├── .gitignore ├── README.md ├── RPP_figures.R ├── data_allformats ├── RPPdataConverted.csv ├── RPPdataConverted.dta ├── RPPdataConverted.rds ├── RPPdataConverted.sav ├── RPPdataConverted.tsv ├── RPPdataConverted.xlsx ├── convertDataset.R └── original_converted_variablenames.rtf ├── figures ├── RPP_ES.eps ├── RPP_ES_density.pdf ├── RPP_ES_density_bl.pdf ├── RPP_ES_density_tr.pdf ├── RPP_pv.eps ├── RPP_pv_Main0.png ├── RPP_pv_Main1.png ├── RPP_pv_main2.png ├── RPP_pv_sub0.png ├── RPP_pv_sub2.png ├── RPP_pvalues0.pdf ├── RPP_pvalues1.pdf ├── RPP_pvalues2.pdf ├── RPP_vioQtileTST.pdf ├── figure s1.eps ├── figure s1.jpg ├── figure s2.eps ├── figure s2.jpg ├── figure s3.eps ├── figure s3.jpg ├── figure s4.eps ├── figure s4.jpg ├── figure s5.eps ├── figure s5.jpg ├── figure s6.eps ├── figure s6.jpg ├── figure s7.eps ├── figure s7.jpg ├── figures fred.zip └── figures tilburg.zip ├── functions.r ├── masterscript.R ├── output.txt ├── rpp_reproduce.zip └── vieland evidence nonsig.R /.gitignore: -------------------------------------------------------------------------------- 1 | # History files 2 | .Rhistory 3 | 4 | # Example code in package build process 5 | *-Ex.R 6 | 7 | # R data files from past sessions 8 | # .Rdata 9 | 10 | # RStudio files 11 | .Rproj.user/ 12 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Reproducibility Project: Psychology 2 | Repository for working on the Reproducibility Project: Psychology. These files represent the analyses conducted in Tilburg and 3 | reported in the Science publication, as well as the code used to generate Figures 1-3. The Github repository also operates as the issue tracker for the analysis committee. 4 | 5 | The data are contained in this repository and were retrieved from rpp_data.csv. The analysis script includes a function to download the .csv file directly from the Open Science Framework project page. If you would like to view the .csv file separately, you can visit it here: https://osf.io/fgjvw/. Differences might arise due to changes made subsequently to the data. The data used to generate the Figures 1-3 is downloaded from the OSF. 6 | 7 | ## Running the analyses for the RP:P project 8 | There are three ways of getting the files required to reproduce all analyses in the RPP manuscript: 9 | 10 | 1. Download the `masterscript.R` file, and run this in R (downloads all dependencies from the OSF) 11 | 2. Download the zip file `rpp_reproduce.zip` and extract the folder (this is for the non-git users). You can use [this](https://github.com/centerforopenscience/rpp/raw/master/rpp_reproduce.zip) link to do that. 12 | 3. Clone this git repository and run the `masterscript.R` (this is for the git-users. The command to do this would be `git clone https://github.com/centerforopenscience/rpp FOLDERNAME`, where FOLDERNAME is the name of the folder these files will be contained (note your working directory to know where this folder will be placed) 13 | 14 | Once the files are downloaded, running the analyses has been made user-friendly (please make sure you have the R statistical package installed, downloadable [here](https://cran.r-project.org/)). 15 | 16 | 1. Open the `masterscript.R` file in R. 17 | 2. Run all 18 | 3. Select the directory where you downloaded the files (i.e., the folder where `masterscript.R`, `functions.R`, `RPP_figures.R`, and `rpp_data.csv` are located) 19 | 4. Now you can run all the results. 20 | 21 | ### `masterscript.R` explained 22 | The `masterscript.R` contains all the analyses conducted by the analysis team, save for the additional figures. These have been ordered to correspond with the *Statistical Analyses* section of the manuscript in the Supplemental Information (note there might be some changes in the ordering that were not picked up, but in general it corresponds; if you cannot find something in direct chronological order it usually follows later in that section). 23 | 24 | Note that there was some code run in TURBO PASCAL, which was not run in R and is only available in the supplement. 25 | 26 | Lines 46-56 in `masterscript.R` correspond to the *Preliminary analyses* section. 27 | 28 | Lines 58-186 in `masterscript.R` correspond to the *Evaluating replication effect against null hypothesis of no effect* section. Note that this section also includes the part with TURBO PASCAL code. 29 | 30 | Lines 188-261 in `masterscript.R` correspond to the *Comparing original and replication effect sizes* section. 31 | 32 | Lines 263-307 in `masterscript.R` correspond to the figure in *Evaluating replication effect against original effect size* section. Code from [A5] is in lines 541-767 33 | 34 | Lines 309-539 are data-ordering code. 35 | 36 | Lines 783-928 in `masterscript.R` correspond to the *Combining original and replication effect sizes for cumulative evidence​* section. 37 | 38 | Lines 981-1051 in `masterscript.R` correspond to the *Meta­analysis of all original study effects, and of all replication study effects* section. 39 | 40 | Lines 930-979 in `masterscript.R` correspond to the *Meta­analysis of difference of effect size between original and replication study* section. 41 | 42 | Lines 1109-1455 in `masterscript.R` correspond to the *Moderator Analyses* section. 43 | 44 | ### Versions of packages used in the analyses 45 | - `R` package: `v3.2.1` 46 | - `metafor` package: `v1.9-7` 47 | - `Hmisc` package: `v3.16-0` 48 | - `httr` package: `v1` 49 | 50 | 51 | ****** 52 | ****** 53 | 54 | 55 | ## Generating the Figures in the RP:P project publication 56 | 57 | The `R` code that can be used to generate Figure 1-3 of the RP:P Science article is in the file `RPP_figures.R` 58 | Download this file either by cloning this repository as explained above, or by simply downloading the linked file (e.g. by right-clicking the name) 59 | 60 | To generate the Figures: 61 | 62 | 1. Open your local of the file `RPP_figures.R` in your `R` environment. 63 | 2. Select `run all` from your `R` GUI or use the `R` console to run: `source('RPP_figures.R', echo=TRUE)` (this assumes the `RPP_figures.R` file is in current working directory) 64 | 65 | > **Note:** You will need an internet connection to run the script. 66 | 67 | This will create 3 PDF files in the current working directory: 68 | 69 | 1. `RPP_Figure1_vioQtile.pdf` - **Figure 1**: Violin Quantile Plots displaying Original and Replication study densities of p-values and effect sizes. 70 | 2. `RPP_Figure2_pvalues.pdf` - **Figure 2**: Scatterplot of Original and Replication study p-values, with a 'blow-up' of the X-axis. 71 | 3. `RPP_Figure3_ESdensity.pdf` - **Figure 3**: Scatterplot of Original and Replication study Effect sizes, with X and Y margin density plots. 72 | 73 | > **Note** Figure appearance will not be exactly the same as in the Science publication. 74 | 75 | ### `RPP_figures.R` explained 76 | The `R` source file contains three main sections, *SETUP*, *FIGURE 1*, *FIGURE 2*, *FIGURE 3*. 77 | 78 | 79 | ****** 80 | 81 | 82 | #### Section: SETUP 83 | All the custom built functions that are required to recreate the Figures are available in a GitHub sourceable file: `C-3PR.R`. It is available [here](https://github.com/FredHasselman/toolboxR), but it is not required (or recommended) to download the file to your local environment. 84 | 85 | **Explanation of code in this section:** 86 | 87 | *Lines 22-23*: 88 | ```{r} 89 | require(devtools) 90 | source_url('https://raw.githubusercontent.com/FredHasselman/toolboxR/master/C-3PR.R') 91 | ``` 92 | Source `C-3PR.R` directly from GitHub using `source_url()` in package `devtools`. 93 | 94 | *Line 26*: 95 | ``` 96 | in.IT(c('plyr','dplyr','ggplot2','RColorBrewer','scales','lattice','gridExtra')) 97 | ``` 98 | Load (and if necessary install) libraries used for data management and plotting using the `in.IT()` function that is now available due to sourcing `C-3PR` 99 | 100 | *Line 30*: 101 | ``` 102 | RPPdata <- get.OSFfile(code='https://osf.io/fgjvw/',dfCln=T)$df 103 | ``` 104 | Read the data from the OSF storage 105 | > *Note:* get.OSFfile() returns a list with the CSV data (df) and information (info) containing the URL download timestamp and original column and rownames (these names will be changed if dfCln=TRUE). 106 | 107 | *Lines 32-41*: 108 | These lines contain code to select the correct studies and prepare some variables needed for plotting. 109 | 110 | 111 | ****** 112 | 113 | 114 | #### Section: FIGURE 1 115 | This section contains the code to generate the Violin Quantile Plots. The functions are inspired by the code [Troy](http://stackoverflow.com/questions/22278951/combining-violin-plot-with-box-plot) posted on [Stack Overflow](http://stackoverflow.com) on 10-03-2014. Thank you Troy! 116 | 117 | **Explanation of code in this section:** 118 | 119 | *Lines 48-55*: 120 | This code creates a temporary dataset convenient for plotting the violin plots (i.e. long format data with `Study` as a factor indicating an original or replication study). 121 | 122 | *Lines 56-82*: 123 | This code plots panel A of the figure containing the p-values. 124 | First the quantiles are caclulated from the data and output to the `R` console: 125 | ``` 126 | > ori 127 | freq 128 | [0,0.0004] 24 129 | (0.0004,0.0069] 26 130 | (0.0069,0.0232] 22 131 | (0.0232,0.912] 26 132 | 133 | > rep 134 | freq 135 | [0,0.0077] 24 136 | (0.0077,0.198] 26 137 | (0.198,0.534] 24 138 | (0.534,0.975] 24 139 | ``` 140 | 141 | Then the plot is created as follows: 142 | ``` 143 | # Get regular violinplot using package ggplot2 144 | g.pv <- ggplot(df,aes(x=grp,y=p.value)) + 145 | geom_violin(aes(group=grp),scale="width",color="grey30",fill="grey30",trim=T,adjust=.7) 146 | # Cut at quantiles using vioQtile() in C-3PR 147 | g.pv0 <- vioQtile(g.pv,qtiles,probs) 148 | # Garnish 149 | g.pv1 <- g.pv0 + geom_hline(aes(yintercept=.05),linetype=2) + 150 | ggtitle("A") + xlab("") + ylab("p-value") + 151 | mytheme 152 | # View 153 | g.pv1 154 | 155 | ## Uncomment to save panel A as a seperate file 156 | # ggsave("RPP_F1_VQPpv.eps",plot=g.pv1) 157 | ``` 158 | 159 | 160 | *Lines 83-109*: 161 | This code plots panel B of the figure containing the effect sizes. 162 | First the quantiles are caclulated from the data and output to the `R` console: 163 | ``` 164 | > ori 165 | freq 166 | [0.00464,0.222] 24 167 | (0.222,0.372] 25 168 | (0.372,0.551] 25 169 | (0.551,0.86] 24 170 | 171 | > rep 172 | freq 173 | [-0.45,0.0194] 24 174 | (0.0194,0.12] 24 175 | (0.12,0.323] 25 176 | (0.323,0.923] 24 177 | ``` 178 | 179 | Then the plot is created as follows: 180 | ``` 181 | # Get regular violinplot using package ggplot2 182 | g.es <- ggplot(df,aes(x=grp,y=EffectSize)) + 183 | geom_violin(aes(group=grpN),scale="width",fill="grey40",color="grey40",trim=T,adjust=1) 184 | # Cut at quantiles using vioQtile() in C-3PR 185 | g.es0 <- vioQtile(g.es,qtiles=qtiles,probs=probs) 186 | # Garnish 187 | g.es1 <- g.es0 + 188 | ggtitle("B") + xlab("") + ylab("Effect Size") + 189 | scale_y_continuous(breaks=c(-.25,-.5,0,.25,.5,.75,1),limits=c(-.5,1)) + mytheme 190 | # View 191 | g.es1 192 | 193 | # # Uncomment to save panel B as a seperate file 194 | # ggsave("RPP_F1_VQPes.eps",plot=g.es1) 195 | ``` 196 | 197 | *Lines 110-118*: 198 | To view and save the combined plot the function `multi.PLOT()` is used. This function was copied from the online version of [Winston Chang's Cookbook for R](http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_(ggplot2)/). Thanks Winston! 199 | ``` 200 | # VIEW panels in one plot using the multi.PLOT() function from C-3PR 201 | multi.PLOT(g.pv1,g.es1,cols=2) 202 | 203 | # SAVE combined plots as PDF 204 | pdf("RPP_Figure1_vioQtile.pdf",pagecentre=T, width=20,height=8 ,paper = "special") 205 | multi.PLOT(g.pv1,g.es1,cols=2) 206 | dev.off() 207 | ``` 208 | 209 | 210 | ****** 211 | 212 | 213 | #### Section: FIGURE 2 214 | This section contains the code to generate the scatterplot comparing Original and Replication study p-values. The Figure contains a subplot which is a magnification of the scale of the X-axis. The grey lines in the published figure were added using image editing software. 215 | 216 | 217 | **Explanation of code in this section:** 218 | 219 | *Lines 130-145*: 220 | This code sets up the viewport such that the main scatterplot and the magnification are plotted correctly. 221 | ``` 222 | # One of many ways to manipulate positions of multiple plots in one output device 223 | vpM <- viewport(width = 0.6, height = 0.6, x=0.4, y=.7) 224 | vpZ <- viewport(width = 0.6, height = 0.3, x=.4, y=.25) 225 | grid.show.viewport(vpM) 226 | grid.show.viewport(vpZ) 227 | # This will position a ggplot "main" and "sub" in their respective slots 228 | full <- function() { 229 | plot.new() 230 | print(main,vp = vpM) 231 | print(sub, vp = vpZ) 232 | } 233 | ``` 234 | If the function `full()` is called, an object `main` and `sub` will be arranged on the viewport according to information provided in `vpM` and `vpZ`. The command `grid.show.viewport()` of package `grid` will reveal the coordinates defined int hese variables. 235 | 236 | 237 | *Lines 146-171*: 238 | This code creates two ggplot objects, `main` and `sub`. 239 | ``` 240 | main <- ggplot(RPPdata,aes(x=T.pval.USE.O,y=T.pval.USE.R))+ 241 | scale_x_continuous(breaks=c(0,.01,.05),limits=c(0,.06)) + 242 | ggtitle("") + xlab("Original Study p-value") + ylab("Replication p-value") + 243 | scale_color_brewer(name="Journal",palette="Set2") + 244 | scale_size_continuous(name="Replication:\nPower",breaks=seq(0,1,length=11)) + ylim(c(0,1)) + 245 | geom_vline(aes(xintercept=0.05),linetype=2,color=mypalette[9]) + 246 | geom_hline(aes(yintercept=0.05),linetype=2,color=mypalette[9]) + 247 | geom_point(aes(size=Power.Rn,color=Journal.O),alpha=.8) + mytheme 248 | 249 | ## Uncomment to save subplot 250 | # ggsave("RPP_F2_pvMain.png",plot=main) 251 | 252 | sub <- ggplot(RPPdata,aes(x=T.pval.USE.O,y=T.pval.USE.R)) + 253 | geom_vline(aes(xintercept=0.05),linetype=2,color=mypalette[9]) + 254 | geom_hline(aes(yintercept=0.05),linetype=2,color=mypalette[9]) + 255 | geom_point(aes(size=Power.Rn,color=Journal.O),alpha=.8) + 256 | scale_x_continuous(breaks=c(0,.001,.005),limits=c(0,.005)) + 257 | ggtitle("0 < p < 0.005") + 258 | xlab("")+ ylab("") + 259 | scale_size_continuous(guide=F) + 260 | scale_color_brewer(palette="Set2",guide=F) + 261 | ylim(c(0,1)) + mytheme 262 | 263 | ## Uncomment to save subplot 264 | # ggsave("RPP_F2_pvsub.png",plot=sub) 265 | ``` 266 | 267 | *Lines 172-179*: 268 | To view and save the combined plot call the function `full()` defined above. 269 | ``` 270 | # VIEW combined plots 271 | full() 272 | 273 | # SAVE combined plots as PDF (note: the lines emphasizing the 'blow-up' figure in the publication were drawn in later using image editing software) 274 | pdf("RPP_Figure2_pvalues.pdf",pagecentre=T, width=15,height=8 ,paper = "special") 275 | full() 276 | dev.off() 277 | ``` 278 | 279 | 280 | ****** 281 | 282 | 283 | #### Section: FIGURE 3 284 | This section contains the code to generate the scatterplot comparing Original and Replication study effect sizes. The Figure margins contain density plots of the X and Y axis. 285 | 286 | 287 | **Explanation of code in this section:** 288 | 289 | *Lines 187-202*: 290 | This code sets up some variables needed to create the plots. 291 | The function plotHolder() is used to create a 'dummy' plot needed to create the margin plot. 292 | 293 | *Lines 203-243*: 294 | This code creates the marginplots `xDense` and `yDense` and the main scatterplot `scatterP`. 295 | ``` 296 | # X margin density plot (note: gg.theme() from C-3PR can be used directly in a ggplot2() call) 297 | xDense <- ggplot(RPPdata, aes(x=T.r.O, fill=oriSig)) + 298 | geom_density(aes(y= ..count..),trim=F,alpha=.5) + 299 | xlab("") + ylab("") + xlim(0,1) + 300 | gg.theme("noax") + 301 | theme(legend.position = "none",plot.margin = unit(c(0,0,0,4), "lines")) 302 | 303 | ## Uncomment to save subplot 304 | # ggsave("RPP_F3_xDense.png",plot=xDense) 305 | 306 | # Y margin density plot (note: gg.theme() from C-3PR can be used directly in a ggplot2() call) 307 | yDense <- ggplot(RPPdata, aes(x=T.r.R, fill=repSig)) + 308 | geom_density(aes(y= ..count..),trim=F,alpha=.5) + 309 | xlab("") + ylab("") + xlim(-.5,1) + 310 | coord_flip() + 311 | gg.theme("noax") + 312 | theme(legend.position = "none", plot.margin = unit(c(0,0,3,0), "lines")) 313 | 314 | ## Uncomment to save subplot 315 | # ggsave("RPP_F3_yDense.png",plot=yDense) 316 | 317 | # The main scatterplot (note: gg.theme() from C-3PR can be used directly in a ggplot2() call) 318 | scatterP<- 319 | ggplot(RPPdata,aes(x=T.r.O,y=T.r.R)) + 320 | geom_hline(aes(yintercept=0),linetype=2) + 321 | geom_abline(intercept=0,slope=1,color="Grey60")+ 322 | geom_point(aes(size=Power.Rn,fill=repSig),color="Grey30",shape=21,alpha=.8) + 323 | geom_rug(aes(color=oriSig),size=1,sides="b",alpha=.6) + 324 | geom_rug(aes(color=repSig),,size=1,sides="l",alpha=.6) + 325 | scale_x_continuous(name="Original Effect Size",limits=c(0,1),breaks=c(0,.25,.5,.75,1)) + 326 | scale_y_continuous(name="Replication Effect Size",limits=c(-.5,1),breaks=c(-.5,-.25,0,.25,.5,.75,1)) + 327 | ggtitle("") + xlab("") + ylab("") + 328 | scale_size_continuous(name="Replication Power",range=c(2,9)) + 329 | scale_color_discrete(name="p-value") + 330 | scale_fill_discrete(name="p-value") + 331 | gg.theme("clean") + 332 | theme(legend.position=c(.9,.6), plot.margin = unit(c(-2,-1.5,2,2), "lines")) 333 | 334 | ## Uncomment to save subplot 335 | # ggsave("RPP_F3_scatter.png",plot=scatterP) 336 | ``` 337 | 338 | *Lines 244-250*: 339 | To view and save the combined plots the function `grid.arrange()` from the `gridExtra` package is used. 340 | ``` 341 | grid.arrange(xDense, blankPlot, scatterP, yDense, ncol=2, nrow=2, widths=c(4, 1.4), heights=c(1.4, 4)) 342 | 343 | # SAVE combined plots as PDF 344 | pdf("RPP_Figure3_ESdensity.pdf",pagecentre=T, width=15,height=12 ,paper = "special") 345 | grid.arrange(xDense, blankPlot, scatterP, yDense, ncol=2, nrow=2, widths=c(4, 1.4), heights=c(1.4, 4)) 346 | dev.off() 347 | ``` 348 | 349 | ### Versions of packages used to generate the figures (not showing imports) 350 | - `R` version: `3.1.3 (2015-03-09)` 351 | - `devtools` version: `1.8.0` 352 | - `dplyr` version: `0.4.2` 353 | - `ggplot2` version: `1.0.1` 354 | - `gridExtra` version: `2.0.0` 355 | - `lattice` version: `0.20-33` 356 | - `httr` version: `1.0.0` 357 | - `plyr` version: `1.8.3` 358 | - `RColorBrewer` version: `1.1-2` 359 | 360 | 361 | 362 | -------------------------------------------------------------------------------- /RPP_figures.R: -------------------------------------------------------------------------------- 1 | ############################################################ 2 | # RPP Manuscript Figure 1,2,3 # 3 | # # 4 | # Created by Fred Hasselman on behalf of the RPP intiative # 5 | ############################################################ 6 | 7 | # All the custom functions that are required to recreate the Figures are available in a GitHub sourceable file: `C-3PR.R` 8 | # It is available here: https://github.com/FredHasselman/toolboxR 9 | 10 | ############################################################ 11 | # C-3PR: Evaluating Scientific *C*laims: *P*ost *P*ublication *P*eer *R*eview, *R*eplication and *R*eproduction. 12 | # 13 | # An R function library created in the contex of analysing and visualising data generated by several Open Science intiatives. 14 | # 15 | # C-3PR Created by [Fred Hasselman](https://osf.io/ujgs6/) on behalf of the ManyLabs1, ManyLabs2, RPP and Curate Science projects 16 | ############################################################ 17 | 18 | 19 | # SETUP local R------------------------------------------------------------------- 20 | 21 | # Use this code (from the devtools package) to source C-3PR directly from GitHub: 22 | require(devtools) 23 | source_url('https://raw.githubusercontent.com/FredHasselman/toolboxR/master/C-3PR.R') 24 | 25 | # This will load and (if necessary) install libraries frequently used for data management and plotting 26 | in.IT(c('ggplot2','RColorBrewer','lattice','gridExtra','plyr','dplyr','httr')) 27 | 28 | # Read the data from the OSF storage 29 | # Note: get.OSFfile() returns a list with the .csv data (df) and information (info) containing the URL download timestamp and original column and rownames (these names will be changed if dfCln=TRUE). 30 | RPPdata <- get.OSFfile(code='https://osf.io/fgjvw/',dfCln=T)$df 31 | 32 | ## If you dowloaded the csv file to your harddrive use this code: 33 | # RPPdata<-read.csv('rpp_data.csv',stringsAsFactors=F ) 34 | # RPPdata<-df.Clean(RPPdata) 35 | # RPPdata<-RPPdata$df 36 | 37 | # Select the completed replication studies 38 | RPPdata <- dplyr::filter(RPPdata, !is.na(T.pval.USE.O),!is.na(T.pval.USE.R)) 39 | # We have 99 studies for which p-values and effect sizes could be calculated 40 | nrow(RPPdata) 41 | # We have 97 studies for which p-values of the original effect were significantly below .05 42 | idOK <- complete.cases(RPPdata$T.r.O,RPPdata$T.r.R) 43 | sum(idOK) 44 | 45 | # Get ggplot2 themes predefined in C-3PR 46 | mytheme <- gg.theme("clean") 47 | 48 | ############ 49 | # FIGURE 1 50 | # VIOLIN QUANTILE PLOTS (VQP) ----------------------------------------------- 51 | ############ 52 | 53 | # Restructure the data to "long" format: Study type will be a factor 54 | df <- dplyr::select(RPPdata,starts_with("T.")) 55 | df <- data.frame(EffectSize=as.numeric(c(df$T.r.O,df$T.r.R)),p.value=as.numeric(c(df$T.pval.USE.O,df$T.pval.USE.R)),grp=factor(c(rep("Original Studies",times=length(df$T.r.O)),rep("Replications",times=length(df$T.r.R))))) 56 | 57 | # Create some variables for plotting 58 | df$grpN <- as.numeric(df$grp) 59 | probs <- seq(0,1,.25) 60 | 61 | # VQP PANEL A: p-value ------------------------------------------------- 62 | 63 | # Get p-value quantiles and frequencies from data 64 | qtiles <- ldply(unique(df$grpN),function(gr) quantile(round(df$p.value[df$grpN==gr],digits=4),probs,na.rm=T,type=3)) 65 | freqs <- ldply(unique(df$grpN),function(gr) table(cut(df$p.value[df$grpN==gr],breaks=qtiles[gr,],na.rm=T,include.lowest=T,right=T))) 66 | labels <- sapply(unique(df$grpN),function(gr)levels(cut(round(df$p.value[df$grpN==gr],digits=4), breaks = qtiles[gr,],na.rm=T,include.lowest=T,right=T))) 67 | 68 | # Check the Quantile bins! 69 | ori <-cbind(freq=as.numeric(t(freqs[1,]))) 70 | rownames(ori) <- labels[,1] 71 | ori 72 | 73 | rep <-cbind(freq=as.numeric(t(freqs[2,]))) 74 | rownames(rep) <- labels[,2] 75 | rep 76 | 77 | # Get regular violinplot using package ggplot2 78 | g.pv <- ggplot(df,aes(x=grp,y=p.value)) + geom_violin(aes(group=grp),scale="width",color="grey30",fill="grey30",trim=T,adjust=.7) 79 | # Cut at quantiles using vioQtile() in C-3PR 80 | g.pv0 <- vioQtile(g.pv,qtiles,probs) 81 | # Garnish 82 | g.pv1 <- g.pv0 + geom_hline(aes(yintercept=.05),linetype=2) + 83 | ggtitle("A") + xlab("") + ylab("p-value") + 84 | mytheme 85 | # View 86 | g.pv1 87 | 88 | ## Uncomment to save panel A as a seperate file 89 | # ggsave("RPP_F1_VQPpv.eps",plot=g.pv1) 90 | 91 | # VQP PANEL B: effect size ------------------------------------------------- 92 | 93 | # Get effect size quantiles and frequencies from data 94 | qtiles <- ldply(unique(df$grpN),function(gr) quantile(df$EffectSize[df$grpN==gr],probs,na.rm=T,type=3,include.lowest=T)) 95 | freqs <- ldply(unique(df$grpN),function(gr) table(cut(df$EffectSize[df$grpN==gr],breaks=qtiles[gr,],na.rm=T,include.lowest=T))) 96 | labels <- sapply(unique(df$grpN),function(gr)levels(cut(round(df$EffectSize[df$grpN==gr],digits=4), breaks = qtiles[gr,],na.rm=T,include.lowest=T,right=T))) 97 | 98 | # Check the Quantile bins! 99 | ori <-cbind(freq=as.numeric(t(freqs[1,]))) 100 | rownames(ori) <- labels[,1] 101 | ori 102 | 103 | rep <-cbind(freq=as.numeric(t(freqs[2,]))) 104 | rownames(rep) <- labels[,2] 105 | rep 106 | 107 | # Get regular violinplot using package ggplot2 108 | g.es <- ggplot(df,aes(x=grp,y=EffectSize)) + geom_violin(aes(group=grpN),scale="width",fill="grey40",color="grey40",trim=T,adjust=1) 109 | # Cut at quantiles using vioQtile() in C-3PR 110 | g.es0 <- vioQtile(g.es,qtiles=qtiles,probs=probs) 111 | # Garnish 112 | g.es1 <- g.es0 + 113 | ggtitle("B") + xlab("") + ylab("Effect Size") + 114 | scale_y_continuous(breaks=c(-.25,-.5,0,.25,.5,.75,1),limits=c(-.5,1)) + mytheme 115 | # View 116 | g.es1 117 | 118 | # # Uncomment to save panel B as a seperate file 119 | # ggsave("RPP_F1_VQPes.eps",plot=g.es1) 120 | 121 | # VIEW panels in one plot using the multi.PLOT() function from C-3PR 122 | multi.PLOT(g.pv1,g.es1,cols=2) 123 | 124 | # SAVE combined plots as PDF 125 | pdf("RPP_Figure1_vioQtile.pdf",pagecentre=T, width=20,height=8 ,paper = "special") 126 | multi.PLOT(g.pv1,g.es1,cols=2) 127 | dev.off() 128 | 129 | 130 | ######################## 131 | # FIGURE 2 132 | # P-VALUE PLOTS ----------------------------------------------------------- 133 | ######################## 134 | 135 | # One of many ways to manipulate positions of multiple plots in one output device 136 | vpM <- viewport(width = 0.6, height = 0.6, x=0.4, y=.7) 137 | vpZ <- viewport(width = 0.6, height = 0.3, x=.4, y=.25) 138 | grid.show.viewport(vpZ) 139 | # This will position a ggplot "main" and "sub" in their respective slots 140 | full <- function() { 141 | plot.new() 142 | print(main,vp = vpM) 143 | print(sub, vp = vpZ) 144 | } 145 | 146 | # Choose a palette from RColorBrewer 147 | mypalette <- brewer.pal(9,"YlOrRd") 148 | # Get the Replication observed power 149 | RPPdata$Power.Rn <- as.numeric(RPPdata$Power.R) 150 | 151 | # Scatter with size=power.replication and journal -------------------------------------------------- 152 | main <- ggplot(RPPdata,aes(x=T.pval.USE.O,y=T.pval.USE.R))+ 153 | scale_x_continuous(breaks=c(0,.01,.05),limits=c(0,.06)) + 154 | ggtitle("") + xlab("Original Study p-value") + ylab("Replication p-value") + 155 | scale_color_brewer(name="Journal",palette="Set2") + 156 | scale_size_continuous(name="Replication:\nPower",breaks=seq(0,1,length=11)) + ylim(c(0,1)) + 157 | geom_vline(aes(xintercept=0.05),linetype=2,color=mypalette[9]) + 158 | geom_hline(aes(yintercept=0.05),linetype=2,color=mypalette[9]) + 159 | geom_point(aes(size=Power.Rn,color=Journal.O),alpha=.8) + mytheme 160 | 161 | ## Uncomment to save subplot 162 | # ggsave("RPP_F2_pvMain.png",plot=main) 163 | 164 | sub <- ggplot(RPPdata,aes(x=T.pval.USE.O,y=T.pval.USE.R)) + 165 | geom_vline(aes(xintercept=0.05),linetype=2,color=mypalette[9]) + 166 | geom_hline(aes(yintercept=0.05),linetype=2,color=mypalette[9]) + 167 | geom_point(aes(size=Power.Rn,color=Journal.O),alpha=.8) + 168 | scale_x_continuous(breaks=c(0,.001,.005),limits=c(0,.005)) + 169 | ggtitle("0 < p < 0.005") + 170 | xlab("")+ ylab("") + 171 | scale_size_continuous(guide=F) + 172 | scale_color_brewer(palette="Set2",guide=F) + 173 | ylim(c(0,1)) + mytheme 174 | 175 | ## Uncomment to save subplot 176 | # ggsave("RPP_F2_pvsub.png",plot=sub) 177 | 178 | # VIEW combined plots 179 | full() 180 | 181 | # SAVE combined plots as PDF (note: the lines emphasizing the 'blow-up' figure in the publication were drawn in later using image editing software) 182 | pdf("RPP_Figure2_pvalues.pdf",pagecentre=T, width=15,height=8 ,paper = "special") 183 | full() 184 | dev.off() 185 | 186 | 187 | ######################## 188 | # FIGURE 3 189 | # EFFECT SIZE DENSITY PLOTS ------------------------------------------------------------- 190 | ######################## 191 | 192 | # Setup some variables 193 | RPPdata$oriSig <- "Not Significant" 194 | # 3 studies claimed an effect at .05 < p < .06 195 | RPPdata$oriSig[RPPdata$T.pval.USE.O<=.06] <- "Significant" 196 | RPPdata$oriSig <- factor(RPPdata$oriSig) 197 | 198 | RPPdata$repSig <- "Not Significant" 199 | RPPdata$repSig[RPPdata$T.pval.USE.R<=.05] <- "Significant" 200 | RPPdata$repSig <- factor(RPPdata$repSig) 201 | RPPdata$repSig <- factor(RPPdata$repSig) 202 | 203 | # Create a scatterplot with density margin plots 204 | 205 | # The plotHolder() function from C-3PR creates a blank plot template that will hold the figures 206 | blankPlot <- plotHolder() 207 | 208 | # X margin density plot (note: gg.theme() from C-3PR can be used directly in a ggplot2() call) 209 | xDense <- ggplot(RPPdata, aes(x=T.r.O, fill=oriSig)) + 210 | geom_density(aes(y= ..count..),trim=F,alpha=.5) + 211 | xlab("") + ylab("") + xlim(0,1) + 212 | gg.theme("noax") + 213 | theme(legend.position = "none",plot.margin = unit(c(0,0,0,4), "lines")) 214 | 215 | ## Uncomment to save subplot 216 | # ggsave("RPP_F3_xDense.png",plot=xDense) 217 | 218 | # Y margin density plot (note: gg.theme() from C-3PR can be used directly in a ggplot2() call) 219 | yDense <- ggplot(RPPdata, aes(x=T.r.R, fill=repSig)) + 220 | geom_density(aes(y= ..count..),trim=F,alpha=.5) + 221 | xlab("") + ylab("") + xlim(-.5,1) + 222 | coord_flip() + 223 | gg.theme("noax") + 224 | theme(legend.position = "none", plot.margin = unit(c(0,0,3,0), "lines")) 225 | 226 | ## Uncomment to save subplot 227 | # ggsave("RPP_F3_yDense.png",plot=yDense) 228 | 229 | # The main scatterplot (note: gg.theme() from C-3PR can be used directly in a ggplot2() call) 230 | scatterP<- 231 | ggplot(RPPdata,aes(x=T.r.O,y=T.r.R)) + 232 | geom_hline(aes(yintercept=0),linetype=2) + 233 | geom_abline(intercept=0,slope=1,color="Grey60")+ 234 | geom_point(aes(size=Power.Rn,fill=repSig),color="Grey30",shape=21,alpha=.8) + 235 | geom_rug(aes(color=oriSig),size=1,sides="b",alpha=.6) + 236 | geom_rug(aes(color=repSig),,size=1,sides="l",alpha=.6) + 237 | scale_x_continuous(name="Original Effect Size",limits=c(0,1),breaks=c(0,.25,.5,.75,1)) + 238 | scale_y_continuous(name="Replication Effect Size",limits=c(-.5,1),breaks=c(-.5,-.25,0,.25,.5,.75,1)) + 239 | ggtitle("") + xlab("") + ylab("") + 240 | scale_size_continuous(name="Replication Power",range=c(2,9)) + 241 | scale_color_discrete(name="p-value") + 242 | scale_fill_discrete(name="p-value") + 243 | gg.theme("clean") + 244 | theme(legend.position=c(.9,.6), plot.margin = unit(c(-2,-1.5,2,2), "lines")) 245 | 246 | ## Uncomment to save subplot 247 | # ggsave("RPP_F3_scatter.png",plot=scatterP) 248 | 249 | # Yet another way to organise plots: grid.arrange() from the gridExtra package. 250 | grid.arrange(xDense, blankPlot, scatterP, yDense, ncol=2, nrow=2, widths=c(4, 1.4), heights=c(1.4, 4)) 251 | 252 | # SAVE combined plots as PDF 253 | pdf("RPP_Figure3_ESdensity.pdf",pagecentre=T, width=15,height=12 ,paper = "special") 254 | grid.arrange(xDense, blankPlot, scatterP, yDense, ncol=2, nrow=2, widths=c(4, 1.4), heights=c(1.4, 4)) 255 | dev.off() 256 | 257 | ######################## 258 | # DONE! ------------------------------------------------------------------- 259 | ######################## 260 | 261 | # Contact me if you have any questions: https://osf.io/ujgs6/ 262 | 263 | 264 | 265 | 266 | -------------------------------------------------------------------------------- /data_allformats/RPPdataConverted.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CenterForOpenScience/rpp/a7c5b87f15f60178e6f5d25f983217d39f5ac654/data_allformats/RPPdataConverted.csv -------------------------------------------------------------------------------- /data_allformats/RPPdataConverted.dta: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CenterForOpenScience/rpp/a7c5b87f15f60178e6f5d25f983217d39f5ac654/data_allformats/RPPdataConverted.dta -------------------------------------------------------------------------------- /data_allformats/RPPdataConverted.rds: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CenterForOpenScience/rpp/a7c5b87f15f60178e6f5d25f983217d39f5ac654/data_allformats/RPPdataConverted.rds -------------------------------------------------------------------------------- /data_allformats/RPPdataConverted.sav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CenterForOpenScience/rpp/a7c5b87f15f60178e6f5d25f983217d39f5ac654/data_allformats/RPPdataConverted.sav -------------------------------------------------------------------------------- /data_allformats/RPPdataConverted.tsv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CenterForOpenScience/rpp/a7c5b87f15f60178e6f5d25f983217d39f5ac654/data_allformats/RPPdataConverted.tsv -------------------------------------------------------------------------------- /data_allformats/RPPdataConverted.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CenterForOpenScience/rpp/a7c5b87f15f60178e6f5d25f983217d39f5ac654/data_allformats/RPPdataConverted.xlsx -------------------------------------------------------------------------------- /data_allformats/convertDataset.R: -------------------------------------------------------------------------------- 1 | # Load packages 2 | library(devtools) 3 | source_url('https://raw.githubusercontent.com/FredHasselman/ManyLabRs/master/manylabRs/R/getData.R') 4 | source_url('https://raw.githubusercontent.com/FredHasselman/ManyLabRs/master/manylabRs/R/inIT.R') 5 | # This will load and (if necessary) install libraries frequently used for data management and plotting 6 | in.IT(c('plyr','dplyr','httr','rio','haven')) 7 | 8 | # Read the data from OSF storage 9 | # 10 | # Note: get.OSFfile() returns a list with the .csv data (df) and information (info) containing the URL download timestamp and original column and rownames (these names will be changed if dfCln=TRUE). 