Mixing
How to include spatial location (x,y) in GAM predict?
I've developed my generalised additive model using the mgcv pakcage in R. A simpler version of my gam model is
data:
mydata
raster file
library(readxl)
library(mgcv)
mydata <- read_excel("C:/Desktop/mydata.xlsx")
mygam <- gam(conc ~ s(var1)+ s(var2) + s(var3) + s(X,Y), data = mydata)
var1, var2 and var3 are land use variables; (X,Y) are point locations of each sampled data conc. My aim now is to create a predicted surface from this model, using predict.gam. I have all my variables in individual raster files. Thanks to one of the answers in this post I can predict using
mygam2 <- gam(conc ~ s(var1)+ s(var2) + s(var3), data = mydata)
library(raster)
rs_stack(var1,var2,var3)
predict(rs_stack,mygam2,filename="file_name.tif")
(the prediction may take a few minutes)
But this does not include the spatial location(X,Y), as they are in point format, therefore can't be transformed to a raster layer as the land use variables. My question is how can I account for (X,Y) in the prediction, with the rest of the raster layers?
Any thoughts would be very much appreciated!
r gis raster r-raster gam
asked Nov 20 '18 at 16:25
veonlyveonly
103
add a comment |
I've developed my generalised additive model using the mgcv pakcage in R. A simpler version of my gam model is
data:
mydata
raster file
library(readxl)
library(mgcv)
mydata <- read_excel("C:/Desktop/mydata.xlsx")
mygam <- gam(conc ~ s(var1)+ s(var2) + s(var3) + s(X,Y), data = mydata)
var1, var2 and var3 are land use variables; (X,Y) are point locations of each sampled data conc. My aim now is to create a predicted surface from this model, using predict.gam. I have all my variables in individual raster files. Thanks to one of the answers in this post I can predict using
mygam2 <- gam(conc ~ s(var1)+ s(var2) + s(var3), data = mydata)
library(raster)
rs_stack(var1,var2,var3)
predict(rs_stack,mygam2,filename="file_name.tif")
(the prediction may take a few minutes)
But this does not include the spatial location(X,Y), as they are in point format, therefore can't be transformed to a raster layer as the land use variables. My question is how can I account for (X,Y) in the prediction, with the rest of the raster layers?
Any thoughts would be very much appreciated!
r gis raster r-raster gam
asked Nov 20 '18 at 16:25
veonlyveonly
103
I think a potential workaround is to extract all values of variables from point locations, export them as tables, and then do the prediction asdata.frame. but I have a very very large area, with 25m resolution, so working with raster would be a much better option (if it's achievable...)
– veonly
Nov 20 '18 at 16:25
You could create a raster layer containing the x coordinates and another containing the y coordinates.
– Florian
Nov 20 '18 at 16:38
A MCVE would help: stackoverflow.com/help/mcve
– Florian
Nov 20 '18 at 16:39
@Florian I've edited my question with data provided.
– veonly
Nov 20 '18 at 17:31
@Florian If I inlcude X,Y as separate raster files, won't they be accounted as 'values' instead of sptial location?
– veonly
Nov 20 '18 at 17:32
add a comment |
I've developed my generalised additive model using the mgcv pakcage in R. A simpler version of my gam model is
data:
mydata
raster file
library(readxl)
library(mgcv)
mydata <- read_excel("C:/Desktop/mydata.xlsx")
mygam <- gam(conc ~ s(var1)+ s(var2) + s(var3) + s(X,Y), data = mydata)
var1, var2 and var3 are land use variables; (X,Y) are point locations of each sampled data conc. My aim now is to create a predicted surface from this model, using predict.gam. I have all my variables in individual raster files. Thanks to one of the answers in this post I can predict using
mygam2 <- gam(conc ~ s(var1)+ s(var2) + s(var3), data = mydata)
library(raster)
rs_stack(var1,var2,var3)
predict(rs_stack,mygam2,filename="file_name.tif")
(the prediction may take a few minutes)
But this does not include the spatial location(X,Y), as they are in point format, therefore can't be transformed to a raster layer as the land use variables. My question is how can I account for (X,Y) in the prediction, with the rest of the raster layers?
Any thoughts would be very much appreciated!
r gis raster r-raster gam
asked Nov 20 '18 at 16:25
veonlyveonly
103
I've developed my generalised additive model using the mgcv pakcage in R. A simpler version of my gam model is
data:
mydata
raster file
library(readxl)
library(mgcv)
mydata <- read_excel("C:/Desktop/mydata.xlsx")
mygam <- gam(conc ~ s(var1)+ s(var2) + s(var3) + s(X,Y), data = mydata)
var1, var2 and var3 are land use variables; (X,Y) are point locations of each sampled data conc. My aim now is to create a predicted surface from this model, using predict.gam. I have all my variables in individual raster files. Thanks to one of the answers in this post I can predict using
mygam2 <- gam(conc ~ s(var1)+ s(var2) + s(var3), data = mydata)
library(raster)
rs_stack(var1,var2,var3)
predict(rs_stack,mygam2,filename="file_name.tif")
(the prediction may take a few minutes)
But this does not include the spatial location(X,Y), as they are in point format, therefore can't be transformed to a raster layer as the land use variables. My question is how can I account for (X,Y) in the prediction, with the rest of the raster layers?
