## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----setup-------------------------------------------------------------------- library(BMEmapping) ## ----------------------------------------------------------------------------- data("utsnowload") head(utsnowload) ## ----eval=FALSE--------------------------------------------------------------- # ?utsnowload ## ----------------------------------------------------------------------------- # prediction location x <- data.matrix(utsnowload[228:232, c("latitude", "longitude")]) x ## ----------------------------------------------------------------------------- # hard data locations ch <- data.matrix(utsnowload[1:67, c("latitude", "longitude")]) # soft data locations cs <- data.matrix(utsnowload[68:227, c("latitude", "longitude")]) # hard data values zh <- c(utsnowload[1:67, c("hard")]) # lower bounds a <- c(utsnowload[68:227, c("lower")]) # upper bounds b <- c(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 # #> coord.1 coord.2 observed mean variance residual fold # #> 1 40.44 -112.24 0.09696012 -0.1742 0.2811 0.2712 1 # #> 2 39.94 -112.41 0.12258678 -0.3519 0.2940 0.4745 2 # #> 3 37.51 -113.40 -0.02302358 0.0162 0.2168 -0.0392 3 # #> 4 37.49 -113.85 0.50354362 -0.1098 0.2483 0.6133 4 # #> 5 39.31 -109.53 -0.68611327 -0.3871 0.3520 -0.2990 5 # #> 6 40.72 -109.54 -0.53000397 -0.6945 0.1586 0.1645 6 # #> 7 40.61 -109.89 -0.71923519 -0.8164 0.2002 0.0972 7 # #> 8 40.91 -109.96 -1.31503404 -1.2461 0.1879 -0.0689 8 # #> 9 40.74 -109.67 -0.94879597 -0.6540 0.1480 -0.2948 9 # #> 10 40.92 -110.19 -1.39798035 -1.0320 0.2295 -0.3660 10 # #> 11 40.95 -110.48 -1.21900906 -1.0311 0.0588 -0.1879 11 # #> 12 40.60 -110.43 -1.24787225 -0.9276 0.1412 -0.3203 12 # #> 13 40.55 -110.69 -0.55027484 -0.6074 0.1044 0.0571 13 # #> 14 40.91 -110.50 -1.06708711 -1.0355 0.0633 -0.0316 14 # #> 15 40.72 -110.47 -1.14044998 -1.0730 0.1463 -0.0674 15 # #> 16 40.58 -110.59 -0.94551554 -0.6273 0.1167 -0.3182 16 # #> 17 40.86 -110.80 -0.83840015 -0.6303 0.1204 -0.2081 17 # #> 18 40.77 -110.01 -1.24671792 -1.2210 0.1835 -0.0257 18 # #> 19 40.80 -110.88 -0.65036211 -0.7069 0.1086 0.0565 19 # #> 20 40.68 -110.95 -0.37127802 -0.5523 0.1481 0.1810 20 # #> 21 39.89 -110.75 -0.80367306 -0.4423 0.2055 -0.3614 21 # #> 22 39.96 -110.99 -0.54230365 -0.3535 0.1659 -0.1888 22 # #> 23 41.38 -111.94 0.94099563 1.3172 0.0495 -0.3762 23 # #> 24 41.31 -111.45 0.24796667 0.0396 0.2536 0.2084 24 # #> 25 41.41 -111.83 0.47642403 0.8217 0.1410 -0.3453 25 # #> 26 41.38 -111.92 1.25233814 0.7392 0.0298 0.5131 26 # #> 27 41.90 -111.63 0.61655171 0.0708 0.2713 0.5458 27 # #> 28 41.68 -111.42 0.18443361 -0.0449 0.2339 0.2293 28 # #> 29 41.41 -111.54 0.11223798 0.1876 0.0916 -0.0754 29 # #> 30 41.47 -111.50 0.10561343 0.1456 0.0924 -0.0400 30 # #> 31 40.85 -111.05 -0.10690304 -0.3928 0.0506 0.2859 31 # #> 32 40.89 -111.07 -0.29946212 -0.2690 0.0496 -0.0305 32 # #> 33 40.16 -111.21 0.00344554 -0.1073 0.2126 0.1107 33 # #> 34 40.99 -111.82 0.78786432 0.1035 0.1912 0.6844 34 # #> 35 40.43 -111.62 0.39822325 0.1897 0.2016 0.2085 35 # #> 36 40.36 -111.09 -0.24414027 -0.1348 0.1680 -0.1093 36 # #> 37 40.61 -111.10 -0.52669066 -0.2962 0.1611 -0.2305 37 # #> 38 40.76 -111.63 0.14568497 0.3546 0.1824 -0.2089 38 # #> 39 40.79 -111.12 -0.10923301 -0.2849 0.1393 0.1757 39 # #> 40 39.68 -111.32 -0.08382941 -0.3556 0.1434 0.2718 40 # #> 41 39.31 -111.43 -0.78984433 -0.4174 0.1735 -0.3724 41 # #> 42 39.14 -111.56 -0.38648680 -0.5594 0.1321 0.1729 42 # #> 43 39.05 -111.47 -0.57739062 -0.6028 0.1091 0.0254 43 # #> 44 39.87 -111.28 -0.22947205 -0.0083 0.0417 -0.2212 44 # #> 45 39.89 -111.25 -0.03805984 -0.2372 0.0346 0.1991 45 # #> 46 39.45 -111.27 -0.42606551 -0.5614 0.1873 0.1353 46 # #> 47 39.13 -111.44 -0.52777166 -0.5837 0.1149 0.0559 47 # #> 48 39.01 -111.58 -0.81486819 -0.4709 0.1300 -0.3440 48 # #> 49 39.93 -111.63 0.06849776 -0.1495 0.1982 0.2180 49 # #> 50 38.77 -111.68 -0.68746363 -0.4619 0.0430 -0.2256 50 # #> 51 38.68 -111.60 -1.04793061 -0.7220 0.1395 -0.3259 51 # #> 52 38.21 -111.48 -1.40848147 -0.7562 0.2956 -0.6523 52 # #> 53 38.80 -111.68 -0.43759896 -0.6781 0.0433 0.2405 53 # #> 54 37.84 -111.88 -0.73581358 -0.7046 0.3145 -0.0312 54 # #> 55 38.51 -112.02 -0.90807705 -0.7549 0.2387 -0.1532 55 # #> 56 38.48 -112.39 -0.67118202 -0.8935 0.2731 0.2223 56 # #> 57 38.30 -112.36 -0.76527983 -0.4291 0.1105 -0.3362 57 # #> 58 38.30 -112.44 -0.51835705 -0.4790 0.0702 -0.0394 58 # #> 59 38.88 -112.25 -0.24704072 -0.5178 0.2858 0.2708 59 # #> 60 37.58 -112.90 -0.42302609 -0.2958 0.0683 -0.1272 60 # #> 61 37.49 -112.58 0.00732065 0.0269 0.0663 -0.0196 61 # #> 62 37.49 -112.51 0.02427501 0.0493 0.0417 -0.0250 62 # #> 63 37.66 -112.74 -0.76376457 -0.3283 0.1760 -0.4355 63 # #> 64 37.57 -112.84 -0.28791382 -0.4297 0.0607 0.1418 64 # #> 65 37.53 -113.05 -0.07280592 -0.2826 0.1556 0.2098 65 # #> 66 38.48 -109.27 -0.90950964 -0.4185 0.2903 -0.4910 66 # #> 67 37.81 -109.49 -0.39635792 -0.5006 0.3202 0.1042 67 # #> # #> $metrics # #> ME MAE RMSE # #> 1 -0.0127 0.2259 0.2769 #