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.
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I am running a quantile regression (as my residuals in linear regression were not normally distributed) for a study on the association of mediterranean diet and inflammatory markers. As I was building the model I got outputs for beta coefficients and standard error plus p-values, and confidence intervals. However, once I stratified for low and high levels of exercise, there was no longer an output for standard error. Any ideas?enter image description here
By default rq does not returns standard errors, but rather the confidence interval by inverting a rank test. If you want standard errors you have to specify another method for se, such as se="boot".
Note: the whole point of quantile regression is to move away from means and SD, so this may not be the most adequate estimate for your problem.
I'm looking for a function for interaction effects visualization which has a correspondence with ivreg or plm. My model is 2sls with fixed effects but it seems there are no packages available for calculating interaction effects in R.
I'd be pleased if someone could solve my concern.
You might want to have a look at interplot(). You can use this function to visualize e.g. the estimated coefficient of regressor X on outcome Y conditional on values of instrument Z by simply plugging in the fitted values from ivreg(). (The confidence intervals are trickier, but you are probably less interested in those in the first instance.)
https://cran.r-project.org/web/packages/interplot/vignettes/interplot-vignette.html
I would like to plot the predictions of my probit model and include the 95% confidence interval based on the twoway clustered standard errors that I obtained using cluster.vcov()-function from the multiwaycov package.
However, the predict-functions that are out there do not allow me to pass them my correct variance-covariance-matrix and it seems a workaround is necessary (see workaround for lm-models by Joris Mey and Michael at https://stackoverflow.com/a/3793955).
Does anyone have an idea to go about this for probit (glm-models)? Or, are there other ways to go about this?
I am interested in calculating p-values within a Cox PH model based upon the maximal test statistic, to get very robust estimates. Does anyone have experience with this?
I have played around a bit with the R package 'coxphf' that incorporates Firth's penalized likelihood, but it seems to be giving me different coefficients and p-values if I chose firth=FALSE vs. use the standard coxph function in 'survival'.
I do not discount being completely lost on this, so any advice would be useful.
Thanks!
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"