R: Sensitivity, specificity, positive and negative likelihood...
NW_above
R Documentation
Sensitivity, specificity, positive and negative likelihood ratios for prediction of excessive GWG for normal weight women
Description
This dataset include the sensitivity and specificity for prediction of excessive GWG at the end of pregnancy from exceeding the respective cut-off values in each respective month as well as the likelihood ratio to assess how much the information on exceeding the cut-off values improves the a priori probabilities for excessive and adequate total GWG for normal weight women (starting with the 2nd month or week 4/1-8/0). The data is based on 367 normal weight women from two German cohorts (see Description).
Usage
data("NW_above")
Format
A data frame with 9 observations on the following 13 variables.
month
month of pregnancy
se.est
vector of sensitivity estimators
se.lower
vector of lower bound of the confidence interval of the sensitivity estimators
se.upper
vector of upper bound of the confidence interval of the sensitivity estimators
sp.est
vector of specificity estimators
sp.lower
vector of lower bound of the confidence interval of the specificity estimators
sp.upper
vector of upper bound of the confidence interval of the specificity estimators
lr.pos.est
vector of positive likelihood ratio estimators
lr.pos.lower
vector of lower bound of the confidence interval of the positive likelihood ratio estimators
lr.pos.upper
vector of upper bound of the confidence interval of the positive likelihood ratio estimators
lr.neg.est
vector of negative likelihood ratio estimators
lr.neg.lower
vector of lower bound of the confidence interval of the negative likelihood ratio estimators
lr.neg.upper
vector of upper bound of the confidence interval of the negative likelihood ratio estimators
Details
Exact binomial 95% confidence intervals (CI) were calculated for sensitivity and specificity and the 95% CI of the likelihood ratios were calculated as suggested by Simel et al. (1991).
Source
Knabl J, Riedel C, Gmach J et al. (2013). Prediction of excessive or inadequate gestational weight gain from week-specific IOM/NRC cut-off values. submitted.
References
Simel D, Samsa G, Matchar D (1991). Likelihood ratios with confidence: Sample size estimation for diagnostic test studies. Journal of Clinical Epidemiology 44.p 763 - 770.
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(GWG)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/GWG/NW_above.Rd_%03d_medium.png", width=480, height=480)
> ### Name: NW_above
> ### Title: Sensitivity, specificity, positive and negative likelihood
> ### ratios for prediction of excessive GWG for normal weight women
> ### Aliases: NW_above
> ### Keywords: datasets
>
> ### ** Examples
>
> data("NW_above")
>
> #Plot of positive likelihood ratios for excessive GWG for normal weight women
> plot(NW_above$month, NW_above$lr.pos.est,
+ xlim=c(1,10),
+ ylim=c(0,20),
+ main="LR+ for excessive GWG",
+ ylab="likelihood ratio",
+ xlab="month",
+ cex.main=2,
+ font.main=1,
+ cex.lab=1.9,
+ cex.axis=1.5)
> lines(NW_above$month, NW_above$lr.pos.est, lty=1,lwd=4)
> lines(NW_above$month, NW_above$lr.pos.lower, lty=2,lwd=4)
> lines(NW_above$month, NW_above$lr.pos.upper, lty=2,lwd=4)
> abline(h=2,lwd=1, col="grey")
> abline(h=4,lwd=1, col="grey")
> abline(h=6,lwd=1, col="grey")
> abline(h=8,lwd=1, col="grey")
> abline(h=10,lwd=1, col="grey")
> abline(h=12,lwd=1, col="grey")
> abline(h=14,lwd=1, col="grey")
> abline(h=16,lwd=1, col="grey")
> abline(h=18,lwd=1, col="grey")
> axis(1, c(3,5,7,9), cex.axis=1.5)
> legend("topleft", c("lr+ estimate", "lr+ confidence interval"),
+ col=c("black", "black"), lty=c(1,2),
+ bg="white", cex=1.5, lwd=c(3,3))
>
>
>
>
>
> dev.off()
null device
1
>