Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", echo = FALSE)
## ----prepare_master_matrix, eval=FALSE, echo=TRUE-----------------------------
# # Data
# data("mx", package = "biosurvey")
# data("preselected", package = "biosurvey")
# variables <- raster::stack(system.file("extdata/variables.tif",
# package = "biosurvey"))
# names(variables) <- c("Mean_temperature", "Max_temperature", "Min_temperature",
# "Annual_precipitation", "Prec_wettest_month",
# "Prec_driest_month")
#
# # Create master matrix object
# m_matrixp <- prepare_master_matrix(region = mx, variables = variables,
# preselected_sites = preselected,
# do_pca = TRUE, center = TRUE, scale = TRUE)
# #> Processing raster layers, please wait...
# #> Performing PCA analysis
#
# summary(m_matrixp)
# #>
# #> Summary of a master_matrix object
# #> ---------------------------------------------------------------------------
# #>
# #> Data matrix summary:
# #> Longitude Latitude Mean_temperature Max_temperature
# #> Min. :-116.92 Min. :14.58 Min. : 85.0 Min. :180
# #> 1st Qu.:-106.79 1st Qu.:19.92 1st Qu.:174.0 1st Qu.:309
# #> Median :-102.58 Median :24.25 Median :203.0 Median :335
# #> Mean :-102.52 Mean :23.95 Mean :203.6 Mean :330
# #> 3rd Qu.: -98.75 3rd Qu.:27.92 3rd Qu.:236.0 3rd Qu.:354
# #> Max. : -86.92 Max. :32.58 Max. :291.0 Max. :425
# #> Min_temperature Annual_precipitation Prec_wettest_month Prec_driest_month
# #> Min. :-60.00 Min. : 53.0 Min. : 9.0 Min. : 0.00
# #> 1st Qu.: 30.00 1st Qu.: 352.0 1st Qu.: 78.0 1st Qu.: 3.00
# #> Median : 60.00 Median : 619.0 Median :144.5 Median : 6.00
# #> Mean : 70.66 Mean : 768.8 Mean :164.7 Mean : 10.67
# #> 3rd Qu.:112.00 3rd Qu.:1046.2 3rd Qu.:224.0 3rd Qu.: 12.00
# #> Max. :213.00 Max. :4103.0 Max. :750.0 Max. :140.00
# #> PC1 PC2
# #> Min. :-2.5770 Min. :-5.9018
# #> 1st Qu.:-1.3957 1st Qu.:-0.7861
# #> Median :-0.6003 Median : 0.1946
# #> Mean : 0.0000 Mean : 0.0000
# #> 3rd Qu.: 1.0825 3rd Qu.: 0.9541
# #> Max. : 9.1506 Max. : 3.1070
# #>
# #>
# #> Sites preselected by user:
# #> Site Longitude Latitude
# #> 1 Chamela -105.04479 19.50090
# #> 2 Los Tuxtlas -95.07419 18.58489
# #> 3 Chajul -90.94067 16.17000
# #> 4 Parque de Tlalpan -99.19778 19.29139
# #> 5 Parque Chipinque -100.35940 25.61750
# #>
# #>
# #> Region of interest:
# #> class : SpatialPolygonsDataFrame
# #> features : 1
# #> extent : -118.4042, -86.7014, 14.55055, 32.71846 (xmin, xmax, ymin, ymax)
# #> crs : +proj=longlat +datum=WGS84 +no_defs
# #> variables : 11
# #> names : FIPS, ISO2, ISO3, UN, NAME, AREA, POP2005, REGION, SUBREGION, LON, LAT
# #> value : MX, MX, MEX, 484, Mexico, 190869, 104266392, 19, 13, -102.535, 23.951
## ----explore_data_EG, eval=FALSE, echo=TRUE-----------------------------------
# # Plot using Principal Components resulted
# explore_data_EG(m_matrixp, variable_1 = "PC1", variable_2 = "PC2")
#
## ---- fig.height=4, fig.width=6-----------------------------------------------
knitr::include_graphics("vignette_img/V3_f1.png")
## ----make_blocks, eval=FALSE, echo=TRUE---------------------------------------
# # Creating blocks
# m_blocks <- make_blocks(m_matrixp, variable_1 = "PC1",
# variable_2 = "PC2", n_cols = 15, n_rows = 15,
# block_type = "equal_area")
#
## ----plot_blocks_EG, eval=FALSE, echo=TRUE------------------------------------
# # plotting all blocks
# plot_blocks_EG(master = m_blocks, block_ID = TRUE)
## ---- fig.height=4, fig.width=6-----------------------------------------------
knitr::include_graphics("vignette_img/V3_f2.png")
