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revisit: a "Statistical Audit" for Statistical Reproducibility and Alternate Analysis

The reproducibility crisis

In recent years, scientists, especially those who run academic journals or fund research projects, have been greatly concerned about lack of reproducibility of research. A study performed by one research group, with certain findings, is then attempted by another group, with different findings.

In addition, there is a related problem, lack of transparency. In reading a paper reporting on certain research, it is often not clear exactly what procedures the authors used.

As reported for instance in the journal Nature, the problem is considered by many to have reached crisis stage.

Much of the concern is statistical in nature. As noted in the above Nature report (emphasis added):

The survey asked scientists what led to problems in reproducibility. More than 60% of respondents said that each of two factors — pressure to publish and selective reporting — always or often contributed. More than half pointed to insufficient replication in the lab, poor oversight or low statistical power.

Respondents were asked to rate 11 different approaches to improving reproducibility in science, and all got ringing endorsements. Nearly 90% — more than 1,000 people — ticked "More robust experimental design" "better statistics"...

The package is, made available as open source, is aimed to help remedy such problems with data handling and statistics.

The revisit package

In one sense, the package might be said to enable a statistical audit, allowing users to check the statistical analyses of the original authors of a study, but it really is much more. In our referring to "users" below, keep in mind that this could mean various kinds of people, such as:

  • The various authors of the study, during the period when the study is being conducted. The package will facilitate collaboration among the authors at that time.

  • Reviewers of a manuscript on the study, presented for possible publication. The package will facilitate the reviewers' checking of the statistical analyses in the paper, not only verifying the steps but, even more importantly, allowing the reviewer to explore alternative analyses.

  • Other scientists, who are reading a published paper on the study. The package will facilitate these scientists also in exploring various alternative analyses.

The package has two main aspects:


It makes it easier and more convenient for the user to explore the effects of various changes that might be made to the analyses. The package facilitates

  • replaying,

  • changing, and

  • recording

various new versions of the analyses.


The package spots troublesome statistical situations, and issues advice and warnings, in light of concerns that too many "false positives" are being reported in published research. For example, the package may:

  • Point out that a p-value is small and may not to correspond to an effect of practical importance.

  • Point out that a "nonsignificant" value corresponds to a confidence interval containing both large positive and large negative values, so n is too small for a "no significant difference" finding.

  • Suggest that the user employ a multiple inference procedure (provided in the package).

  • Detect the presence of highly influential outliers, and suggest that a robust method, e.g. quantile regression be used.

  • Detect evidence of possible overfitting.

  • Etc.

(Only some of these are implemented yet.)

More specifically, the user might go through the following thought processes, and take action using the facilities in the package:

  • The user thinks of questions involving alternate scenarios. What, for instance, would occur if one were to more aggressively weed out outliers, or use outlier-resistant methods? How would the results change? What if different predictor variables were used, or squared and interaction terms added? How valid are the models and assumptions? What about entirely different statistical approaches?

  • In exploring such questions, the user will modify the original code, producing at least one new version of the code, and likely several versions. Say for instance the user is considering making two changes to the original analysis, one to possibly use outlier-resistant methods and another to use multiple-inference procedures. That potentially sets up four different versions. The revisit package facilitates this, making easier for the user to make changes, try them out and record them into different branches of the code. In other words, the package facilitates exploration of alternative analyses.

  • In addition, the user may wish to share the results of her exploration of alternate analyses of the data with others. Since each of her branches is conveniently packaged into a separate file, she then simply sends the files to the other researchers. The package allows the latter to easily "replay" the analyses, and they in turn may use the package to produce further branches.

  • Note, though, that the package is not aimed to "automate" statistical analysis. The user decides which analyses to try, with the package consisting of tools to help by making it easy to explore, record and package alternative analyses.

If the original data file is, say, x.R, then the branches will be given names by the users, say x.1.R, x.2.R and so on. Each branch will have a brief, user-supplied description.

Language and interface

The software is written in R, and is currently in prototype form. The author of the original research is assumed to do all data/statistical analysis in R.

Both text-based and graphical (GUI) interfaces are available. The GUI uses RStudio add-ins. The text-based version provides more flexiblity, while the GUI provides convenience.

