on Cardinal, Quantilic Limits or Data-Driven Mining
Source:R/env_characterizaton_src.R
env_typing.RdReturns environmental typologies (envirotypes) that can be used as envirotype
markers. Typologies are given by cardinals (discrete intervals for each variable), by
empirical quantiles, or – when envirotype_mining = TRUE – learned from the data:
for each variable a K-means clustering is run over a grid of candidate k values and
the optimal number of envirotypes is selected by the Calinski-Harabasz (variance ratio)
criterion.
Usage
env_typing(
env.data,
var.id,
env.id,
cardinals = NULL,
days.id = NULL,
time.window = NULL,
names.window = NULL,
quantiles = NULL,
id.names = NULL,
by.interval = FALSE,
scale = FALSE,
format = NULL,
ratio = FALSE,
joint = FALSE,
joint.by.interval = TRUE,
combine.features = FALSE,
combine.max.order = 2L,
envirotype_mining = FALSE,
k.range = 2:10,
nstart = 25,
iter.max = 100,
seed = 1234,
plot_envirotypes = FALSE,
min.window = 2L,
verbose = TRUE
)Arguments
- env.data
data.frame of environmental variables from
get_weather.- var.id
character. Which variables will be used in the analysis.
- env.id
character. Column used as id for environments.
- cardinals
list (numeric). Cardinal thresholds per variable. If
NULL, seequantiles. Ignored whenenvirotype_mining = TRUE.- days.id
character. Name of the column indicating the days from start.
- time.window
vector (numeric). If
by.interval = TRUE, the temporal breaks.- names.window
vector (character). If
by.interval = TRUE, the interval names. Note thatcut()is applied with an open-ended final break, sotime.window = c(0,40,80,120)defines FOUR intervals; supply either 4 names or 3, in which case the trailing one is auto-named.- quantiles
vector (numeric). Probability quantiles used when
cardinalsisNULL. Defaultc(0.01, .25, .50, .75, .99). Ignored when mining.- id.names
vector (character). Columns used as id for the environmental variables.
- by.interval
boolean. Compute temporal intervals inside each environment. Default
FALSE.- scale
boolean. If
TRUE, variables assume \(x \sim N(0,1)\). DefaultFALSE.- format
character. Output shape,
'long'(default) or'wide'.- ratio
boolean. If
TRUE,Freqis rescaled to a relative frequency in \([0,1]\) within each environment x interval x variable group. DefaultFALSE.- joint
boolean. Only used when
envirotype_mining = TRUE. IfFALSE(default) one univariate K-means is run per variable. IfTRUE, the variables invar.idare COMBINED: the vector of all variables observed at the same time point is clustered jointly, so each envirotype is a recurring multi-variable state of the environment (analogous to a haplotype across loci).- joint.by.interval
boolean. Only used when
joint = TRUE. IfTRUE(default) a separate K-means is fitted inside each time interval; ifFALSEa single K-means is fitted over all intervals pooled.- combine.features
boolean. Only valid when
envirotype_mining = TRUEandjoint = FALSE. IfTRUE, envirotypes are mined for every unique COMBINATION of the variables invar.id: each variable on its own, then every pair, every triple, and so on. A combination of two or more variables is clustered jointly (as injoint = TRUE); a single-variable combination reduces to the univariate case. Each combination receives a compact generic id (FC001,FC002, ...) and the returnedfeature_combinationstable maps every id back to its variables and time intervals. DefaultFALSE.- combine.max.order
integer or
NULL. Largest combination size to enumerate whencombine.features = TRUE. Because the number of combinations grows as a power set (\(2^n - 1\)), this defaults to2(single variables and pairs only), which stays tractable for dozens of variables. Increase it to include triples and higher (e.g.3), or setNULLto enumerate alllength(var.id)orders. A guard stops the run if the requested number of combinations is impractically large.- envirotype_mining
boolean. If
TRUE, envirotype classes are mined from the data by K-means withkchosen by the Calinski-Harabasz index. DefaultFALSE.- k.range
integer vector. Candidate numbers of envirotypes tested when mining. Default
2:10.- nstart
numeric. Random starts passed to
stats::kmeans. Default 25.- iter.max
numeric. Maximum iterations passed to
stats::kmeans. Default 100.- seed
numeric. Random seed for reproducible K-means. Default 1234;
NULLskips seeding.- plot_envirotypes
boolean. If
TRUE, draws a barplot of the frequency (or ratio) of each envirotype per environment, faceted by variable (univariate) or by time interval (joint). Uses ggplot2 when available, base graphics otherwise. DefaultFALSE.- min.window
integer. Minimum number of observations required in the open-ended trailing interval. Default 2; use 0 or
NULLto always keep it.- verbose
boolean. If
TRUE(default) prints progress messages.
Value
If envirotype_mining = FALSE (default) and plot_envirotypes = FALSE: a
data.frame (or matrix, if format = 'wide') of envirotype frequencies.
Otherwise a list with:
typologiesdata.frame (long) or matrix (wide) of envirotype frequencies.
envirotype_descriptiondata.frame describing the numeric range captured by each envirotype: variable, label, cluster index,
n,min,max,mean,sd,medianand thelower/uppercut boundaries.mining_summarydata.frame with the optimal
k, the CH index at the optimum and the full CH profile overk.range.feature_combinationsonly when
combine.features = TRUE: a data.frame mapping each generic combination id to its number of features (n_features), thefeaturesthemselves and the timeintervals.plotthe plot object, or
NULL.
Details
The Calinski-Harabasz index for a partition into \(k\) groups of \(n\) observations is
$$CH(k) = \frac{BGSS/(k-1)}{WGSS/(n-k)}$$
where BGSS is the between-group and WGSS the within-group sum of squares. The \(k\)
maximising CH is retained. Clusters are relabelled in increasing order of their centres so
that Envirotype_<var>_00001 is always the lowest range of the variable. For joint
(multivariate) mining there is no natural ordering, so centroids are ordered along their
first principal component and variables are standardised within the window before
clustering so that no feature dominates the Euclidean distance.
