Question

pcaRasters
$call
rasterPCA(img = predictors)

$model
Call:
princomp(cor = spca, covmat = covMat[[1]])

Standard deviations:
      Comp.1       Comp.2       Comp.3       Comp.4       Comp.5       Comp.6       Comp.7       Comp.8       Comp.9 
498.96308248 356.19955279 166.82560362  79.75533403  28.30786958  18.01329729  11.05097697   5.90091966   4.85153037 
     Comp.10      Comp.11      Comp.12      Comp.13      Comp.14      Comp.15      Comp.16      Comp.17      Comp.18 
  3.96912826   2.92429575   2.32486057   1.74476578   1.37242353   0.99700591   0.69100295   0.52470761   0.38599513 
     Comp.19      Comp.20      Comp.21      Comp.22      Comp.23 
  0.30199746   0.12861497   0.05112695   0.01751713   0.00000000 

 23  variables and  1034761 observations.

$map
class       : RasterBrick 
dimensions  : 959, 1079, 1034761, 23  (nrow, ncol, ncell, nlayers)
resolution  : 0.008333334, 0.008333334  (x, y)
extent      : 24.99168, 33.98334, -23.00833, -15.01666  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +ellps=WGS84 +no_defs 
data source : in memory
names       :           PC1,           PC2,           PC3,           PC4,           PC5,           PC6,           PC7,           PC8,           PC9,          PC10,          PC11,          PC12,          PC13,          PC14,          PC15, ... 
min values  : -1.525414e+03, -8.294717e+02, -1.597420e+03, -2.924969e+02, -4.018654e+02, -9.054122e+01, -4.005998e+01, -1.802074e+01, -2.699063e+01, -2.808965e+01, -1.337488e+01, -1.268085e+01, -1.224565e+01, -1.060565e+01, -4.378304e+00, ... 
max values  :  1.589643e+03,  1.964028e+03,  3.989713e+02,  3.699300e+02,  1.310118e+02,  7.833018e+01,  6.450310e+01,  2.629923e+01,  3.463626e+01,  2.732504e+01,  1.044373e+01,  1.601244e+01,  3.073991e+01,  5.426831e+00,  7.680870e+00, ...

Answer 1

#sample data

data(rlogo)
ggRGB(rlogo, 1,2,3)
pcaRasters <- rasterPCA(rlogo)

访问值

 getValues(pcaRasters$map)
                  PC1           PC2         PC3
   [1,] -118.27810276  -4.054375764  0.726071167
   [2,] -118.27810276  -4.054375764  0.726071167
   [3,] -118.27810276  -4.054375764  0.726071167

绘制栅格：

plot(pcaRasters$map)

保存到磁盘：

writeRaster(pcaRasters$map, "filename.tif")

在栅格上从PCA中提取值

1 个答案: