I have the following code:
fit_lm=lm(z~x y)
mix <- 2
max <- 12
miy <- 2
may <- 12
griddf <- expand.grid(x = seq(mix,max, length.out = 10),
y = seq( miy,may,length.out = 10))
Prediction_data <- data.frame(griddf)
colnames(Prediction_data) <- c("x", "y")
coordinates(Prediction_data ) <- ~ x y
terrain_lm <- predict(fit_lm, Prediction_data)
I want that terrain_lm is a numeric matrix, in such a way, that I can use
fig <- plot_ly()
fig <- fig %>% add_surface(terrain_lm)
but I get a 1d array with 100 elements.
CodePudding user response:
The result of predict
is a vector. You need to add it to the x and y values and then use xtabs
to transform into a suitable matrix for a surface plot.
library(plotly)
#test data
x <- runif(20, 4, 10)
y <- runif(20, 3, 6)
z <- 3*x y runif(20, 0, 2)
fit_lm <- lm(z~x y)
mix <- 2
max <- 12
miy <- 2
may <- 12
griddf <- expand.grid(x = seq(mix,max, length.out = 10),
y = seq( miy,may,length.out = 10))
terrain_lm <- data.frame(griddf)
terrain_lm$z <- predict(fit_lm, terrain_lm)
fig <- plot_ly(z = ~xtabs(z ~ x y, data = terrain_lm))
fig <- fig %>% add_surface()