See also
W_matrix, env_kernel, T_matrix
Examples
# \donttest{
data("maizeWTH")
env.data <- maizeWTH[maizeWTH$daysFromStart < 100, ]
## 1. Generic time intervals
env_typing(env.data = env.data, env.id = 'env', var.id = 'T2M', by.interval = TRUE)
#> ---------------------------------------------------------------
#> env_typing -- mines environmental types from weather data
#> Last updated at 09/27/2026
#> ---------------------------------------------------------------
#> - assigning records to time-interval stages
#> env.variable Freq env interval var
#> 1 T2M_(17.4,22]_Interval_0 0 NM Interval_0 T2M
#> 2 T2M_(22,24.1]_Interval_0 3 NM Interval_0 T2M
#> 3 T2M_(24.1,25.2]_Interval_0 4 NM Interval_0 T2M
#> 4 T2M_(25.2,27.9]_Interval_0 2 NM Interval_0 T2M
#> 5 T2M_(17.4,22]_Interval_10 0 NM Interval_10 T2M
#> 6 T2M_(22,24.1]_Interval_10 0 NM Interval_10 T2M
#> 7 T2M_(24.1,25.2]_Interval_10 8 NM Interval_10 T2M
#> 8 T2M_(25.2,27.9]_Interval_10 2 NM Interval_10 T2M
#> 9 T2M_(17.4,22]_Interval_20 0 NM Interval_20 T2M
#> 10 T2M_(22,24.1]_Interval_20 3 NM Interval_20 T2M
#> 11 T2M_(24.1,25.2]_Interval_20 5 NM Interval_20 T2M
#> 12 T2M_(25.2,27.9]_Interval_20 2 NM Interval_20 T2M
#> 13 T2M_(17.4,22]_Interval_30 0 NM Interval_30 T2M
#> 14 T2M_(22,24.1]_Interval_30 2 NM Interval_30 T2M
#> 15 T2M_(24.1,25.2]_Interval_30 7 NM Interval_30 T2M
#> 16 T2M_(25.2,27.9]_Interval_30 1 NM Interval_30 T2M
#> 17 T2M_(17.4,22]_Interval_40 0 NM Interval_40 T2M
#> 18 T2M_(22,24.1]_Interval_40 0 NM Interval_40 T2M
#> 19 T2M_(24.1,25.2]_Interval_40 4 NM Interval_40 T2M
#> 20 T2M_(25.2,27.9]_Interval_40 6 NM Interval_40 T2M
#> 21 T2M_(17.4,22]_Interval_50 0 NM Interval_50 T2M
#> 22 T2M_(22,24.1]_Interval_50 0 NM Interval_50 T2M
#> 23 T2M_(24.1,25.2]_Interval_50 10 NM Interval_50 T2M
#> 24 T2M_(25.2,27.9]_Interval_50 0 NM Interval_50 T2M
#> 25 T2M_(17.4,22]_Interval_60 0 NM Interval_60 T2M
#> 26 T2M_(22,24.1]_Interval_60 0 NM Interval_60 T2M
#> 27 T2M_(24.1,25.2]_Interval_60 7 NM Interval_60 T2M
#> 28 T2M_(25.2,27.9]_Interval_60 3 NM Interval_60 T2M
#> 29 T2M_(17.4,22]_Interval_70 0 NM Interval_70 T2M
#> 30 T2M_(22,24.1]_Interval_70 4 NM Interval_70 T2M
#> 31 T2M_(24.1,25.2]_Interval_70 6 NM Interval_70 T2M
#> 32 T2M_(25.2,27.9]_Interval_70 0 NM Interval_70 T2M
#> 33 T2M_(17.4,22]_Interval_80 0 NM Interval_80 T2M
#> 34 T2M_(22,24.1]_Interval_80 4 NM Interval_80 T2M
#> 35 T2M_(24.1,25.2]_Interval_80 6 NM Interval_80 T2M
#> 36 T2M_(25.2,27.9]_Interval_80 0 NM Interval_80 T2M
#> 37 T2M_(17.4,22]_Interval_90 0 NM Interval_90 T2M
#> 38 T2M_(22,24.1]_Interval_90 3 NM Interval_90 T2M
#> 39 T2M_(24.1,25.2]_Interval_90 6 NM Interval_90 T2M
#> 40 T2M_(25.2,27.9]_Interval_90 1 NM Interval_90 T2M
#> 41 T2M_(17.4,22]_Interval_0 0 PM Interval_0 T2M
#> 42 T2M_(22,24.1]_Interval_0 7 PM Interval_0 T2M
#> 43 T2M_(24.1,25.2]_Interval_0 2 PM Interval_0 T2M
#> 44 T2M_(25.2,27.9]_Interval_0 0 PM Interval_0 T2M
#> 45 T2M_(17.4,22]_Interval_10 5 PM Interval_10 T2M
#> 46 T2M_(22,24.1]_Interval_10 5 PM Interval_10 T2M
#> 47 T2M_(24.1,25.2]_Interval_10 0 PM Interval_10 T2M
#> 48 T2M_(25.2,27.9]_Interval_10 0 PM Interval_10 T2M
#> 49 T2M_(17.4,22]_Interval_20 4 PM Interval_20 T2M
#> 50 T2M_(22,24.1]_Interval_20 5 PM Interval_20 T2M
#> 51 T2M_(24.1,25.2]_Interval_20 1 PM Interval_20 T2M
#> 52 T2M_(25.2,27.9]_Interval_20 0 PM Interval_20 T2M
#> 53 T2M_(17.4,22]_Interval_30 1 PM Interval_30 T2M
#> 54 T2M_(22,24.1]_Interval_30 4 PM Interval_30 T2M
#> 55 T2M_(24.1,25.2]_Interval_30 5 PM Interval_30 T2M
#> 56 T2M_(25.2,27.9]_Interval_30 0 PM Interval_30 T2M
#> 57 T2M_(17.4,22]_Interval_40 3 PM Interval_40 T2M
#> 58 T2M_(22,24.1]_Interval_40 7 PM Interval_40 T2M
#> 59 T2M_(24.1,25.2]_Interval_40 0 PM Interval_40 T2M
#> 60 T2M_(25.2,27.9]_Interval_40 0 PM Interval_40 T2M
#> 61 T2M_(17.4,22]_Interval_50 1 PM Interval_50 T2M
#> 62 T2M_(22,24.1]_Interval_50 7 PM Interval_50 T2M
#> 63 T2M_(24.1,25.2]_Interval_50 2 PM Interval_50 T2M
#> 64 T2M_(25.2,27.9]_Interval_50 0 PM Interval_50 T2M
#> 65 T2M_(17.4,22]_Interval_60 9 PM Interval_60 T2M
#> 66 T2M_(22,24.1]_Interval_60 1 PM Interval_60 T2M
#> 67 T2M_(24.1,25.2]_Interval_60 0 PM Interval_60 T2M
#> 68 T2M_(25.2,27.9]_Interval_60 0 PM Interval_60 T2M
#> 69 T2M_(17.4,22]_Interval_70 10 PM Interval_70 T2M
#> 70 T2M_(22,24.1]_Interval_70 0 PM Interval_70 T2M
#> 71 T2M_(24.1,25.2]_Interval_70 0 PM Interval_70 T2M
#> 72 T2M_(25.2,27.9]_Interval_70 0 PM Interval_70 T2M
#> 73 T2M_(17.4,22]_Interval_80 10 PM Interval_80 T2M
#> 74 T2M_(22,24.1]_Interval_80 0 PM Interval_80 T2M
#> 75 T2M_(24.1,25.2]_Interval_80 0 PM Interval_80 T2M
#> 76 T2M_(25.2,27.9]_Interval_80 0 PM Interval_80 T2M
