Comment ajouter de la couleur à un contourplot en treillis?

Question

Je suis en train de faire un tracé de contour (ou parcelle de niveau, je ne me soucie pas particulièrement que l'on) qui ressemble à ceci:

J'ai ce code:

contourplot(z~x*y, data=df1, xlim=c(0,100), ylim=c(0,50), 
      scales=list(x=list(at=c(0,20,40,60,80,100)), 
         y=list(at=c(0,10,20,30,40,50))), 
      at=seq(0,5000,by=500)) 

qui fonctionne très bien, mais quand j'essaie d'ajouter la couleur comme ceci:

contourplot(z~x*y, data=df1, xlim=c(0,100), ylim=c(0,50), 
      scales=list(x=list(at=c(0,20,40,60,80,100)), 
         y=list(at=c(0,10,20,30,40,50))), 
      at=seq(0,5000,by=500), region=T, 
      colorkey=list(at=seq(0,5000,by=10)), col.regions=rainbow(5000)) 

Je reçois ce résultat horrible

Je pense que le problème est surtout d'échelle, mais peu importe que je les valeurs pour essayer at ou col.regions Je ne peux pas changer le résultat. J'ai même essayé l'option interpolate, mais ça ne fait pas la différence. J'ai beaucoup cherché mais je dois dire que je trouve la documentation de treillis un peu compliquée.

Le data.frame est la suivante:

structure(list(x = c(99.9735523336143, 99.9735523336143, 99.9735523336143, 
9.99735523336143, 99.9735523336143, 99.9735523336143, 99.9735523336143, 
99.9735523336143, 9.99735523336143, 99.9735523336143, 9.99735523336143, 
9.99735523336143, 9.99735523336143, 9.99735523336143, 9.99735523336143, 
9.99735523336143, 19.9947104667229, 19.9947104667229, 19.9947104667229, 
19.9947104667229, 19.9947104667229, 19.9947104667229, 19.9947104667229, 
19.9947104667229, 29.9920657000843, 29.9920657000843, 29.9920657000843, 
29.9920657000843, 29.9920657000843, 29.9920657000843, 29.9920657000843, 
29.9920657000843, 39.9103904572927, 39.9103904572927, 39.9103904572927, 
39.9103904572927, 39.9103904572927, 39.9103904572927, 39.9103904572927, 
39.9103904572927, 49.9867761668072, 49.9867761668072, 49.9867761668072, 
4.99867761668072, 49.9867761668072, 49.9867761668072, 49.9867761668072, 
49.9867761668072, 4.99867761668072, 49.9867761668072, 4.99867761668072, 
4.99867761668072, 4.99867761668072, 4.99867761668072, 4.99867761668072, 
4.99867761668072, 60.0631618763217, 60.0631618763217, 60.0631618763217, 
60.0631618763217, 60.0631618763217, 60.0631618763217, 60.0631618763217, 
60.0631618763217, 69.9419713954535, 69.9419713954535, 69.9419713954535, 
69.9419713954535, 69.9419713954535, 69.9419713954535, 69.9419713954535, 
69.9419713954535, 79.8207809145854, 79.8207809145854, 79.8207809145854, 
79.8207809145854, 79.8207809145854, 79.8207809145854, 79.8207809145854, 
79.8207809145854, 90.0947428144825, 90.0947428144825, 90.0947428144825, 
90.0947428144825, 90.0947428144825, 90.0947428144825, 90.0947428144825, 
90.0947428144825), y = c(0.0999735523336143, 0.499867761668072, 
9.99735523336143, 0.0999735523336143, 0.999735523336143, 19.9947104667229, 
1.99947104667229, 49.9867761668072, 0.499867761668072, 4.99867761668072, 
9.99735523336143, 0.999735523336143, 19.9947104667229, 1.99947104667229, 
49.9867761668072, 4.99867761668072, 0.0999735523336143, 0.499867761668072, 
9.99735523336143, 0.999735523336143, 19.9947104667229, 1.99947104667229, 
49.9867761668072, 4.99867761668072, 0.0999735523336143, 0.499867761668072, 
9.99735523336143, 0.999735523336143, 19.9947104667229, 1.99947104667229, 
49.9867761668072, 4.99867761668072, 0.0999735523336143, 0.499867761668072, 
9.99735523336143, 0.999735523336143, 19.9947104667229, 1.99947104667229, 
49.9867761668072, 4.99867761668072, 0.0999735523336143, 0.499867761668072, 
9.99735523336143, 0.0999735523336143, 0.999735523336143, 19.9947104667229, 
1.99947104667229, 49.9867761668072, 0.499867761668072, 4.99867761668072, 
9.99735523336143, 0.999735523336143, 19.9947104667229, 1.99947104667229, 
49.9867761668072, 4.99867761668072, 0.0999735523336143, 0.499867761668072, 
9.99735523336143, 0.999735523336143, 19.9947104667229, 1.99947104667229, 
49.9867761668072, 4.99867761668072, 0.0999735523336143, 0.499867761668072, 
9.99735523336143, 0.999735523336143, 19.9947104667229, 1.99947104667229, 
49.9867761668072, 4.99867761668072, 0.0999735523336143, 0.499867761668072, 
9.99735523336143, 0.999735523336143, 19.9947104667229, 1.99947104667229, 
49.9867761668072, 4.99867761668072, 0.0999735523336143, 0.499867761668072, 
9.99735523336143, 0.999735523336143, 19.9947104667229, 1.99947104667229, 
49.9867761668072, 4.99867761668072), z = c(7.70262939725884, 
72.4762038498177, 1897.66010932237, 1.58054830233696, 165.070646557113, 
3792.42153530273, 356.278306229365, 9223.47853672023, 14.2758673767137, 
935.839000278543, 341.884101039707, 31.701193606272, 665.004073393329, 
66.9391931173054, 1509.15007815714, 171.754281181158, 2.89064204660586, 
26.2635839730677, 636.27993673818, 58.5156364281774, 1243.02434222159, 
123.885961157754, 2852.46703855471, 318.792649405134, 3.97493187787477, 
36.3111148789109, 889.55438527022, 81.1632049285379, 1744.63858516148, 
172.284250494152, 4044.24262993302, 444.58461882461, 4.86420556288387, 
44.6549593184585, 1105.67331471408, 100.121759918144, 2176.12813241023, 
213.072583938431, 5090.54862806588, 551.33727936112, 5.608738003459, 
51.7306506719939, 1294.26413375778, 0.827199861040891, 116.33438144459, 
2555.52749317869, 248.208540590425, 6028.1869116069, 7.44790194067698, 
643.985400190343, 177.310068151686, 16.5109365887457, 344.080667093976, 
34.8178598080871, 776.206332364582, 89.2039775124962, 6.21939893932657, 
57.6140181031486, 1455.92520463951, 129.934909189257, 2883.12188866332, 
277.918147781392, 6852.31788369484, 722.963381835502, 6.70936909598539, 
62.4057616449996, 1591.87930397262, 141.11465788703, 3160.532277617, 
302.546915494946, 7561.72493964819, 789.007565625702, 7.10911809960051, 
66.3821496606545, 1708.59117356901, 150.482643243694, 3400.26770836962, 
323.370848584576, 8184.13138429934, 845.374097718433, 7.44465742618642, 
69.7893126020086, 1812.39459545251, 158.596014882138, 3614.91792337778, 
341.585243605719, 8749.51748025968, 895.192251986793)), .Names = c("x", 
"y", "z"), row.names = c(NA, -88L), class = "data.frame") 

Answer 1

Ajout panel.2dsmoother du package latticeextra vous donnera (relativement) lisser les couleurs à la recherche. Bien que si vous regardez de près, les bords sont encore un peu en dents de scie:

contourplot(z~x*y, data=df1, xlim=c(0,100), ylim=c(0,50), 
      scales=list(x=list(at=c(0,20,40,60,80,100)), 
         y=list(at=c(0,10,20,30,40,50))), 
      at=seq(0,5000,by=500), region=T, 
      colorkey=list(at=seq(0,5000,by=10)), 
      col.regions=rainbow(5000), 
      panel = latticeExtra::panel.2dsmoother) 

Si vous n'êtes pas limité aux options du paquet de treillis, filled.contour du paquet grpahics semble plutôt agréable. Vous aurez besoin de faire quelques empoignades de données:

df2 <- tidyr::spread(df1, y, z) 
df.x <- df2$x 
df.y <- as.numeric(colnames(df2)[-1]) 
df.z <- as.matrix(df2[,-1]) 

filled.contour(df.x, df.y, df.z, 
       nlevels = 10, 
       col = rainbow(10), 
       plot.axes = { 
       axis(1) 
       axis(2) 
       contour(df.x, df.y, df.z, add = T)} 
       )