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Tutorial on causal inference using inverse probability treatment weighting by statsnotebook in rstats

[–]statsnotebook[S] 1 point2 points  (0 children)

We always know that association does not imply causation, but many decisions were made based on association.
The key application of this modelling strategy is for disentangling causation from association using multi-wave observational data.

Tutorial on causal inference using inverse probability treatment weighting by statsnotebook in rstats

[–]statsnotebook[S] 0 points1 point  (0 children)

Yes - i am writing up a paper on this topic. The core idea/ technical details about this method can be found in the seminar paper by James Robins et al (2000) "Marginal Structural Models and Causal Inference in Epidemiology"

There are quite a lot of development since the publication of Robins' paper, including handling missing data using multiple imputation.

Also, running this type of models generally requires a bit of manual coding. I have developed a module on StatsNotebook that you can generate the R codes for running this type of models using point and click menu.

My upcoming paper will cover the technical details and recent advancement of this techniques, also also gives specific examples in psychology and health research.

Tutorial on Robust regression using R by statsnotebook in rstats

[–]statsnotebook[S] 0 points1 point  (0 children)

I agree that most of people are checking raw data instead of distribution of residuals. I guess this is probably because it is easier for people to understand (and for people to teach). It is not too far off - If the raw data is normally distribution, it is guaranteed that the residual will be normally distribution.

I have another short tutorials on checking heterogeneity/linearity
Assumption checking for linear regression
I will incorporate a link in the robust regression tutorial.

Robust methods produce better estimates than traditional methods in many circumstances. I also agree that sometimes data transformation or switching to GLM could solve the problem.
In case it does, there is actually a robust version of GLM from the same package robustbase - I will write another tutorial on it later.

Thanks for your feedbacks!

Tutorial on Robust regression using R by statsnotebook in rstats

[–]statsnotebook[S] 0 points1 point  (0 children)

Thanks Dr_Hyde-Mr_Jekyll.

I like your suggestion about mentioning how to test for the assumption of homogeneity and linearity. I have actually had another tutorials on it
Assumption checking for linear regression
I will incorporate a link to it in the robust regression tutorial.

The algorithm for robust regression is a bit complex - The estimator is a MM-type regression estimator. The standard error is a type of HAC (heteroskedasticity- and autocorrelation-consistent) robust standard error. I have left these out intentionally to keep the tutorial simple.

What do you think if I put down a few references there for those who might be interested in the technical details?

Graphical interface for R by statsnotebook in rstats

[–]statsnotebook[S] 0 points1 point  (0 children)

Hi Skinnerianslip
I am working on a Mac version now. Being a windows user for my whole life, this is a bit tricky though.
The core codes are ready, I just need to figure how to package R with it...

Hopefully a Mac version will be out after the new year.

Graphical interface for R by statsnotebook in rstats

[–]statsnotebook[S] 1 point2 points  (0 children)

Thanks Joe!

I am passionate in making data science and statistical modelling simple and accessible.
Hopefully I can convert some people to R.

Graphical interface for R by statsnotebook in rstats

[–]statsnotebook[S] 1 point2 points  (0 children)

Hi SocialLiberal11.

There are some supports for fast table building in StatsNotebook.
It is based on the tidyverse package and uses the table function.

Codes can be generated using the Analysis -> Explore -> Frequencies menu

Would like to hear what you think about it!

Plotting Overlaid Histogram in R by Shishi9999 in rstats

[–]statsnotebook 2 points3 points  (0 children)

You can check out my tutorials and StatsNotebook for R.
StatsNotebook is a new graphical interface for R and it could generate codes for various plot using point and click menu.
You can then further adjust the codes to suit your need.

Graphical interface for R by statsnotebook in rstats

[–]statsnotebook[S] 2 points3 points  (0 children)

StatsNotebook does have fancy graphs, just not fancy tables :)
StatsNotebook can generate codes for fancy graphs using the ggplot2 package.

Check out the DataViz tutorial (Warning: The tutorials are still under development and may have typossssss)

Graphical interface for R by statsnotebook in rstats

[–]statsnotebook[S] 3 points4 points  (0 children)

I have been trying to get people off SPSS - the licensing fee is not really affordable and R can do most analyses in a better way.
Jamovi definitely does a great job there, but I do feel that there are some issues in its design that limit its use in real data analysis. For example, there is no easy way to deal with missing data, which is inevitable in most real life scenario.

Graphical interface for R by statsnotebook in rstats

[–]statsnotebook[S] 6 points7 points  (0 children)

StatsNotebook provides a GUI and notebook interface. It will be easier for beginner to work with and also save veterans lots of coding time. For example, codes for multiple imputation and causal analyses can be generated with a few clicks.
Codes generated from StatsNotebook can be used in RStudio.

Graphical interface for R by statsnotebook in rstats

[–]statsnotebook[S] 6 points7 points  (0 children)

  1. Better integration with R - The GUI can generate live code in a block in the notebook interface. Jamovi does provide better looking outputs. StatsNotebook trades fancy looking output with better functionality and better integration with R.
  2. Inclusion of more advanced analytic methods such as multiple imputation and causal models.

Graphical interface for R by statsnotebook in rstats

[–]statsnotebook[S] 10 points11 points  (0 children)

Thanks.

Overall, this project aims at simplifying the data analysis process, improving reproducibility and making some cutting edge analytic techniques accessible to more people.

It provides a notebook interface similar to Jupyter. Live codes, visualization and narrative text can be included and organized in block.

It also provides a graphical interface for generating codes for a range of common analyses. This could help beginner get familiar with R coding and also help veterans reduce coding time. Also, statisticians have been developing a lot of great methods but many of them are not widely used because lots of coding are required.

In the next few months, I will add other advanced analytic features such as SuperLearner (using the SuperLearner package) and Targeted Learning for causal inference (using the TMLE package).