#> 77 T2M_(17.4,22]_Interval_90 9 PM Interval_90 T2M
#> 78 T2M_(22,24.1]_Interval_90 1 PM Interval_90 T2M
#> 79 T2M_(24.1,25.2]_Interval_90 0 PM Interval_90 T2M
#> 80 T2M_(25.2,27.9]_Interval_90 0 PM Interval_90 T2M
#> 81 T2M_(17.4,22]_Interval_0 0 IP Interval_0 T2M
#> 82 T2M_(22,24.1]_Interval_0 3 IP Interval_0 T2M
#> 83 T2M_(24.1,25.2]_Interval_0 3 IP Interval_0 T2M
#> 84 T2M_(25.2,27.9]_Interval_0 3 IP Interval_0 T2M
#> 85 T2M_(17.4,22]_Interval_10 0 IP Interval_10 T2M
#> 86 T2M_(22,24.1]_Interval_10 2 IP Interval_10 T2M
#> 87 T2M_(24.1,25.2]_Interval_10 6 IP Interval_10 T2M
#> 88 T2M_(25.2,27.9]_Interval_10 2 IP Interval_10 T2M
#> 89 T2M_(17.4,22]_Interval_20 0 IP Interval_20 T2M
#> 90 T2M_(22,24.1]_Interval_20 1 IP Interval_20 T2M
#> 91 T2M_(24.1,25.2]_Interval_20 7 IP Interval_20 T2M
#> 92 T2M_(25.2,27.9]_Interval_20 2 IP Interval_20 T2M
#> 93 T2M_(17.4,22]_Interval_30 1 IP Interval_30 T2M
#> 94 T2M_(22,24.1]_Interval_30 9 IP Interval_30 T2M
#> 95 T2M_(24.1,25.2]_Interval_30 0 IP Interval_30 T2M
#> 96 T2M_(25.2,27.9]_Interval_30 0 IP Interval_30 T2M
#> 97 T2M_(17.4,22]_Interval_40 0 IP Interval_40 T2M
#> 98 T2M_(22,24.1]_Interval_40 1 IP Interval_40 T2M
#> 99 T2M_(24.1,25.2]_Interval_40 5 IP Interval_40 T2M
#> 100 T2M_(25.2,27.9]_Interval_40 4 IP Interval_40 T2M
#> 101 T2M_(17.4,22]_Interval_50 0 IP Interval_50 T2M
#> 102 T2M_(22,24.1]_Interval_50 2 IP Interval_50 T2M
#> 103 T2M_(24.1,25.2]_Interval_50 7 IP Interval_50 T2M
#> 104 T2M_(25.2,27.9]_Interval_50 1 IP Interval_50 T2M
#> 105 T2M_(17.4,22]_Interval_60 0 IP Interval_60 T2M
#> 106 T2M_(22,24.1]_Interval_60 2 IP Interval_60 T2M
#> 107 T2M_(24.1,25.2]_Interval_60 6 IP Interval_60 T2M
#> 108 T2M_(25.2,27.9]_Interval_60 2 IP Interval_60 T2M
#> 109 T2M_(17.4,22]_Interval_70 0 IP Interval_70 T2M
#> 110 T2M_(22,24.1]_Interval_70 6 IP Interval_70 T2M
#> 111 T2M_(24.1,25.2]_Interval_70 3 IP Interval_70 T2M
#> 112 T2M_(25.2,27.9]_Interval_70 1 IP Interval_70 T2M
#> 113 T2M_(17.4,22]_Interval_80 2 IP Interval_80 T2M
#> 114 T2M_(22,24.1]_Interval_80 8 IP Interval_80 T2M
#> 115 T2M_(24.1,25.2]_Interval_80 0 IP Interval_80 T2M
#> 116 T2M_(25.2,27.9]_Interval_80 0 IP Interval_80 T2M
#> 117 T2M_(17.4,22]_Interval_90 10 IP Interval_90 T2M
#> 118 T2M_(22,24.1]_Interval_90 0 IP Interval_90 T2M
#> 119 T2M_(24.1,25.2]_Interval_90 0 IP Interval_90 T2M
#> 120 T2M_(25.2,27.9]_Interval_90 0 IP Interval_90 T2M
#> 121 T2M_(17.4,22]_Interval_0 0 SE Interval_0 T2M
#> 122 T2M_(22,24.1]_Interval_0 6 SE Interval_0 T2M
#> 123 T2M_(24.1,25.2]_Interval_0 3 SE Interval_0 T2M
#> 124 T2M_(25.2,27.9]_Interval_0 0 SE Interval_0 T2M
#> 125 T2M_(17.4,22]_Interval_10 3 SE Interval_10 T2M
#> 126 T2M_(22,24.1]_Interval_10 7 SE Interval_10 T2M
#> 127 T2M_(24.1,25.2]_Interval_10 0 SE Interval_10 T2M
#> 128 T2M_(25.2,27.9]_Interval_10 0 SE Interval_10 T2M
#> 129 T2M_(17.4,22]_Interval_20 3 SE Interval_20 T2M
#> 130 T2M_(22,24.1]_Interval_20 6 SE Interval_20 T2M
#> 131 T2M_(24.1,25.2]_Interval_20 1 SE Interval_20 T2M
#> 132 T2M_(25.2,27.9]_Interval_20 0 SE Interval_20 T2M
#> 133 T2M_(17.4,22]_Interval_30 6 SE Interval_30 T2M
#> 134 T2M_(22,24.1]_Interval_30 4 SE Interval_30 T2M
#> 135 T2M_(24.1,25.2]_Interval_30 0 SE Interval_30 T2M
#> 136 T2M_(25.2,27.9]_Interval_30 0 SE Interval_30 T2M
#> 137 T2M_(17.4,22]_Interval_40 0 SE Interval_40 T2M
#> 138 T2M_(22,24.1]_Interval_40 7 SE Interval_40 T2M
#> 139 T2M_(24.1,25.2]_Interval_40 3 SE Interval_40 T2M
#> 140 T2M_(25.2,27.9]_Interval_40 0 SE Interval_40 T2M
#> 141 T2M_(17.4,22]_Interval_50 9 SE Interval_50 T2M
#> 142 T2M_(22,24.1]_Interval_50 1 SE Interval_50 T2M
#> 143 T2M_(24.1,25.2]_Interval_50 0 SE Interval_50 T2M
#> 144 T2M_(25.2,27.9]_Interval_50 0 SE Interval_50 T2M
#> 145 T2M_(17.4,22]_Interval_60 8 SE Interval_60 T2M
#> 146 T2M_(22,24.1]_Interval_60 0 SE Interval_60 T2M
#> 147 T2M_(24.1,25.2]_Interval_60 0 SE Interval_60 T2M
#> 148 T2M_(25.2,27.9]_Interval_60 0 SE Interval_60 T2M
#> 149 T2M_(17.4,22]_Interval_70 10 SE Interval_70 T2M
#> 150 T2M_(22,24.1]_Interval_70 0 SE Interval_70 T2M
#> 151 T2M_(24.1,25.2]_Interval_70 0 SE Interval_70 T2M
#> 152 T2M_(25.2,27.9]_Interval_70 0 SE Interval_70 T2M
#> 153 T2M_(17.4,22]_Interval_80 6 SE Interval_80 T2M
#> 154 T2M_(22,24.1]_Interval_80 2 SE Interval_80 T2M
#> 155 T2M_(24.1,25.2]_Interval_80 0 SE Interval_80 T2M
#> 156 T2M_(25.2,27.9]_Interval_80 0 SE Interval_80 T2M
#> 157 T2M_(17.4,22]_Interval_90 9 SE Interval_90 T2M
#> 158 T2M_(22,24.1]_Interval_90 0 SE Interval_90 T2M
#> 159 T2M_(24.1,25.2]_Interval_90 0 SE Interval_90 T2M
#> 160 T2M_(25.2,27.9]_Interval_90 0 SE Interval_90 T2M