First example

Let's get started, using the GUI. (See installation instructions below.)

We start up RStudio (either by icon or by typing 'rstudio' into a terminal window), then load the revisit library by typing 'library(revisit)' into the RStudio R console, then (near the top of the screen) select Addins | Revisit.

This example uses the famous Pima diabetes study at the UCI data repository. The following table shows the 9 variables in pima.txt, followed by their descriptions from this link:

Variable Description
NPreg Number of times pregnant
Gluc Plasma glucose concentration a 2 hours in an oral glucose tolerance test
BP Diastolic blood pressure (mm Hg)
Thick Triceps skin fold thickness (mm)
Insul 2-Hour serum insulin (mu U/ml)
BMI Body mass index (weight in kg/(height in m)^2)
Genet Diabetes pedigree function
Age Age (years)
Diab Class variable (0 or 1)

As an illustration, suppose code were written by the author of the study, in our package file examples/pima.R. We copy that to the file pima.R in the directory from which we launched RStudio. (We could do this by hand before launch, or via the function getexample() included in the package. In the GUI version, we'd run the latter command before starting the add-in.) We then type 'pima' into the Filename box, and click Load Code.

The screen now looks like this:

alt text

RStudio is running in the background window, and the foreground window shows the revisit add-in. The original author's code is shown in the editor portion in the bottom half of that window. One can edit the code, re-run in full or in part (Run/Continue and Next buttons), save/load branches (Save Code and Load Code buttons) and so on. All output will be displayed in the R console portion of the background window.

By the way, if Load Branch # is 0 and the branch 0 file cannot be found, revisit will attempt to load the original author code. If that file is found, revisit will automatically create the branch 0 file, identical to the original author code, but with an identifying comment line.

To replay the author's code without modfication, we click Run/Continue . The new screen is:

alt text

The results of the 8 confidence interval computations are shown in the R console. (There are 8 variables other than Diab, so the intervals concern differences between diabetics and nondiabetics.)

We as the user may now think, "Hmm, one really should use a multiple inference procedure here." So we change line 12 to use revisit's own function, employing the Bonferroni method with number of comparisons equal to 8.

We then re-run. If we fail to reset the Run Start Line and Run Through Line first, however, we will get the error shown in red in the console below. This is because revisit had already run through the end of the code. There is no need to start from the beginning, so we change the Run Start Line box to 11, reset the Run Through Line box to the last line (if necessary) and click Run/Continue, yielding:

alt text

Ah, the confidence intervals did indeed get wider, as expected, in line with statistical fairness. (Note that the Run Start Line box has again moved one past the last line of code.)

Say we believe this branch is worth saving. The Save Branch # box tells us the next branch will be named branch 1 (we could change that). Before saving, we are required to type in a Description of the change. If we now click Save Code, the new branch will be reloaded with the description now visible in the last line of the revisit history at the top of the file as shown below:

alt text

By the way, look at the comments at the top of the code,

# RV history start
original code
Use t.test.rv with bonf = 8
# RV history end

Each time we save a new branch, the description comments of the source branch are accumulated, thus providing a change hsitory.

We should also check whether the author did a good job of data cleaning. As a crude measure, we can find the range of each of the variables, say by running the code


We could simply run this code directly if we were in the text-based version of revisit, since there we would have direct control of the R console. This is not the case in the GUI version. So instead, we add the code temporarily at the end of code editor, as line 16. We change the Run Start Line box to 16, and hit Run/Continue:

alt text

Those 0s are troubling. How can variables such as Glucose and BMI be 0? The descriptions of the variables above suggest that the 0s for the variables Gluc, BP, Thick, Insul, and BMI actually represent missing values. Those 0s can be set to missing with the first five of the following statements:

pima$Gluc[pima$Gluc == 0] <- NA
pima$BP[pima$BP == 0] <- NA
pima$Thick[pima$Thick == 0] <- NA
pima$Insul[pima$Insul == 0] <- NA
pima$BMI[pima$BMI == 0] <- NA
ccs <- complete.cases(pima[,2:7])
pima <- pima[ccs,]

The last two statements will drop all cases that contain one or more suspicious 0s. Suppose we add all seven statements to the code immediately after pima is loaded, reset Run Start Line and Run Through Line to run all of the code, and click Run/Continue. Following is the result:

alt text

As can be seen from the output of the ranges, NPreg is now the only variable that contains 0s, the one variable for which they make sense. We can also see that the confidence intervals have changed. They should be more accurate now that cases that contain 0s which are actually missing values have been removed. We can then delete the last line which prints the ranges (print(apply(pima[,1:8],2,range))) as this was intended just for temporary use.

