Nothing
inst/doc/Introduction_to_BMEmapping.R
In BMEmapping: Spatial Interpolation using Bayesian Maximum Entropy (BME)
## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup--------------------------------------------------------------------
library(BMEmapping)
## -----------------------------------------------------------------------------
data("utsnowload")
head(utsnowload)
## ----eval=FALSE---------------------------------------------------------------
# ?utsnowload
## -----------------------------------------------------------------------------
# prediction location
x <- utsnowload[228:232, c("latitude", "longitude")]
x
## -----------------------------------------------------------------------------
# hard data locations
ch <- utsnowload[1:67, c("latitude", "longitude")]
# soft data locations
cs <- utsnowload[68:227, c("latitude", "longitude")]
# hard data values
zh <- utsnowload[1:67, c("hard")]
# lower bounds
a <- utsnowload[68:227, c("lower")]
# upper bounds
b <- utsnowload[68:227, c("upper")]
## -----------------------------------------------------------------------------
# variogram model and parameters
model <- "exp"
nugget <- 0.0953
sill <- 0.3639
range <- 1.0787
## ----fig.width = 4, fig.height = 4.5, fig.align='center'----------------------
prob_zk(x[1,], ch, cs, zh, a, b, model, nugget, sill, range, plot = TRUE)
## -----------------------------------------------------------------------------
# posterior mode
bme_predict(x, ch, cs, zh, a, b, model, nugget, sill, range, type = "mode")
# posterior mean
bme_predict(x, ch, cs, zh, a, b, model, nugget, sill, range, type = "mean")
## ----eval=FALSE---------------------------------------------------------------
# bme_cv(ch, cs, zh, a, b, model, nugget, sill, range, type = "mean")
#
# #> $results
# #> latitude longitude observed mean variance residual fold
# #> 1 40.44 -112.24 0.09696012 -0.2065 0.3598 0.3035 1
# #> 2 39.94 -112.41 0.12258678 -0.3423 0.3427 0.4649 2
# #> 3 37.51 -113.40 -0.02302358 -0.0726 0.3514 0.0496 3
# #> 4 37.49 -113.85 0.50354362 -0.1631 0.3900 0.6666 4
# #> 5 39.31 -109.53 -0.68611327 -0.2303 0.4444 -0.4558 5
# #> 6 40.72 -109.54 -0.53000397 -0.7366 0.3024 0.2066 6
# #> 7 40.61 -109.89 -0.71923519 -0.8916 0.3152 0.1724 7
# #> 8 40.91 -109.96 -1.31503404 -1.0151 0.2933 -0.2999 8
# #> 9 40.74 -109.67 -0.94879597 -0.7044 0.2795 -0.2444 9
# #> 10 40.92 -110.19 -1.39798035 -1.0139 0.3175 -0.3841 10
# #> 11 40.95 -110.48 -1.21900906 -0.9611 0.2218 -0.2579 11
# #> 12 40.60 -110.43 -1.24787225 -0.8706 0.2713 -0.3773 12
# #> 13 40.55 -110.69 -0.55027484 -0.6954 0.2599 0.1451 13
# #> 14 40.91 -110.50 -1.06708711 -1.0866 0.2119 0.0195 14
# #> 15 40.72 -110.47 -1.14044998 -0.9950 0.2578 -0.1454 15
# #> 16 40.58 -110.59 -0.94551554 -0.8009 0.2416 -0.1446 16
# #> 17 40.86 -110.80 -0.83840015 -0.5465 0.2681 -0.2919 17
# #> 18 40.77 -110.01 -1.24671792 -1.0531 0.2734 -0.1936 18
# #> 19 40.80 -110.88 -0.65036211 -0.4763 0.2321 -0.1741 19
# #> 20 40.68 -110.95 -0.37127802 -0.4399 0.2586 0.0686 20
# #> 21 39.89 -110.75 -0.80367306 -0.3605 0.3668 -0.4432 21
# #> 22 39.96 -110.99 -0.54230365 -0.2677 0.2912 -0.2746 22
# #> 23 41.38 -111.94 0.94099563 0.7969 0.1807 0.1441 23
# #> 24 41.31 -111.45 0.24796667 0.0273 0.2867 0.2207 24
# #> 25 41.41 -111.83 0.47642403 0.6856 0.2460 -0.2092 25
# #> 26 41.38 -111.92 1.25233814 0.6507 0.1735 0.6016 26
# #> 27 41.90 -111.63 0.61655171 0.0339 0.3443 0.5827 27
# #> 28 41.68 -111.42 0.18443361 -0.0173 0.3117 0.2017 28
# #> 29 41.41 -111.54 0.11223798 0.2098 0.2246 -0.0976 29
# #> 30 41.47 -111.50 0.10561343 0.1328 0.2329 -0.0272 30
# #> 31 40.85 -111.05 -0.10690304 -0.3160 0.1908 0.2091 31
# #> 32 40.89 -111.07 -0.29946212 -0.2456 0.2007 -0.0539 32
# #> 33 40.16 -111.21 0.00344554 -0.1387 0.3134 0.1421 33
# #> 34 40.99 -111.82 0.78786432 0.0856 0.2765 0.7023 34
# #> 35 40.43 -111.62 0.39822325 0.0749 0.2780 0.3233 35
# #> 36 40.36 -111.09 -0.24414027 -0.2252 0.3183 -0.0189 36
# #> 37 40.61 -111.10 -0.52669066 -0.2218 0.2720 -0.3049 37
# #> 38 40.76 -111.63 0.14568497 0.2201 0.2832 -0.0744 38
# #> 39 40.79 -111.12 -0.10923301 -0.3191 0.2304 0.2099 39
# #> 40 39.68 -111.32 -0.08382941 -0.2960 0.2652 0.2122 40
# #> 41 39.31 -111.43 -0.78984433 -0.4473 0.2903 -0.3425 41
# #> 42 39.14 -111.56 -0.38648680 -0.6416 0.2396 0.2551 42
# #> 43 39.05 -111.47 -0.57739062 -0.5946 0.2228 0.0172 43
# #> 44 39.87 -111.28 -0.22947205 -0.0731 0.1994 -0.1564 44
# #> 45 39.89 -111.25 -0.03805984 -0.1976 0.2003 0.1595 45
# #> 46 39.45 -111.27 -0.42606551 -0.4756 0.3043 0.0495 46
# #> 47 39.13 -111.44 -0.52777166 -0.5962 0.2269 0.0684 47
# #> 48 39.01 -111.58 -0.81486819 -0.4973 0.2491 -0.3176 48
# #> 49 39.93 -111.63 0.06849776 -0.0867 0.2983 0.1552 49
# #> 50 38.77 -111.68 -0.68746363 -0.6272 0.1908 -0.0603 50
# #> 51 38.68 -111.60 -1.04793061 -0.6279 0.2834 -0.4200 51
# #> 52 38.21 -111.48 -1.40848147 -0.6012 0.3933 -0.8073 52
# #> 53 38.80 -111.68 -0.43759896 -0.7310 0.1964 0.2934 53
# #> 54 37.84 -111.88 -0.73581358 -0.4816 0.4018 -0.2542 54
# #> 55 38.51 -112.02 -0.90807705 -0.7382 0.3365 -0.1699 55
# #> 56 38.48 -112.39 -0.67118202 -0.6298 0.2905 -0.0414 56
# #> 57 38.30 -112.36 -0.76527983 -0.5643 0.2435 -0.2010 57
# #> 58 38.30 -112.44 -0.51835705 -0.5553 0.2232 0.0369 58
# #> 59 38.88 -112.25 -0.24704072 -0.4462 0.3438 0.1992 59
# #> 60 37.58 -112.90 -0.42302609 -0.3781 0.2050 -0.0449 60
# #> 61 37.49 -112.58 0.00732065 -0.1742 0.2318 0.1815 61
# #> 62 37.49 -112.51 0.02427501 -0.1263 0.2205 0.1506 62
# #> 63 37.66 -112.74 -0.76376457 -0.3345 0.2746 -0.4293 63
# #> 64 37.57 -112.84 -0.28791382 -0.4501 0.2057 0.1622 64
# #> 65 37.53 -113.05 -0.07280592 -0.3232 0.2927 0.2504 65
# #> 66 38.48 -109.27 -0.90950964 -0.3653 0.3869 -0.5442 66
# #> 67 37.81 -109.49 -0.39635792 -0.3522 0.3680 -0.0442 67
# #>
# #> $metrics
# #> ME MAE RMSE
# #> 1 -0.0102 0.2378 0.2953
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BMEmapping documentation built on July 2, 2025, 9:07 a.m.
