Thanks for looking. No, for the quantiles it fails to instantiate the
collection of designs correctly, whether hard-coding the variable name or using
make.formula. 'with' passes make.formula correctly when calculating the mean,
e.g. this works:
MIcombine( with(des, svymean(make.formula(get('var_name')))))
# Here's a reproducible example.
DF1 <- data.frame(SDMVPSU = c(1,1,1,1,1,2,2,2,2,2),
SDMVSTRA = c(22, 20, 24, 18, 20, 22, 20, 24, 18, 20),
WTSPO2YR = c(252605, 82199, 24946, 147236, 3679, 294959,
65085, 21765, 197775, 49931),
LBXTCD = c(20.4, 29.7, 8.8, 18.0, 22.2, 10.4, 43.9, 15.3,
13.8, 84.5))
DF2 <- data.frame(SDMVPSU = c(1,1,1,1,1,2,2,2,2,2),
SDMVSTRA = c(22, 20, 24, 18, 20, 22, 20, 24, 18, 20),
WTSPO2YR = c(252605, 82199, 24946, 147236, 3679, 294959,
65085, 21765, 197775, 49931),
LBXTCD = c(21.9, 29.7, 9.2, 5.9, 32.8, 8.9, 43.9, 7.4, 10.5,
84.5))
var_name <- "LBXTCD"
# Individually svyquantile (and svymean) work:
des_single1 <- svydesign(id=~SDMVPSU, strat=~SDMVSTRA, weight=~WTSPO2YR,
data=Df1_red, nest=TRUE)
svyquantile(make.formula(get('var_name')), des_single1, c(.5), na.rm = FALSE)
des_single2 <- svydesign(id=~SDMVPSU, strat=~SDMVSTRA, weight=~WTSPO2YR,
data=Df2_red, nest=TRUE)
svyquantile(make.formula(get('var_name')), des_single2, c(.5), na.rm = FALSE)
Imputed_list <- c()
Imputed_list[[1]] <- DF1
Imputed_list[[2]] <- DF2
# svymean works (so the svydesign object is fine?) but svyquantile doesn't:
des_mult <- svydesign(id=~SDMVPSU, strat=~SDMVSTRA, weight=~WTSPO2YR,
data=imputationList(Imputed_list), nest=TRUE)
M_mean <- with(des_mult, svymean(make.formula(get('var_name'))))
summary(M_mean)
M_quantile <- with(des_mult, svyquantile(make.formula(get('var_name')),
quantiles = c(.5)))
summary(M_quantile)
Thanks again,
Brennan
www.toxstrategies.com
From: Anthony Damico <ajdam...@gmail.com>
Date: Tuesday, May 10, 2016 at 10:37 PM
To: Anne Bichteler <abichte...@toxstrategies.com>
Cc: "r-help@r-project.org" <r-help@r-project.org>
Subject: Re: [R] Quantiles on multiply imputed survey data - mitools
is the `with` not passing make.formula( get( 'var_name' ) ) through to
svyquantile for some reason? does this work?
MIcombine( with(des, svyquantile(~LBXTCD, .5)))
if that's not it, could you make a minimal reproducible example that includes
the data download? code to download and import nhanes here
https://github.com/ajdamico/asdfree/tree/master/National%20Health%20and%20Nutrition%20Examination%20Survey
On Tue, May 10, 2016 at 4:33 PM, Anne Bichteler
<abichte...@toxstrategies.com> wrote:
Hello, and thank you for considering this question:
The svystat object created with multiply imputed NHANES data files is failing
on calling survey::svyquantile. I'm wondering if I'm diagnosing the issue
correctly, whether the behavior is expected, and whether y'all might have any
ideas for workarounds.
I'm following T. Lumley's general method outlined here:
http://faculty.washington.edu/tlumley/old-survey/svymi.html
<http://faculty.washington.edu/tlumley/old-survey/svymi.html>, but with data
files I've imputed myself on the 2001/2002 biennial. Each file has 1081
observations and no missing values.
### Create the survey design object with list of imputed data files
ImputedList0102.
des <- svydesign(id=~SDMVPSU, strat=~SDMVSTRA, weight=~WTSPO2YR,
data=imputationList(ImputedList0102), nest=TRUE)
### Blood analyte of interest
var_name <- "LBXTCD" # analyte in blood serum
### All is well calculating the mean:
M <- with(des, svymean(make.formula(get('var_name'))))
summary(M)
Result <- MIcombine(M)
Result$coefficients
# LBXTCD
# 17.41635
### but svystat object fails to calculate a 50th percentile:
### it fails when hard-coding the name rather than using make.formula;
### it fails regardless of number of files or choices in handling ties or
interval type.
### There are 16 ties in each data file.
M1 <- with(des, svyquantile(make.formula(get('var_name')), quantiles = c(.5)))
summary(M1)
# Length Class Mode
#[1,] 1 -none- numeric
#[2,] 1 -none- numeric
#[3,] 1 -none- numeric
### The quantile is successfully calculated on one file at a time, however, and
is different for each file.
### (had thought perhaps there was a lack-of-variance issue). The quantile
calculated on each file
### is the same regardless of interval.type.
des_single1 <- svydesign(id=~SDMVPSU, strat=~SDMVSTRA, weight=~WTSPO2YR,
data=ImputedList0102[[1]], nest=TRUE)
svyquantile(make.formula(get('var_name')), des_single1, c(.5))
# 0.5
# LBXTCD 13.5554
des_single2 <- svydesign(id=~SDMVPSU, strat=~SDMVSTRA, weight=~WTSPO2YR,
data=ImputedList0102[[2]], nest=TRUE)
svyquantile(make.formula(get('var_name')), des_single2, c(.5))
# 0.5
# LBXTCD 14.06154
# The number of observations exceeding the 50th percentile differs for each
file, which I can't claim to understand.
# I removed the 16 ties, but no help. Do the ties and/or different number of
observations above/below prevent the svydesigns from being combined?
nrow(subset(ImputedList0102[[1]], LBXTCD > 13.5554))
# [1] 516
nrow(subset(ImputedList0102[[2]], LBXTCD > 14.06154))
# [1] 512
I'm hoping someone can point me to some gross error I'm making or another
function parameter or data manipulation or another survey-savvy method
altogether to calculate a 50th percentile across multiply imputed data files.
Thanks for any advice,
Brennan
www.toxstrategies.com <http://www.toxstrategies.com>
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