Say we then believe this branch is worth saving. The Save Branch # box tells us the next branch will be named branch 2 (as before, we could change that). Before saving, we are again required to type in a Description of the change. If we do that and then click Save Code, the new branch will be reloaded with the description now visible in the last line of the revisit history at the top of the file as shown below:

alt text

Main text-based functions

Here are the functions available in the text-based prototype. Many more coming!

rvinit(): Initializes the revisit system.

makebranch0(origcodenm): Inputs the original author's code and forms a branch copy of it.

loadb(br): Loads a branch.

runb(startline,throughline): Runs a branch through a user-specified set of lines in the code, pausing at the end. By default, these are the numbers of the first and last lines of the code, but other numbers can be specified. Use 'c' to continue from the current line, or 's' to execute just the current line. (Restriction: The start and finish lines cannot be inside a loops or if-then-else constructs.)

saveb(branchnum,desc): Save all the code changes to a new branch with the given name and brief description.

pause(): Insert this call to pause execution at the given point, useful for instance immediately following a plotting function to give the user a chance to view the plot.

t.test.rv(x,y,alpha,bonf): Substitute for R's t.test(). Calls the latter but warns the user if a small p-value arises merely from a large sample size rather than from a substantial effect. If bonf is larger than 1, alpha will be divided by bonf to implement Bonferroni Inequality-based multiple inference. (Holm option coming soon.)

coef.rv(lmobj,alpha=0.05,usebonf=TRUE): Substitute for R's coef(), which extracts estimated coefficients from the output of lm() and glm(). If usebonf is true, then Bonferroni adjustments will be made to the p-values in testing H0: βi = 0, with the Bonferroni factor equal to the number of coefficients. Again, statistically significant effects that may not be of practical significance will be flagged.

edt(): Make a change to the current code. Calls R's edit(), thus invoking a text editor of the user's choice (or default).

lcc(): Display the current code.

Second example

Here we look at the MovieLens data. The owners of this data forbid redistribution, so it is not included here, but it can be downloaded from the [MovieLens data site]( movielens/100k/). This version of the data consists of 100,000 movie ratings. There are about 1000 users and 1700 films. There is some covariate information, including age and gender for the users, as well as for the movies, e.g. year of release and genre. Suppose someone had done a study of this data, focusing on the effects of age and gender.

We'll use the text version of revisit here, both to make our presentation less cluttered and to illustrate an advantage to using this version.

Suppose the author of a study of this data had analyzed with the code examples/movielens.R in this package. We'll assume that that file is in our current directory, which we can conveniently arrange by running

> getexample('movielens.R')

So, let's see the code:

> lcc()
[1] "next line to execute indicated by ***"
1 *** # RV history start 
2 # original code 
3 # RV history end 
5 # analysis of MovieLens data,  
6 # 
8 # read ratings data (userID, movieID, rating, timestamp) 
9 ud <- read.table('',header=F,sep='\t') 
10 # read user covariate data (user, age, gender, ...( 
11 uu <- read.table('u.user',header=F,sep='|') 
13 ud <- ud[,-4]   # remove timestamp, leaving user, item, rating 
14 uu <- uu[,1:3]   
15 names(ud) <- c('user','item','rating') 
16 names(uu) <- c('user','age','gender') 
17 uu$gender <- as.integer(uu$gender == 'M') 
18 uall <- merge(ud,uu) 
20 # investigate effect of age, gender on user mean ratings 
21 usermeans <- tapply(uall$rating,uall$user,mean) 
22 lmout <- lm(usermeans ~ uu[,2] + uu[,3]) 
23 print(summary(lmout))  # get estimates, p-values etc. 