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## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup--------------------------------------------------------------------
library(BMEmapping)
## -----------------------------------------------------------------------------
data("utsnowload")
head(utsnowload)
## ----eval=FALSE---------------------------------------------------------------
# ?utsnowload
## -----------------------------------------------------------------------------
# prediction location
x <- utsnowload[228:232, c("latitude", "longitude")]
x
## -----------------------------------------------------------------------------
# hard data locations
ch <- utsnowload[1:67, c("latitude", "longitude")]
# soft data locations
cs <- utsnowload[68:227, c("latitude", "longitude")]
# hard data values
zh <- utsnowload[1:67, c("hard")]
# lower bounds
a <- utsnowload[68:227, c("lower")]
# upper bounds
b <- utsnowload[68:227, c("upper")]
## -----------------------------------------------------------------------------
# variogram model and parameters
model <- "exp"
nugget <- 0.0953
sill <- 0.3639
range <- 1.0787
## ----fig.width = 4, fig.height = 4.5, fig.align='center'----------------------
prob_zk(x[1,], ch, cs, zh, a, b, model, nugget, sill, range, plot = TRUE)
## -----------------------------------------------------------------------------
# posterior mode
bme_predict(x, ch, cs, zh, a, b, model, nugget, sill, range, type = "mode")
# posterior mean
bme_predict(x, ch, cs, zh, a, b, model, nugget, sill, range, type = "mean")
## ----eval=FALSE---------------------------------------------------------------
# bme_cv(ch, cs, zh, a, b, model, nugget, sill, range, type = "mean")
#
# #> $results
# #> latitude longitude observed mean variance residual fold
# #> 1 40.44 -112.24 0.09696012 -0.2065 0.3598 0.3035 1
# #> 2 39.94 -112.41 0.12258678 -0.3423 0.3427 0.4649 2
# #> 3 37.51 -113.40 -0.02302358 -0.0726 0.3514 0.0496 3
# #> 4 37.49 -113.85 0.50354362 -0.1631 0.3900 0.6666 4
# #> 5 39.31 -109.53 -0.68611327 -0.2303 0.4444 -0.4558 5
# #> 6 40.72 -109.54 -0.53000397 -0.7366 0.3024 0.2066 6
# #> 7 40.61 -109.89 -0.71923519 -0.8916 0.3152 0.1724 7
# #> 8 40.91 -109.96 -1.31503404 -1.0151 0.2933 -0.2999 8
# #> 9 40.74 -109.67 -0.94879597 -0.7044 0.2795 -0.2444 9
# #> 10 40.92 -110.19 -1.39798035 -1.0139 0.3175 -0.3841 10
# #> 11 40.95 -110.48 -1.21900906 -0.9611 0.2218 -0.2579 11
# #> 12 40.60 -110.43 -1.24787225 -0.8706 0.2713 -0.3773 12
# #> 13 40.55 -110.69 -0.55027484 -0.6954 0.2599 0.1451 13
# #> 14 40.91 -110.50 -1.06708711 -1.0866 0.2119 0.0195 14
# #> 15 40.72 -110.47 -1.14044998 -0.9950 0.2578 -0.1454 15
# #> 16 40.58 -110.59 -0.94551554 -0.8009 0.2416 -0.1446 16
# #> 17 40.86 -110.80 -0.83840015 -0.5465 0.2681 -0.2919 17
# #> 18 40.77 -110.01 -1.24671792 -1.0531 0.2734 -0.1936 18
# #> 19 40.80 -110.88 -0.65036211 -0.4763 0.2321 -0.1741 19
# #> 20 40.68 -110.95 -0.37127802 -0.4399 0.2586 0.0686 20
# #> 21 