mpspline2 (version 0.1.9)

mpspline_compact: Spline discrete soils data - multiple sites

Description

These functions implement the mass-preserving spline method of Bishop et al (1999) (tools:::Rd_expr_doi("10.1016/S0016-7061(99)00003-8")) for interpolating between measured soil attributes down a soil profile, across multiple sites' worth of data. mpspline_compact() returns results as matrices while mpspline_tidy() returns results as data frames.

Usage

mpspline_compact(
  obj = NULL,
  var_name = NULL,
  lam = 0.1,
  d = c(0, 5, 15, 30, 60, 100, 200),
  vlow = 0,
  vhigh = 1000
)
mpspline_tidy(
  obj = NULL,
  var_name = NULL,
  lam = 0.1,
  d = c(0, 5, 15, 30, 60, 100, 200),
  vlow = 0,
  vhigh = 1000
)

Value

mpspline_compact() returns a four-item list containing matrices: predicted values over input depth ranges, output depth ranges, 1cm predictions, and RMSE values. Site identifiers are in rownames.

mpspline_tidy() returns a four-item list of data frames with the same predictions but in tidy format, with an added VARIABLE column when processing multiple variables.

Arguments

obj

data.frame or matrix. Column 1 must contain site identifiers. Columns 2 and 3 must contain upper and lower sample depths, respectively, measured in centimeters. Subsequent columns will contain measured values for those depths.

var_name

character or integer vector denoting the column(s) in obj in which target data is stored. If not supplied, the fourth column of the input object is assumed to contain the target data.

lam

number; smoothing parameter for spline. Defaults to 0.1.

d

sequential integer vector; denotes the output depth ranges in cm. Defaults to c(0, 5, 15, 30, 60, 100, 200) after the GlobalSoilMap specification, giving output predictions over intervals 0-5cm, 5-15cm, etc.

vlow

numeric; constrains the minimum predicted value to a realistic number. Defaults to 0.

vhigh

numeric; constrains the maximum predicted value to a realistic number. Defaults to 1000.

Examples

dat <- data.frame("SID" = c( 1,  1,  1,  1,   2,   2,   2,   2),
                   "UD" = c( 0, 20, 40, 60,   0,  15,  45,  80),
                   "LD" = c(10, 30, 50, 70,   5,  30,  60, 100),
                  "VAL" = c( 6,  4,  3, 10, 0.1, 0.9, 2.5,   6),
                   stringsAsFactors = FALSE)
# single variable
result <- mpspline_compact(obj = dat, var_name = 'VAL')

# multiple variables
dat_multi <- data.frame( "SID" = c( 1,  1,  1,  1,   2,   2,   2,   2),
                          "UD" = c( 0, 20, 40, 60,   0,  15,  45,  80),
                          "LD" = c(10, 30, 50, 70,   5,  30,  60, 100),
                        "VAL1" = c( 6,  4,  3, 10, 0.1, 0.9, 2.5,   6),
                        "VAL2" = c( 5,  3,  2,  9, 0.2, 1.0, 2.0,   5),
                        stringsAsFactors = FALSE)
result_multi <- mpspline_compact(obj = dat_multi, var_name = c('VAL1', 'VAL2'))
# \dontshow{
# Reuse example data from mpspline_compact
dat <- data.frame("SID" = c( 1,  1,  1,  1,   2,   2,   2,   2),
                   "UD" = c( 0, 20, 40, 60,   0,  15,  45,  80),
                   "LD" = c(10, 30, 50, 70,   5,  30,  60, 100),
                  "VAL" = c( 6,  4,  3, 10, 0.1, 0.9, 2.5,   6),
                   stringsAsFactors = FALSE)
# }
# Single variable with tidy output
result <- mpspline_tidy(obj = dat, var_name = 'VAL')

# Multiple variables
dat_multi <- data.frame( "SID" = c( 1,  1,  1,  1,   2,   2,   2,   2),
                          "UD" = c( 0, 20, 40, 60,   0,  15,  45,  80),
                          "LD" = c(10, 30, 50, 70,   5,  30,  60, 100),
                        "VAL1" = c( 6,  4,  3, 10, 0.1, 0.9, 2.5,   6),
                        "VAL2" = c( 5,  3,  2,  9, 0.2, 1.0, 2.0,   5),
                        stringsAsFactors = FALSE)
result_multi <- mpspline_tidy(obj = dat_multi, var_name = c('VAL1', 'VAL2'))
subset(result_multi$est_dcm, VARIABLE == 'VAL1')