washb_prescreen

Usage

washb_prescreen(Y, Ws, family = "gaussian", pval = 0.2, print = TRUE)

Arguments

Y

Outcome variable (continuous, such as LAZ, or binary, such as diarrhea)

Ws

data frame that includes candidate adjustment covariates to screen

family

GLM model family (gaussian, binomial, poisson, or negative binomial). Use "neg.binom" for Negative binomial.

pval

The p-value threshold: any variables with a p-value from the lielihood ratio test below this threshold will be returned. Defaults to 0.2

print

Logical for whether to print function output, defaults to TRUE.

Examples

#Prescreen function applied to the Bangladesh diarrheal disease outcome.
#The function will test a matrix of covariates and return those related to child diarrheal disease with
#a <0.2 p-value from a likelihood ratio test.

#Load diarrhea data:
library(washb)
data(washb_bd_diar)
data(washb_bd_enrol)

 # drop svydate and month because they are superceded in the child level diarrhea data
washb_bd_enrol$svydate <- NULL
washb_bd_enrol$month <- NULL

# merge the baseline dataset to the follow-up dataset
ad <- merge(washb_bd_enrol,washb_bd_diar,by=c("dataid","clusterid","block","tr"),all.x=F,all.y=T)

# subset to the relevant measurement
# Year 1 or Year 2
ad <- subset(ad,svy==1|svy==2)

#subset the diarrhea to children <36 mos at enrollment
### (exlude new births that are not target children)
ad <- subset(ad,sibnewbirth==0)
ad <- subset(ad,gt36mos==0)

# Exclude children with missing data
ad <- subset(ad,!is.na(ad$diar7d))

#Re-order the tr factor for convenience
ad$tr <- factor(ad$tr,levels=c("Control","Water","Sanitation","Handwashing","WSH","Nutrition","Nutrition + WSH"))

#Ensure that month is coded as a factor
ad$month <- factor(ad$month)

#Sort the data for perfect replication when using V-fold cross-validation
ad <- ad[order(ad$block,ad$clusterid,ad$dataid,ad$childid),]


###Subset to a new dataframe the variables to be screened:
Ws <- subset(ad,select=c("fracode","month","agedays","sex","momage","momedu","momheight","hfiacat","Nlt18","Ncomp","watmin","elec","floor","walls","roof","asset_wardrobe","asset_table","asset_chair","asset_khat","asset_chouki","asset_tv","asset_refrig","asset_bike","asset_moto","asset_sewmach","asset_mobile"))

###Run the washb_prescreen function
prescreened_varnames<-washb_prescreen(Y=ad$diar7d,Ws,family="binomial")
Warning message:
glm.fit: fitted probabilities numerically 0 or 1 occurred

Likelihood Ratio Test P-values:
      [,1]             [,2]       
 [1,] "fracode"        "P = 0.125"
 [2,] "month"          "P = 0.000"
 [3,] "agedays"        "P = 0.000"
 [4,] "sex"            "P = 0.159"
 [5,] "momage"         "P = 0.858"
 [6,] "momedu"         "P = 0.001"
 [7,] "momheight"      "P = 0.837"
 [8,] "hfiacat"        "P = 0.000"
 [9,] "Nlt18"          "P = 0.146"
[10,] "Ncomp"          "P = 0.858"
[11,] "watmin"         "P = 0.017"
[12,] "elec"           "P = 0.002"
[13,] "floor"          "P = 0.009"
[14,] "walls"          "P = 0.173"
[15,] "roof"           "P = 0.446"
[16,] "asset_wardrobe" "P = 0.003"
[17,] "asset_table"    "P = 0.278"
[18,] "asset_chair"    "P = 0.264"
[19,] "asset_khat"     "P = 0.054"
[20,] "asset_chouki"   "P = 0.883"
[21,] "asset_tv"       "P = 0.109"
[22,] "asset_refrig"   "P = 0.015"
[23,] "asset_bike"     "P = 0.005"
[24,] "asset_moto"     "P = 0.233"
[25,] "asset_sewmach"  "P = 0.004"
[26,] "asset_mobile"   "P = 0.713"


Covariates selected (P<0.2):
      [,1]             [,2]       
 [1,] "fracode"        "P = 0.125"
 [2,] "month"          "P = 0.000"
 [3,] "agedays"        "P = 0.000"
 [4,] "sex"            "P = 0.159"
 [5,] "momedu"         "P = 0.001"
 [6,] "hfiacat"        "P = 0.000"
 [7,] "Nlt18"          "P = 0.146"
 [8,] "watmin"         "P = 0.017"
 [9,] "elec"           "P = 0.002"
[10,] "floor"          "P = 0.009"
[11,] "walls"          "P = 0.173"
[12,] "asset_wardrobe" "P = 0.003"
[13,] "asset_khat"     "P = 0.054"
[14,] "asset_tv"       "P = 0.109"
[15,] "asset_refrig"   "P = 0.015"
[16,] "asset_bike"     "P = 0.005"
[17,] "asset_sewmach"  "P = 0.004"


###Rerun the function with a stricter p=value
prescreened_varname2s<-washb_prescreen(Y=ad$diar7d,Ws,family="binomial", pval=0.5)
Warning message:
glm.fit: fitted probabilities numerically 0 or 1 occurred

Likelihood Ratio Test P-values:
      [,1]             [,2]       
 [1,] "fracode"        "P = 0.125"
 [2,] "month"          "P = 0.000"
 [3,] "agedays"        "P = 0.000"
 [4,] "sex"            "P = 0.159"
 [5,] "momage"         "P = 0.858"
 [6,] "momedu"         "P = 0.001"
 [7,] "momheight"      "P = 0.837"
 [8,] "hfiacat"        "P = 0.000"
 [9,] "Nlt18"          "P = 0.146"
[10,] "Ncomp"          "P = 0.858"
[11,] "watmin"         "P = 0.017"
[12,] "elec"           "P = 0.002"
[13,] "floor"          "P = 0.009"
[14,] "walls"          "P = 0.173"
[15,] "roof"           "P = 0.446"
[16,] "asset_wardrobe" "P = 0.003"
[17,] "asset_table"    "P = 0.278"
[18,] "asset_chair"    "P = 0.264"
[19,] "asset_khat"     "P = 0.054"
[20,] "asset_chouki"   "P = 0.883"
[21,] "asset_tv"       "P = 0.109"
[22,] "asset_refrig"   "P = 0.015"
[23,] "asset_bike"     "P = 0.005"
[24,] "asset_moto"     "P = 0.233"
[25,] "asset_sewmach"  "P = 0.004"
[26,] "asset_mobile"   "P = 0.713"


Covariates selected (P<0.5):
      [,1]             [,2]       
 [1,] "fracode"        "P = 0.125"
 [2,] "month"          "P = 0.000"
 [3,] "agedays"        "P = 0.000"
 [4,] "sex"            "P = 0.159"
 [5,] "momedu"         "P = 0.001"
 [6,] "hfiacat"        "P = 0.000"
 [7,] "Nlt18"          "P = 0.146"
 [8,] "watmin"         "P = 0.017"
 [9,] "elec"           "P = 0.002"
[10,] "floor"          "P = 0.009"
[11,] "walls"          "P = 0.173"
[12,] "roof"           "P = 0.446"
[13,] "asset_wardrobe" "P = 0.003"
[14,] "asset_table"    "P = 0.278"
[15,] "asset_chair"    "P = 0.264"
[16,] "asset_khat"     "P = 0.054"
[17,] "asset_tv"       "P = 0.109"
[18,] "asset_refrig"   "P = 0.015"
[19,] "asset_bike"     "P = 0.005"
[20,] "asset_moto"     "P = 0.233"
[21,] "asset_sewmach"  "P = 0.004"