haotu : an open lab notebook

2017/07/18

Make ggplot look like base plot in R

Filed under: ggplot, R, R graphics — Tags: , , , , , , — S @ 04:46
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"))

here

 

 

 

2017/06/22

Automatic MIT License for R Packages

Filed under: errors in R, R, R Stats — Tags: , — S @ 07:01
devtools::use_mit_license()

2016/10/28

The relationship between VIF and R2 (r squared)

Filed under: Math and Stats, R, R Stats — Tags: , , — S @ 09:08

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:

{\displaystyle \mathrm {VIF_{i}} ={\frac {1}{1-R_{i}^{2}}}}

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?

rplot

 

 

#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")

2016/07/14

aggregate by removing NA

Filed under: Manipulate Data in R, R, R Stats, Uncategorized — Tags: — S @ 08:43
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)

2016/06/24

save rda data file with compression

Filed under: errors in R, R, R, R Stats — Tags: , , , , — S @ 06:36
save(mydata,file="mydata.rda",compress="xz")

find non-ascii in R

Filed under: errors in R, R, R, R Stats, Uncategorized — Tags: , , , , — S @ 06:34
tools::showNonASCII(readLines("myfiles.R"))

2016/04/18

raster cell area size 1 degree cell size km2

Filed under: R, R spatial, R Stats, Uncategorized — Tags: , — S @ 08:14

For a 1×1 degree cell size raster

r <- raster(ncol=360, nrow=180) #or just the default r<-raster()
area(r)
area(r)$values

 

http://gis.stackexchange.com/questions/177622/r-calculate-raster-cell-size-in-map-units

 

http://gis.stackexchange.com/questions/29734/how-to-calculate-area-of-1-x-1-degree-cells-in-a-raster

2016/03/01

select multiple columns of a data.table by column name

Filed under: Manipulate Data in R, R, R Stats — Tags: , — S @ 12:39
myDT[,.(mycolname1,mycolname2,mycolname3)]

2015/03/10

check a phylo object

Filed under: Uncategorized — Tags: , , — S @ 09:07

See here:

https://github.com/emmanuelparadis/checkValidPhylo

or forked

https://github.com/mrhelmus/checkValidPhylo

2015/01/30

aggregate more than one column in data.table

Filed under: R — Tags: , , , , — S @ 11:15
# Average ability by grade
agg1<- fm1[, j=list(mean(x0, na.rm = TRUE),mean(x1, na.rm = TRUE)),by = key]

http://rprogramming.net/aggregate-data-in-r-using-data-table/

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