[PILOT/COPILOT - TEXT IN SQUARE BRACKETS IS HERE FOR GUIDANCE. COPILOT PLEASE DELETE BEFORE KNITTING THE FINAL REPORT]
[PILOT/COPILOT ENTER RELEVANT REPORT DETAILS HERE]
articleID <- "CARPS_EXT_20_7_2015" # insert the article ID code here e.g., "10-3-2015_PS"
reportType <- 'pilot' # specify whether this is the 'pilot' report or 'final' report
pilotNames <- "Griffin Dietz" # insert the pilot's name here e.g., "Tom Hardwicke". If there are multiple cpilots enter both names in a character string e.g., "Tom Hardwicke, Bob Dylan"
copilotNames <- NA # # insert the co-pilot's name here e.g., "Michael Frank". If there are multiple co-pilots enter both names in a character string e.g., "Tom Hardwicke, Bob Dylan"
pilotTTC <- 180 # insert the pilot's estimated time to complete (in minutes, fine to approximate) e.g., 120
copilotTTC <- NA # insert the co-pilot's estimated time to complete (in minutes, fine to approximate) e.g., 120
pilotStartDate <- "10/31/18" # insert the pilot's start date in US format e.g., as.Date("01/25/18", format = "%m/%d/%y")
copilotStartDate <- NA # insert the co-pilot's start date in US format e.g., as.Date("01/25/18", format = "%m/%d/%y")
completionDate <- NA # copilot insert the date of final report completion (after any necessary rounds of author assistance) in US format e.g., as.Date("01/25/18", format = "%m/%d/%y")The authors presented children with a computer animation of one object chasing another. The objects moved for 5 seconds, followed by a 4.2 second pause. In the training trials, when the scene paused, a downward pointing index finger appeared above the chaser accompanied by auditory stimuli naming the chaser object with a novel word. In the test trials, when the scene paused, a bulls-eye appeard between the two objects and the infant was asked, “Where is the [novel name of chaser object]?”. This was followed by a 3 second response phase, in which eye tracking data was gathed from the infant. Children were alternatingly presented with trials that presented the trained or untrained label.
The authors calculated average difference scores ranging from -1 (looking only at the target) to 1 (looking only at the chaser) for each infant in three time ranges (0–1 s, 0–2 s, and 0–3 s) with the formula: [(time looking at unlabeled object) - (time looking at chaser object)] / (time looking ta both objects). They compared these values across conditions using two-tailed paired t-tests. They also compared these difference scores to 0 (no preference for either object) using one-sample t-tests. In both cases authors report Cohen’s d for the effect size.
To ensure the effects are not the result of the specifically selected timepoint, the authors also preformed a prmutation-based t-test.
The average number of valid training trials was 4.81 (see Table S1 in the Supplemental Material). We analyzed the total looking times in different phases of the test trials. We found no significant difference between the trainedword condition and the untrained-word condition during either the 5-s first exposure phase, t(15) = 1.72, p = .106, d = 0.43, 95% confidence interval (CI) for the lookingtime difference = [–0.05, 0.46], or the 3-s response phase, t(15) = 1.52, p = .149, d = 0.38, 95% CI = [–0.04, 0.24]. Figure 2a depicts the time course of changing preference during the response phase. As expected, during the test trials, the infants looked longer at the object that had acted as the chaser when they heard the trained word than when they heard the untrained word. This was true for all three predefined time ranges within the response phase—0–1 s: t(15) = 5.73, p < .001, d = 1.43, 95% CI = [0.66, 1.00]; 0–2 s: t(15) = 3.23, p = .006, d = 0.81, 95% CI = [0.13, 0.62]; 0–3 s: t(15) = 2.47, p = .026, d = 0.62, 95% CI = [0.04, 0.53] (see Fig. 2a). These effects remained significant when looking-time differences during the first exposure phase were included as covariates in the analyses, p < .001, p = .018, and p = .037, respectively, for the three time ranges. Such differences were not due to chance: Using permutation-based t tests, we found that the difference scores in the trained-word condition and the untrained-word condition were significantly different from 0.33 to 2.06 s (ps < .050). In further analyses, we found that the difference scores for all three time ranges were significantly above the chance level in the trained-word condition—0–1 s: t(15) = 5.88, p < .001, d = 1.47, 95% CI = [0.45, 0.96]; 0–2 s: t(15) = 2.59, p = .021, d = 0.65, 95% CI = [0.04, 0.45]; 0–3 s: t(15) = 2.84, p = .012, d = 0.71, 95% CI = [0.06, 0.43]. This finding suggests that the infants identified the chaser as the referent of the trained word. Although the same analysis did not reveal a significant difference from zero in the untrained-word condition, an initial tendency to look longer at the target was observed—0–1 s: t(15) = 1.91, p = .075, d = 0.48, 95% CI = [–0.76, 0.04]; 0–2 s: t(15) = 0.93, p = .368, d = 0.23, 95% CI = [–0.43, 0.17]; 0–3 s: t(15) = 0.29, p = .774, d = 0.07, 95% CI = [–0.26, 0.20].
[PILOT/COPILOT DO NOT CHANGE THE CODE IN THE CHUNK BELOW]
[PILOT/COPILOT Some useful packages are being loaded below. You can add any additional ones you might need too.]
# load packages
library(tidyverse) # for data munging
library(knitr) # for kable table formating
library(haven) # import and export 'SPSS', 'Stata' and 'SAS' Files
library(readxl) # import excel files
library(CARPSreports) # custom report functions
library(RVAideMemoire) # for permutation t-test
library(lsr)[PILOT/COPILOT DO NOT MAKE CHANGES TO THE CODE CHUNK BELOW]
# Prepare report object. This will be updated automatically by the reproCheck function each time values are compared
reportObject <- data.frame(dummyRow = TRUE, reportedValue = NA, obtainedValue = NA, valueType = NA, percentageError = NA, comparisonOutcome = NA, eyeballCheck = NA)data <- read_excel("data/Experiment1.xlsx")condData <- data %>%
gather(item, value, contains("-")) %>%
mutate(condition = (ifelse(grepl("Trained", item), "Trained", "Untrained")),
item = sub(".*-", "", item))Generating data frame that include total looking time (at either Chaser or Chasee) in ms by child and condition.
totalLookingTime <- condData[(condData$item != 'DS'),] %>%
group_by(SubNO, condition) %>%
summarise(time = (sum(value) * 16.67))Difference scores for each of the intervals in the response phase for both conditions.
diffScoreData0to3 <- condData[(condData$item == 'DS' & condData$FrameCount == 180),]
diffScoreData0to2 <- condData[(condData$item == 'DS' & condData$FrameCount == 120),]
diffScoreData0to1 <- condData[(condData$item == 'DS' & condData$FrameCount == 60),]Difference scores for the permutation test from the specified time range and from all frames (commented out, but present in case the time range came from test itself rather than preprocessing).
###5) Response Phase Difference Score Permutation T-Tests
#if timeframe reported are from preprocessing
diffScoresData <- condData[(condData$item == 'DS' & condData$FrameCount >= (330 / 16.67) & condData$FrameCount <= (2060 / 16.67)),]
#if time frames reported are from test results
# diffScoresData <- condData[(condData$item == 'DS'),]
diffScoresDataTrained <- condData[(condData$item == 'DS' & condData$condition == 'Trained'),]
diffScoresDataUntrained <- condData[(condData$item == 'DS' & condData$condition == 'Untrained'),]Difference scores for each of the intervals in the response phase for the trained word conditions.
diffScoreTrainedData0to3 <- condData[(condData$item == 'DS' & condData$FrameCount == 180 & condData$condition == "Trained"),]
diffScoreTrainedData0to2 <- condData[(condData$item == 'DS' & condData$FrameCount == 120 & condData$condition == "Trained"),]
diffScoreTrainedData0to1 <- condData[(condData$item == 'DS' & condData$FrameCount == 60 & condData$condition == "Trained"),]Difference scores for each of the intervals in the response phase for the untrained word conditions.
