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Merge pull request #68 from stscl/dev
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update `gccm` vignette
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@SpatLyu
SpatLyu authored Jan 10, 2025
2 parents 0ec9a7f + 9406b70 commit 97763ee
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154 changes: 77 additions & 77 deletions vignettes/GCCM.Rmd
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Expand Up @@ -156,66 +156,66 @@ pd_res = gccm(cause = "Pre",
nb = popd_nb,
trendRM = TRUE)
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endTime = Sys.time()
print(difftime(endTime,startTime, units ="mins"))
## Time difference of 19.82833 mins
## Time difference of 19.80848 mins
pd_res
## libsizes popDensity->Pre Pre->popDensity
## 1 10 0.01130395 0.06607465
Expand Down Expand Up @@ -254,7 +254,7 @@ Visualize the result:
``` r
windowsFonts(TNR = windowsFont("Times New Roman"))

plot(pd_res,limits = c(0, 2800),family = "TNR")
plot(pd_res,family = "TNR",xlimits = c(0, 2800))
```

![**Figure 1**. The cross-mapping prediction outputs between population density and county-level Precipitation.](../man/figures/gccm/fig1-1.png)
Expand Down Expand Up @@ -286,7 +286,7 @@ terra::plot(npp,nc = 3,

![**Figure 2**. Maps of farmland NPP and climate factors.](../man/figures/gccm/fig2-1.png)

*To save the computation time, we will aggregate the data by 3 times and select 4500 non-NA pixels to predict:*
*To save the computation time, we will aggregate the data by 3 times and select 3000 non-NA pixels to predict:*


``` r
Expand All @@ -305,7 +305,7 @@ dim(pred)
## [1] 6920 2

set.seed(42)
indices = sample(nrow(pred), size = 4500, replace = FALSE)
indices = sample(nrow(pred), size = 3000, replace = FALSE)
pred = pred[indices,]
```

Expand All @@ -320,49 +320,49 @@ npp_res = gccm(cause = "pre",
libsizes = seq(10,130,10),
E = 3, RowCol = pred)
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##
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endTime = Sys.time()
print(difftime(endTime,startTime, units ="mins"))
## Time difference of 20.42028 mins
## Time difference of 14.03368 mins
npp_res
## libsizes npp->pre pre->npp
## 1 10 0.06239891 0.08543342
## 2 20 0.10372288 0.14573580
## 3 30 0.13166531 0.17997571
## 4 40 0.15325455 0.21750843
## 5 50 0.18346558 0.27622460
## 6 60 0.23659107 0.28531139
## 7 70 0.24305683 0.23985740
## 8 80 0.18414529 0.12021939
## 9 90 0.10716495 0.02814977
## 10 100 0.12567428 -0.05565077
## 1 10 0.05366658 0.07974064
## 2 20 0.09168397 0.14103020
## 3 30 0.12385334 0.18137790
## 4 40 0.15205226 0.22147635
## 5 50 0.18402074 0.27559982
## 6 60 0.23504915 0.26947293
## 7 70 0.24022627 0.20894394
## 8 80 0.20491112 0.09716466
## 9 90 0.14005787 0.01715257
## 10 100 0.17271242 -0.02868893
```

Visualize the result:


``` r
plot(npp_res,limits = c(9, 101),family = "TNR")
plot(npp_res,family = "TNR",xlimits = c(9, 101),ylimits = c(0,0.5))
## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).
```
Expand Down
2 changes: 1 addition & 1 deletion vignettes/GCCM.Rmd.orig
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Expand Up @@ -117,7 +117,7 @@ pd_res

Visualize the result:

```{r fig1,fig.width=6.75,fig.height=5.5,fig.dpi=100,fig.cap=knitr::asis_output("**Figure 1**. The cross-mapping prediction outputs between population density and county-level Precipitation.")}
```{r fig1,fig.width=6.15,fig.height=5.05,fig.dpi=100,fig.cap=knitr::asis_output("**Figure 1**. The cross-mapping prediction outputs between population density and county-level Precipitation.")}
windowsFonts(TNR = windowsFont("Times New Roman"))

plot(pd_res,family = "TNR",xlimits = c(0, 2800))
Expand Down

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