hi, https://github.com/davidbrae/swmap might help some but probably not everything you need
On Feb 20, 2018 11:26 AM, "Bedilu Ejigu" <bedi...@gmail.com> wrote: > I am analyzing geospatial data come from malaria intervention survey, > to compare standard multilevel models with spatial models. Some of > the variables in my dataset are the following: > > > > 1. malaria-malaria test result(1-presence, 0-absence) which is > our outcome variable > > 2. LATNUM-coordinates of the survey cluster > > 3. LONGNUM- coordinates of the survey cluster > > 4. hv024-region (categorical variable) > > 5. hv025-residence (urban/rural) > > 6. hv227 -net use (yes/no) > > 7. hv270 -wealth index(poorest, poorer, middle, richer, richest) > > 8. hc1 – age in days > > 9. hc27- sex (male/female) > > 10. hc68-educational level (no education, primary, secondary) > > 11. anebin- Anemia level(1-anemic,0-nonanemic) > > > > > > What I want to fit is a spatial logistic regression model by using > the aforementioned variables using any of the packages in R which can > handle the task (i.e. prevMap, geoRglm). Can anyone help me on how to > fit such a spatial logistic regression model? If possible, and someone > did similar tasks before, could you share me your R code? > > > > Sample dataset, which shows the structure of my dataset: > > > > hv024 > > hv025 > > hv227 > > hv270 > > hc1 > > hc27 > > hc68 > > LATNUM > > LONGNUM > > anebin > > malaria > > western > > rural > > yes > > middle > > 18 > > female > > middle/jss/jhs > > 5.076585 > > -2.88716 > > 0 > > 0 > > western > > rural > > yes > > poorer > > 42 > > female > > middle/jss/jhs > > 5.076585 > > -2.88716 > > 0 > > 0 > > western > > rural > > yes > > poorer > > 15 > > male > > middle/jss/jhs > > 5.076585 > > -2.88716 > > 1 > > 0 > > western > > rural > > yes > > poorer > > 30 > > male > > middle/jss/jhs > > 5.076585 > > -2.88716 > > 1 > > 0 > > western > > rural > > yes > > middle > > 39 > > male > > primary > > 5.076585 > > -2.88716 > > 0 > > 0 > > western > > rural > > yes > > middle > > 19 > > male > > primary > > 5.076585 > > -2.88716 > > 1 > > 0 > > western > > rural > > no > > poorer > > 28 > > male > > no education > > 5.076585 > > -2.88716 > > 1 > > 0 > > western > > rural > > no > > poorer > > 8 > > male > > primary > > 5.076585 > > -2.88716 > > 1 > > 0 > > western > > rural > > yes > > middle > > 32 > > male > > no education > > 5.076585 > > -2.88716 > > 1 > > 0 > > western > > rural > > yes > > middle > > 59 > > male > > middle/jss/jhs > > 5.076585 > > -2.88716 > > 0 > > 0 > > western > > rural > > yes > > middle > > 40 > > male > > NA > > 5.076585 > > -2.88716 > > 1 > > 0 > > western > > rural > > yes > > poorer > > 36 > > male > > middle/jss/jhs > > 5.076585 > > -2.88716 > > 0 > > 0 > > western > > rural > > yes > > poorer > > 19 > > male > > no education > > 5.076585 > > -2.88716 > > 1 > > 0 > > western > > rural > > yes > > poorer > > 19 > > female > > NA > > 5.076585 > > -2.88716 > > 1 > > 0 > > western > > urban > > yes > > richer > > 9 > > female > > middle/jss/jhs > > 5.286215 > > -2.76342 > > 0 > > 0 > > western > > urban > > no > > richest > > 48 > > female > > primary > > 5.286215 > > -2.76342 > > 0 > > 0 > > > > > > With best regards, > > > > Bedilu > > > *_______________________________________________* > > [[alternative HTML version deleted]] > > _______________________________________________ > R-sig-Geo mailing list > R-sig-Geo@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-geo > [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo