If your working directory is C:Parent/Child

Then to read a file from Parent use ..

file="../MY_FILE.RData"

https://stackoverflow.com/questions/24319168/how-to-move-backward-parent-folder

If your working directory is C:Parent/Child

Then to read a file from Parent use ..

file="../MY_FILE.RData"

https://stackoverflow.com/questions/24319168/how-to-move-backward-parent-folder

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myplot + theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))

myplot + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))

R intersect polygons and points

```
library(sf)
library(tidyverse)
# example data from raster package
soil <- st_read(system.file("external/lux.shp", package="raster")) %>%
# add in some fake soil type data
mutate(soil = LETTERS[c(1:6,1:6)]) %>%
select(soil)
# field polygons
field <- c("POLYGON((6 49.75,6 50,6.4 50,6.4 49.75,6 49.75))",
"POLYGON((5.8 49.5,5.8 49.7,6.2 49.7,6.2 49.5,5.8 49.5))") %>%
st_as_sfc(crs = st_crs(soil)) %>%
st_sf(field = c('x','y'), geoms = ., stringsAsFactors = FALSE)
# intersect - note that sf is intelligent with attribute data!
pi <- st_intersection(soil, field)
plot(soil$geometry, axes = TRUE)
plot(field$geoms, add = TRUE)
plot(pi$geoms, add = TRUE, col='red')
# add in areas in m2
attArea <- pi %>%
mutate(area = st_area(.) %>% as.numeric())
# for each field, get area per soil type
attArea %>%
as_tibble() %>%
group_by(field, soil) %>%
summarize(area = sum(area))
```

Variance Inflation Factor (VIF) is a common simple stat used to quantify multicollinearity in least squares regressions. It is calculated for each covariate in a regression, with higher values meaning that the covariate is more colinear with the other covariates. It technically measures “how much the variance (the square of the estimate’s standard deviation) of an estimated regression coefficient is increased because of collinearity.” The equation is:

where R2i is from the regression of the covariate i on all the other covariates. The problem is where to draw the cutoff? Is a VIF > 2.5 too high? >5? or how about VIF>10, all have been used as cutoffs. Here is a figure of R2 vs VIF. As you can see, a cuttoff of 2.5 is an R2 of 0.60 and 10 is 0.90! While statistically, you could perhaps get away with these high inflations, what does it mean for your particular question? If you are dealing with a relationship among covariates that is as strong as 0.90, can you really be sure that the model and your interpretations are valid?

`#VIF function`

r<-function(x){1-(1/x)} #r is R2 and x is VIF

x<-seq(1,15,.1) #seq of VIFs

y<-sapply(x,r) #seq of R2

#plot

par(las=1)

plot(x,y,type="l",xlab="VIF",ylab="R2 of regression of focal covariate on all other covariates")

# common VIF cutoffs = 2.5, 5, 10

ly<-c(y[x==2.5],y[x==5],y[x==10])

lx<-c(2.5,5,10)

segments(lx,0,lx,ly,col="red")

segments(lx,ly,0,ly,col="red")

na.collapse<-function(x) { x.<-unique(x[!is.na(x)]) if(length(x.)==0) { return(NA) } else { if(length(x.)==1){ return(x.) } else { return(paste(x.,collapse="|")) } } } na.collapse(x)

http://support.esri.com/technical-article/000009381

In the map document, open the attribute table for the polygon feature class by right-clicking the layer name.

In the attribute table, navigate to Table Options > Add Field and add two new fields of type Double. Name one ‘Longitude’ and the other ‘Latitude’.

Right-click the Longitude field and select Calculate Geometry.

In the Calculate Geometry dialog box, select ‘X Coordinate of Centroid’ from the Property drop-down menu. Click OK.

Right-click the Latitude field and select Calculate Geometry.

In the Calculate Geometry dialog box, select ‘Y Coordinate of Centroid’ from the Property drop-down menu. Click OK.