#> 161 T2M_(17.4,22]_Interval_0 0 SO Interval_0 T2M
#> 162 T2M_(22,24.1]_Interval_0 0 SO Interval_0 T2M
#> 163 T2M_(24.1,25.2]_Interval_0 0 SO Interval_0 T2M
#> 164 T2M_(25.2,27.9]_Interval_0 9 SO Interval_0 T2M
#> 165 T2M_(17.4,22]_Interval_10 0 SO Interval_10 T2M
#> 166 T2M_(22,24.1]_Interval_10 0 SO Interval_10 T2M
#> 167 T2M_(24.1,25.2]_Interval_10 0 SO Interval_10 T2M
#> 168 T2M_(25.2,27.9]_Interval_10 9 SO Interval_10 T2M
#> 169 T2M_(17.4,22]_Interval_20 0 SO Interval_20 T2M
#> 170 T2M_(22,24.1]_Interval_20 0 SO Interval_20 T2M
#> 171 T2M_(24.1,25.2]_Interval_20 0 SO Interval_20 T2M
#> 172 T2M_(25.2,27.9]_Interval_20 9 SO Interval_20 T2M
#> 173 T2M_(17.4,22]_Interval_30 0 SO Interval_30 T2M
#> 174 T2M_(22,24.1]_Interval_30 0 SO Interval_30 T2M
#> 175 T2M_(24.1,25.2]_Interval_30 0 SO Interval_30 T2M
#> 176 T2M_(25.2,27.9]_Interval_30 9 SO Interval_30 T2M
#> 177 T2M_(17.4,22]_Interval_40 0 SO Interval_40 T2M
#> 178 T2M_(22,24.1]_Interval_40 0 SO Interval_40 T2M
#> 179 T2M_(24.1,25.2]_Interval_40 2 SO Interval_40 T2M
#> 180 T2M_(25.2,27.9]_Interval_40 8 SO Interval_40 T2M
#> 181 T2M_(17.4,22]_Interval_50 0 SO Interval_50 T2M
#> 182 T2M_(22,24.1]_Interval_50 0 SO Interval_50 T2M
#> 183 T2M_(24.1,25.2]_Interval_50 0 SO Interval_50 T2M
#> 184 T2M_(25.2,27.9]_Interval_50 10 SO Interval_50 T2M
#> 185 T2M_(17.4,22]_Interval_60 0 SO Interval_60 T2M
#> 186 T2M_(22,24.1]_Interval_60 1 SO Interval_60 T2M
#> 187 T2M_(24.1,25.2]_Interval_60 3 SO Interval_60 T2M
#> 188 T2M_(25.2,27.9]_Interval_60 6 SO Interval_60 T2M
#> 189 T2M_(17.4,22]_Interval_70 0 SO Interval_70 T2M
#> 190 T2M_(22,24.1]_Interval_70 0 SO Interval_70 T2M
#> 191 T2M_(24.1,25.2]_Interval_70 2 SO Interval_70 T2M
#> 192 T2M_(25.2,27.9]_Interval_70 8 SO Interval_70 T2M
#> 193 T2M_(17.4,22]_Interval_80 0 SO Interval_80 T2M
#> 194 T2M_(22,24.1]_Interval_80 0 SO Interval_80 T2M
#> 195 T2M_(24.1,25.2]_Interval_80 0 SO Interval_80 T2M
#> 196 T2M_(25.2,27.9]_Interval_80 10 SO Interval_80 T2M
#> 197 T2M_(17.4,22]_Interval_90 0 SO Interval_90 T2M
#> 198 T2M_(22,24.1]_Interval_90 0 SO Interval_90 T2M
#> 199 T2M_(24.1,25.2]_Interval_90 0 SO Interval_90 T2M
#> 200 T2M_(25.2,27.9]_Interval_90 8 SO Interval_90 T2M
## 2. Specific time intervals with names
env_typing(env.data = env.data, env.id = 'env', var.id = 'T2M',
by.interval = TRUE,
time.window = c(0, 15, 35, 65, 90),
names.window = c('1-initial growing', '2-leaf expansion I',
'3-leaf expansion II', '4-flowering', '5-grain filling'))
#> ---------------------------------------------------------------
#> env_typing -- mines environmental types from weather data
#> Last updated at 09/27/2026
#> ---------------------------------------------------------------
#> - assigning records to time-interval stages
#> env.variable Freq env interval var
#> 1 T2M_(17.4,22]_1-initial growing 0 NM 1-initial growing T2M
#> 2 T2M_(22,24.1]_1-initial growing 3 NM 1-initial growing T2M
#> 3 T2M_(24.1,25.2]_1-initial growing 8 NM 1-initial growing T2M
#> 4 T2M_(25.2,27.9]_1-initial growing 3 NM 1-initial growing T2M
#> 5 T2M_(17.4,22]_2-leaf expansion I 0 NM 2-leaf expansion I T2M
#> 6 T2M_(22,24.1]_2-leaf expansion I 5 NM 2-leaf expansion I T2M
#> 7 T2M_(24.1,25.2]_2-leaf expansion I 12 NM 2-leaf expansion I T2M
#> 8 T2M_(25.2,27.9]_2-leaf expansion I 3 NM 2-leaf expansion I T2M
#> 9 T2M_(17.4,22]_3-leaf expansion II 0 NM 3-leaf expansion II T2M
#> 10 T2M_(22,24.1]_3-leaf expansion II 0 NM 3-leaf expansion II T2M
#> 11 T2M_(24.1,25.2]_3-leaf expansion II 22 NM 3-leaf expansion II T2M
#> 12 T2M_(25.2,27.9]_3-leaf expansion II 8 NM 3-leaf expansion II T2M
#> 13 T2M_(17.4,22]_4-flowering 0 NM 4-flowering T2M
#> 14 T2M_(22,24.1]_4-flowering 8 NM 4-flowering T2M
#> 15 T2M_(24.1,25.2]_4-flowering 15 NM 4-flowering T2M
#> 16 T2M_(25.2,27.9]_4-flowering 2 NM 4-flowering T2M
#> 17 T2M_(17.4,22]_5-grain filling 0 NM 5-grain filling T2M
#> 18 T2M_(22,24.1]_5-grain filling 3 NM 5-grain filling T2M
#> 19 T2M_(24.1,25.2]_5-grain filling 6 NM 5-grain filling T2M
#> 20 T2M_(25.2,27.9]_5-grain filling 1 NM 5-grain filling T2M
#> 21 T2M_(17.4,22]_1-initial growing 2 PM 1-initial growing T2M
#> 22 T2M_(22,24.1]_1-initial growing 10 PM 1-initial growing T2M
#> 23 T2M_(24.1,25.2]_1-initial growing 2 PM 1-initial growing T2M
#> 24 T2M_(25.2,27.9]_1-initial growing 0 PM 1-initial growing T2M
#> 25 T2M_(17.4,22]_2-leaf expansion I 7 PM 2-leaf expansion I T2M
#> 26 T2M_(22,24.1]_2-leaf expansion I 7 PM 2-leaf expansion I T2M
#> 27 T2M_(24.1,25.2]_2-leaf expansion I 6 PM 2-leaf expansion I T2M
#> 28 T2M_(25.2,27.9]_2-leaf expansion I 0 PM 2-leaf expansion I T2M
