[R] Archer-Lemeshow Goodness of Fit Test for Survey Data with Log. Regression

[Courtney Benjamin]

​Thank you, Anthony.  Your approach does work; however, I am concerned to some degree about subsetting the data prior to creating the new svrepdesign as I know that it is not recommended to subset the data prior to creating the svrepdesign object.  I am not sure if this is a significant concern in this context as the model was fitted with the original svrepdesign that was created prior to subsetting any data and the new svrepdesign is being used to run the diagnostic for the model.  Any thoughts on that issue?

Also, from my understanding of the outcome of the diagnostic, low p values indicate a poor model fit.

Sincerely,

Courtney


Courtney Benjamin

Broome-Tioga BOCES

Automotive Technology II Teacher

Located at Gault Toyota

Doctoral Candidate-Educational Theory & Practice

State University of New York at Binghamton

cbenjami at btboces.org<mailto:cbenjami at btboces.org>

607-763-8633

________________________________
From: Anthony Damico <ajdamico at gmail.com>
Sent: Thursday, November 17, 2016 4:28 AM
To: Courtney Benjamin
Cc: r-help at r-project.org
Subject: Re: [R] Archer-Lemeshow Goodness of Fit Test for Survey Data with Log. Regression

great minimal reproducible example, thanks.  does something like this work?



#Log. Reg. model-all curric. concentrations including F1RTRCC as a predictor
allCC <- svyglm(formula=F3ATTAINB~F1PARED+BYINCOME+F1RACE+F1SEX+F1RGPP2+F1HIMATH+F1RTRCC,family="binomial",design=elsq1ch_brr,subset=BYSCTRL==1&G10COHRT==1,na.action=na.exclude)
summary(allCC)

r <- residuals(allCC, type="response")
f<-fitted(allCC)
your_g<- cut(f, c(-Inf, quantile(f,  (1:9)/10, Inf)))

elsq1ch[ elsq1ch$BYSCTRL==1&elsq1ch$G10COHRT==1 , 'your_g' ] <- your_g
elsq1ch[ elsq1ch$BYSCTRL==1&elsq1ch$G10COHRT==1 , 'r' ] <- r
newdesign<-svrepdesign(variables = elsq1ch, repweights = elsq1ch[,18:217], weights = elsq1ch[,17], combined.weights = TRUE, type = "BRR")

decilemodel<- svyglm(r~your_g, design=newdesign,subset=BYSCTRL==1&G10COHRT==1)
regTermTest(decilemodel, ~your_g)




On Wed, Nov 16, 2016 at 10:15 PM, Courtney Benjamin <cbenjami at btboces.org<mailto:cbenjami at btboces.org>> wrote:
?Hello R Experts,

I am trying to implement the Archer-Lemeshow GOF Test for survey data on a logistic regression model using the survey package based upon an R Help Archive post that I found where Dr. Thomas Lumley advised how to do it: http://r.789695.n4.nabble.com/Goodness-of-t-tests-for-Complex-Survey-Logistic-Regression-td4668233.html

Everything is going well until I get to the point where I have to add the objects 'r' and 'g' as variables to the data frame by either using the transform function or the update function to update the svrepdesign object.  The log. regression model involved uses a subset of data and some of the values in the data frame are NA, so that is affecting my ability to add 'r' and 'g' as variables; I am getting an error because I only have 8397 rows for the new variables and 16197 in the data frame and svrepdesign object.  I am not sure how to overcome this error.

The following is a MRE:

##Archer Lemeshow Goodness of Fit Test for Complex Survey Data with Logistic Regression

library(RCurl)
library(survey)

data <- getURL("https://raw.githubusercontent.com/cbenjamin1821/careertech-ed/master/elsq1adj.csv")
elsq1ch <- read.csv(text = data)

#Specifying the svyrepdesign object which applies the BRR weights
elsq1ch_brr<-svrepdesign(variables = elsq1ch[,1:16], repweights = elsq1ch[,18:217], weights = elsq1ch[,17], combined.weights = TRUE, type = "BRR")
elsq1ch_brr

##Resetting baseline levels for predictors
elsq1ch_brr <- update( elsq1ch_brr , F1HIMATH = relevel(F1HIMATH,"PreAlg or Less") )
elsq1ch_brr <- update( elsq1ch_brr , BYINCOME = relevel(BYINCOME,"0-25K") )
elsq1ch_brr <- update( elsq1ch_brr , F1RACE = relevel(F1RACE,"White") )
elsq1ch_brr <- update( elsq1ch_brr , F1SEX = relevel(F1SEX,"Male") )
elsq1ch_brr <- update( elsq1ch_brr , F1RTRCC = relevel(F1RTRCC,"Academic") )

#Log. Reg. model-all curric. concentrations including F1RTRCC as a predictor
allCC <- svyglm(formula=F3ATTAINB~F1PARED+BYINCOME+F1RACE+F1SEX+F1RGPP2+F1HIMATH+F1RTRCC,family="binomial",design=elsq1ch_brr,subset=BYSCTRL==1&G10COHRT==1,na.action=na.omit)
summary(allCC)

#Recommendations from Lumley (from R Help Archive) on implementing the Archer Lemeshow GOF test
r <- residuals(allCC, type="response")
f<-fitted(allCC)
g<- cut(f, c(-Inf, quantile(f,  (1:9)/10, Inf)))

# now create a new design object with r and g added as variables
#This is the area where I am having problems as my model involves a subset and some values are NA as well
#I am also not sure if I am naming/specifying the new variables of r and g properly
transform(elsq1ch,r=r,g=g)
elsq1ch_brr <- update(elsq1ch_brr,tag=g,tag=r)
#then:
decilemodel<- svyglm(r~g, design=newdesign)
regTermTest(decilemodel, ~g)
#is the F-adjusted mean residual test from the Archer Lemeshow paper

Thank you,
Courtney

?

Courtney Benjamin

Broome-Tioga BOCES

Automotive Technology II Teacher

Located at Gault Toyota

Doctoral Candidate-Educational Theory & Practice

State University of New York at Binghamton

cbenjami at btboces.org<mailto:cbenjami at btboces.org><mailto:cbenjami at btboces.org<mailto:cbenjami at btboces.org>>

607-763-8633<tel:607-763-8633>

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