diffScoreUntrainedData0to3 <- condData[(condData$item == 'DS' & condData$FrameCount == 180 & condData$condition == "Untrained"),]
diffScoreUntrainedData0to2 <- condData[(condData$item == 'DS' & condData$FrameCount == 120 & condData$condition == "Untrained"),]
diffScoreUntrainedData0to1 <- condData[(condData$item == 'DS' & condData$FrameCount == 60 & condData$condition == "Untrained"),]We found no significant difference between the trainedword condition and the untrained-word condition during either the 5-s first exposure phase, t(15) = 1.72, p = .106, d = 0.43, 95% confidence interval (CI) for the lookingtime difference = [–0.05, 0.46], or the 3-s response phase, t(15) = 1.52, p = .149, d = 0.38, 95% CI = [–0.04, 0.24]. (from Yin & Csibra p. 5)
#Commented out because insufficient info
# t(15) = 1.52, p = .149, d = 0.38, 95% CI = [–0.04, 0.24]
# lookingTimeTest <- t.test(time ~ condition, data=totalLookingTime)
# lookingTimeTest
# cohensD(time ~ condition, data = totalLookingTime, method = "paired")The infants looked longer at the object that had acted as the chaser when they heard the trained word than when they heard the untrained word. This was true for all three predefined time ranges within the response phase—0–1 s: t(15) = 5.73, p < .001, d = 1.43, 95% CI = [0.66, 1.00]; 0–2 s: t(15) = 3.23, p = .006, d = 0.81, 95% CI = [0.13, 0.62]; 0–3 s: t(15) = 2.47, p = .026, d = 0.62, 95% CI = [0.04, 0.53] (see Fig. 2a). (from Yin & Csibra p. 5)
# "0–1 s: t(15) = 5.73, p < .001, d = 1.43, 95% CI = [0.66, 1.00]"
differenceScoreTest0to1 <- t.test(value ~ condition, data = diffScoreData0to1, paired = T)
differenceScoreTest0to1##
## Paired t-test
##
## data: value by condition
## t = 5.7255, df = 15, p-value = 4.014e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.6644522 1.4525478
## sample estimates:
## mean of the differences
## 1.0585cohensD(value ~ condition, data = diffScoreData0to1, method = "paired")## [1] 1.431387reproCheck(reportedValue = '5.73', obtainedValue = 5.7255, valueType = 't')## [1] "MATCH for t. The reported value (5.73) and the obtained value (5.73) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 5.73 5.73 t 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '<.001', obtainedValue = 4.014e-05, valueType = 'p', eyeballCheck = TRUE)## [1] "MATCH for p. Eyeball comparison only."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE <NA> NA <NA> NA
## 2 FALSE <.001 4.014e-05 p NA
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH TRUEreproCheck(reportedValue = '1.43', obtainedValue = 1.431387, valueType = 'd')## [1] "MATCH for d. The reported value (1.43) and the obtained value (1.43) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 1.43 1.43 d 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.66', obtainedValue = 0.6644522, valueType = 'ci')## [1] "MATCH for ci. The reported value (0.66) and the obtained value (0.66) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.66 0.66 ci 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '1.00', obtainedValue = 1.4525478, valueType = 'ci') #incorrect upper CI## [1] "MAJOR_ERROR for ci. The reported value (1) and the obtained value (1.45) differed by 45%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 1 1.45 ci 45
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MAJOR_ERROR NA# "0–2 s: t(15) = 3.23, p = .006, d = 0.81, 95% CI = [0.13, 0.62]"
differenceScoreTest0to2 <- t.test(value ~ condition, data = diffScoreData0to2, paired = T)
differenceScoreTest0to2##
## Paired t-test
##
## data: value by condition
## t = 3.2296, df = 15, p-value = 0.005613
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.1272748 0.6213502
## sample estimates:
## mean of the differences
## 0.3743125cohensD(value ~ condition, data = diffScoreData0to2, method = "paired")## [1] 0.8073951reproCheck(reportedValue = '3.23', obtainedValue = 3.2296, valueType = 't')## [1] "MATCH for t. The reported value (3.23) and the obtained value (3.23) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 3.23 3.23 t 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '.006', obtainedValue = 0.005613, valueType = 'p')## [1] "MATCH for p. The reported value (0.006) and the obtained value (0.006) differed by 0%. Note that the obtained value was rounded to 3 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.006 0.006 p 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.81', obtainedValue = 0.8073951, valueType = 'd')## [1] "MATCH for d. The reported value (0.81) and the obtained value (0.81) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.81 0.81 d 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.13', obtainedValue = 0.1272748, valueType = 'ci')## [1] "MATCH for ci. The reported value (0.13) and the obtained value (0.13) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.13 0.13 ci 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.62', obtainedValue = 0.6213502, valueType = 'ci')## [1] "MATCH for ci. The reported value (0.62) and the obtained value (0.62) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.62 0.62 ci 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NA# "0–3 s: t(15) = 2.47, p = .026, d = 0.62, 95% CI = [0.04, 0.53]"
differenceScoreTest0to3 <- t.test(value ~ condition, data = diffScoreData0to3, paired = T)
differenceScoreTest0to3##
## Paired t-test
##
## data: value by condition
## t = 2.4721, df = 15, p-value = 0.02589
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.0389262 0.5260738
## sample estimates:
## mean of the differences
## 0.2825cohensD(value ~ condition, data = diffScoreData0to3, method = "paired")## [1] 0.6180206reproCheck(reportedValue = '2.47', obtainedValue = 2.4721, valueType = 't')## [1] "MATCH for t. The reported value (2.47) and the obtained value (2.47) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 2.47 2.47 t 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '.026', obtainedValue = 0.02589, valueType = 'p')## [1] "MATCH for p. The reported value (0.026) and the obtained value (0.026) differed by 0%. Note that the obtained value was rounded to 3 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.026 0.026 p 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.62', obtainedValue = 0.6180206, valueType = 'd')## [1] "MATCH for d. The reported value (0.62) and the obtained value (0.62) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.62 0.62 d 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.04', obtainedValue = 0.0389262, valueType = 'ci')## [1] "MATCH for ci. The reported value (0.04) and the obtained value (0.04) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.04 0.04 ci 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.53', obtainedValue = 0.5260738, valueType = 'ci')## [1] "MATCH for ci. The reported value (0.53) and the obtained value (0.53) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.53 0.53 ci 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAUsing permutation-based t tests, we found that the difference scores in the trained-word condition and the untrained-word condition were significantly different from 0.33 to 2.06 s (ps < .050). (from Yin & Csibra p. 5)
# Commented out because takes several minutes to run
# permTest2 <- RVAideMemoire::perm.t.test(formula = value ~ condition, data = diffScoresData, nperm = 50000, paired = T)
# permTest2We found that the difference scores for all three time ranges were significantly above the chance level in the trained-word condition—0–1 s: t(15) = 5.88, p < .001, d = 1.47, 95% CI = [0.45, 0.96]; 0–2 s: t(15) = 2.59, p = .021, d = 0.65, 95% CI = [0.04, 0.45]; 0–3 s: t(15) = 2.84, p = .012, d = 0.71, 95% CI = [0.06, 0.43]. (from Yin & Csibra p. 5)
# "0–1 s: t(15) = 5.88, p < .001, d = 1.47, 95% CI = [0.45, 0.96]"
trainedDifferenceScoreTest0to1 <- t.test(diffScoreTrainedData0to1$value)
trainedDifferenceScoreTest0to1##
## One Sample t-test
##
## data: diffScoreTrainedData0to1$value
## t = 5.8756, df = 15, p-value = 3.049e-05
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.4467443 0.9553807
