"sill" must be positive.??

[sue-shine]

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

[andrewzm]

Hi Sue, this seems to be a problem with fitting vgmST to your specific data. Often this happens because there is a "trend" component you have not removed. I see your formula is p ~ 1, try putting in some covariates and see if things improve. Also, look closely at the "empirical" spatial and temporal variograms so you have an idea what you are fitting the theoretical variogram too. Hope this helps. Andrew

…

On Mon, Jul 6, 2020 at 7:00 PM sue-shine ***@***.***> wrote: 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 <#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 — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#2>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AABEU3NKQOZPIPPTXZCE5SDR2GHEHANCNFSM4ORMR4KQ> .

[sue-shine]

Hi, I try to put covariates in, but it didn't work, I wonder if it is convenient to check my exercise data for you.really need help.Thank you!