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Extract random effects from CARBayes output



Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)
Announcing the arrival of Valued Associate #679: Cesar Manara
Unicorn Meta Zoo #1: Why another podcast?Extract values from raster into points: data loss using extract function from raster packageSpatial weight matrix with boundary effects in R?What to do with the intercept output from regression analsyis?Is there really random distribution of points?Using Random Forest for big data?Predicting Enhanced Vegetation Index using Random Forest in RHow to extract specific values with point coordinates from Kriging interpolations made in R?Plotting/Exporting the Spatial Random Effects in BYM model - CARBayes - in REstimate spatial kernel density from raster and output kernel values in a gridExtract random coordinate points using classes (ranges) of values from a raster image



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We conducted a study at the U.S. census tract level looking at the relationship between environmental factors and social mobility in the ten largest U.S. cities. We used CARBayes::S.CARmultilevel in R to control for spatial autocorrelation between neighboring tracts and higher-order nested effects of cities (the city was included as a random effect variable).



We want to know how each city influenced the results in the fully adjusted model: In other words, what is the median coefficient for each city, relative to the reference group. We were able to calculate the median random effect coefficients as well as standard deviations and confidence intervals using the following code:



CAR_nwide_m4_re <- as.data.frame(CAR_nwide_m4[["samples"]][["zeta"]])
CAR_nwide_m4_re_median <- as.data.frame(apply(CAR_nwide_m4_re,2,FUN=median))
CAR_nwide_m4_re_ci <- as.data.frame(apply(as.matrix(CAR_nwide_m4_re), 2, function(x)mean(x)+c(-1.96,1.96)*sd(x)/sqrt(length(x))))
CAR_nwide_m4_re_sd <- as.data.frame(apply(CAR_nwide_m4_re,2,FUN=sd))


Our questions relate to which city is "V1" "V2" etc in the output?



Also, there are values for all 10 variables, so we are unclear what city was left out of the model and is therefore the reference group.










share|improve this question
























  • Welcome to GIS SE. As a new user, please take the Tour, which emphasizes the importance of asking One question per Question. Additional questions can be asked in other Questions, but you should probably go through the process once before adding new questions. Please remember to always specify the exact version of software in the body of the question, and to format code with the button.

    – Vince
    Apr 10 at 11:05

















0















We conducted a study at the U.S. census tract level looking at the relationship between environmental factors and social mobility in the ten largest U.S. cities. We used CARBayes::S.CARmultilevel in R to control for spatial autocorrelation between neighboring tracts and higher-order nested effects of cities (the city was included as a random effect variable).



We want to know how each city influenced the results in the fully adjusted model: In other words, what is the median coefficient for each city, relative to the reference group. We were able to calculate the median random effect coefficients as well as standard deviations and confidence intervals using the following code:



CAR_nwide_m4_re <- as.data.frame(CAR_nwide_m4[["samples"]][["zeta"]])
CAR_nwide_m4_re_median <- as.data.frame(apply(CAR_nwide_m4_re,2,FUN=median))
CAR_nwide_m4_re_ci <- as.data.frame(apply(as.matrix(CAR_nwide_m4_re), 2, function(x)mean(x)+c(-1.96,1.96)*sd(x)/sqrt(length(x))))
CAR_nwide_m4_re_sd <- as.data.frame(apply(CAR_nwide_m4_re,2,FUN=sd))


Our questions relate to which city is "V1" "V2" etc in the output?



Also, there are values for all 10 variables, so we are unclear what city was left out of the model and is therefore the reference group.










share|improve this question
























  • Welcome to GIS SE. As a new user, please take the Tour, which emphasizes the importance of asking One question per Question. Additional questions can be asked in other Questions, but you should probably go through the process once before adding new questions. Please remember to always specify the exact version of software in the body of the question, and to format code with the button.

    – Vince
    Apr 10 at 11:05













0












0








0








We conducted a study at the U.S. census tract level looking at the relationship between environmental factors and social mobility in the ten largest U.S. cities. We used CARBayes::S.CARmultilevel in R to control for spatial autocorrelation between neighboring tracts and higher-order nested effects of cities (the city was included as a random effect variable).



We want to know how each city influenced the results in the fully adjusted model: In other words, what is the median coefficient for each city, relative to the reference group. We were able to calculate the median random effect coefficients as well as standard deviations and confidence intervals using the following code:



CAR_nwide_m4_re <- as.data.frame(CAR_nwide_m4[["samples"]][["zeta"]])
CAR_nwide_m4_re_median <- as.data.frame(apply(CAR_nwide_m4_re,2,FUN=median))
CAR_nwide_m4_re_ci <- as.data.frame(apply(as.matrix(CAR_nwide_m4_re), 2, function(x)mean(x)+c(-1.96,1.96)*sd(x)/sqrt(length(x))))
CAR_nwide_m4_re_sd <- as.data.frame(apply(CAR_nwide_m4_re,2,FUN=sd))


Our questions relate to which city is "V1" "V2" etc in the output?



Also, there are values for all 10 variables, so we are unclear what city was left out of the model and is therefore the reference group.










share|improve this question
















We conducted a study at the U.S. census tract level looking at the relationship between environmental factors and social mobility in the ten largest U.S. cities. We used CARBayes::S.CARmultilevel in R to control for spatial autocorrelation between neighboring tracts and higher-order nested effects of cities (the city was included as a random effect variable).



We want to know how each city influenced the results in the fully adjusted model: In other words, what is the median coefficient for each city, relative to the reference group. We were able to calculate the median random effect coefficients as well as standard deviations and confidence intervals using the following code:



CAR_nwide_m4_re <- as.data.frame(CAR_nwide_m4[["samples"]][["zeta"]])
CAR_nwide_m4_re_median <- as.data.frame(apply(CAR_nwide_m4_re,2,FUN=median))
CAR_nwide_m4_re_ci <- as.data.frame(apply(as.matrix(CAR_nwide_m4_re), 2, function(x)mean(x)+c(-1.96,1.96)*sd(x)/sqrt(length(x))))
CAR_nwide_m4_re_sd <- as.data.frame(apply(CAR_nwide_m4_re,2,FUN=sd))


Our questions relate to which city is "V1" "V2" etc in the output?



Also, there are values for all 10 variables, so we are unclear what city was left out of the model and is therefore the reference group.







r spatial-statistics regression






share|improve this question















share|improve this question













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edited Apr 10 at 11:04









TomazicM

1,8171516




1,8171516










asked Apr 10 at 10:11









mutelbrowningmutelbrowning

1




1












  • Welcome to GIS SE. As a new user, please take the Tour, which emphasizes the importance of asking One question per Question. Additional questions can be asked in other Questions, but you should probably go through the process once before adding new questions. Please remember to always specify the exact version of software in the body of the question, and to format code with the button.

    – Vince
    Apr 10 at 11:05

















  • Welcome to GIS SE. As a new user, please take the Tour, which emphasizes the importance of asking One question per Question. Additional questions can be asked in other Questions, but you should probably go through the process once before adding new questions. Please remember to always specify the exact version of software in the body of the question, and to format code with the button.

    – Vince
    Apr 10 at 11:05
















Welcome to GIS SE. As a new user, please take the Tour, which emphasizes the importance of asking One question per Question. Additional questions can be asked in other Questions, but you should probably go through the process once before adding new questions. Please remember to always specify the exact version of software in the body of the question, and to format code with the button.

– Vince
Apr 10 at 11:05





Welcome to GIS SE. As a new user, please take the Tour, which emphasizes the importance of asking One question per Question. Additional questions can be asked in other Questions, but you should probably go through the process once before adding new questions. Please remember to always specify the exact version of software in the body of the question, and to format code with the button.

– Vince
Apr 10 at 11:05










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