## ----EG_selection, eval=FALSE, echo=TRUE--------------------------------------
# # Selecting sites uniformly in E and G spaces
# EG_sel <- EG_selection(master = m_blocks, n_blocks = 20)
# #> Preparing data for analysis
# #> Running distance optimization, please wait...
# #> Selecting relevant environmental blocks, please wait...
# #> Running algorithm for selecting sites, please wait...
# #> Process 1 of 10
# #> Process 2 of 10
# #> Process 3 of 10
# #> Process 4 of 10
# #> Process 5 of 10
# #> Process 6 of 10
# #> Process 7 of 10
# #> Process 8 of 10
# #> Process 9 of 10
# #> Process 10 of 10
# #> Total number of sites selected: 26
#
# summary(EG_sel)
# #>
# #> Summary of a master_selection object
# #> ---------------------------------------------------------------------------
# #>
# #> Sites preselected by user:
# #> Site Longitude Latitude
# #> 1 Chamela -105.04479 19.50090
# #> 2 Los Tuxtlas -95.07419 18.58489
# #> 3 Chajul -90.94067 16.17000
# #> 4 Parque de Tlalpan -99.19778 19.29139
# #> 5 Parque Chipinque -100.35940 25.61750
# #>
# #>
# #> 2 columns and 6 rows of first elements are shown
# #>
# #> Sites selected randomly:
# #> Empty
# #>
# #>
# #> Sites selected uniformly in G space:
# #> Empty
# #>
# #>
# #> Sites selected uniformly in E space:
# #> Empty
# #>
# #>
# #> Sites selected uniformly in E space, considering G structure:
# #> Longitude Latitude
# #> 1 -105.04479 19.50090
# #> 2 -95.07419 18.58489
# #> 3 -90.94067 16.17000
# #> 4 -99.19778 19.29139
# #> 5 -100.35940 25.61750
# #> 4477 -98.08333 20.41667
#
## ----plot_sites_EG EG, eval=FALSE, echo=TRUE----------------------------------
# # Plotting sites selected uniformly in the geographic and in the environmental spaces
# plot_sites_EG(EG_sel, selection_type = "EG")
## ---- fig.height=4, fig.width=6-----------------------------------------------
knitr::include_graphics("vignette_img/V3_f3.png")
## ----random_selection, eval=FALSE, echo=TRUE----------------------------------
# # Selecting sites randomly
# EG_r_selection <- random_selection(EG_sel, n_sites = 26, n_samplings = 5)
# #> Selecting sampling sites randomly
# #> Total number of sites selected: 26
#
# summary(EG_r_selection)
# #>
# #> Summary of a master_selection object
# #> ---------------------------------------------------------------------------
# #>
# #> Sites preselected by user:
# #> Site Longitude Latitude
# #> 1 Chamela -105.04479 19.50090
# #> 2 Los Tuxtlas -95.07419 18.58489
# #> 3 Chajul -90.94067 16.17000
# #> 4 Parque de Tlalpan -99.19778 19.29139
# #> 5 Parque Chipinque -100.35940 25.61750
# #>
# #>
# #> 2 columns and 6 rows of first elements are shown
# #>
# #> Sites selected randomly:
# #> Longitude Latitude
# #> 1 -105.04479 19.50090
# #> 2 -95.07419 18.58489
# #> 3 -90.94067 16.17000
# #> 4 -99.19778 19.29139
# #> 5 -100.35940 25.61750
# #> 1017 -103.58333 29.08333
# #>
# #>
# #> Sites selected uniformly in G space:
# #> Empty
# #>
# #>
# #> Sites selected uniformly in E space:
# #> Empty
# #>
# #>
# #> Sites selected uniformly in E space, considering G structure:
# #> Longitude Latitude
# #> 1 -105.04479 