A very useful blog by Zev Ross on reading spatial data into R using rgdal package:

http://zevross.com/blog/2016/01/13/tips-for-reading-spatial-files-into-r-with-rgdal/

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# Category: Analysis

## Reading spatial data

## Detecting Outliers

## Clipping (Subsetting) a point layer over a polygon layer

## A very brief introduction to multilevel models

## mapply in R – an example

## Interpreting logit/logistic – by ATS UCLA

## Analysis example of logit models using R – from ATS UCLA

## The statistical methods in logistic regression – its mechanics

## Resources on Spatial Statistics by ESRI

## Point density in ArcGIS

A blog -hopefully- with good balance of epidemiology and statistics

A very useful blog by Zev Ross on reading spatial data into R using rgdal package:

http://zevross.com/blog/2016/01/13/tips-for-reading-spatial-files-into-r-with-rgdal/

Reblogged from Datascience+

- Why outliers detection is important?
- Detect Outliers
- Outliers Test
- Outliers package

More here :

http://datascienceplus.com/outlier-detection-and-treatment-with-r/

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.’

mapply() looks like an interesting function in R. here an example of what you can do with mapply() function

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 |
library(powerSurvEpi) ssizeEpi.default(power, theta, p, psi, rho2, alpha = 0.05) #Arguments #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. #example #set power at 80%, 90% and 95% #set proportion at 30%, 40% and 50% mapply(function (sprop,spsi) ss<-ssizeEpi.default(power=0.8, theta=1.5, p=sprop, psi=spsi, rho2=.3, alpha = 0.05), sprop =c(0.30,0.60,0.90,0.80,0.60,0.80,0.60,0.70,0.70), spsi =c(0.43,0.55,0.78,0.54,0.67,0.58,0.46,0.22,0.32) ) |

The results are :

1 |
[1] 756 517 972 790 425 735 618 1477 1015 |

questions:

- log odds
- odds ratios
- probability
- interaction

http://www.ats.ucla.edu/stat/mult_pkg/faq/general/odds_ratio.htm

nice written by ATS UCLA team (as always)

- describing data
- estimation
- plotting

A nice blog on the mechanics of logistic regression:

https://nightlordtw.wordpress.com/2011/12/01/logistic-regression/

Resources on Spatial Statistics by ESRI

http://blogs.esri.com/esri/arcgis/2010/07/13/spatial-statistics-resources/