11 | RPPdata <- get.OSFfile(code='https://osf.io/fgjvw/',dfCln=TRUE)$df 12 | 13 | # Figure out how many NAs result from changing to numeric 14 | errs <- llply(1:ncol(RPPdata), function(c) try.CATCH(sum(is.na(as.numeric(RPPdata[c][[1]]))))) 15 | 16 | nNA <- laply(seq_along(errs), function(e) if(is.integer(errs[[e]]$value)){errs[[e]]$value}else{168}) 17 | # Which variables are numeric 18 | nINT <- colwise(is.numeric)(RPPdata) 19 | 20 | # These variables are likely Character format and should be changed to Numeric format 21 | varnames <- colnames(RPPdata[,(nNA<120)&!nINT]) 22 | # [1] "Surprising.result.O" "Exciting.result.O" "Replicated.study.number.R" "N.O" 23 | # [5] "80.power" "90.power" "95.power" "Planned.Power" 24 | # [9] "Original.Author.s.Assessment" "P.value.R" "Power.R" 25 | 26 | 27 | # Fix it! 28 | for(cn in varnames){ 29 | RPPdata[cn] <- as.numeric(RPPdata[cn][[1]]) 30 | } 31 | 32 | # Convert empty cells to NA 33 | l_ply(1:ncol(RPPdata), function(c) if(is.character(RPPdata[c][[1]])){ 34 | RPPdata[c][[1]] <<- zap_empty(RPPdata[c][[1]]) 35 | } 36 | ) 37 | 38 | # Use package rio for export to: 39 | # 40 | # Comma seperated 41 | rio::export(RPPdata,"RPPdataConverted.csv") 42 | # Tab delimeted 43 | rio::export(RPPdata,"RPPdataConverted.tsv") 44 | # SPSS 45 | haven::write_sav(RPPdata,"RPPdataConverted.sav") 46 | # Excel 47 | rio::export(RPPdata,"RPPdataConverted.xlsx") 48 | # Stata 49 | rio::export(RPPdata,"RPPdataConverted.dta") 50 | # R (serialized object) 51 | rio::export(RPPdata,"RPPdataConverted.rds") 52 | -------------------------------------------------------------------------------- /data_allformats/original_converted_variablenames.rtf: -------------------------------------------------------------------------------- 1 | {\rtf1\ansi\ansicpg1252\cocoartf1404\cocoasubrtf340 2 | {\fonttbl\f0\fmodern\fcharset0 Courier;} 3 | {\colortbl;\red255\green255\blue255;} 4 | \paperw11900\paperh16840\margl1440\margr1440\vieww21720\viewh14340\viewkind0 5 | \pard\tx566\tx1133\tx1700\tx2267\tx2834\tx3401\tx3968\tx4535\tx5102\tx5669\tx6236\tx6803\pardirnatural\partightenfactor0 6 | 7 | \f0\fs24 \cf0 ori.colnames colnames(RPPdata$df)\ 8 | 1 Study Num Local.ID\ 9 | 2 Study Title (O) Study.Title.O\ 10 | 3 Authors (O) Authors.O\ 11 | 4 Journal (O) Journal.O\ 12 | 5 Volume (O) Volume.O\ 13 | 6 Issue (O) Issue.O\ 14 | 7 Pages (O) Pages.O\ 15 | 8 Project URL Project.URL\ 16 | 9 Descriptors (O) Descriptors.O\ 17 | 10 Number of Authors (O) Number.of.Authors.O\ 18 | 11 Number of Authors (R) Number.of.Authors.R\ 19 | 12 1st author (O) 1st.author.O\ 20 | 13 Citation Count, 1st author (O) Citation.Count.1st.author.O\ 21 | 14 Institution 1st author (O) Institution.1st.author.O\ 22 | 15 Senior author (O) Senior.author.O\ 23 | 16 Citation count, senior author (O) Citation.count.senior.author.O\ 24 | 17 Institution senior author (O) Institution.senior.author.O\ 25 | 18 1st author (R) 1st.author.R\ 26 | 19 Citation count, 1st author (R) Citation.count.1st.author.R\ 27 | 20 Institution 1st author (R) Institution.1st.author.R\ 28 | 21 Senior author (R) Senior.author.R\ 29 | 22 Citation count, senior author (R) Citation.count.senior.author.R\ 30 | 23 Institution senior author (R) Institution.senior.author.R\ 31 | 24 Citation count, paper (O) Citation.count.paper.O\ 32 | 25 Institution prestige, 1st author (O) Institution.prestige.1st.author.O\ 33 | 26 Institution prestige, senior author (O) Institution.prestige.senior.author.O\ 34 | 27 Institution prestige, 1st author (R) Institution.prestige.1st.author.R\ 35 | 28 Institution prestige, senior author (R) Institution.prestige.senior.author.R\ 36 | 29 Number of Studies (O) Number.of.Studies.O\ 37 | 30 Discipline (O) Discipline.O\ 38 | 31 Number of research sites (O) Number.of.research.sites.O\ 39 | 32 Secondary data (O) Secondary.data.O\ 40 | 33 Methodology expertise required (O) Methodology.expertise.required.O\ 41 | 34 Opportunity for expectancy bias (O) Opportunity.for.expectancy.bias.O\ 42 | 35 Opportunity for lack of diligence (O) Opportunity.for.lack.of.diligence.O\ 43 | 36 Surprising result (O) Surprising.result.O\ 44 | 37 Exciting result (O) Exciting.result.O\ 45 | 38 Internal conceptual replications (O) Internal.conceptual.replications.O\ 46 | 39 Successful conceptual replications (O) Successful.conceptual.replications.O\ 47 | 40 Internal direct replications (O) Internal.direct.replications.O\ 48 | 41 Successful direct replications (O) Successful.direct.replications.O\ 49 | 42 Feasibility (O) Feasibility.O\ 50 | 43 Status (R) Status.R\ 51 | 44 Completion (R) Completion.R\ 52 | 45 Secondary (R) Secondary.R\ 53 | 46 Contact Researcher (R) Contact.Researcher.R\ 54 | 47 Study claim date (R) Study.claim.date.R\ 55 | 48 Analysis completion date (R) Analysis.completion.date.R\ 56 | 49 Coder's email (R) Coder.s.email.R\ 57 | 50 Replicated study number (R) Replicated.study.number.R\ 58 | 51 Test statistic (O) Test.statistic.O\ 59 | 52 N (O) N.O\ 60 | 53 Reported P-value (O) Reported.P.value.O\ 61 | 54 Calculated P-value (O) Calculated.P.value.O\ 62 | 55 # Tails (O) Tails.O\ 63 | 56 Type of analysis (O) Type.of.analysis.O.56\ 64 | 57 Type of effect (O) Type.of.effect.O\ 65 | 58 Description of effect (O) Description.of.effect.O\ 66 | 59 Effect size (O) Effect.size.O\ 67 | 60 Actual Power (O) Actual.Power.O\ 68 | 61 80% power 80.power\ 69 | 62 90% power 90.power\ 70 | 63 95% power 95.power\ 71 | 64 Collect materials from authors Collect.materials.from.authors\ 72 | 65 Planned Sample Planned.Sample\ 73 | 66 Planned Power Planned.Power\ 74 | 67 Original Author's Assessment Original.Author.s.Assessment\ 75 | 68 OSC reviewer (O) OSC.reviewer.O\ 76 | 69 Test statistic (R) Test.statistic.R\ 77 | 70 N (R) N.R\ 78 | 71 P-value (R) P.value.R\ 79 | 72 Direction (R) Direction.R\ 80 | 73 # Tails (R) Tails.R\ 81 | 74 Type of analysis (R) Type.of.analysis.R.74\ 82 | 75 Type of effect (R) Type.of.effect.R\ 83 | 76 Replicate (R) Replicate.R\ 84 | 77 Power (R) Power.R\ 85 | 78 Effect Size (R) Effect.Size.R\ 86 | 79 OSC reviewer (R) OSC.reviewer.R\ 87 | 80 Notes (R) Notes.R\ 88 | 81 Project audit complete (R) Project.audit.complete.R\ 89 | 82 R check location (R) R.check.location.R\ 90 | 83 Degree (R) Degree.R\ 91 | 84 Year of highest degree (R) Year.of.highest.degree.R\ 92 | 85 Current position (R) Current.position.R\ 93 | 86 Domain expertise (R) Domain.expertise.R\ 94 | 87 Method expertise (R) Method.expertise.R\ 95 | 88 Total publications (R) Total.publications.R\ 96 | 89 Peer-reviewed articles (R) Peer.reviewed.articles.R\ 97 | 90 Citations (R) Citations.R\ 98 | 91 Implementation quality (R) Implementation.quality.R\ 99 | 92 Data collection quality (R) Data.collection.quality.R\ 100 | 93 Replication similarity (R) Replication.similarity.R\ 101 | 94 Differences (R) Differences.R\ 102 | 95 Effect similarity (R) Effect.similarity.R\ 103 | 96 Findings similarity (R) Findings.similarity.R\ 104 | 97 Difficulty of implimentation (R) Difficulty.of.implimentation.R\ 105 | 98 Surprise of outcome (R) Surprise.of.outcome.R\ 106 | 99 Dummy Dummy\ 107 | 100 Number of Research sites (R) Number.of.Research.sites.R\ 108 | 101 Secondary data (R) Secondary.data.R\ 109 | 102 Area of expertise (R) Area.of.expertise.R\ 110 | 103 T_N..O. T.N.O\ 111 | 104 T_Test.Statistic..O. T.Test.Statistic.O\ 112 | 105 T_TestComparison..O. T.TestComparison.O\ 113 | 106 T_df1..O. T.df1.O\ 114 | 107 T_df2..O. T.df2.O\ 115 | 108 T_Test.value..O. T.Test.value.O\ 116 | 109 T_p.comparison..O. T.p.comparison.O\ 117 | 110 T_pval..O. T.pval.O\ 118 | 111 T_pval.recalc..O. T.pval.recalc.O\ 119 | 112 T_pval_USE..O. T.pval.USE.O\ 120 | 113 T_sign..O. T.sign.O.113\ 121 | 114 T_r..O. T.r.O\ 122 | 115 T_N..R. T.N.R\ 123 | 116 T_Test.Statistic..R. T.Test.Statistic.R\ 124 | 117 T_Test.Comparison..R. T.Test.Comparison.R\ 125 | 118 T_df1..R. T.df1.R\ 126 | 119 T_df2..R. T.df2.R\ 127 | 120 T_Test.value..R. T.Test.value.R\ 128 | 121 T_p.comparison..R. T.p.comparison.R\ 129 | 122 T_pval..R. T.pval.R\ 130 | 123 T_pval.recalc..R. T.pval.recalc.R\ 131 | 124 T_pval_USE..R. T.pval.USE.R\ 132 | 125 T_sign..R. T.sign.R.125\ 133 | 126 T_r..R. T.r.R\ 134 | 127 T_Comparison.effects..R.O. T.Comparison.effects.R.O\ 135 | 128 Type of analysis (O) Type.of.analysis.O.128\ 136 | 129 Type of analysis (R) Type.of.analysis.R.129\ 137 | 130 V130 V130\ 138 | 131 T_sign_O T.sign.O.131\ 139 | 132 T_sign_R T.sign.R.132\ 140 | 133 T_O_larger T.O.larger\ 141 | 134 T_N_O_for_tables T.N.O.for.tables\ 142 | 135 T_N_R_for_tables T.N.R.for.tables\ 143 | 136 Meta-analytic estimate (Fz) Meta.analytic.estimate.Fz\ 144 | 137 O within CI R O.within.CI.R\ 145 | 138 Meta-analysis significant Meta.analysis.significant} -------------------------------------------------------------------------------- /figures/RPP_ES.eps: -------------------------------------------------------------------------------- 