diff --git a/articles/Introduction-to-SoilProfileCollection-Objects.html b/articles/Introduction-to-SoilProfileCollection-Objects.html index a47a6d460..4ecf24c76 100644 --- a/articles/Introduction-to-SoilProfileCollection-Objects.html +++ b/articles/Introduction-to-SoilProfileCollection-Objects.html @@ -364,8 +364,8 @@

Horizon and Site Data#> [1] 21 27 32 55 25 34 3 15 27 32 25 31 33 13 21 23 15 17 12 19 14 14 22 25 40 51 67 24 25 32 
 sp4$elevation # vector of simulated elevation (site data)
-
#>  [1]  843.0698 1366.8370 1028.9023 1233.5746 1027.0144  658.0877  912.6135 1086.5831 1143.6326
-#> [10] 1015.9048
+
#>  [1] 1047.3747  743.3900 1162.8973 1035.2875  891.4111 1130.0727 1194.4019 1071.4525 1227.0159
+#> [10] 1185.8546
 # unit-length value explicitly targeting site data
 site(sp4)$collection_id <- 1
@@ -421,12 +421,12 @@ 
Horizon and Site Data#> 
 #> ----- Sites (6 / 10 rows  |  5 / 5 columns) -----
 #>         id elevation collection_id constant group
-#>     colusa  843.0698             1        1     A
-#>      glenn 1366.8370             1        1     B
-#>      kings 1028.9023             1        1     A
-#>   mariposa 1233.5746             1        1     B
-#>  mendocino 1027.0144             1        1     A
-#>       napa  658.0877             1        1     B
+#>     colusa 1047.3747             1        1     A
+#>      glenn  743.3900             1        1     B
+#>      kings 1162.8973             1        1     A
+#>   mariposa 1035.2875             1        1     B
+#>  mendocino  891.4111             1        1     A
+#>       napa 1130.0727             1        1     B
 #> [... more sites ...]
 #> 
 #> Spatial Data:
@@ -585,16 +585,16 @@ 
Spatial Data# extract coordinates as matrix
 getSpatial(sp4)
#>              x       y
-#>  [1,] 353964.9 4109637
-#>  [2,] 353988.5 4109419
-#>  [3,] 353709.3 4109539
-#>  [4,] 353838.6 4109561
-#>  [5,] 353981.3 4109532
-#>  [6,] 354012.4 4109571
-#>  [7,] 353877.3 4109368
-#>  [8,] 354085.6 4109540
-#>  [9,] 353961.5 4109429
-#> [10,] 353899.8 4109452
+#> [1,] 353984.6 4109483 +#> [2,] 354022.5 4109556 +#> [3,] 354000.4 4109568 +#> [4,] 354056.8 4109543 +#> [5,] 354065.4 4109442 +#> [6,] 354015.1 4109489 +#> [7,] 354222.4 4109585 +#> [8,] 353970.0 4109366 +#> [9,] 354134.6 4109584 +#> [10,] 354081.8 4109438 
 # get/set spatial reference system using prj()<-
 prj(sp4) <- '+proj=utm +zone=11 +datum=NAD83'
@@ -1639,14 +1639,14 @@ 
Aggregation over “slabs”# note: result is in long-format
 # note: horizon names are lost due to aggregation
 head(d.gsm, 7)
-
#>   variable id     value contributing_fraction top bottom
-#> 1       p1  1 11.904899             1.0000000   0      5
-#> 2       p1  1 11.907170             1.0000000   5     15
-#> 3       p1  1 11.910578             1.0000000  15     30
-#> 4       p1  1 14.993850             1.0000000  30     60
-#> 5       p1  1 17.402573             1.0000000  60    100
-#> 6       p1  1 14.966393             0.7090909 100    200
-#> 7       p1  2  6.125664             1.0000000   0      5
+
#>   variable id    value contributing_fraction top bottom
+#> 1       p1  1 12.85900                 1.000   0      5
+#> 2       p1  1 12.99266                 1.000   5     15
+#> 3       p1  1 13.84828                 1.000  15     30
+#> 4       p1  1 17.29098                 1.000  30     60
+#> 5       p1  1 17.33966                 0.425  60    100
+#> 6       p1  1      NaN                 0.000 100    200
+#> 7       p1  2 14.83760                 1.000   0      5

