For reasons, I need to compute the F-statistic of R2 change, for regression models. While SPSS is easy in R I can't seem to find a package in R.
The algorithm is the following:
((SSE_reduced-SSE_full)/(numbercoefficients_full-numbercoefficients_reduced))/(SSE_full/(numberobservations_full-numbercoefficients_full))
where,
Full is a model with interactions.
reduced with only control variables.
Thanks in advance.
Related
I am in my first experience using mixed models in R for my statistical analysis. Due to my data being comprised of binary outcome variables, I have managed to build a logistic model using the glmer function of the lme4 package that I think works as I wanted it to.
I am now aiming to investigate the statistical significance of my model coefficients. I have read that generally, the best approach for generalized mixed models is to bootstrap confidence intervals, but I haven't managed to find a good, clear, explanation of how to do this in R.
Would anyone have any suggestions? Are there any packages in R that expedite this process, or do people generally build their own functions for this? I haven't really done any bootstrapping before so I'd appreciate some more in-depth answers.
If you want to compute parametric bootstrap confidence intervals, the built-in functionality
confint(fitted_model, method = "boot")
should work (see ?confint.merMod)
Also see this answer (which illustrates both parametric and nonparametric bootstrapping for user-defined quantities).
If you have multiple cores, you can speed this up by adding parallel = "multicore", ncpus = parallel::detectCores()-1 (or some other appropriate number of cores to use): see ?lme4::bootMer for details.
I’m currently working with a dataset that has lots zeros in the predictor variables as well as the response variable too. The response variable is continuous and it is very skewed to the right.
I’m trying to apply a discrete-continous model where in the first level i perform a binomial logit model to model the zero o and in the second level i perform a regression model for nonzero observations.
Stata program allows you to do this type of analysis very easily but i am using RStudio and did not find any clear packages that implement such apprach. I’d greatly appreciate it if someone can point me to which package i should be using and showing an example would be greatly appreciated too.
I'm trying to do a hurdle model with random effects in either r or stata. I've looked at the glmmADMB package, but am running into problems getting it download in R and I can't find any documentation on the package in Cran. Is this package still available? Has anyone used it successfully to estimate a hurdle model with random effects?
Alternatively, is there a way to estimate this in stata? Is there a way to estimate random effects with any type of count data in stata?
Any advice would be greatly appreciated.
Jennifer
In Stata, xtnbreg and xtpoisson have the random effects estimator as the default option. You can always estimate the two parts separately by hand. See the count-data chapter of Cameron and Trivedi's Stata book for cross-sectional examples.
You also have the user-written hplogit and hnlogit for hurdle count models. These use a logit/probit for the first-stage and a zero-truncated poisson/negative binomial for the second stage. Also, a finite mixture model might be a nice approach (see user-written fmm). There's also ztpnm. All these are cross-sectional models.
Does anyone know about a R package that supports fixed effect, instrumental variable regression like xtivreg in stata (FE IV regression). Yes, I can just include dummy variables but that just gets impossible when the number of groups increases.
Thanks!
I can just include dummy variables but that just gets impossible when the number of groups increases
By "impossible," do you mean "computationally impossible"? If so, check out the plm package, which was designed to handle cases that would otherwise be computationally infeasible, and which permits fixed-effects IV.
Start with the plm vignette. It will quickly make clear whether plm is what you're looking for.
Update 2018 December 03: the estimatr package will also do what you want. It's faster and easier to use than the plm package.
As you may know, for many fixed effects and random effects models {I should mention FE and RE from econometrics and education standpoint since the definitions in statistics are different}, you can create an equivalent SEM (Structural Equation Modeling) model. There are two packages in R that can be used for that purpose: 1)SEM 2) LAVAAN
Another solution is to use SAS. In SAS, you can use Proc GLM which enables you to use "absorb" statement which automatically takes care of the dummies as well as finding (x - xbar) per each observation.
Hope it helps.
Try the ivreg command from the AER package.
I have a problem at hand which I'd think is fairly common amongst
groups were R is being adopted for Analytics in place of SAS.
Users would like to obtain results for logistic regression in R that
they have become accustomed to in SAS.
Towards this end, I was able to propose the Design package in R which
contains many functions to extract the various metrics that SAS
reports.
If you have suggestions pertaining to other packages, or sample code
that replicates some of the SAS outputs for logistic regression, I
would be glad to hear of them.
Some of the requirements are:
Stepwise variable selection for logistic regression
Choose base level for factor variables
The Hosmer-Lemeshow statistic
concordant and discordant
Tau C statistic
Thank you for your suggestions.
Just because SAS does it, doesn't necessarily mean it's good statistical practice. Step-wise regression is particularly problematic.
What I have found so far is that the Design and rms package to be the best (and only) package for these outputs.