Any thoughts would be very much appreciated!
r gis raster r-raster gam
r gis raster r-raster gam
asked Nov 20 '18 at 16:25
veonlyveonly
103
asked Nov 20 '18 at 16:25
veonlyveonly
103
edited Nov 20 '18 at 17:30
veonly
asked Nov 20 '18 at 16:25
veonlyveonly
103
asked Nov 20 '18 at 16:25
veonlyveonly
103
asked Nov 20 '18 at 16:25
veonlyveonly
103
103
I think a potential workaround is to extract all values of variables from point locations, export them as tables, and then do the prediction asdata.frame. but I have a very very large area, with 25m resolution, so working with raster would be a much better option (if it's achievable...)
– veonly
Nov 20 '18 at 16:25
You could create a raster layer containing the x coordinates and another containing the y coordinates.
– Florian
Nov 20 '18 at 16:38
A MCVE would help: stackoverflow.com/help/mcve
– Florian
Nov 20 '18 at 16:39
@Florian I've edited my question with data provided.
– veonly
Nov 20 '18 at 17:31
@Florian If I inlcude X,Y as separate raster files, won't they be accounted as 'values' instead of sptial location?
– veonly
Nov 20 '18 at 17:32
add a comment |
I think a potential workaround is to extract all values of variables from point locations, export them as tables, and then do the prediction asdata.frame. but I have a very very large area, with 25m resolution, so working with raster would be a much better option (if it's achievable...)
– veonly
Nov 20 '18 at 16:25
You could create a raster layer containing the x coordinates and another containing the y coordinates.
– Florian
Nov 20 '18 at 16:38
A MCVE would help: stackoverflow.com/help/mcve
– Florian
Nov 20 '18 at 16:39
@Florian I've edited my question with data provided.
– veonly
Nov 20 '18 at 17:31
@Florian If I inlcude X,Y as separate raster files, won't they be accounted as 'values' instead of sptial location?
– veonly
Nov 20 '18 at 17:32
I think a potential workaround is to extract all values of variables from point locations, export them as tables, and then do the prediction as
data.frame. but I have a very very large area, with 25m resolution, so working with raster would be a much better option (if it's achievable...)– veonly
Nov 20 '18 at 16:25
I think a potential workaround is to extract all values of variables from point locations, export them as tables, and then do the prediction as
data.frame. but I have a very very large area, with 25m resolution, so working with raster would be a much better option (if it's achievable...)– veonly
Nov 20 '18 at 16:25
You could create a raster layer containing the x coordinates and another containing the y coordinates.
– Florian
Nov 20 '18 at 16:38
You could create a raster layer containing the x coordinates and another containing the y coordinates.
– Florian
Nov 20 '18 at 16:38
A MCVE would help: stackoverflow.com/help/mcve
– Florian
Nov 20 '18 at 16:39
A MCVE would help: stackoverflow.com/help/mcve
– Florian
Nov 20 '18 at 16:39
@Florian I've edited my question with data provided.
– veonly
Nov 20 '18 at 17:31
@Florian I've edited my question with data provided.
– veonly
Nov 20 '18 at 17:31
@Florian If I inlcude X,Y as separate raster files, won't they be accounted as 'values' instead of sptial location?
– veonly
Nov 20 '18 at 17:32
@Florian If I inlcude X,Y as separate raster files, won't they be accounted as 'values' instead of sptial location?
– veonly
Nov 20 '18 at 17:32
add a comment |
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I think a potential workaround is to extract all values of variables from point locations, export them as tables, and then do the prediction as
data.frame. but I have a very very large area, with 25m resolution, so working with raster would be a much better option (if it's achievable...)– veonly
Nov 20 '18 at 16:25
You could create a raster layer containing the x coordinates and another containing the y coordinates.
– Florian
Nov 20 '18 at 16:38
A MCVE would help: stackoverflow.com/help/mcve
– Florian
Nov 20 '18 at 16:39
@Florian I've edited my question with data provided.
– veonly
Nov 20 '18 at 17:31
@Florian If I inlcude X,Y as separate raster files, won't they be accounted as 'values' instead of sptial location?
– veonly
Nov 20 '18 at 17:32