A very useful blog by Zev Ross on reading spatial data into R using rgdal package:
Reblogged from Datascience+
- Why outliers detection is important?
- Detect Outliers
- Outliers Test
- Outliers package
More here :
Nice tutorials by Robin Lovelace on Clipping
‘This miniature vignette shows how to clip spatial data based on different spatial objects in R and a ‘bounding box’. Spatial overlays are common in GIS applications and R users are fortunate that the clipping and spatial subsetting functions are mature and fairly fast. We’ll also write a new function called
gClip(), that will make clipping by bounding boxes easier.’
- The motivation of doing Multilevel Model
- Models such as Random Intercept and Random Slope models
- Graphical display of Multilevel Model
mapply() looks like an interesting function in R. here an example of what you can do with mapply() function
ssizeEpi.default(power, theta, p, psi, rho2, alpha = 0.05)
#power postulated power.
#theta postulated hazard ratio.
#p proportion of subjects taking value one for the covariate of interest.
#psi proportion of subjects died of the disease of interest.
#rho2 square of the correlation between the covariate of interest and the other covariate.
#alpha type I error rate.
#set power at 80%, 90% and 95%
#set proportion at 30%, 40% and 50%
ss<-ssizeEpi.default(power=0.8, theta=1.5, p=sprop, psi=spsi, rho2=.3, alpha = 0.05),
The results are :
 756 517 972 790 425 735 618 1477 1015
- log odds
- odds ratios
nice written by ATS UCLA team (as always)
- describing data
A nice blog on the mechanics of logistic regression:
Resources on Spatial Statistics by ESRI
Use spatial point patterns
Toolbox – spatial analyst