#> 29 T2M_(17.4,22]_3-leaf expansion II 10 PM 3-leaf expansion II T2M
#> 30 T2M_(22,24.1]_3-leaf expansion II 18 PM 3-leaf expansion II T2M
#> 31 T2M_(24.1,25.2]_3-leaf expansion II 2 PM 3-leaf expansion II T2M
#> 32 T2M_(25.2,27.9]_3-leaf expansion II 0 PM 3-leaf expansion II T2M
#> 33 T2M_(17.4,22]_4-flowering 24 PM 4-flowering T2M
#> 34 T2M_(22,24.1]_4-flowering 1 PM 4-flowering T2M
#> 35 T2M_(24.1,25.2]_4-flowering 0 PM 4-flowering T2M
#> 36 T2M_(25.2,27.9]_4-flowering 0 PM 4-flowering T2M
#> 37 T2M_(17.4,22]_5-grain filling 9 PM 5-grain filling T2M
#> 38 T2M_(22,24.1]_5-grain filling 1 PM 5-grain filling T2M
#> 39 T2M_(24.1,25.2]_5-grain filling 0 PM 5-grain filling T2M
#> 40 T2M_(25.2,27.9]_5-grain filling 0 PM 5-grain filling T2M
#> 41 T2M_(17.4,22]_1-initial growing 0 IP 1-initial growing T2M
#> 42 T2M_(22,24.1]_1-initial growing 5 IP 1-initial growing T2M
#> 43 T2M_(24.1,25.2]_1-initial growing 5 IP 1-initial growing T2M
#> 44 T2M_(25.2,27.9]_1-initial growing 4 IP 1-initial growing T2M
#> 45 T2M_(17.4,22]_2-leaf expansion I 0 IP 2-leaf expansion I T2M
#> 46 T2M_(22,24.1]_2-leaf expansion I 6 IP 2-leaf expansion I T2M
#> 47 T2M_(24.1,25.2]_2-leaf expansion I 11 IP 2-leaf expansion I T2M
#> 48 T2M_(25.2,27.9]_2-leaf expansion I 3 IP 2-leaf expansion I T2M
#> 49 T2M_(17.4,22]_3-leaf expansion II 1 IP 3-leaf expansion II T2M
#> 50 T2M_(22,24.1]_3-leaf expansion II 9 IP 3-leaf expansion II T2M
#> 51 T2M_(24.1,25.2]_3-leaf expansion II 15 IP 3-leaf expansion II T2M
#> 52 T2M_(25.2,27.9]_3-leaf expansion II 5 IP 3-leaf expansion II T2M
#> 53 T2M_(17.4,22]_4-flowering 2 IP 4-flowering T2M
#> 54 T2M_(22,24.1]_4-flowering 14 IP 4-flowering T2M
#> 55 T2M_(24.1,25.2]_4-flowering 6 IP 4-flowering T2M
#> 56 T2M_(25.2,27.9]_4-flowering 3 IP 4-flowering T2M
#> 57 T2M_(17.4,22]_5-grain filling 10 IP 5-grain filling T2M
#> 58 T2M_(22,24.1]_5-grain filling 0 IP 5-grain filling T2M
#> 59 T2M_(24.1,25.2]_5-grain filling 0 IP 5-grain filling T2M
#> 60 T2M_(25.2,27.9]_5-grain filling 0 IP 5-grain filling T2M
#> 61 T2M_(17.4,22]_1-initial growing 0 SE 1-initial growing T2M
#> 62 T2M_(22,24.1]_1-initial growing 11 SE 1-initial growing T2M
#> 63 T2M_(24.1,25.2]_1-initial growing 3 SE 1-initial growing T2M
#> 64 T2M_(25.2,27.9]_1-initial growing 0 SE 1-initial growing T2M
#> 65 T2M_(17.4,22]_2-leaf expansion I 9 SE 2-leaf expansion I T2M
#> 66 T2M_(22,24.1]_2-leaf expansion I 10 SE 2-leaf expansion I T2M
#> 67 T2M_(24.1,25.2]_2-leaf expansion I 1 SE 2-leaf expansion I T2M
#> 68 T2M_(25.2,27.9]_2-leaf expansion I 0 SE 2-leaf expansion I T2M
#> 69 T2M_(17.4,22]_3-leaf expansion II 17 SE 3-leaf expansion II T2M
#> 70 T2M_(22,24.1]_3-leaf expansion II 10 SE 3-leaf expansion II T2M
#> 71 T2M_(24.1,25.2]_3-leaf expansion II 3 SE 3-leaf expansion II T2M
#> 72 T2M_(25.2,27.9]_3-leaf expansion II 0 SE 3-leaf expansion II T2M
#> 73 T2M_(17.4,22]_4-flowering 19 SE 4-flowering T2M
#> 74 T2M_(22,24.1]_4-flowering 2 SE 4-flowering T2M
#> 75 T2M_(24.1,25.2]_4-flowering 0 SE 4-flowering T2M
#> 76 T2M_(25.2,27.9]_4-flowering 0 SE 4-flowering T2M
#> 77 T2M_(17.4,22]_5-grain filling 9 SE 5-grain filling T2M
#> 78 T2M_(22,24.1]_5-grain filling 0 SE 5-grain filling T2M
#> 79 T2M_(24.1,25.2]_5-grain filling 0 SE 5-grain filling T2M
#> 80 T2M_(25.2,27.9]_5-grain filling 0 SE 5-grain filling T2M
#> 81 T2M_(17.4,22]_1-initial growing 0 SO 1-initial growing T2M
#> 82 T2M_(22,24.1]_1-initial growing 0 SO 1-initial growing T2M
#> 83 T2M_(24.1,25.2]_1-initial growing 0 SO 1-initial growing T2M
#> 84 T2M_(25.2,27.9]_1-initial growing 13 SO 1-initial growing T2M
#> 85 T2M_(17.4,22]_2-leaf expansion I 0 SO 2-leaf expansion I T2M
#> 86 T2M_(22,24.1]_2-leaf expansion I 0 SO 2-leaf expansion I T2M
#> 87 T2M_(24.1,25.2]_2-leaf expansion I 0 SO 2-leaf expansion I T2M
#> 88 T2M_(25.2,27.9]_2-leaf expansion I 18 SO 2-leaf expansion I T2M
#> 89 T2M_(17.4,22]_3-leaf expansion II 0 SO 3-leaf expansion II T2M
#> 90 T2M_(22,24.1]_3-leaf expansion II 0 SO 3-leaf expansion II T2M
#> 91 T2M_(24.1,25.2]_3-leaf expansion II 3 SO 3-leaf expansion II T2M
#> 92 T2M_(25.2,27.9]_3-leaf expansion II 27 SO 3-leaf expansion II T2M
#> 93 T2M_(17.4,22]_4-flowering 0 SO 4-flowering T2M
#> 94 T2M_(22,24.1]_4-flowering 1 SO 4-flowering T2M
#> 95 T2M_(24.1,25.2]_4-flowering 4 SO 4-flowering T2M
#> 96 T2M_(25.2,27.9]_4-flowering 20 SO 4-flowering T2M
#> 97 T2M_(17.4,22]_5-grain filling 0 SO 5-grain filling T2M
#> 98 T2M_(22,24.1]_5-grain filling 0 SO 5-grain filling T2M
#> 99 T2M_(24.1,25.2]_5-grain filling 0 SO 5-grain filling T2M
#> 100 T2M_(25.2,27.9]_5-grain filling 8 SO 5-grain filling T2M
