R: Spatial analysis with spatstat, sidebar does not show correct values

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I'm trying to create a map with the package spatstat of the R so that the sidebar shows the values of the third (preferably) or fourth column of my data frame and that the tmb colors reflect that third ( or fourth column).

My script: 

#dados

x=c(6.839887, 6.671494, 6.651083, 6.655289, 6.591903, 6.653641, 6.661709, 6.671664, 6.660044, 6.624659, 6.648162, 6.536877, 6.654134, 6.674678,6.618935, 6.677705, 6.643918, 6.644119, 6.670517, 6.583619, 6.649991, 6.647649, 6.656308, 6.645772, 6.648740, 6.643103, 6.652199, 6.666641,6.633400, 6.621282, 6.635427, 6.646127, 6.630862, 6.657919, 6.671616, 6.622935, 6.648225, 6.676911, 6.640234, 6.719334, 6.653202, 6.656747,6.724692, 6.639747, 6.630575, 6.657916, 6.618957, 6.640006, 6.645280, 6.614058, 6.576136, 6.631994, 6.617391, 6.782351, 6.620072, 6.661061,6.597216, 6.648755, 6.618436, 6.659507, 6.653993, 6.663255, 6.630893, 6.656322, 6.617265, 6.649022, 6.629346, 6.595224, 6.540263, 6.623435,6.652709, 6.608565, 6.618335, 6.645100, 6.790914, 6.643620, 6.462808, 6.680115, 6.716004, 6.668781, 6.765199, 6.674251, 6.647542, 6.724564,6.724556)

y=c(17.16749, 17.16727, 17.16678, 17.16673, 17.16813, 17.16663, 17.16652, 17.16636, 17.16629, 17.16856, 17.16521, 17.16519, 17.17002, 17.16465,17.17015, 17.16407, 17.16356, 17.17122, 17.16334, 17.17152, 17.16282, 17.16278, 17.16272, 17.17257, 17.16198, 17.17279, 17.16169, 17.16161,17.16146, 17.17352, 17.17389, 17.16076, 17.17420, 17.16046, 17.15917, 17.17571, 17.15895, 17.15881, 17.15860, 17.15827, 17.15797, 17.15776,17.17761, 17.15664, 17.15622, 17.15610, 17.15571, 17.15561, 17.15527,17.15514, 17.15494, 17.15447, 17.15438, 17.18041, 17.18053, 17.15402,17.18090, 17.15384, 17.18121, 17.15355, 17.15352, 17.15349, 17.18213,17.15242, 17.15201, 17.14978, 17.18591, 17.18688, 17.18707, 17.18761,17.14712, 17.18788, 17.18794, 17.14619, 17.18868, 17.14588, 17.14511,17.14471, 17.14440, 17.14430, 17.19116, 17.19140, 17.14222, 17.14123,17.33627)

z=c(32.23228,526.46061, -1300.03539, -376.04329, 139.67322,-913.24800,  -526.46061, 354.55511, 483.48424, 161.16141, 182.64960, 419.0196, 75.20866, -225.62598, -1536.40546, -397.53148, -1106.64169,  -440.50786, 118.18504,-290.09054, -1471.94089, 440.50786,-848.78343, -1385.98814, -676.87793, -1622.35821, -1450.45271,75.20866, -1557.89365, 161.16141, 376.04329, 354.55511, -32.23228,-1171.10626,-75.20866, 547.94880, -805.80706, 870.27162, -698.36612,-32.23228, -2331.46842,  -182.64960, 75.20866, -719.85431,-1837.24009,913.24800, -1106.64169, 698.36612, 483.48424, -676.87793, -3019.09045, 891.75981, 1106.64169, 333.06692, -913.24800,333.06692, 934.73619, 354.55511, 75.20866, -891.75981, -247.11416, -1966.16922, 139.67322, -784.31887, -569.43699, -118.18504,-440.50786, 397.53148, -655.38974, 139.67322, 53.72047, -633.90155,-633.90155, 419.01967, -547.94880, 75.20866, 569.43699, 290.09054, -376.04329, 547.94880, 75.20866, -10.74409, 182.64960,-397.53148, -479.53833 )

