I wanted to computed zonal percentiles in R for raster values with a gridcode, using the DoBy package. Certain percentiles work perfectly, others produce more or less NA values, in this case -9999. What can be the cause of this discrepancy?
NOTE: All NA values appear in polygons that are not fully overlaping with the raster i'm interested in. This doesn't however explain why some percentile compute, others do not.
My applied percentile functions:
quant1<-function(x,na.rm=TRUE){(quantile(x, c(.01), na.rm = TRUE))}
quant5<-function(x,na.rm=TRUE){(quantile(x, c(.05), na.rm = TRUE))}
quant15<-function(x,na.rm=TRUE){(quantile(x, c(.15), na.rm = TRUE))}
quant25<-function(x,na.rm=TRUE){(quantile(x, c(.25), na.rm = TRUE))}
quant75<-function(x,na.rm=TRUE){(quantile(x, c(.75), na.rm = TRUE))}
quant85<-function(x,na.rm=TRUE){(quantile(x, c(.85), na.rm = TRUE))}
quant95<-function(x,na.rm=TRUE){(quantile(x, c(.95), na.rm = TRUE))}
quant99<-function(x,na.rm=TRUE){(quantile(x, c(.99), na.rm = TRUE))}
Application of functions:
q1<-summaryBy(RASTERVALU~grid_code,data=depth, FUN=quant1, keep.names=T)
q5<-summaryBy(RASTERVALU~grid_code,data=depth, FUN=quant5, keep.names=T)
q15<-summaryBy(RASTERVALU~grid_code,data=depth, FUN=quant15, keep.names=T)
q25<-summaryBy(RASTERVALU~grid_code,data=depth, FUN=quant25, keep.names=T)
q75<-summaryBy(RASTERVALU~grid_code,data=depth, FUN=quant75, keep.names=T)
q85<-summaryBy(RASTERVALU~grid_code,data=depth, FUN=quant85, keep.names=T)
q95<-summaryBy(RASTERVALU~grid_code,data=depth, FUN=quant95, keep.names=T)
q99<-summaryBy(RASTERVALU~grid_code,data=depth, FUN=quant99, keep.names=T)
This should to my understanding remove NA values before computation and produce results for all polygons, no matter the pixel count.
Follows an output table of the computation. Percentile 1 and 15 are most plagued by NA values.
q1 q5 q15 q25 q75 q85 q95 q99
-9999 -82.7934989929199 -9999 -9999 -82.6172466278076 -82.5455478668213 -82.4335483551025 -82.3479396057129
-9999 -82.0279998779297 -9999 -9999 -81.7740020751953 -81.6869964599609 -81.6319034576416 -81.6039762878418
-9999 -68.5245018005371 -9999 -68.6367511749268 -68.4617481231689 -68.4400024414062 -68.4128520965576 -68.318980255127
-9999 -68.1529998779297 -9999 -68.6242504119873 -67.9940013885498 -67.9098526000977 -67.8197998046875 -67.6305767059326
-9999 -66.0965003967285 -9999 -9999 -65.9724998474121 -65.9219970703125 -65.7605514526367 -65.6639614868164
-9999 -65.2804985046387 -9999 -9999 -64.8652515411377 -64.7608005523682 -64.5118511199951 -64.4869895172119
-9999 -62.7045001983643 -9999 -9999 -62.5639991760254 -62.4821016311646 -62.2751508712769 -62.1119895172119
-9999 -62.5089988708496 -9999 -62.6920013427734 -62.3634986877441 -62.2785495758057 -62.1639013290405 -62.1359615707397
-9999 -62.3390007019043 -9999 -9999 -62.1464996337891 -62.088650894165 -62.031251335144 -61.9182501983643
-9999 -58.6590003967285 -9999 -9999 -58.3725004196167 -58.1434007644653 -57.8779983520508 -57.6537393188477
-9999 -58.4934997558594 -9999 -58.8072500228882 -58.3682489395142 -58.3038509368896 -58.1767482757568 -58.0499892425537
-9999 -56.1360015869141 -9999 -56.3297491073608 -56.0120010375977 -55.9688512802124 -55.8899494171143 -55.8329501724243
-9999 -53.9230003356934 -9999 -54.2092504501343 -53.8269996643066 -53.7830009460449 -53.6978017807007 -53.6589603805542
-9999 -48.9314994812012 -9999 -9999 -48.7105007171631 -48.6123992919922 -48.5179513931274 -48.4739890289307
-9999 -42.2854995727539 -9999 -42.8615007400513 -41.8812494277954 -41.6038501739502 -41.3591510772705 -40.5169882965088
-9999 -42.1614990234375 -9999 -42.2462491989136 -42.0267505645752 -41.9099998474121 -41.8459506988525 -41.8219386291504
-9999 -38.1544990539551 -9999 -9999 -37.8874998092651 -37.6593996047974 -37.4445497512817 -37.3039208602905
-9999 -33.1689987182617 -9999 -33.2529983520508 -33.1027507781982 -33.0769996643066 -33.0347497940063 -33.0139999389648
-9999 -29.6655006408691 -9999 -9999 -29.5527496337891 -29.5060005187988 -29.4157503128052 -29.3299599266052
-9999 -29.638500213623 -9999 -29.7770004272461 -29.5520000457764 -29.5209999084473 -29.4968997955322 -29.4849806022644
-9999 -29.0570001602173 -9999 -9999 -28.9180006980896 -28.8618495941162 -28.7558505058289 -28.6939602470398
-9999 -27.1759996414185 -9999 -9999 -27.0627498626709 -27.0209999084473 -26.9890003204346 -26.9459791564941
-9999 -25.918999671936 -9999 -25.9869995117188 -25.8700008392334 -25.8515495300293 -25.8089500427246 -25.7189898681641
What could explain this effect?
Note: My data has no NA values. Certain grids just have a lower pixel count.
UPDATE: Yes, the data has -9999 for NA values, silly and easy mistake. Thanks for the help!
I would do:
library(raster)
r <- raster(ncols=10, nrows=10)
r[] <- runif(ncell(r)) * 1:ncell(r)
z <- r
z[] <- rep(1:5, each=20)
quants <- c(0.01, 0.05, 0.15, 0.25, 0.75, 0.85, 0.95, 0.99)
q <- zonal(r, z, function(i, ...) quantile(i, quants))
colnames(q) <- c("zone", quants)
q
Now, your data clearly has values of -9999. These are not NA, they are -9999. That is why your lowest quantile values have that value. If you want them to be NA, you may try:
NAvalue(r) <- -9999
q <- zonal(r, z, function(i, ...) quantile(i, quants, na.rm=TRUE))
or
x <- reclassify(r, cbind(-9999, NA))
q <- zonal(r, z, function(i, ...) quantile(i, quants, na.rm=TRUE))