Excuse me, sorry to bother you. I have met a question when coding.as the code follows.
<loc<-read.csv("F:/1Guichi1991-2015/schdata.SU/location.csv")
<date<-read.csv("F:/1Guichi1991-2015/schdata.SU/date.csv")
<data<-read.csv("F:/1Guichi1991-2015/schdata.SU/data.csv")
<library("dplyr")
<library("tidyr")
<library("STRbook")
<library("sp")
<library("spacetime")
#STFDF
<spat_part <- SpatialPoints(coords = loc[,c("lon", "lat")])
<date$time<-as.character(date$time)
<temp_part<-date$time
<temp_part <- as.Date(temp_part)
<class(date$time)
<data$VillageID<-as.integer(data$VillageID)
<all(unique(data$VillageID) == loc$VillageID)
<STOBJ1<- STFDF(sp = spat_part,
time = temp_part,
data = data)
<proj4string(STOBJ1) <- CRS("+proj=longlat +ellps=WGS84")
<library(gstat)
<vv<-variogram(object=p~1,
data=STOBJ1,
width=2,
cutoff=28,
tlags=0.01:6.1,tunit="days")
<plot(vv)
#separable model
<sepVgm <- vgmST(stModel = "separable",
space = vgm(10, "Exp", 400, nugget = 0.1),
time = vgm(10, "Exp", 1, nugget = 0.1),
sill = 20)
<sepVgm <- fit.StVariogram(vv, sepVgm)
##Error in vgmST("separable", space = vgm(1 - par[2], as.character(model$space$model[2]), :
"sill" must be positive.
I can find the problem with my exercise data, could you please help me?
<spat_pred_grid<- expand.grid(
< lon = seq(117, 118, length = 20),
< lat = seq(30, 31, length = 20)) %>%
< SpatialPoints(proj4string = CRS(proj4string(STOBJ1)))
<gridded(spat_pred_grid) <- TRUE
<temp_pred_grid <- as.Date("1991-06-01") + seq(1, 6, length = 6)
<DE_pred<- STF(sp = spat_pred_grid,
time = temp_pred_grid)
<STOBJ1 <- as(STOBJ1[,"1991-06-01::1991-06-25"], "STIDF")
<STOBJ1 <- subset(STOBJ1, !is.na(STOBJ1$p))
<pred_kriged <- krigeST(p ~ #1,
data = STOBJ1,
newdata = DE_pred, # prediction grid
modelList = sepVgm, # semivariogram
computeVar = TRUE)
##Error in chol.default(A) : the leading minor of order 88 is not positive definite
Excuse me, sorry to bother you. I have met a question when coding.as the code follows.
<loc<-read.csv("F:/1Guichi1991-2015/schdata.SU/location.csv")
<date<-read.csv("F:/1Guichi1991-2015/schdata.SU/date.csv")
<data<-read.csv("F:/1Guichi1991-2015/schdata.SU/data.csv")
<library("dplyr")
<library("tidyr")
<library("STRbook")
<library("sp")
<library("spacetime")
#STFDF
<spat_part <- SpatialPoints(coords = loc[,c("lon", "lat")])
<date$time<-as.character(date$time)
<temp_part<-date$time
<temp_part <- as.Date(temp_part)
<class(date$time)
<data$VillageID<-as.integer(data$VillageID)
<all(unique(data$VillageID) == loc$VillageID)
<STOBJ1<- STFDF(sp = spat_part,
time = temp_part,
data = data)
<proj4string(STOBJ1) <- CRS("+proj=longlat +ellps=WGS84")
<library(gstat)
<vv<-variogram(object=p~1,
data=STOBJ1,
width=2,
cutoff=28,
tlags=0.01:6.1,tunit="days")
<plot(vv)
#separable model
<sepVgm <- vgmST(stModel = "separable",
space = vgm(10, "Exp", 400, nugget = 0.1),
time = vgm(10, "Exp", 1, nugget = 0.1),
sill = 20)
<sepVgm <- fit.StVariogram(vv, sepVgm)
##Error in vgmST("separable", space = vgm(1 - par[2], as.character(model$space$model[2]), :
"sill" must be positive.
I can find the problem with my exercise data, could you please help me?
<spat_pred_grid<- expand.grid(
< lon = seq(117, 118, length = 20),
< lat = seq(30, 31, length = 20)) %>%
< SpatialPoints(proj4string = CRS(proj4string(STOBJ1)))
<gridded(spat_pred_grid) <- TRUE
<temp_pred_grid <- as.Date("1991-06-01") + seq(1, 6, length = 6)
<DE_pred<- STF(sp = spat_pred_grid,
time = temp_pred_grid)
<STOBJ1 <- as(STOBJ1[,"1991-06-01::1991-06-25"], "STIDF")
<STOBJ1 <- subset(STOBJ1, !is.na(STOBJ1$p))
<pred_kriged <- krigeST(p ~ #1,
data = STOBJ1,
newdata = DE_pred, # prediction grid
modelList = sepVgm, # semivariogram
computeVar = TRUE)
##Error in chol.default(A) : the leading minor of order 88 is not positive definite