How to fit Spatial logistic regression models to DHS data

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How to fit Spatial logistic regression models to DHS data

Bedilu Ejigu
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


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Re: How to fit Spatial logistic regression models to DHS data

Anthony Damico
hi, https://github.com/davidbrae/swmap  might help some but probably not
everything you need

On Feb 20, 2018 11:26 AM, "Bedilu Ejigu" <[hidden email]> 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
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>

        [[alternative HTML version deleted]]

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Re: How to fit Spatial logistic regression models to DHS data

Thierry Onkelinx
In reply to this post by Bedilu Ejigu
Have a look at Zuur et al (2017) Beginner's Guide to Spatial, Temporal
and Spatial-Temporal Ecological Data Analysis with R-INLA

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE
AND FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
[hidden email]
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

///////////////////////////////////////////////////////////////////////////////////////////
To call in the statistician after the experiment is done may be no
more than asking him to perform a post-mortem examination: he may be
able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does
not ensure that a reasonable answer can be extracted from a given body
of data. ~ John Tukey
///////////////////////////////////////////////////////////////////////////////////////////




2018-02-20 12:26 GMT+01:00 Bedilu Ejigu <[hidden email]>:

> 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
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo

_______________________________________________
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