19.50090
# #> 2 -95.07419 18.58489
# #> 3 -90.94067 16.17000
# #> 4 -99.19778 19.29139
# #> 5 -100.35940 25.61750
# #> 4477 -98.08333 20.41667
## ----plot_sites_EG random, eval=FALSE, echo=TRUE------------------------------
# # Plotting randomly selected sites
# plot_sites_EG(EG_r_selection, selection_type = "random", variable_1 = "PC1",
# variable_2 = "PC2")
## ---- fig.height=4, fig.width=6-----------------------------------------------
knitr::include_graphics("vignette_img/V3_f4.png")
## ----uniformG_selection, eval=FALSE, echo=TRUE--------------------------------
# # Selecting sites uniformly in G space
# EG_r_G_selection <- uniformG_selection(EG_r_selection, expected_points = 26)
# #> Running distance optimization, please wait...
# #> Running algorithm for selecting sites, please wait...
# #> Distance 181.62 resulted in 30 points
# #> Distance 199.782 resulted in 24 points
# #> Distance 217.944 resulted in 23 points
# #> Distance 236.106 resulted in 20 points
# #> Distance 217.944 resulted in 23 points
# #> Distance 219.7602 resulted in 22 points
# #> Distance 221.5764 resulted in 22 points
# #> Distance 223.3926 resulted in 22 points
# #> Distance 225.2088 resulted in 22 points
# #> Distance 227.025 resulted in 22 points
# #> Distance 228.8412 resulted in 20 points
# #> Distance 227.025 resulted in 22 points
# #> Distance 227.20662 resulted in 22 points
# #> Distance 227.38824 resulted in 21 points
# #> Total number of sites selected: 26
#
# summary(EG_r_G_selection)
# #>
# #> Summary of a master_selection object
# #> ---------------------------------------------------------------------------
# #>
# #> Sites preselected by user:
# #> Site Longitude Latitude
# #> 1 Chamela -105.04479 19.50090
# #> 2 Los Tuxtlas -95.07419 18.58489
# #> 3 Chajul -90.94067 16.17000
# #> 4 Parque de Tlalpan -99.19778 19.29139
# #> 5 Parque Chipinque -100.35940 25.61750
# #>
# #>
# #> 2 columns and 6 rows of first elements are shown
# #>
# #> Sites selected randomly:
# #> Longitude Latitude
# #> 1 -105.04479 19.50090
# #> 2 -95.07419 18.58489
# #> 3 -90.94067 16.17000
# #> 4 -99.19778 19.29139
# #> 5 -100.35940 25.61750
# #> 1017 -103.58333 29.08333
# #>
# #>
# #> Sites selected uniformly in G space:
# #> Longitude Latitude
# #> 1 -105.04479 19.50090
# #> 2 -95.07419 18.58489
# #> 3 -90.94067 16.17000
# #> 4 -99.19778 19.29139
# #> 5 -100.35940 25.61750
# #> 1017 -103.58333 29.08333
# #>
# #>
# #> Sites selected uniformly in E space:
# #> Empty
# #>
# #>
# #> Sites selected uniformly in E space, considering G structure:
# #> Longitude Latitude
# #> 1 -105.04479 19.50090
# #> 2 -95.07419 18.58489
# #> 3 -90.94067 16.17000
# #> 4 -99.19778 19.29139
# #> 5 -100.35940 25.61750
# #> 4477 -98.08333 20.41667
#
## ----plot_sites_EG G_selection, eval=FALSE, echo=TRUE-------------------------
# # Plotting sites selected uniformly in the geographic space
# plot_sites_EG(EG_r_G_selection, selection_type = "G", variable_1 = "PC1",
# variable_2 = "PC2")
## ---- fig.height=4, fig.width=6-----------------------------------------------
knitr::include_graphics("vignette_img/V3_f5.png")
## ----uniformE_selection, eval=FALSE, echo=TRUE--------------------------------
# # Selecting sites uniformly in E space
# EG_r_G_E_selection <- uniformE_selection(EG_r_G_selection, expected_points = 26)
# #> Running distance optimization, please wait...
# #> Running algorithm for selecting sites, please wait...
# #> Distance 0.81 resulted in 35 points
# #> Distance 0.891 resulted in 29 points
# #> Distance 0.972 resulted in 27 points
# #> Distance 1.053 resulted in 22 points
# #> Distance 1.134 resulted in 20 points
# #> Distance 1.053 resulted in 22 points
# #> Distance 1.0611 resulted in 22 points
# #> Distance 1.0692 resulted in 22 points
# #> Distance 1.0773 resulted in 22 points
# #> Distance 1.0854 resulted in 22 points
# #> Distance 1.0935 resulted in 22 points
# #> Distance 1.1016 resulted in 20 points
# #> Distance 1.0935 resulted in 22 points
# #> Distance 1.09431 resulted in 22 points
# #> Distance 1.09512 resulted in 21 points
# #> Total number of sites selected: 26
#
# summary(EG_r_G_E_selection)
# #>
# #> Summary of a master_selection object
# #> ---------------------------------------------------------------------------
# #>
# #> Sites preselected by user:
# #> Site Longitude Latitude
# #> 1 Chamela -105.04479 19.50090
# #> 2 Los Tuxtlas -95.07419 18.58489
# #> 3 Chajul -90.94067 16.17000
# #> 4 Parque de Tlalpan -99.19778 19.29139
# #> 5 Parque Chipinque -100.35940 25.61750
# #>
# #>
# #> 2 columns and 6 rows of first elements are shown
# #>
# #> Sites selected randomly:
# #> Longitude Latitude
# #> 1 -105.04479 19.50090
# #> 2 -95.07419 18.58489
# #> 3 -90.94067 16.17000
# #> 4 -99.19778 19.29139
# #> 5 -100.35940 25.61750
# #> 1017 -103.58333 29.08333
# #>
# #>
# #> Sites selected uniformly in G space:
# #> Longitude Latitude
# #> 1 -105.04479 19.50090
# #> 2 -95.07419 18.58489
# #> 3 -90.94067 16.17000
# #> 4 -99.19778 19.29139
# #> 5 -100.35940 25.61750
# #> 1017 -103.58333 29.08333
# #>
# #>
# #> Sites selected uniformly in E space:
# #> Longitude Latitude
# #> 1 -105.04479 19.50090
# #> 2 -95.07419 18.58489
# #> 3 -90.94067 16.17000
# #> 4 -99.19778 19.29139
# #> 5 -100.35940 25.61750
# #> 1081 -105.41667 28.91667
# #>
# #>
# #> Sites selected uniformly in E space, considering G structure:
# #> Longitude Latitude
# #> 1 -105.04479 19.50090
# #> 2 -95.07419 18.58489
# #> 3 -90.94067 16.17000
# #> 4 -99.19778 19.29139
# #> 5 -100.35940 25.61750
# #> 4477 -98.08333 20.41667
## ----plot_sites_EG E_all, eval=FALSE, echo=TRUE-------------------------------
# # Plotting sites selected uniformly in the environmental space
# plot_sites_EG(EG_r_G_E_selection, selection_type = "E")
## ---- fig.height=4, fig.width=6-----------------------------------------------
knitr::include_graphics("vignette_img/V3_f6.png")
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