## 3. Cardinal (ecophysiological) thresholds
env_typing(env.data = env.data, env.id = 'env',
var.id = c('T2M', 'PRECTOT'),
cardinals = list(T2M = c(0, 9, 22, 32, 45), PRECTOT = c(0, 5, 10, 25, 100)))
#> ---------------------------------------------------------------
#> env_typing -- mines environmental types from weather data
#> Last updated at 09/27/2026
#> ---------------------------------------------------------------
#> - assigning records to time-interval stages
#> env.variable Freq env interval var
#> 1 T2M_(0,9] 0 NM by environment T2M
#> 2 T2M_(9,22] 0 NM by environment T2M
#> 3 T2M_(22,32] 99 NM by environment T2M
#> 4 T2M_(32,45] 0 NM by environment T2M
#> 5 T2M_(0,9] 0 PM by environment T2M
#> 6 T2M_(9,22] 53 PM by environment T2M
#> 7 T2M_(22,32] 46 PM by environment T2M
#> 8 T2M_(32,45] 0 PM by environment T2M
#> 9 T2M_(0,9] 0 IP by environment T2M
#> 10 T2M_(9,22] 13 IP by environment T2M
#> 11 T2M_(22,32] 86 IP by environment T2M
#> 12 T2M_(32,45] 0 IP by environment T2M
#> 13 T2M_(0,9] 0 SE by environment T2M
#> 14 T2M_(9,22] 60 SE by environment T2M
#> 15 T2M_(22,32] 39 SE by environment T2M
#> 16 T2M_(32,45] 0 SE by environment T2M
#> 17 T2M_(0,9] 0 SO by environment T2M
#> 18 T2M_(9,22] 0 SO by environment T2M
#> 19 T2M_(22,32] 99 SO by environment T2M
#> 20 T2M_(32,45] 0 SO by environment T2M
#> 21 PRECTOT_(0,5] 70 NM by environment PRECTOT
#> 22 PRECTOT_(5,10] 14 NM by environment PRECTOT
#> 23 PRECTOT_(10,25] 8 NM by environment PRECTOT
#> 24 PRECTOT_(25,100] 1 NM by environment PRECTOT
#> 25 PRECTOT_(0,5] 62 PM by environment PRECTOT
#> 26 PRECTOT_(5,10] 14 PM by environment PRECTOT
#> 27 PRECTOT_(10,25] 6 PM by environment PRECTOT
#> 28 PRECTOT_(25,100] 0 PM by environment PRECTOT
#> 29 PRECTOT_(0,5] 68 IP by environment PRECTOT
#> 30 PRECTOT_(5,10] 16 IP by environment PRECTOT
#> 31 PRECTOT_(10,25] 13 IP by environment PRECTOT
#> 32 PRECTOT_(25,100] 0 IP by environment PRECTOT
#> 33 PRECTOT_(0,5] 53 SE by environment PRECTOT
#> 34 PRECTOT_(5,10] 12 SE by environment PRECTOT
#> 35 PRECTOT_(10,25] 8 SE by environment PRECTOT
#> 36 PRECTOT_(25,100] 1 SE by environment PRECTOT
#> 37 PRECTOT_(0,5] 80 SO by environment PRECTOT
#> 38 PRECTOT_(5,10] 8 SO by environment PRECTOT
#> 39 PRECTOT_(10,25] 2 SO by environment PRECTOT
#> 40 PRECTOT_(25,100] 0 SO by environment PRECTOT
## 4. Data-driven univariate mining (K-means + Calinski-Harabasz)
out <- env_typing(env.data = env.data, env.id = 'env',
var.id = c('T2M', 'PRECTOT'),
envirotype_mining = TRUE, k.range = 2:8)
#> ---------------------------------------------------------------
#> env_typing -- mines environmental types from weather data
#> Last updated at 09/27/2026
#> ---------------------------------------------------------------
#> - assigning records to time-interval stages
#> Mining envirotypes for: T2M
#> Mining envirotypes for: PRECTOT
out$typologies
#> env.variable Freq env interval var
#> 1 Envirotype_T2M_00001 0 NM by environment T2M
#> 2 Envirotype_T2M_00002 0 NM by environment T2M
#> 3 Envirotype_T2M_00003 0 NM by environment T2M
#> 4 Envirotype_T2M_00004 8 NM by environment T2M
#> 5 Envirotype_T2M_00005 37 NM by environment T2M
#> 6 Envirotype_T2M_00006 49 NM by environment T2M
#> 7 Envirotype_T2M_00007 5 NM by environment T2M
#> 8 Envirotype_T2M_00008 0 NM by environment T2M
#> 9 Envirotype_T2M_00001 0 PM by environment T2M
#> 10 Envirotype_T2M_00002 17 PM by environment T2M
#> 11 Envirotype_T2M_00003 37 PM by environment T2M
#> 12 Envirotype_T2M_00004 23 PM by environment T2M
#> 13 Envirotype_T2M_00005 19 PM by environment T2M
#> 14 Envirotype_T2M_00006 3 PM by environment T2M
#> 15 Envirotype_T2M_00007 0 PM by environment T2M
#> 16 Envirotype_T2M_00008 0 PM by environment T2M
#> 17 Envirotype_T2M_00001 0 IP by environment T2M
#> 18 Envirotype_T2M_00002 2 IP by environment T2M
#> 19 Envirotype_T2M_00003 12 IP by environment T2M
#> 20 Envirotype_T2M_00004 22 IP by environment T2M
#> 21 Envirotype_T2M_00005 20 IP by environment T2M
#> 22 Envirotype_T2M_00006 34 IP by environment T2M
#> 23 Envirotype_T2M_00007 8 IP by environment T2M
#> 24 Envirotype_T2M_00008 1 IP by environment T2M
#> 25 Envirotype_T2M_00001 14 SE by environment T2M
#> 26 Envirotype_T2M_00002 25 SE by environment T2M
#> 27 Envirotype_T2M_00003 22 SE by environment T2M
#> 28 Envirotype_T2M_00004 24 SE by environment T2M
#> 29 Envirotype_T2M_00005 10 SE by environment T2M
#> 30 Envirotype_T2M_00006 4 SE by environment T2M
#> 31 Envirotype_T2M_00007 0 SE by environment T2M
#> 32 Envirotype_T2M_00008 0 SE by environment T2M
#> 33 Envirotype_T2M_00001 0 SO by environment T2M
#> 34 Envirotype_T2M_00002 0 SO by environment T2M
