I would like to calculate a summary odds ratio value for two or more papers where the only information I have is the individual odds ratios with their 95% confidence intervals. Is this possible? I have been poking around in the meta package, and only figured out how to do it with crude counts.
Thanks so much!
It is quite simple.
You just need to use the natural logarithm of the odds ratio (logOR), and its standard errror (and corresponding variance). These can be easily back-calculated from the 95% confidence intervals according to the normal distribution. Finally, pool logORs with their variance.
For instance, after you have built a data frame (eg called mydata) with logOR and variance for each study, you can easily proceed with a random effect meta-analysis with the metafor package in R as follows:
res <- rma(logOR, variance, data=mydata, method="DL")
forest(res)
In the future, you may consider posting similar questions in CrossValidated.
Related
I have been tasked with calculating the SE for logistic regression point estimates (where all my predictor variables are factors). I typically use ggpredict to estimate my predictions which provides CI's. However, we are comparing our results to estimates from program MARK and we find readers have a better grasp at understanding our plots with SE as opposed to 95% CI's.
Based on reading the package notes, it appears I can simply calculate (conf.high - predicted value)/1.96). Am I correct? Or am I missing something and that is not the correct way to calculate SE for the predicted estimates. If I am wrong, any ideas on how I can do this or do I need to just use CI's?
Thank you very much for your help.
When using the GAMLSS package in R, there are many different ways to fit a distribution to a set of data. My data is a single vector of values, and I am fitting a distribution over these values.
My question is this: what is the main difference between using fitDist() and gamlss() since they give similar but different answers for parameter values, and different worm plots?
Also, using the function confint() works for gamlss() fitted objects but not for objects fitted with fitDist(). Is there any way to produce confidence intervals for parameters fitted with the fitDist() function? Is there an accuracy difference between the two procedures? Thanks!
m1 <- fitDist()
fits many distributions and chooses the best according to a
generalized Akaike information criterion, GAIC(k), wit penalty k for each
fitted parameter in the distribution, where k is specified by the user,
e.g. k=2 for AIC,
k = log(n) for BIC,
k=4 for a Chi-squared test (rounded from 3.84, the 5% critical value of a Chi-squared distribution with 1 degree of fereedom), which is my preference.
m1$fits
gives the full results from the best to worst distribution according to GAIC(k).
This seems like a problem that has an accepted, statistically and mathematically sound answer, but I can't seem to find it.
When estimating confidence intervals from sample proportions, I generally use the normal approximation technique described here: https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Normal_approximation_interval
However, this fails spectacularly for proportions where my sample is close to 0 or 1, notably having symmetrical distribution which causes it to go above 1 or below 0. Generally, since proportion estimates "behave better" when modeled using a logit, I assume there is some way to apply a logit transform to the confidence intervals which would result in an asymmetric confidence interval that would never cross 0 or 1.
However, instead of trying to hack together my own technique with freshman calculus and MBA statistics as my highest formal mathematical training, I have been searching the web to see if such a technique has already been described by someone more qualified.
Is anyone aware of a way to do this?
A straightforward derivation via the usual change of variables formula shows that y = logit(x) where x has a beta distribution (the posterior distribution for the binomial proportion assuming a beta prior), has a distribution with pdf (exp(y)^a)/((1 + exp(y))^(a + b))/beta(a, b) where beta(a, b) = gamma(a)*gamma(b)/gamma(a + b).
That pdf has a somewhat Gaussian-like shape, but it's less symmetrical the more different a and b are. It probably has a name, although I don't recognize it.
It's not clear that taking y = logit(x) here is helpful. For several other approaches, see: Binomial proportion confidence interval
Statistics problems should probably go to stats.stackexchange.com.
This is a programming question for people who like to use the ez package in R. I am accustomed to using linear mixed effects models with lmer(). Among the useful outputs of lmer (), I get a coefficient value for each of my experimental factors, and using pvals.fnc() I can easily get 95% confidence intervals (CI) to report together with the model coefficients.
I have recently started using ezANOVA, and I would like to know: Is there a mainstream way to get the same output? That is, I'd like to get a value for the coefficient of an experimental factor and a CI to go along with it. Here is sample code to make this concrete:
library(languageR) #necessary to use pvals.fnc()
library(lme4) #necessary for lmer()
library(ez) #necessary for ezANOVA
data(ANT) #load sample data
If I were using lmer, I would estimate my model and then get 95% CIs for the coefficients:
model_lmer = lmer( formula = rt ~ cue*flank + (1|subnum), data = ANT)
pvals.fnc(model_lmer, withMCMC=T)$fixed
So, for example, I know that the estimate of the interaction between cue and flank (when cue has the level "center" and flank has the level "congruent") is -3.9511 and the 95% CI is [-12.997, 5.535]
Now say that I want to run an anova by-subjects and by-items using ezANOVA, and I want to get 95% CIs for the by-subject estimates. This is my model:
model.f1 = ezANOVA(data=ANT, dv=rt,wid=subnum,within=.(cue,flank),return_aov=T)
But in the output, I don't see the model estimates when I do:
model.f1$ANOVA
And I don't know how to calculate the 95% CIs corresponding to those estimates. I think I should be able to use ezBoot() but I tried and I'm not sure how to implement it.
Any suggestions? Thanks for your help!
This answer was provided by the author of the "ez" package in another forum. I'm copying it here in case someone else finds it useful:
"One somewhat hacky way to get CIs for effects is to use ezStats () to get the means
and FLSD, compute the difference between the means to get the effect,
and divide the FLSD by sqrt(2) to get the CI"
I am generating ecological niche models for a set of species and I would like to use AUC as a metric for ecological niche quality. Steven Phillips, who developed Maxent, provides code in his Maxent manual for calculating the AUC in R. However, I am reading papers that report partial AUC ratios as a more robust and conceptually sound metric. I think I understand how to calculate partial AUC using the ROCR R package, but how does one calculate AUC ratio?
Here is the tutorial script from Phillips:
presence<-read.csv("bradypus_variegatus_samplePredictions.csv")
background<-read.csv("bradypus_variegatus_backgroundPredictions.csv")
pp<-presence$Logistic.prediction
testpp<-pp[presence$Test.or.train=="test"]
trainpp<-pp[presence$Test.or.train=="train"]
bb<-background$logistic
combined<-c(testpp,bb)
label<-c(rep(1,length(testpp)),rep(0,length(bb)))
pred<-prediction(combined,label)
perf<-performance(pred,"tpr","fpr")
plot(perf,colorize=TRUE)
performance(pred,"auc")#y.values[[1]] #RETURNS AUC
AUC<-function(p,ind){
pres<-p[ind]
combined<-c(pres,bb)
label<-c(rep(1,length(pres)),rep(0,length(bb)))
predic<-prediction(combined,label)
return(performance(predic,'auc')#y.values[[1]])
}
b1<-boot(testpp,AUC,100) #RETURNS AUC WITH STANDARD ERROR
b1
Any advice or suggestions would be greatly appreciated! Thank you.
Without knowing the specifics of your dataset and application,
Partial AUC: The area under only a portion of the curve. (usually picked because it is more robust or otherwise desirable, like you said)
AUC ratio: The ratio of one AUC to another. (usually a reference of some sort)
Soo...
Partial AUC ratio: The ratio of one partial AUC to another.
Package ROCR can calculate partial AUC values using the fpr.stop= parameter. As John said the ratio is just this value divided by the same calculation for your reference model.