spmultinom: Multinomial logistic regression on spatial objects

In GSIF: Global Soil Information Facilities

Description

Runs the multinomial logistic regression via nnet::multinom to produce spatial predictions of the target factor-type variable. It requires point locations of observed classes and a list of covariate layers provided as "SpatialPixelsDataFrame-class" object. The resulting predicted classes are then used to estimate class centres and variances per class.

Usage

## S4 method for signature 
## 'formula,SpatialPointsDataFrame,SpatialPixelsDataFrame'
spmultinom(formulaString,
     observations, covariates, class.stats = TRUE, predict.probs = TRUE, ...)

Arguments

formulaString	formula string
observations	object of type "SpatialPointsData"; occurrences of factors
covariates	object of type "SpatialPixelsData"; list of covariate layers
class.stats	logical; species wether to estimate class centres
predict.probs	logical; species wether to predict probabilities per class
...	optional arguments

Value

Returns an object of type "SpatialMemberships" with following slots: predicted (classes predicted by the multinomial logistic regression, model (the multinomial logistic regression model), mu (probabilities derived using the mutinom model), class.c (derived class centres), class.sd (derived class deviations), confusion (confusion matrix).

Author(s)

Bas Kempen and Tomislav Hengl

References

• Multinomial logistic regression (http://en.wikipedia.org/wiki/Multinomial_logit)

• Nnet package (https://CRAN.R-project.org/package=nnet)

See Also

spfkm, SpatialMemberships-class

Examples

# load data:
library(plotKML)
library(sp)

data(eberg)
# subset to 20%:
eberg <- eberg[runif(nrow(eberg))<.2,]
data(eberg_grid)
coordinates(eberg) <- ~X+Y
proj4string(eberg) <- CRS("+init=epsg:31467")
gridded(eberg_grid) <- ~x+y
proj4string(eberg_grid) <- CRS("+init=epsg:31467")
# derive soil predictive components:
eberg_spc <- spc(eberg_grid, ~PRMGEO6+DEMSRT6+TWISRT6+TIRAST6)
# predict memberships:
formulaString = soiltype ~ PC1+PC2+PC3+PC4+PC5+PC6+PC7+PC8+PC9+PC10
eberg_sm <- spmultinom(formulaString, eberg, eberg_spc@predicted)
## Not run: # plot memberships:
pal = seq(0, 1, 1/50)
spplot(eberg_sm@mu, col.regions=pal)
image(eberg_sm@mu[1], col=pal)
text(eberg@coords, paste(eberg$soiltype), cex=.6, col="black")
# classes predicted:
Ls = length(levels(eberg_sm@predicted$soiltype))
pnts = list("sp.points", eberg, pch="+", cex=.6, col="black")
spplot(eberg_sm@predicted, col.regions=rainbow(Ls)[rank(runif(Ls))], sp.layout=pnts)

## End(Not run)

Example output

GSIF version 0.5-5.1 (2019-01-04)
URL: http://gsif.r-forge.r-project.org/
plotKML version 0.6-1 (2020-03-08)
URL: http://plotkml.r-forge.r-project.org/
Warning message:
In showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj = prefer_proj) :
  Discarded datum Deutsches_Hauptdreiecksnetz in CRS definition
Warning message:
In showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj = prefer_proj) :
  Discarded datum Deutsches_Hauptdreiecksnetz in CRS definition
Converting PRMGEO6 to indicators...
Converting covariates to principal components...
Warning message:
In proj4string(obj) : CRS object has comment, which is lost in output
Fitting a multinomial logistic regression model...
# weights:  132 (110 variable)
initial  value 1222.926589 
iter  10 value 862.881524
iter  20 value 844.249559
iter  30 value 831.862150
iter  40 value 827.678064
iter  50 value 825.941255
iter  60 value 825.149303
iter  70 value 824.914320
iter  80 value 824.825869
final  value 824.824380 
converged
Estimated Cohen Kappa (weighted): 0.2045
Map purity: 32.1
Warning message:
In nnet::multinom(formulaString, ov, ...) : groups ‘Ha’ ‘Hw’ are empty

GSIF documentation built on March 26, 2020, 7:01 p.m.