And now run it:

> runb()

lm(formula = usermeans ~ uu[, 2] + uu[, 3])

     Min       1Q   Median       3Q      Max 
-2.06903 -0.25972  0.03078  0.27967  1.34615 

             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 3.4725821  0.0482655  71.947  < 2e-16 ***
uu[, 2]     0.0033891  0.0011860   2.858  0.00436 ** 
uu[, 3]     0.0002862  0.0318670   0.009  0.99284    
Signif. codes:  0***0.001**0.01*0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4438 on 940 degrees of freedom
Multiple R-squared:  0.008615,  Adjusted R-squared:  0.006505 
F-statistic: 4.084 on 2 and 940 DF,  p-value: 0.01714

This indicates a highly-significant and positive effect of age on ratings. But let's look closer, by running the revisit function coef.rv(). We could do this by calling edt(), adding a call to coef.rv() to the code, and then calling runb() again. But it is more convenient to simply run that command directly, since (unlike the GUI case) we do have control of the R console:

> coef.rv(lmout)
          est.         left       right      p-val warning
1 3.4725821093  3.357035467 3.588128752 0.00000000        
2 0.0033891042  0.000549889 0.006228319 0.01280424       X
3 0.0002862087 -0.076002838 0.076575255 1.00000000        

The resulting p-value indicates a "significant" result, but there is an X in the warning column. The estimated age coefficient here, about 0.0034 is tiny; a 10-year difference in age corresponds to a difference in mean rating of only abou 0.034, minuscule for ratings in the range of 1 to 5. This "significant" result is likely of no practical interest.

Third example

Here we analyze the data set ols262. (Data and code courtesy of M. Zavodny, Stata translated to R by R. Davis. There are some differences in the analysis here, e.g. the author used clustered standard errors.)

The data involve a study of the impact of H-1B, a controversial work visa program. The author found that for every 100 visa workers, about 262 new jobs are created. This kind of finding has been debated but our concern here will not be on the economic issues, rather on how revisit might be used on this analysis.

The data consist of employment figures for each of the 50 states, in each of the years 2000-2010.

Again, we'll use the text version of revisit. Assume that we've copied examples/ols262.R to the current directory, from which we will load it.

> library(revisit)
> rvinit()  # required initialization
> loadb('ols262.0.R')  # load the branch
> lcc()  # list the code
1 # RV history start 
2 # original code 
3 # RV history end 
4 # the file public.dta and the Stata code on which this R code is based 
5 # is courtesy of M. Zavodny 
7 data(zav) # zav.txt is .txt version of Zav. file public.dta 
8 zav = zav[zav$year < 2008,] # 2000-2007 (first year in zav.txt is 2000) 
10 ##### traditional employment rate and immigrant share model ##### 
12 # employment rate for natives = employed natives divided by total native 
13 # population 
14 zav$emprate_native   <- zav$emp_native / zav$pop_native * 100 
16 # immigrant stem workers with advanced degrees from U.S. universities,  
17 # share of total workers employed 
19 zav$immshare_emp_stem_e_grad   <- zav$emp_edus_stem_grad / zav$emp_total * 100 
28 zav$lnimmshare_emp_stem_n_grad <- log(zav$immshare_emp_stem_n_grad) 
30 # year in sample (in this case, 2000-2007), converted to factors for 
31 # use as an instrumental variable 
32 zav$fyear  <- as.factor(zav$year) 
34 # 50 states plus Washington D.C., converted to factors for use as an 
35 # instrumental variable 
36 zav$fstate <- as.factor(zav$statefip) 
38 # need to normalize population weights so that each year has same weight; 
39 # otherwise, too much weight assigned to later years 
40 # do for total native population (age 16-64) 
41 zav$sum_pop_native <- with(zav, ave(pop_native, year, FUN=sum)) 
42 zav$weight_native <- zav$pop_native / zav$sum_pop_native 
44 # remove non-positive values so that their logs are valid values (not 
45 # NaN or infinite, plus or minus) 
46 kk <- zav[zav$emp_edus_stem_grad > 0 & zav$emp_nedus_stem_grad > 0,] 
48 # do regression with two independent variables, two instrumental 
49 # variables, and weights to treat years equally 
51 mm <- (with(kk,  
52    lm(lnemprate_native ~  
53       lnimmshare_emp_stem_e_grad +  
54       lnimmshare_emp_stem_n_grad +  
55       fyear +  
56       fstate,  
57       weights=weight_native))) 
59 # extract slope and p-value from summary 
60 slope <- mm$coefficients[2] 
61 pvalue <- summary(mm)$coefficients[2,4] 
63 # calculate number of native jobs associated with change in immigrant share 
64 ols <- round(slope, 3) 
65 jobs <- sum(zav$emp_native)/sum(zav$emp_edus_stem_grad) * ols * 100 
66 print(paste("Slope   =", slope)) 
67 print(paste("P-value =", pvalue)) 
68 print(paste("Jobs    =", jobs)) 