1 | %!PS-Adobe-3.0 EPSF-3.0 2 | %%DocumentNeededResources: font Helvetica 3 | %%+ font Helvetica-Bold 4 | %%+ font Helvetica-Oblique 5 | %%+ font Helvetica-BoldOblique 6 | %%+ font Symbol 7 | %%Title: R Graphics Output 8 | %%Creator: R Software 9 | %%Pages: (atend) 10 | %%BoundingBox: 0 0 713 455 11 | 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| 508.09 104.08 2.70 c p3 769 | 509.78 107.91 2.70 c p3 770 | 512.04 111.74 2.70 c p3 771 | 512.91 119.40 2.70 c p3 772 | 514.89 123.23 2.70 c p3 773 | 518.14 127.06 2.70 c p3 774 | 518.73 130.89 2.70 c p3 775 | 518.86 134.72 2.70 c p3 776 | 519.44 138.55 2.70 c p3 777 | 520.07 142.38 2.70 c p3 778 | 520.58 146.21 2.70 c p3 779 | 522.32 150.04 2.70 c p3 780 | 522.42 153.87 2.70 c p3 781 | 523.17 157.70 2.70 c p3 782 | 523.23 161.53 2.70 c p3 783 | 523.77 165.36 2.70 c p3 784 | 524.20 169.19 2.70 c p3 785 | 525.20 173.02 2.70 c p3 786 | 525.54 176.85 2.70 c p3 787 | 526.74 180.68 2.70 c p3 788 | 530.78 184.51 2.70 c p3 789 | 534.77 188.34 2.70 c p3 790 | 537.33 192.17 2.70 c p3 791 | 539.08 196.00 2.70 c p3 792 | 542.08 199.83 2.70 c p3 793 | 543.29 203.66 2.70 c p3 794 | 547.06 207.49 2.70 c p3 795 | 548.52 211.32 2.70 c p3 796 | 550.12 215.15 2.70 c p3 797 | 552.41 218.98 2.70 c p3 798 | 553.98 222.81 2.70 c p3 799 | 557.31 226.64 2.70 c p3 800 | 559.87 230.47 2.70 c p3 801 | 560.65 234.30 2.70 c p3 802 | 561.55 238.13 2.70 c p3 803 | 561.82 241.96 2.70 c p3 804 | 563.06 245.79 2.70 c p3 805 | 566.60 249.62 2.70 c p3 806 | 567.49 253.45 2.70 c p3 807 | 569.07 257.28 2.70 c p3 808 | 569.99 261.12 2.70 c p3 809 | 570.71 264.95 2.70 c p3 810 | 571.00 268.78 2.70 c p3 811 | 571.06 272.61 2.70 c p3 812 | 571.09 276.44 2.70 c p3 813 | 571.33 280.27 2.70 c p3 814 | 571.59 284.10 2.70 c p3 815 | 572.13 287.93 2.70 c p3 816 | 572.90 291.76 2.70 c p3 817 | 576.37 295.59 2.70 c p3 818 | 577.73 299.42 2.70 c p3 819 | 585.39 303.25 2.70 c p3 820 | 585.55 307.08 2.70 c p3 821 | 586.93 310.91 2.70 c p3 822 | 587.50 314.74 2.70 c p3 823 | 591.73 318.57 2.70 c p3 824 | 596.53 322.40 2.70 c p3 825 | 603.32 326.23 2.70 c p3 826 | 607.28 330.06 2.70 c p3 827 | 607.83 333.89 2.70 c p3 828 | 608.95 337.72 2.70 c p3 829 | 609.46 341.55 2.70 c p3 830 | 613.56 345.38 2.70 c p3 831 | 615.92 349.21 2.70 c p3 832 | 624.10 353.04 2.70 c p3 833 | 625.09 356.87 2.70 c p3 834 | 626.96 360.70 2.70 c p3 835 | 627.24 364.53 2.70 c p3 836 | 630.08 368.36 2.70 c p3 837 | 637.05 372.19 2.70 c p3 838 | 637.42 376.02 2.70 c p3 839 | 637.53 379.85 2.70 c p3 840 | 637.62 383.68 2.70 c p3 841 | 642.48 387.51 2.70 c p3 842 | 646.24 391.34 2.70 c p3 843 | 654.39 395.17 2.70 c p3 844 | 661.18 399.00 2.70 c p3 845 | 663.96 402.83 2.70 c p3 846 | 665.25 406.66 2.70 c p3 847 | 669.31 410.49 2.70 c p3 848 | 670.08 414.32 2.70 c p3 849 | 674.01 418.15 2.70 c p3 850 | 674.39 421.98 2.70 c p3 851 | 676.46 425.81 2.70 c p3 852 | 676.66 429.64 2.70 c p3 853 | 681.34 433.47 2.70 c p3 854 | 690.80 437.30 2.70 c p3 855 | 716.27 441.13 2.70 c p3 856 | 718.68 444.96 2.70 c p3 857 | 0.7020 0.7020 0.7020 srgb 858 | 0.75 setlinewidth 859 | [ 2.25 3.75] 0 setdash 860 | np 861 | 455.04 73.44 m 862 | 306.72 0 l 863 | o 864 | np 865 | 455.04 444.96 m 866 | 306.72 0 l 867 | o 868 | 0 0 0 srgb 869 | 0.75 setlinewidth 870 | [] 0 setdash 871 | np 872 | 249.57 73.44 m 873 | 67.39 0 l 874 | o 875 | np 876 | 316.96 77.27 m 877 | 69.71 0 l 878 | o 879 | np 880 | 386.67 81.10 m 881 | 2.35 0 l 882 | o 883 | np 884 | 389.02 84.93 m 885 | 12.20 0 l 886 | o 887 | np 888 | 401.22 88.76 m 889 | 12.38 0 l 890 | o 891 | np 892 | 413.60 92.59 m 893 | 7.51 0 l 894 | o 895 | np 896 | 421.11 96.42 m 897 | 2.76 0 l 898 | o 899 | np 900 | 423.87 100.25 m 901 | 15.13 0 l 902 | o 903 | np 904 | 439.00 104.08 m 905 | 1.04 0 l 906 | o 907 | np 908 | 440.04 107.91 m 909 | 2.07 0 l 910 | o 911 | np 912 | 442.11 111.74 m 913 | 1.60 0 l 914 | o 915 | np 916 | 443.71 115.57 m 917 | 0.82 0 l 918 | o 919 | np 920 | 444.53 119.40 m 921 | 0.08 0 l 922 | o 923 | np 924 | 444.61 123.23 m 925 | 1.08 0 l 926 | o 927 | np 928 | 445.69 127.06 m 929 | 0.10 0 l 930 | o 931 | np 932 | 445.79 130.89 m 933 | 7.38 0 l 934 | o 935 | np 936 | 453.17 134.72 m 937 | 0.42 0 l 938 | o 939 | np 940 | 453.59 138.55 m 941 | 2.32 0 l 942 | o 943 | np 944 | 455.91 142.38 m 945 | 0.05 0 l 946 | o 947 | np 948 | 455.96 146.21 m 949 | 2.68 0 l 950 | o 951 | np 952 | 458.64 150.04 m 953 | 0.40 0 l 954 | o 955 | np 956 | 459.04 153.87 m 957 | 1.42 0 l 958 | o 959 | np 960 | 460.46 157.70 m 961 | 0.19 0 l 962 | o 963 | np 964 | 460.65 161.53 m 965 | 0.03 0 l 966 | o 967 | np 968 | 460.68 165.36 m 969 | 0.32 0 l 970 | o 971 | np 972 | 461.00 169.19 m 973 | 1.35 0 l 974 | o 975 | np 976 | 462.35 173.02 m 977 | 1.71 0 l 978 | o 979 | np 980 | 464.06 176.85 m 981 | 2.25 0 l 982 | o 983 | np 984 | 466.31 180.68 m 985 | 2.31 0 l 986 | o 987 | np 988 | 468.62 184.51 m 989 | 0.07 0 l 990 | o 991 | np 992 | 468.69 188.34 m 993 | 5.28 0 l 994 | o 995 | np 996 | 473.97 192.17 m 997 | 0.90 0 l 998 | o 999 | np 1000 | 474.87 196.00 m 1001 | 0.45 0 l 1002 | o 1003 | np 1004 | 475.32 199.83 m 1005 | 2.22 0 l 1006 | o 1007 | np 1008 | 477.54 203.66 m 1009 | 0.14 0 l 1010 | o 1011 | np 1012 | 477.68 207.49 m 1013 | 0.89 0 l 1014 | o 1015 | np 1016 | 478.57 211.32 m 1017 | 2.00 0 l 1018 | o 1019 | np 1020 | 480.57 215.15 m 1021 | 2.43 0 l 1022 | o 1023 | np 1024 | 483.00 218.98 m 1025 | 0.88 0 l 1026 | o 1027 | np 1028 | 483.88 222.81 m 1029 | 2.36 0 l 1030 | o 1031 | np 1032 | 486.24 226.64 m 1033 | 0.19 0 l 1034 | o 1035 | np 1036 | 486.43 230.47 m 1037 | 0.28 0 l 1038 | o 1039 | np 1040 | 486.71 234.30 m 1041 | 0.52 0 l 1042 | o 1043 | np 1044 | 487.23 238.13 m 1045 | 0.29 0 l 1046 | o 1047 | np 1048 | 487.52 241.96 m 1049 | 1.26 0 l 1050 | o 1051 | np 1052 | 488.78 245.79 m 1053 | 1.18 0 l 1054 | o 1055 | np 1056 | 489.96 249.62 m 1057 | 1.52 0 l 1058 | o 1059 | np 1060 | 491.48 253.45 m 1061 | 0.39 0 l 1062 | o 1063 | np 1064 | 491.87 257.28 m 1065 | 0.14 0 l 1066 | o 1067 | np 1068 | 492.01 261.12 m 1069 | 3.34 0 l 1070 | o 1071 | np 1072 | 495.35 264.95 m 1073 | 1.04 0 l 1074 | o 1075 | np 1076 | 496.39 268.78 m 1077 | 2.84 0 l 1078 | o 1079 | np 1080 | 499.23 272.61 m 1081 | 1.31 0 l 1082 | o 1083 | np 1084 | 500.54 276.44 m 1085 | 0.58 0 l 1086 | o 1087 | np 1088 | 501.12 280.27 m 1089 | 1.47 0 l 1090 | o 1091 | np 1092 | 502.59 284.10 m 1093 | 0.11 0 l 1094 | o 1095 | np 1096 | 502.70 287.93 m 1097 | 1.74 0 l 1098 | o 1099 | np 1100 | 504.44 291.76 m 1101 | 5.15 0 l 1102 | o 1103 | np 1104 | 509.59 295.59 m 1105 | 0.47 0 l 1106 | o 1107 | np 1108 | 510.06 299.42 m 1109 | 6.32 0 l 1110 | o 1111 | np 1112 | 516.38 303.25 m 1113 | 2.31 0 l 1114 | o 1115 | np 1116 | 518.69 307.08 m 1117 | 3.08 0 l 1118 | o 1119 | np 1120 | 521.77 310.91 m 1121 | 2.52 0 l 1122 | o 1123 | np 1124 | 524.29 314.74 m 1125 | 1.17 0 l 1126 | o 1127 | np 1128 | 525.46 318.57 m 1129 | 5.30 0 l 1130 | o 1131 | np 1132 | 530.76 322.40 m 1133 | 0.96 0 l 1134 | o 1135 | np 1136 | 531.72 326.23 m 1137 | 1.27 0 l 1138 | o 1139 | np 1140 | 532.99 330.06 m 1141 | 4.86 0 l 1142 | o 1143 | np 1144 | 537.85 333.89 m 1145 | 1.02 0 l 1146 | o 1147 | np 1148 | 538.87 337.72 m 1149 | 3.36 0 l 1150 | o 1151 | np 1152 | 542.23 341.55 m 1153 | 1.67 0 l 1154 | o 1155 | np 1156 | 543.90 345.38 m 1157 | 8.06 0 l 1158 | o 1159 | np 1160 | 551.96 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1229 | 0.07 0 l 1230 | o 1231 | np 1232 | 655.32 418.15 m 1233 | 8.77 0 l 1234 | o 1235 | np 1236 | 664.09 421.98 m 1237 | 6.85 0 l 1238 | o 1239 | np 1240 | 670.94 425.81 m 1241 | 14.26 0 l 1242 | o 1243 | np 1244 | 685.20 429.64 m 1245 | 4.02 0 l 1246 | o 1247 | np 1248 | 689.22 433.47 m 1249 | 5.28 0 l 1250 | o 1251 | np 1252 | 694.50 437.30 m 1253 | 14.81 0 l 1254 | o 1255 | np 1256 | 709.31 441.13 m 1257 | 28.81 0 l 1258 | o 1259 | np 1260 | 738.12 444.96 m 1261 | 53.88 0 l 1262 | o 1263 | /bg { 0 0 0 srgb } def 1264 | 316.96 77.27 2.70 c p3 1265 | 386.67 81.10 2.70 c p3 1266 | 389.02 84.93 2.70 c p3 1267 | 401.22 88.76 2.70 c p3 1268 | 413.60 92.59 2.70 c p3 1269 | 421.11 96.42 2.70 c p3 1270 | 423.87 100.25 2.70 c p3 1271 | 439.00 104.08 2.70 c p3 1272 | 440.04 107.91 2.70 c p3 1273 | 442.11 111.74 2.70 c p3 1274 | 443.71 115.57 2.70 c p3 1275 | 444.53 119.40 2.70 c p3 1276 | 444.61 123.23 2.70 c p3 1277 | 445.69 127.06 2.70 c p3 1278 | 445.79 130.89 2.70 c p3 1279 | 453.17 134.72 2.70 c p3 1280 | 453.59 138.55 2.70 c p3 1281 | 455.91 142.38 2.70 c p3 1282 | 455.96 146.21 2.70 c p3 1283 | 458.64 150.04 2.70 c p3 1284 | 459.04 153.87 2.70 c p3 1285 | 460.46 157.70 2.70 c p3 1286 | 460.65 161.53 2.70 c p3 1287 | 460.68 165.36 2.70 c p3 1288 | 461.00 169.19 2.70 c p3 1289 | 462.35 173.02 2.70 c p3 1290 | 464.06 176.85 2.70 c p3 1291 | 466.31 180.68 2.70 c p3 1292 | 468.62 184.51 2.70 c p3 1293 | 468.69 188.34 2.70 c p3 1294 | 473.97 192.17 2.70 c p3 1295 | 474.87 196.00 2.70 c p3 1296 | 475.32 199.83 2.70 c p3 1297 | 477.54 203.66 2.70 c p3 1298 | 477.68 207.49 2.70 c p3 1299 | 478.57 211.32 2.70 c p3 1300 | 480.57 215.15 2.70 c p3 1301 | 483.00 218.98 2.70 c p3 1302 | 483.88 222.81 2.70 c p3 1303 | 486.24 226.64 2.70 c p3 1304 | 486.43 230.47 2.70 c p3 1305 | 486.71 234.30 2.70 c p3 1306 | 487.23 238.13 2.70 c p3 1307 | 487.52 241.96 2.70 c p3 1308 | 488.78 245.79 2.70 c p3 1309 | 489.96 249.62 2.70 c p3 1310 | 491.48 253.45 2.70 c p3 1311 | 491.87 257.28 2.70 c p3 1312 | 492.01 261.12 2.70 c p3 1313 | 495.35 264.95 2.70 c p3 1314 | 496.39 268.78 2.70 c p3 1315 | 499.23 272.61 2.70 c p3 1316 | 500.54 276.44 2.70 c p3 1317 | 501.12 280.27 2.70 c p3 1318 | 502.59 284.10 2.70 c p3 1319 | 502.70 287.93 2.70 c p3 1320 | 504.44 291.76 2.70 c p3 1321 | 509.59 295.59 2.70 c p3 1322 | 510.06 299.42 2.70 c p3 1323 | 516.38 303.25 2.70 c p3 1324 | 518.69 307.08 2.70 c p3 1325 | 521.77 310.91 2.70 c p3 1326 | 524.29 314.74 2.70 c p3 1327 | 525.46 318.57 2.70 c p3 1328 | 530.76 322.40 2.70 c p3 1329 | 531.72 326.23 2.70 c p3 1330 | 532.99 330.06 2.70 c p3 1331 | 537.85 333.89 2.70 c p3 1332 | 538.87 337.72 2.70 c p3 1333 | 542.23 341.55 2.70 c p3 1334 | 543.90 345.38 2.70 c p3 1335 | 551.96 349.21 2.70 c p3 1336 | 554.03 353.04 2.70 c p3 1337 | 562.33 356.87 2.70 c p3 1338 | 571.46 360.70 2.70 c p3 1339 | 576.45 364.53 2.70 c p3 1340 | 578.32 368.36 2.70 c p3 1341 | 581.21 372.19 2.70 c p3 1342 | 584.89 376.02 2.70 c p3 1343 | 588.31 379.85 2.70 c p3 1344 | 598.11 383.68 2.70 c p3 1345 | 602.49 387.51 2.70 c p3 1346 | 607.51 391.34 2.70 c p3 1347 | 608.46 395.17 2.70 c p3 1348 | 609.98 399.00 2.70 c p3 1349 | 615.93 402.83 2.70 c p3 1350 | 616.78 406.66 2.70 c p3 1351 | 642.11 410.49 2.70 c p3 1352 | 655.25 414.32 2.70 c p3 1353 | 655.32 418.15 2.70 c p3 1354 | 664.09 421.98 2.70 c p3 1355 | 670.94 425.81 2.70 c p3 1356 | 685.20 429.64 2.70 c p3 1357 | 689.22 433.47 2.70 c p3 1358 | 694.50 437.30 2.70 c p3 1359 | 709.31 441.13 2.70 c p3 1360 | 738.12 444.96 2.70 c p3 1361 | 0.7020 0.7020 0.7020 srgb 1362 | 0.75 setlinewidth 1363 | [ 2.25 3.75] 0 setdash 1364 | np 1365 | 455.04 73.44 m 1366 | 306.72 0 l 1367 | o 1368 | np 1369 | 455.04 444.96 m 1370 | 306.72 0 l 1371 | o 1372 | 0.7451 0.7451 0.7451 srgb 1373 | 0.75 setlinewidth 1374 | [] 0 setdash 1375 | np 1376 | 603.86 244.80 m 1377 | 21.60 0 l 1378 | o 1379 | 0 0 0 srgb 1380 | np 1381 | 603.86 230.40 m 1382 | 21.60 0 l 1383 | o 1384 | /Font1 findfont 12 s 1385 | 636.26 240.49 (Or) 0 ta 1386 | 0.180 (iginal, k = 97) tb gr 1387 | 636.26 226.09 (Replication, k = 97) 0 0 t 1388 | ep 1389 | %%Trailer 1390 | %%Pages: 1 1391 | %%EOF 1392 | -------------------------------------------------------------------------------- /figures/figure s3.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CenterForOpenScience/rpp/a7c5b87f15f60178e6f5d25f983217d39f5ac654/figures/figure s3.jpg -------------------------------------------------------------------------------- /figures/figure s4.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CenterForOpenScience/rpp/a7c5b87f15f60178e6f5d25f983217d39f5ac654/figures/figure s4.jpg -------------------------------------------------------------------------------- /figures/figure s5.jpg: -------------------------------------------------------------------------------- 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color. 9 | # Define transparancy with an integer between 0 and 255 10 | # 0 being fully transparant and 255 being fully visable 11 | # Works with either color and trans a vector of equal length, 12 | # or one of the two of length 1. 13 | 14 | if (length(color)!=length(trans)&!any(c(length(color),length(trans))==1)) stop("Vector lengths not correct") 15 | if (length(color)==1 & length(trans)>1) color <- rep(color,length(trans)) 16 | if (length(trans)==1 & length(color)>1) trans <- rep(trans,length(color)) 17 | 18 | num2hex <- function(x) 19 | { 20 | hex <- unlist(strsplit("0123456789ABCDEF",split="")) 21 | return(paste(hex[(x-x%%16)/16+1],hex[x%%16+1],sep="")) 22 | } 23 | rgb <- rbind(col2rgb(color),trans) 24 | res <- paste("#",apply(apply(rgb,2,num2hex),2,paste,collapse=""),sep="") 25 | return(res) 26 | } 27 | 28 | ### 29 | # Effect size computation 30 | # Written by CHJ Hartgerink 31 | esComp <- function( 32 | x, 33 | df1, 34 | df2, 35 | N, 36 | esType){ 37 | esComp <- ifelse(esType=="t", 38 | sqrt((x^2*(1 / df2)) / (((x^2*1) / df2) + 1)), 39 | ifelse( 40 | esType=="F", 41 | sqrt((x*(df1 / df2)) / (((x*df1) / df2) + 1))*sqrt(1/df1), 42 | ifelse( 43 | esType=="r", 44 | x, 45 | ifelse( 46 | esType=="Chi2", 47 | sqrt(x/N), 48 | ifelse( 49 | esType == "z", 50 | tanh(x * sqrt(1/(N-3))), 51 | NA 52 | ) 53 | ) 54 | ) 55 | )) 56 | 57 | return(esComp) 58 | } 59 | 60 | ### 61 | # Written by CHJ Hartgerink 62 | # The Fisher method applied to test for deviation from uniformity 63 | # In NONSIGNIFICANT P-values 64 | 65 | FisherMethod <- function(# Compute Fisher's exact test for non-significant p-values. 66 | ### This function computes paper level Fisher test statistics, testing whether the distribution of non-significant p-values is uniform. Significant values indicate deviation from uniformity. 67 | ### Returns both the normal Fisher test, as well as the complement test. 68 | ### Computations are done for p*=log(p), where p is all non-significant p-values for each identifier. 69 | x, 70 | ### Vector of p-values. 71 | id, 72 | ### Vector giving paper identifiers. 73 | alpha = .05 74 | ### Indicate what alpha level is being maintained for the study results, which serves as a cut-off for selecting the non-significant p-values. 75 | ){ 76 | Res <- NULL 77 | for(i in 1:length(unique(id))) 78 | { 79 | selP <- x[id==unique(id)[i]] 80 | nSigP <- (na.omit(selP[selP>alpha])-alpha)/(1-alpha) 81 | SigP <- na.omit(selP[selP<=alpha]) 82 | if(!length(nSigP)==0){ 83 | # Compute the Fisher test statistic 84 | FMeth <- -2*sum(log(nSigP)) 85 | pFMeth <- pchisq(q=FMeth, df=2*length(nSigP), lower.tail=F) 86 | } else { 87 | FMeth <- NA 88 | pFMeth <- NA 89 | } 90 | Res <- rbind(Res, data.frame( 91 | Fish = FMeth, 92 | PFish = pFMeth, 93 | CountNSig = length(nSigP), 94 | CountSig = length(SigP), 95 | PercentNonSig = length(nSigP)/length(selP))) 96 | } 97 | return(Res) 98 | } 99 | 100 | ### 101 | # Recalculating p-values 102 | # Written by CHJ Hartgerink and RCM van Aert 103 | 104 | pvalr <- function(x, N) { 105 | fis.r <- 0.5*log((1 + x) / (1 - x)) 106 | se.fis.r <- sqrt(1/(N-3)) 107 | pnorm(fis.r, mean = 0, sd = se.fis.r, lower.tail = FALSE) 108 | } 109 | 110 | # Computes two-tailed p-value 111 | pvalComp <- function( 112 | x, 113 | df1, 114 | df2, 115 | N, 116 | esType){ 117 | pvalComp <- ifelse(esType=="t", 118 | pt(abs(x), df = df2, lower.tail = FALSE) * 2, 119 | ifelse( 120 | esType=="F", 121 | pf(x, df1 = df1, df2 = df2, lower.tail = FALSE), 122 | ifelse( 123 | esType=="r", 124 | pvalr(abs(x), N) * 2, 125 | ifelse( 126 | esType=="Chi2", 127 | pchisq(x, df = df1, lower.tail = FALSE), 128 | ifelse( 129 | esType == "z", 130 | pnorm(abs(x), lower.tail = FALSE) * 2, 131 | NA 132 | ) 133 | ) 134 | ) 135 | )) 136 | 137 | return(pvalComp) 138 | } -------------------------------------------------------------------------------- /output.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CenterForOpenScience/rpp/a7c5b87f15f60178e6f5d25f983217d39f5ac654/output.txt -------------------------------------------------------------------------------- /rpp_reproduce.