A simple graphical comparison of the original and re-aligned soil profile data, after converting slab() result from long -> wide format with {data.table} dcast():

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Examples

# easy z <- fixOverlap(x, thresh = 0.2, trace = TRUE) -#> 5 iterations +#> 3 iterations # harder z <- fixOverlap(x, thresh = 0.6, trace = TRUE) -#> 19 iterations +#> 28 iterations # much harder z <- fixOverlap(x, thresh = 0.9, trace = TRUE) -#> 552 iterations +#> 229 iterations # interpret `trace` output diff --git a/reference/bootstrapSoilTexture.html b/reference/bootstrapSoilTexture.html index f8cf4f976..0cd5b6098 100644 --- a/reference/bootstrapSoilTexture.html +++ b/reference/bootstrapSoilTexture.html @@ -78,11 +78,11 @@

Bootstrap Soil Texture Data

Arguments

ssc
-

a data.frame object with 3 columns: sand, silt, clay and at least three rows of data within the range of 0-100 (percent). NA are automatically removed, but care should be taken to ensure that the sand/silt/clay values add to 100 percent. Simulations are based on these examples.

+

a data.frame object with 3 columns: 'sand', 'silt', 'clay' and at least three rows of data within the range of 0-100 (percent). NA are automatically removed, but care should be taken to ensure that the sand/silt/clay values add to 100 percent. Simulations are based on these examples.

method
-

type of simulation: dirichlet or normal. See details.

+

type of simulation: 'dirichlet' or 'normal'. See details.

n
@@ -93,10 +93,7 @@

Arguments

Value

-

a list containing:

- - -

Details

diff --git a/reference/hzTransitionProbabilities.html b/reference/hzTransitionProbabilities.html index 2dfc7a8fc..c7fd3fabd 100644 --- a/reference/hzTransitionProbabilities.html +++ b/reference/hzTransitionProbabilities.html @@ -1,7 +1,12 @@ Convert cross-tabulation to adjacency matrix. — genhzTableToAdjMat • aqp @@ -73,7 +78,9 @@

Convert cross-tabulation to adjacency matrix.

Convert a cross-tabulation: original, genhz to adjacency matrix.

Functions for creating and working with horizon (sequence) transition probability matrices.

-
+

See the following tutorials for some ideas:

genhzTableToAdjMat(tab)
@@ -94,7 +101,7 @@ 
Arguments

 
 
 
x

-
A SoilProfileCollection object.

+
a SoilProfileCollection object.

 
 
 
name

@@ -102,8 +109,8 @@ 
Arguments

 
 
 
loopTerminalStates

-
should terminal states loop back to themselves?
-This is useful when the transition probability matrix will be used to
+
should terminal states loop back to themselves?

+
This is useful when the transition probability matrix will be used to
 initialize a markovchain object. See examples below.

 
 
@@ -123,37 +130,24 @@ 
Arguments

     
Value

     
 
-
The function hzTransitionProbabilities returns a square
-matrix of transition probabilities. See examples.

+
A square matrix of transition probabilities. See examples.

 
 
-
The function genhzTableToAdjMat returns a square adjacency matrix.
+
The function genhzTableToAdjMat() returns a square adjacency matrix.
 See examples.

 
 
-
The function mostLikelyHzSequence returns the most likely sequence of
+
The function mostLikelyHzSequence() returns the most likely sequence of
 horizons, given a markovchain object initialized from horizon
 transition probabilities and an initial state, t0. See examples.
-
-

Details

-

See the following tutorials for some ideas:

horizon -designation -TP
-

http://ncss-tech.github.io/AQP/aqp/hz-transition-probabilities.html

- -
soil color -TP
-

http://ncss-tech.github.io/AQP/aqp/series-color-TP-graph.html

- -

Note

These functions are still experimental and subject to change.

Author

diff --git a/reference/mixMunsell.html b/reference/mixMunsell.html index 1597c8de3..5d5f9708c 100644 --- a/reference/mixMunsell.html +++ b/reference/mixMunsell.html @@ -146,10 +146,9 @@

Details

References

-

Marcus, R.T. (1998). The Measurement of Color. In K. Nassau (Ed.), Color for Science, Art, and Technology (pp. 32-96). North-Holland.

-

See also