## sample estimates:
## mean of x
## 0.7010625cohensD(x = diffScoreTrainedData0to1$value)## [1] 1.468907reproCheck(reportedValue = '5.88', obtainedValue = 5.8756, valueType = 't')## [1] "MATCH for t. The reported value (5.88) and the obtained value (5.88) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 5.88 5.88 t 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '<.001', obtainedValue = 3.049e-05, valueType = 'p', eyeballCheck = TRUE)## [1] "MATCH for p. Eyeball comparison only."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE <NA> NA <NA> NA
## 2 FALSE <.001 3.049e-05 p NA
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH TRUEreproCheck(reportedValue = '1.47', obtainedValue = 1.468907, valueType = 'd')## [1] "MATCH for d. The reported value (1.47) and the obtained value (1.47) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 1.47 1.47 d 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.45', obtainedValue = 0.4467443, valueType = 'ci')## [1] "MATCH for ci. The reported value (0.45) and the obtained value (0.45) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.45 0.45 ci 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.96', obtainedValue = 0.9553807, valueType = 'ci')## [1] "MATCH for ci. The reported value (0.96) and the obtained value (0.96) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.96 0.96 ci 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NA# "0–2 s: t(15) = 2.59, p = .021, d = 0.65, 95% CI = [0.04, 0.45]"
trainedDifferenceScoreTest0to2 <- t.test(diffScoreTrainedData0to2$value)
trainedDifferenceScoreTest0to2##
## One Sample t-test
##
## data: diffScoreTrainedData0to2$value
## t = 2.588, df = 15, p-value = 0.02059
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.0430738 0.4453012
## sample estimates:
## mean of x
## 0.2441875cohensD(x = diffScoreTrainedData0to2$value)## [1] 0.6469889reproCheck(reportedValue = '2.59', obtainedValue = 2.588, valueType = 't')## [1] "MATCH for t. The reported value (2.59) and the obtained value (2.59) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 2.59 2.59 t 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '.021', obtainedValue = 0.02059, valueType = 'p')## [1] "MATCH for p. The reported value (0.021) and the obtained value (0.021) differed by 0%. Note that the obtained value was rounded to 3 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.021 0.021 p 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.65', obtainedValue = 0.6469889, valueType = 'd')## [1] "MATCH for d. The reported value (0.65) and the obtained value (0.65) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.65 0.65 d 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.04', obtainedValue = 0.0430738, valueType = 'ci')## [1] "MATCH for ci. The reported value (0.04) and the obtained value (0.04) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.04 0.04 ci 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.45', obtainedValue = 0.4453012, valueType = 'ci')## [1] "MATCH for ci. The reported value (0.45) and the obtained value (0.45) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.45 0.45 ci 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NA# "0–3 s: t(15) = 2.84, p = .012, d = 0.71, 95% CI = [0.06, 0.43]"
trainedDifferenceScoreTest0to3 <- t.test(diffScoreTrainedData0to3$value)
trainedDifferenceScoreTest0to3##
## One Sample t-test
##
## data: diffScoreTrainedData0to3$value
## t = 2.8388, df = 15, p-value = 0.01245
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.06254171 0.43945829
## sample estimates:
## mean of x
## 0.251cohensD(x = diffScoreTrainedData0to3$value)## [1] 0.7096979# compareValues(reportedValue = 0.43, obtainedValue = 0.439) #incorrect upper CI
reproCheck(reportedValue = '2.84', obtainedValue = 2.8388, valueType = 't')## [1] "MATCH for t. The reported value (2.84) and the obtained value (2.84) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 2.84 2.84 t 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '.012', obtainedValue = 0.01245, valueType = 'p')## [1] "MATCH for p. The reported value (0.012) and the obtained value (0.012) differed by 0%. Note that the obtained value was rounded to 3 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.012 0.012 p 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.71', obtainedValue = 0.7096979, valueType = 'd')## [1] "MATCH for d. The reported value (0.71) and the obtained value (0.71) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.71 0.71 d 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.06', obtainedValue = 0.06254171, valueType = 'ci')## [1] "MATCH for ci. The reported value (0.06) and the obtained value (0.06) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.06 0.06 ci 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.43', obtainedValue = 0.43945829, valueType = 'ci') #incorrect upper CI## [1] "MINOR_ERROR for ci. The reported value (0.43) and the obtained value (0.44) differed by 2.33%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.43 0.44 ci 2.325581