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CenterForOpenScience/rpp/a7c5b87f15f60178e6f5d25f983217d39f5ac654/rpp_reproduce.zip -------------------------------------------------------------------------------- /vieland evidence nonsig.R: -------------------------------------------------------------------------------- 1 | # Written by CHJ Hartgerink van MALM van Assen 2 | if(!require(httr)){install.packages('httr')} 3 | library(httr) 4 | 5 | # Description of results included here 6 | # Of relevance to Gilbert et al's commentary on the Reproducibility Project: 7 | # Psychology (OSC2015) is whether the nonsignificant results in OSC2015 may 8 | # have arisen from true nonzero effects. We re-analyzed the 64 statistically 9 | # nonsignificant results to estimate their strength of evidence for non-zero 10 | # effects. After excluding one result for which no test statistic was reported, 11 | # Fisher’s test indicated that the hypothesis of 63 zero effects can be rejected 12 | # (χ2(126) = 155.24, p = .039). To get an indication of how strong these 13 | # non-zero effects may be, we hypothesized that X out of 63 results are 14 | # generated by a true non-zero correlation of ρ, and 63-X are generated by true 15 | # zero effects. Using the inversion method for calculating confidence intervals 16 | # based on the observed value of the Fisher test statistic, we determined the 17 | # 95% confidence interval of X for ρ = .1 (weak), .3 (medium), .5 (strong 18 | # effect). Our analyses revealed that 0-63 nonsignificant results may be 19 | # generated by a true small effect (0-100%), 0-21 by a true medium effect 20 | # (0-33.3%), or 0-13 by a true large effect (0-20.6%). These results can be 21 | # reproduced by executing this code.  These results demonstrate that we cannot 22 | # draw strong conclusions about the true effect sizes underlying individual 23 | # nonsignificant OSC2015 results.  The effects may all be essentially zero, but 24 | # some could range from small to medium or even large. Therefore, we agree that 25 | # the OSC2015 findings could underestimate the proportion of “true” effects, 26 | # with the caveat that regardless of statistical significance, many published 27 | # effect sizes are likely to be smaller than initially assumed. 28 | 29 | 30 | # Read in data 31 | info <- GET('https://osf.io/fgjvw/?action=download', write_disk('rpp_data.csv', overwrite = TRUE)) #downloads data file from the OSF 32 | MASTER <- read.csv("rpp_data.csv")[1:167, ] 33 | colnames(MASTER)[1] <- "ID" # Change first column name to ID to be able to load .csv file 34 | 35 | # source function needed 36 | info <- GET('https://osf.io/b2vn7/?action=download', write_disk('functions.r', overwrite = TRUE)) 37 | source('functions.r') 38 | 39 | # number of iterations per condition 40 | # if true probability is .025 or .975, then the SE = .00494 for n.iter = 1,000 41 | n.iter <- 10000 42 | # alpha of the individual test 43 | alpha <- .05 44 | # vector of ES 45 | es <- c(.1, .3, .5) 46 | 47 | # set seed 48 | set.seed(alpha * n.iter * sum(es)) 49 | 50 | # select only the available and nonsignificant replications 51 | # call this M 52 | M <- MASTER[MASTER$T_sign_R == 0 53 | & !is.na(MASTER$T_sign_R) 54 | & (MASTER$T_Test.Statistic..R. == 't' 55 | | MASTER$T_Test.Statistic..R. == 'F' 56 | | MASTER$T_Test.Statistic..R. == 'r' 57 | | MASTER$T_Test.Statistic..R. == 'z' 58 | | MASTER$T_Test.Statistic..R. == 'Chi2'), ] 59 | 60 | Fish_M <- FisherMethod(x = M$T_pval_USE..R., 61 | id = 1, 62 | alpha = .05) 63 | 64 | # selectors for easy selection later on 65 | selr <- M$T_Test.Statistic..R. == 'r' 66 | selz <- M$T_Test.Statistic..R. == 'z' 67 | selChi2 <- M$T_Test.Statistic..R. == 'Chi2' 68 | 69 | # ensure that we can easily compute the effect size r 70 | M$T_df1..R.[selr] <- 1 71 | M$T_df2..R.[selr] <- M$T_N..R.[selr] - 2 72 | # ensure z is tranformed to fisher z 73 | M$sigma[selz] <- sqrt(1 / (M$T_N..R.[selz] - 3)) 74 | 75 | # object to write count to 76 | res <- list(NULL) 77 | res[[1]] <- rep(0, dim(M)[1] + 1) 78 | res[[2]] <- rep(0, dim(M)[1] + 1) 79 | res[[3]] <- rep(0, dim(M)[1] + 1) 80 | 81 | # compute critical value for F and t values 82 | # we assume two-tailed tests everywhere 83 | fCV <- qf(p = alpha, df1 = M$T_df1..R., df2 = M$T_df2..R., lower.tail = FALSE) 84 | # compute critical value for z values 85 | zCV <- qnorm(p = .025, mean = 0, sd = M$sigma[selz], lower.tail = FALSE) 86 | # compute critical value for chi2 values 87 | Chi2CV <- qchisq(p = .05, df = M$T_df1..R.[selChi2], lower.tail = FALSE) 88 | 89 | for(es_loop in 1:length(es)){ 90 | 91 | # L will be the number of nonsignificant studies sampled from studies from M, 92 | # which have true effect es > 0 93 | for (L in 0:dim(M)[1]){ 94 | 95 | # randomly generate a p-value given the effect sizes 96 | # the null effects will yield uniform p-values 97 | for (i in 1:n.iter){ 98 | 99 | ran_L <- sample(dim(M)[1], size = L, replace = FALSE) 100 | 101 | # set population effect for the randomly sampled to es. 102 | pop_effect <- rep(0, dim(M)[1]) 103 | if (!L == 0) pop_effect[ran_L] <- es[es_loop] 104 | 105 | # compute the noncentrality parameter 106 | # for the null effects this will be 0 107 | f2 <- pop_effect^2 / (1 - pop_effect^2) 108 | ncp <- f2 * ifelse(M$T_Test.Statistic..R. == "F", 109 | M$T_df1..R. + M$T_df2..R. + 1, 110 | ifelse(selr, 111 | M$T_df1..R. + M$T_df2..R. + 1, 112 | ifelse(selChi2, 113 | M$T_N..R., 114 | M$T_df1..R. + M$T_df2..R.))) 115 | 116 | # determine area under H1 that corresponds to nonsignificant findings (= beta = Type II error) 117 | beta <- pf(q = fCV, df1 = M$T_df1..R., df2 = M$T_df2..R., ncp = ncp, lower.tail = TRUE) 118 | beta[selz] <- pnorm(zCV, atanh(pop_effect[selz]), M$sigma[selz], lower.tail = TRUE) - pnorm(-zCV, atanh(pop_effect[selz]), M$sigma[selz], lower.tail = TRUE) 119 | beta[selChi2] <- pchisq(Chi2CV, df = M$T_df1..R.[selChi2], ncp = ncp[selChi2], lower.tail = TRUE) 120 | 121 | # compute p-value alternative dist; sample from beta 122 | pA <- runif(length(beta), min = 0, max = beta) 123 | 124 | # test statistics simulated under alternative, based on beta 125 | fA <- qf(p = pA, df1 = M$T_df1..R., df2 = M$T_df2..R., ncp = ncp, lower.tail = TRUE) 126 | fA[selz] <- qnorm(pA[selz] + pnorm(-zCV, atanh(pop_effect[selz]), M$sigma[selz], lower.tail = TRUE), 127 | atanh(pop_effect[selz]), M$sigma[selz], lower.tail = TRUE) 128 | fA[selChi2] <- qchisq(pA[selChi2], df = M$T_df1..R.[selChi2], ncp = ncp[selChi2], lower.tail = TRUE) 129 | 130 | # pValues under null 131 | # convert test statistics under alternative to p-values under h0 132 | p0 <- pf(q = fA, df1 = M$T_df1..R., df2 = M$T_df2..R., lower.tail = FALSE) 133 | p0[selz] <- 1 - 2 * abs(pnorm(fA[selz], 0, M$sigma[selz], lower.tail = FALSE) - .5) 134 | p0[selChi2] <- pchisq(fA[selChi2], M$T_df1..R.[selChi2], lower.tail = FALSE) 135 | 136 | # Result of Fisher test for this run with L true effects > 0 137 | simulated <- FisherMethod(p0, 1, .05) 138 | 139 | res[[es_loop]][L + 1] <- res[[es_loop]][L + 1] + ifelse(simulated$Fish > Fish_M$Fish, 1, 0) 140 | } 141 | } 142 | } 143 | 144 | # pop_effect = .1 145 | # sampled from L = 0:63 146 | # 10000 iterations 147 | # > res[[1]] / n.iter 148 | # [1] 0.0386 0.0428 0.0519 0.0551 0.0576 0.0638 0.0752 0.0818 0.0902 0.0921 0.1018 0.1142 149 | # [13] 0.1287 0.1312 0.1468 0.1595 0.1669 0.1812 0.1828 0.2010 0.2186 0.2352 0.2470 0.2629 150 | # [25] 0.2733 0.2935 0.3037 0.3222 0.3439 0.3596 0.3723 0.3950 0.4047 0.4197 0.4432 0.4584 151 | # [37] 0.4826 0.5048 0.5080 0.5365 0.5536 0.5576 0.5955 0.6062 0.6181 0.6363 0.6505 0.6713 152 | # [49] 0.6738 0.6996 0.7166 0.7239 0.7394 0.7540 0.7760 0.7736 0.7920 0.8046 0.8165 0.8329 153 | # [61] 0.8328 0.8523 0.8621 0.8719 154 | 155 | # pop_effect = .3 156 | # sampled from L = 0:63 157 | # 10000 iterations 158 | # > res[[2]] / n.iter 159 | # [1] 0.0397 0.0590 0.0901 0.1294 0.1726 0.2231 0.2856 0.3542 0.4347 0.4947 0.5769 0.6511 160 | # [13] 0.7153 0.7817 0.8280 0.8777 0.9038 0.9339 0.9532 0.9678 0.9790 0.9851 0.9903 0.9955 161 | # [25] 0.9975 0.9982 0.9989 0.9995 0.9995 0.9996 0.9998 0.9999 1.0000 1.0000 1.0000 1.0000 162 | # [37] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 163 | # [49] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 164 | # [61] 1.0000 1.0000 1.0000 1.0000 165 | 166 | # pop_effect = .5 167 | # sampled from L = 0:63 168 | # 10000 iterations 169 | # > res[[3]] / n.iter 170 | # [1] 0.0386 0.0777 0.1265 0.2085 0.3026 0.4241 0.5481 0.6754 0.7932 0.8589 0.9169 0.9558 171 | # [13] 0.9776 0.9888 0.9958 0.9985 0.9991 0.9995 0.9999 0.9999 1.0000 1.0000 1.0000 1.0000 172 | # [25] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 173 | # [37] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 174 | # [49] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 175 | # [61] 1.0000 1.0000 1.0000 1.0000 176 | --------------------------------------------------------------------------------