R package forsterUVA

Background

This package contained reprocessed data and refactored code from the investigation by the University of Amsterdam (UvA) of the scientific integrity of publications by Jens Forster. The forsterUVA package tidies up the source data and provides standard package documentation to make the analyses reproducible.

A PDF of the UvA report by Peeters, Klassen, and van de Weil can be downloaded here.

The original R scripts on which the package is based can be downloaded in a single zip file, which is available here.

The package contains three main tables: pubs, studies, and study_stats. These were extracted from the original scripts. The table findings has the results of the statistical investigation from the original report. The findings can be reproduced using the functions deltaF.linear() and evi.val().

Installation

You need to have the add-on package devtools installed to be able to install forsterUVA.

library("devtools")

install_github("dalejbarr/forsterUVA")

Working with the package

Below are scripts showing how to reproduce the analyses in two different ways:

1. using functions in base R, or
2. using the add-on package dplyr.

Reproducing analyses using base R

Stats for individual studies

library("forsterUVA")
 
## calculate delta F p-value and V statistic for ea study
ss2 <- merge(studies, study_stats, c("pub", "study"))
ss2$pdF <- apply(as.matrix(ss2[, -c(1:2)]), 1, deltaFp)
ss2$V <- apply(as.matrix(ss2[, -c(1:2)]), 1, eviVal)

dat <- ss2[, c("pub", "study", "pdF", "V")]
head(transform(dat, pdF = round(pdF, 4), V = round(V, 1)), 10)

pub	study	pdF	V
D.JF.L09.JESP	exp1.WA.NS.B1	0.9336	5.5
D.JF.L09.JESP	exp1.WA.NS.B2	0.4927	1.1
D.JF.L09.JESP	exp1.WA.NS.B3	0.4609	1
D.JF.L09.JESP	exp1.WA.S.B1	0.8647	3.2
D.JF.L09.JESP	exp1.WA.S.B2	0.001	1
D.JF.L09.JESP	exp1.WA.S.B3	0.0492	1
D.JF.L09.JESP	exp1.WU.NS.B1	0.9373	6.3
D.JF.L09.JESP	exp1.WU.NS.B2	0.8817	3.7
D.JF.L09.JESP	exp1.WU.NS.B3	0.5455	1.2
D.JF.L09.JESP	exp1.WU.S.B1	0.8462	1.5

Stats for individual publications

## overall stats for each publication
pubdata <- merge(ss2, pubs, c("pub"))
pubsplit <- split(pubdata, pubdata$pub)

pubs2 <- data.frame(pub = names(pubsplit), stringsAsFactors = FALSE)
pubs2$p <- unlist(lapply(pubsplit, combine_pdF))
pubs2$V_prod <- unlist(lapply(pubsplit, combine_V))
pubs2$n_studies <- unlist(lapply(pubsplit, nrow))
dat <- transform(merge(pubs2, pubs, by = "pub"),
                 p = round(p, 4), V_prod = round(V_prod, 4))
dat[order(dat$section, dat$pub), c("pub", "section", "p", "V_prod", "n_studies")]

pub	section	p	V_prod	n_studies
Hagtvedt	control	0.3025	1.6384	2
Hunt	control	0.7776	1	1
Jia	control	0.6911	1	1
Kanten	control	0.4236	1.756	2
Lerouge	control	0.4411	14.8772	4
Malkoc	control	0.0952	5.2558	1
Polman	control	0.3785	1.3369	1
Rook	control	0.5262	1.6933	2
Smith	control	0.2995	8.5948	7
L.JF09.JPSP	JF_co, Amsterdam	0	31319196	18
WCY.JF11.JESP	JF_co, Amsterdam	0.6811	1	1
D.JF.L09.JESP	JF_co, Bremen / Würzburg	0.2544	930785.4679	17
D.JF.LR10.PSPB	JF_co, Bremen / Würzburg	0.9995	40.7372	13
K.JF.D10.SPPS	JF_co, Bremen / Würzburg	0.0122	4025.8013	8
L.JF09.CS	JF_co, Bremen / Würzburg	0.1088	12.7841	6
FG.JF12.MP	JF_co, others	0.9561	7.7661	8
JF.D12.JESP	JF_first	0.0053	250357.4084	12
JF.D12.SPPS	JF_first	0	4227517188560.13	19
JF.EO09.PSPB	JF_first	2e-04	2958.2939	5
JF.LK08.JPSP	JF_first	0.6263	15888.188	20
JF.LS09.JEPG	JF_first	0.002	3723685.4017	20
JF09.JEPG	JF_solo	0	538966994609.498	21
JF10.EJSP	JF_solo	0.0025	65.0263	2
JF11.JEPG	JF_solo	0	nil	18

