Description Usage Arguments Value References Examples
sgstar function return the parameter estimation of Seaonal Generalized Space Time Autoregressive Model by using Generalized Least Square (GLS)
1 |
data |
A dataframe that contain timeseries data with k column as space and n rows as time. |
w |
a spatial weight, matrix ncol(data) * ncol(data) with diagonal = 0. |
p |
an autoregressive order, value must be greater than 0. |
ps |
an autoregressive order for seasonal, value must be greater than 0. |
s |
an order of the seasonal period |
sgstar returns output with detail are shown in the following list :
Coefficiens |
coefficiens parameter model for each location |
Fitted.Values |
a dataframe with fit value for each location based on model |
Residual |
a dataframe that contain residual,that is response minus fitted values based on model |
Performance |
a dataframe containing the following objects: |
MSE : Mean Squared Error (MSE) for all the data combined.
RMSE : Root Mean Squared Error (RMSE) for all the data combined.
AIC : a Version of Akaike's Information Criterion (AIC)
Rsquared : R^2, the ‘fraction of variance explained by the model’.
p |
an autoregressive order |
ps |
an autoregressive order for seasonal |
s |
an order of the seasonal period |
weight |
a spatial weight |
data |
a dataset that used in modeling |
Setiawan, Suhartono, and Prastuti M.(2016).S GSTAR-SUR for Seasonal Spatio Temporal Data Forecasting. Malaysian Journal Of Mathematical Sciences.10.<Corpus ID :189955959>.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | library(sgstar)
data("coords")
data("simulatedata")
#create weight matrix using distance inverse matrix
z<-dist(coords,method = "euclidean")
z <- as.matrix(z)
matriksd <- 1/z
matriksd[is.infinite(matriksd)] <- 0
matriksd_w <- matriksd / rowSums(as.data.frame(matriksd))
fit <- sgstar(data = simulatedata, w = matriksd_w, p = 2,ps = 1, s =4)
fit
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