#> 35 Envirotype_T2M_00003 0 SO by environment T2M
#> 36 Envirotype_T2M_00004 1 SO by environment T2M
#> 37 Envirotype_T2M_00005 1 SO by environment T2M
#> 38 Envirotype_T2M_00006 12 SO by environment T2M
#> 39 Envirotype_T2M_00007 44 SO by environment T2M
#> 40 Envirotype_T2M_00008 41 SO by environment T2M
#> 41 Envirotype_PRECTOT_00001 39 NM by environment PRECTOT
#> 42 Envirotype_PRECTOT_00002 24 NM by environment PRECTOT
#> 43 Envirotype_PRECTOT_00003 15 NM by environment PRECTOT
#> 44 Envirotype_PRECTOT_00004 12 NM by environment PRECTOT
#> 45 Envirotype_PRECTOT_00005 4 NM by environment PRECTOT
#> 46 Envirotype_PRECTOT_00006 4 NM by environment PRECTOT
#> 47 Envirotype_PRECTOT_00007 0 NM by environment PRECTOT
#> 48 Envirotype_PRECTOT_00008 1 NM by environment PRECTOT
#> 49 Envirotype_PRECTOT_00001 51 PM by environment PRECTOT
#> 50 Envirotype_PRECTOT_00002 20 PM by environment PRECTOT
#> 51 Envirotype_PRECTOT_00003 11 PM by environment PRECTOT
#> 52 Envirotype_PRECTOT_00004 8 PM by environment PRECTOT
#> 53 Envirotype_PRECTOT_00005 8 PM by environment PRECTOT
#> 54 Envirotype_PRECTOT_00006 1 PM by environment PRECTOT
#> 55 Envirotype_PRECTOT_00007 0 PM by environment PRECTOT
#> 56 Envirotype_PRECTOT_00008 0 PM by environment PRECTOT
#> 57 Envirotype_PRECTOT_00001 37 IP by environment PRECTOT
#> 58 Envirotype_PRECTOT_00002 24 IP by environment PRECTOT
#> 59 Envirotype_PRECTOT_00003 12 IP by environment PRECTOT
#> 60 Envirotype_PRECTOT_00004 11 IP by environment PRECTOT
#> 61 Envirotype_PRECTOT_00005 6 IP by environment PRECTOT
#> 62 Envirotype_PRECTOT_00006 5 IP by environment PRECTOT
#> 63 Envirotype_PRECTOT_00007 4 IP by environment PRECTOT
#> 64 Envirotype_PRECTOT_00008 0 IP by environment PRECTOT
#> 65 Envirotype_PRECTOT_00001 64 SE by environment PRECTOT
#> 66 Envirotype_PRECTOT_00002 9 SE by environment PRECTOT
#> 67 Envirotype_PRECTOT_00003 6 SE by environment PRECTOT
#> 68 Envirotype_PRECTOT_00004 10 SE by environment PRECTOT
#> 69 Envirotype_PRECTOT_00005 4 SE by environment PRECTOT
#> 70 Envirotype_PRECTOT_00006 2 SE by environment PRECTOT
#> 71 Envirotype_PRECTOT_00007 3 SE by environment PRECTOT
#> 72 Envirotype_PRECTOT_00008 1 SE by environment PRECTOT
#> 73 Envirotype_PRECTOT_00001 71 SO by environment PRECTOT
#> 74 Envirotype_PRECTOT_00002 13 SO by environment PRECTOT
#> 75 Envirotype_PRECTOT_00003 6 SO by environment PRECTOT
#> 76 Envirotype_PRECTOT_00004 6 SO by environment PRECTOT
#> 77 Envirotype_PRECTOT_00005 2 SO by environment PRECTOT
#> 78 Envirotype_PRECTOT_00006 0 SO by environment PRECTOT
#> 79 Envirotype_PRECTOT_00007 1 SO by environment PRECTOT
#> 80 Envirotype_PRECTOT_00008 0 SO by environment PRECTOT
out$envirotype_description
#> variable envirotype cluster n min max mean
#> 1 T2M Envirotype_T2M_00001 1 14 15.09 18.46 17.4542857
#> 2 T2M Envirotype_T2M_00002 2 44 18.65 20.52 19.6634091
#> 3 T2M Envirotype_T2M_00003 3 71 20.60 22.09 21.4129577
#> 4 T2M Envirotype_T2M_00004 4 78 22.16 23.41 22.8153846
#> 5 T2M Envirotype_T2M_00005 5 87 23.44 24.49 24.0488506
#> 6 T2M Envirotype_T2M_00006 6 102 24.52 25.49 24.9660784
#> 7 T2M Envirotype_T2M_00007 7 57 25.50 26.59 26.0212281
#> 8 T2M Envirotype_T2M_00008 8 42 26.73 28.56 27.4154762
#> 9 PRECTOT Envirotype_PRECTOT_00001 1 262 0.00 1.35 0.3102672
#> 10 PRECTOT Envirotype_PRECTOT_00002 2 90 1.39 3.40 2.4115556
#> 11 PRECTOT Envirotype_PRECTOT_00003 3 50 3.46 5.50 4.4398000
#> 12 PRECTOT Envirotype_PRECTOT_00004 4 47 5.76 8.65 7.0387234
#> 13 PRECTOT Envirotype_PRECTOT_00005 5 24 9.07 12.89 10.8316667
#> 14 PRECTOT Envirotype_PRECTOT_00006 6 12 13.28 18.43 15.4133333
#> 15 PRECTOT Envirotype_PRECTOT_00007 7 8 19.52 24.92 23.0287500
#> 16 PRECTOT Envirotype_PRECTOT_00008 8 2 41.67 42.90 42.2850000
#> sd median center lower upper
#> 1 0.9307294 17.605 17.4542857 -Inf 18.555
#> 2 0.5449683 19.725 19.6634091 18.555 20.560
#> 3 0.3983516 21.490 21.4129577 20.560 22.125
#> 4 0.3728643 22.865 22.8153846 22.125 23.425
#> 5 0.2857525 24.090 24.0488506 23.425 24.505
#> 6 0.2470425 24.945 24.9660784 24.505 25.495
#> 7 0.3540534 25.970 26.0212281 25.495 26.660
#> 8 0.4456358 27.355 27.4154762 26.660 Inf
#> 9 0.3746195 0.160 0.3102672 -Inf 1.370
#> 10 0.6606056 2.460 2.4115556 1.370 3.430
#> 11 0.5705976 4.460 4.4398000 3.430 5.630
#> 12 0.8920412 6.960 7.0387234 5.630 8.860
#> 13 1.1609579 10.810 10.8316667 8.860 13.085
#> 14 1.3932065 15.250 15.4133333 13.085 18.975
#> 15 1.8954414 23.595 23.0287500 18.975 33.295
#> 16 0.8697413 42.285 42.2850000 33.295 Inf
out$mining_summary
#> variable k.optimal CH.index k.evaluated
#> 1 T2M 8 2580.297 2,3,4,5,6,7,8