Reproducing analyses using dplyr

Stats for individual studies

library("forsterUVA")
library("dplyr")

grouped_data <- studies %>%
    inner_join(study_stats, c("pub", "study")) %>%
    group_by(pub, study)

## calculate p-value for delta F test for each study
dfp <- grouped_data %>% do(deltaFp(., TRUE)) %>% ungroup()

## calculate evidential value (V statistic) for each study
evi <- grouped_data %>% do(eviVal(., TRUE)) %>% ungroup()

## print out
dfp %>%
    inner_join(evi, c("pub", "study")) %>%
    mutate(pdF = round(pdF, 4), V = round(V, 1)) %>%
    head(10)

pub	study	pdF	V
D.JF.L09.JESP	exp1.WA.NS.B1	0.9336	5.5
D.JF.L09.JESP	exp1.WA.NS.B2	0.4927	1.1
D.JF.L09.JESP	exp1.WA.NS.B3	0.4609	1
D.JF.L09.JESP	exp1.WA.S.B1	0.8647	3.2
D.JF.L09.JESP	exp1.WA.S.B2	0.001	1
D.JF.L09.JESP	exp1.WA.S.B3	0.0492	1
D.JF.L09.JESP	exp1.WU.NS.B1	0.9373	6.3
D.JF.L09.JESP	exp1.WU.NS.B2	0.8817	3.7
D.JF.L09.JESP	exp1.WU.NS.B3	0.5455	1.2
D.JF.L09.JESP	exp1.WU.S.B1	0.8462	1.5

Stats for individual publications

pub_groups <- dfp %>%
    inner_join(evi, c("pub", "study")) %>%
    inner_join(pubs, c("pub")) %>%
    group_by(pub, section)

## overall p value for each publication
p_vals <- pub_groups %>% do(combine_pdF(., TRUE)) %>% ungroup()

## overall V value for each publication
V_vals <- pub_groups %>% do(combine_V(., TRUE)) %>% ungroup()

## number of studies for each publication
n_studies <- pub_groups %>% summarize(n_studies = n()) %>% ungroup()

p_vals %>%
    inner_join(V_vals) %>%
    inner_join(n_studies) %>%
    mutate(p = round(p, 4), V_prod = round(V_prod, 1)) %>%
    arrange(section, pub)

pub	section	p	V_prod	n_studies
L.JF09.JPSP	JF_co, Amsterdam	0	31319196	18
WCY.JF11.JESP	JF_co, Amsterdam	0.6811	1	1
D.JF.L09.JESP	JF_co, Bremen / Würzburg	0.2544	930785.5	17
D.JF.LR10.PSPB	JF_co, Bremen / Würzburg	0.9995	40.7	13
K.JF.D10.SPPS	JF_co, Bremen / Würzburg	0.0122	4025.8	8
L.JF09.CS	JF_co, Bremen / Würzburg	0.1088	12.8	6
FG.JF12.MP	JF_co, others	0.9561	7.8	8
JF.D12.JESP	JF_first	0.0053	250357.4	12
JF.D12.SPPS	JF_first	0	4227517188560.1	19
JF.EO09.PSPB	JF_first	2e-04	2958.3	5
JF.LK08.JPSP	JF_first	0.6263	15888.2	20
JF.LS09.JEPG	JF_first	0.002	3723685.4	20
JF09.JEPG	JF_solo	0	538966994609.5	21
JF10.EJSP	JF_solo	0.0025	65	2
JF11.JEPG	JF_solo	0	nil	18
Hagtvedt	control	0.3025	1.6	2
Hunt	control	0.7776	1	1
Jia	control	0.6911	1	1
Kanten	control	0.4236	1.8	2
Lerouge	control	0.4411	14.9	4
Malkoc	control	0.0952	5.3	1
Polman	control	0.3785	1.3	1
Rook	control	0.5262	1.7	2
Smith	control	0.2995	8.6	7