39.89 -110.75 -0.80367306 -0.3605 0.3668 -0.4432 21
# #> 22 39.96 -110.99 -0.54230365 -0.2677 0.2912 -0.2746 22
# #> 23 41.38 -111.94 0.94099563 0.7969 0.1807 0.1441 23
# #> 24 41.31 -111.45 0.24796667 0.0273 0.2867 0.2207 24
# #> 25 41.41 -111.83 0.47642403 0.6856 0.2460 -0.2092 25
# #> 26 41.38 -111.92 1.25233814 0.6507 0.1735 0.6016 26
# #> 27 41.90 -111.63 0.61655171 0.0339 0.3443 0.5827 27
# #> 28 41.68 -111.42 0.18443361 -0.0173 0.3117 0.2017 28
# #> 29 41.41 -111.54 0.11223798 0.2098 0.2246 -0.0976 29
# #> 30 41.47 -111.50 0.10561343 0.1328 0.2329 -0.0272 30
# #> 31 40.85 -111.05 -0.10690304 -0.3160 0.1908 0.2091 31
# #> 32 40.89 -111.07 -0.29946212 -0.2456 0.2007 -0.0539 32
# #> 33 40.16 -111.21 0.00344554 -0.1387 0.3134 0.1421 33
# #> 34 40.99 -111.82 0.78786432 0.0856 0.2765 0.7023 34
# #> 35 40.43 -111.62 0.39822325 0.0749 0.2780 0.3233 35
# #> 36 40.36 -111.09 -0.24414027 -0.2252 0.3183 -0.0189 36
# #> 37 40.61 -111.10 -0.52669066 -0.2218 0.2720 -0.3049 37
# #> 38 40.76 -111.63 0.14568497 0.2201 0.2832 -0.0744 38
# #> 39 40.79 -111.12 -0.10923301 -0.3191 0.2304 0.2099 39
# #> 40 39.68 -111.32 -0.08382941 -0.2960 0.2652 0.2122 40
# #> 41 39.31 -111.43 -0.78984433 -0.4473 0.2903 -0.3425 41
# #> 42 39.14 -111.56 -0.38648680 -0.6416 0.2396 0.2551 42
# #> 43 39.05 -111.47 -0.57739062 -0.5946 0.2228 0.0172 43
# #> 44 39.87 -111.28 -0.22947205 -0.0731 0.1994 -0.1564 44
# #> 45 39.89 -111.25 -0.03805984 -0.1976 0.2003 0.1595 45
# #> 46 39.45 -111.27 -0.42606551 -0.4756 0.3043 0.0495 46
# #> 47 39.13 -111.44 -0.52777166 -0.5962 0.2269 0.0684 47
# #> 48 39.01 -111.58 -0.81486819 -0.4973 0.2491 -0.3176 48
# #> 49 39.93 -111.63 0.06849776 -0.0867 0.2983 0.1552 49
# #> 50 38.77 -111.68 -0.68746363 -0.6272 0.1908 -0.0603 50
# #> 51 38.68 -111.60 -1.04793061 -0.6279 0.2834 -0.4200 51
# #> 52 38.21 -111.48 -1.40848147 -0.6012 0.3933 -0.8073 52
# #> 53 38.80 -111.68 -0.43759896 -0.7310 0.1964 0.2934 53
# #> 54 37.84 -111.88 -0.73581358 -0.4816 0.4018 -0.2542 54
# #> 55 38.51 -112.02 -0.90807705 -0.7382 0.3365 -0.1699 55
# #> 56 38.48 -112.39 -0.67118202 -0.6298 0.2905 -0.0414 56
# #> 57 38.30 -112.36 -0.76527983 -0.5643 0.2435 -0.2010 57
# #> 58 38.30 -112.44 -0.51835705 -0.5553 0.2232 0.0369 58
# #> 59 38.88 -112.25 -0.24704072 -0.4462 0.3438 0.1992 59
# #> 60 37.58 -112.90 -0.42302609 -0.3781 0.2050 -0.0449 60
# #> 61 37.49 -112.58 0.00732065 -0.1742 0.2318 0.1815 61
# #> 62 37.49 -112.51 0.02427501 -0.1263 0.2205 0.1506 62
# #> 63 37.66 -112.74 -0.76376457 -0.3345 0.2746 -0.4293 63
# #> 64 37.57 -112.84 -0.28791382 -0.4501 0.2057 0.1622 64
# #> 65 37.53 -113.05 -0.07280592 -0.3232 0.2927 0.2504 65
# #> 66 38.48 -109.27 -0.90950964 -0.3653 0.3869 -0.5442 66
# #> 67 37.81 -109.49 -0.39635792 -0.3522 0.3680 -0.0442 67
# #>
# #> $metrics
# #> ME MAE RMSE
# #> 1 -0.0102 0.2378 0.2953
Try the BMEmapping package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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