#> 2 PRECTOT 8 4182.403 2,3,4,5,6,7,8
#> CH.profile
#> 1 1004.275,1063.028,1480.092,1731.761,1862.231,2246.823,2580.297
#> 2 790.549,1038.29,1324.786,1427.687,1413.491,3407.667,4182.403
## 5. Joint (multi-variable) envirotypes per development stage
outj <- env_typing(env.data = env.data, env.id = 'env',
var.id = c('T2M', 'PRECTOT'),
by.interval = TRUE, time.window = c(0, 30, 60, 90),
envirotype_mining = TRUE, joint = TRUE)
#> ---------------------------------------------------------------
#> env_typing -- mines environmental types from weather data
#> Last updated at 09/27/2026
#> ---------------------------------------------------------------
#> - assigning records to time-interval stages
#> Mining joint envirotypes for window: Interval_0
#> Mining joint envirotypes for window: Interval_30
#> Mining joint envirotypes for window: Interval_60
#> Mining joint envirotypes for window: Interval_90
head(outj$envirotype_description)
#> window envirotype cluster variable n min max
#> 1 Interval_0 Envirotype_Interval_0_00001 1 T2M 20 26.26 28.56
#> 2 Interval_0 Envirotype_Interval_0_00001 1 PRECTOT 20 0.01 1.06
#> 3 Interval_0 Envirotype_Interval_0_00002 2 T2M 25 24.49 25.97
#> 4 Interval_0 Envirotype_Interval_0_00002 2 PRECTOT 25 0.00 3.50
#> 5 Interval_0 Envirotype_Interval_0_00003 3 T2M 21 22.86 24.33
#> 6 Interval_0 Envirotype_Interval_0_00003 3 PRECTOT 21 0.20 4.15
#> mean sd median center
#> 1 26.924000 0.6488970 26.655 26.924000
#> 2 0.463500 0.3212521 0.375 0.463500
#> 3 25.134000 0.4637887 25.050 25.134000
#> 4 1.273200 1.1913988 0.610 1.273200
#> 5 23.601429 0.4693110 23.540 23.601429
#> 6 1.472381 1.1584986 1.300 1.472381
## 6. Relative frequencies, wide format (ready for env_kernel)
ET <- env_typing(env.data = env.data, env.id = 'env', var.id = 'T2M',
format = 'wide', ratio = TRUE)
#> ---------------------------------------------------------------
#> env_typing -- mines environmental types from weather data
#> Last updated at 09/27/2026
#> ---------------------------------------------------------------
#> - assigning records to time-interval stages
## 7. Envirotypes for feature combinations (singles + pairs by default)
outc <- env_typing(env.data = env.data, env.id = 'env',
var.id = c('T2M', 'PRECTOT', 'VPD'),
envirotype_mining = TRUE, combine.features = TRUE)
#> ---------------------------------------------------------------
#> env_typing -- mines environmental types from weather data
#> Last updated at 09/27/2026
#> ---------------------------------------------------------------
#> - assigning records to time-interval stages
#> Mining combination FC001 (T2M)
#> Mining combination FC002 (PRECTOT)
#> Mining combination FC003 (VPD)
#> Mining combination FC004 (T2M+PRECTOT)
#> Mining combination FC005 (T2M+VPD)
#> Mining combination FC006 (PRECTOT+VPD)
outc$feature_combinations # generic id -> which variables
#> combo_id n_features features intervals
#> 1 FC001 1 T2M by environment
#> 2 FC002 1 PRECTOT by environment
#> 3 FC003 1 VPD by environment
#> 4 FC004 2 T2M, PRECTOT by environment
#> 5 FC005 2 T2M, VPD by environment
#> 6 FC006 2 PRECTOT, VPD by environment
head(outc$envirotype_description)
#> combo_id features envirotype cluster variable n min max
#> 1 FC001 T2M Envirotype_FC001_00001 1 T2M 8 15.09 17.64
#> 2 FC001 T2M Envirotype_FC001_00002 2 T2M 23 17.96 19.48
#> 3 FC001 T2M Envirotype_FC001_00003 3 T2M 33 19.55 20.74
#> 4 FC001 T2M Envirotype_FC001_00004 4 T2M 55 20.80 21.84
#> 5 FC001 T2M Envirotype_FC001_00005 5 T2M 40 21.86 22.69
#> 6 FC001 T2M Envirotype_FC001_00006 6 T2M 57 22.73 23.62
#> mean sd median center
#> 1 16.88625 0.8502426 17.12 16.88625
#> 2 18.84696 0.4471570 18.91 18.84696
#> 3 20.15091 0.3567944 20.11 20.15091
#> 4 21.39436 0.2936789 21.46 21.39436
#> 5 22.29625 0.2336301 22.31 22.29625
#> 6 23.14105 0.2513868 23.13 23.14105
## include triples too (combine.max.order = 3), or NULL for all orders
outc3 <- env_typing(env.data = env.data, env.id = 'env',
var.id = c('T2M', 'PRECTOT', 'VPD'),
envirotype_mining = TRUE, combine.features = TRUE,
combine.max.order = 3)
#> ---------------------------------------------------------------
#> env_typing -- mines environmental types from weather data
#> Last updated at 09/27/2026
#> ---------------------------------------------------------------
#> - assigning records to time-interval stages
#> Mining combination FC001 (T2M)
#> Mining combination FC002 (PRECTOT)
#> Mining combination FC003 (VPD)
#> Mining combination FC004 (T2M+PRECTOT)
#> Mining combination FC005 (T2M+VPD)
#> Mining combination FC006 (PRECTOT+VPD)
#> Mining combination FC007 (T2M+PRECTOT+VPD)
# }