So, let's run the author's original code:

> runb()
[1] "Slope   = 0.00446438147988468"
[1] "P-value = 0.0140870195483076"
[1] "Jobs    = 262.985782017836"

In this code, the author removed the years 2008-2010. (She later ran a full analysis.) We might wonder how things would change if the full data were used. So, we call edt() (not shown) to remove or comment out line 5, and re-run:

> runb()
[1] "Slope   = 0.00180848722715659"
[1] "P-value = 0.33637275201986"
[1] "Jobs    = 124.352299406043"

Now, the result is no longer significant, and the point estimate has been cut in half.

We might wonder what the adjusted R-squared value was. We can determine this by calling R's summary() function:

> summary(mm)
                             Estimate Std. Error t value
(Intercept)                 4.1640851  0.0121807 341.859
lnimmshare_emp_stem_e_grad  0.0018085  0.0018784   0.963
lnimmshare_emp_stem_n_grad  0.0006618  0.0020183   0.328
fyear2001                  -0.0108863  0.0039629  -2.747
fyear2002                  -0.0323695  0.0040825  -7.929
fyear2003                  -0.0469532  0.0040340 -11.639
fyear2004                  -0.0502315  0.0039222 -12.807
fyear2005                  -0.0437600  0.0040080 -10.918
fyear2006                  -0.0408921  0.0039598 -10.327
fyear2007                  -0.0420868  0.0039926 -10.541
fyear2008                  -0.0526175  0.0039486 -13.325
fyear2009                  -0.0985554  0.0039663 -24.848
fyear2010                  -0.1140401  0.0039186 -29.102
fstate2                     0.0192225  0.0211427   0.909
fstate4                     0.0253171  0.0106471   2.378
Residual standard error: 0.002534 on 324 degrees of freedom
Multiple R-squared:  0.9226,	Adjusted R-squared:  0.9082 
F-statistic: 64.35 on 60 and 324 DF,  p-value: < 2.2e-16

This is a high value. However, what are the main drivers here? We might guess that the effects of the individual states are substantial, so we try re-running the regression without them:

> summary((with(kk,
+    lm(lnemprate_native ~  
+       lnimmshare_emp_stem_e_grad +  
+       lnimmshare_emp_stem_n_grad +  
+       fyear,
+       weights=weight_native))))

lm(formula = lnemprate_native ~ lnimmshare_emp_stem_e_grad + 
    lnimmshare_emp_stem_n_grad + fyear, weights = weight_native)

Weighted Residuals:
      Min        1Q    Median        3Q       Max 
-0.024409 -0.003716  0.001683  0.005383  0.019232 

                             Estimate Std. Error t value
(Intercept)                 4.1780416  0.0106922 390.756
lnimmshare_emp_stem_e_grad -0.0130295  0.0036493  -3.570
lnimmshare_emp_stem_n_grad  0.0005722  0.0040274   0.142
fyear2001                  -0.0098670  0.0104854  -0.941
Residual standard error: 0.006736 on 372 degrees of freedom
Multiple R-squared:  0.372,	Adjusted R-squared:  0.3517 
F-statistic: 18.36 on 12 and 372 DF,  p-value: < 2.2e-16

Now adjusted R-squared is only 0.3517, quite a drop. So, state-to-state difference was the main driver of the high R-squared.