w=c(96326.91, 96769.46, 95127.94, 95960.41, 96423.22, 95476.93, 95825.18,96615.67, 96731.03, 96442.47, 96461.73, 96673.36, 96365.44, 96095.53,94914.31, 95941.10, 95302.53, 95902.47, 96403.96, 96037.64, 94972.60,96692.58, 95535.03, 95050.29, 95689.84, 94836.56, 94992.03, 96365.44,94894.87, 96442.47, 96634.90, 96615.67, 96269.09, 95244.36, 96230.54,96788.68, 95573.74, 97076.62, 95670.50, 96269.09, 94193.69, 96134.12,96365.44, 95651.15, 94642.01, 97114.98, 95302.53, 96923.12, 96731.03,95689.84, 93567.91, 97095.80, 97287.46, 96596.43, 95476.93, 96596.43,97134.15, 96615.67, 96365.44, 95496.30, 96076.24, 94525.17, 96423.22,95593.10, 95786.52, 96191.98, 95902.47, 96654.13, 95709.18, 96423.22,96346.17, 95728.52, 95728.52, 96673.36, 95805.85, 96365.44, 96807.89,96557.96, 95960.41, 96788.68, 96365.44, 96288.37, 96461.73,95941.10, 99451.20)

shap.lo=data.frame(x,y,z,w)

library(spatstat)
shap.lo.win <- owin(range(shap.lo[,1]), range(shap.lo[,2]))
centroid.owin(shap.lo.win) ; area.owin(shap.lo.win)

shap.lo.ppp <- as.ppp(shap.lo[,c(1,2,3)], shap.lo.win) # fazendo um objeto ppp

plot(density(shap.lo.ppp,0.02), col=topo.colors(25), main='', xlab='x', 
     ylab='y')

points(x, y)

The result is the image below:

I would like to know of friends because the sidebar shows values different from those shown in the third column of my data frame, that is, in addition to showing no negative value shows values much larger than those contained in the third column.

Is it possible to do this, ie have the colors and sidebar represent the third or fourth column of the data frame?

Thank you for your help!

    
asked by anonymous 28.04.2018 / 19:53

1 answer

1

Bad News

The sidebar is showing the correct data. The 

plot(density(shap.lo.ppp,0.02), col=topo.colors(25), main='', xlab='x', 
  ylab='y')

is plotting the estimated Gaussian density for shap.lo.ppp . By definition, a density function has no negative values. In addition, the integral of it over the domain in question should equal 1. So, for the volume of this surface that appears in the graph to be equal to 1, some points must be very high peaks, greater than 20,000. p>

This is why your scale starts at zero and ends at a value well above what you expected.

Good News

Fortunately, you can fool R to plot exactly what you want. First, I'll estimate the density of these points of your problem: 

densidade <- density(shap.lo.ppp, 0.02)

With this done, I will calculate the minimum and maximum of v , the object that has the values of the estimated density: 

min(densidade$v)
[1] 1.826526e-12
max(densidade$v)
[1] 23782.84

That is, they are zero and more than 20,000, as we suspected. The minimum and maximum values of the scale that interest you are given by 

min(shap.lo.ppp$marks)
[1] -3019.09
max(shap.lo.ppp$marks)
[1] 1106.642

If we define that the minimum (0) of the density is -3019.09 and its maximum (23782.84) is 1106.642, it is enough to make a linear interpolation in the intermediate values of densidade$v to transform the scale in the one that we want. I wrote a function to do this automatically: 

conversao_densidade <- function(densidade, shap.lo){
  # razao entre as amplitudes
  razao     <- (max(densidade$v)-min(densidade$v))/(max(shap.lo$z)-min(shap.lo$z))

  # deslocamento para o valor minimo
  conversao <- densidade$v/razao+min(shap.lo$z)

  # resultado
  return(conversao)
}

Now just replace densidade$v with the interpolated values and make a new graphic, this time with the desired scale: 

densidade$v <- conversao_densidade(densidade, shap.lo)

plot(densidade, col=topo.colors(25), main='', xlab='x', 
 ylab='y')

    
29.04.2018 / 00:43
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