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MINOR_ERROR NAThe same analysis did not reveal a significant difference from zero in the untrained-word condition, an initial tendency to look longer at the target was observed—0–1 s: t(15) = 1.91, p = .075, d = 0.48, 95% CI = [–0.76, 0.04]; 0–2 s: t(15) = 0.93, p = .368, d = 0.23, 95% CI = [–0.43, 0.17]; 0–3 s: t(15) = 0.29, p = .774, d = 0.07, 95% CI = [–0.26, 0.20]. (from Yin & Csibra p. 5)
# "0–1 s: t(15) = 1.91, p = .075, d = 0.48, 95% CI = [–0.76, 0.04]"
untrainedDifferenceScoreTest0to1 <- t.test(diffScoreUntrainedData0to1$value)
untrainedDifferenceScoreTest0to1##
## One Sample t-test
##
## data: diffScoreUntrainedData0to1$value
## t = -1.9106, df = 15, p-value = 0.07536
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.7561886 0.0413136
## sample estimates:
## mean of x
## -0.3574375cohensD(x = diffScoreUntrainedData0to1$value)## [1] 0.4776539reproCheck(reportedValue = '1.91', obtainedValue = -1.9106, valueType = 't')## [1] "MAJOR_ERROR for t. The reported value (1.91) and the obtained value (-1.91) differed by 200%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 1.91 -1.91 t 200
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MAJOR_ERROR NAreproCheck(reportedValue = '.075', obtainedValue = 0.07536, valueType = 'p')## [1] "MATCH for p. The reported value (0.075) and the obtained value (0.075) differed by 0%. Note that the obtained value was rounded to 3 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.075 0.075 p 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.48', obtainedValue = 0.4776539, valueType = 'd')## [1] "MATCH for d. The reported value (0.48) and the obtained value (0.48) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.48 0.48 d 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '-0.76', obtainedValue = -0.7561886, valueType = 'ci')## [1] "MATCH for ci. The reported value (-0.76) and the obtained value (-0.76) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE -0.76 -0.76 ci 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.04', obtainedValue = 0.0413136, valueType = 'ci')## [1] "MATCH for ci. The reported value (0.04) and the obtained value (0.04) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.04 0.04 ci 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NA# "0–2 s: t(15) = 0.93, p = .368, d = 0.23, 95% CI = [–0.43, 0.17]"
untrainedDifferenceScoreTest0to2 <- t.test(diffScoreUntrainedData0to2$value)
untrainedDifferenceScoreTest0to2##
## One Sample t-test
##
## data: diffScoreUntrainedData0to2$value
## t = -0.92737, df = 15, p-value = 0.3684
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.4292026 0.1689526
## sample estimates:
## mean of x
## -0.130125cohensD(x = diffScoreUntrainedData0to2$value)## [1] 0.2318419reproCheck(reportedValue = '0.93', obtainedValue = -0.92737, valueType = 't') #missing negative sign## [1] "MAJOR_ERROR for t. The reported value (0.93) and the obtained value (-0.93) differed by 200%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.93 -0.93 t 200
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MAJOR_ERROR NAreproCheck(reportedValue = '.368', obtainedValue = 0.3684, valueType = 'p')## [1] "MATCH for p. The reported value (0.368) and the obtained value (0.368) differed by 0%. Note that the obtained value was rounded to 3 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.368 0.368 p 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.23', obtainedValue = 0.2318419, valueType = 'd')## [1] "MATCH for d. The reported value (0.23) and the obtained value (0.23) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.23 0.23 d 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '-0.43', obtainedValue = -0.4292026, valueType = 'ci')## [1] "MATCH for ci. The reported value (-0.43) and the obtained value (-0.43) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE -0.43 -0.43 ci 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.17', obtainedValue = 0.1689526, valueType = 'ci')## [1] "MATCH for ci. The reported value (0.17) and the obtained value (0.17) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.17 0.17 ci 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NA# "0–3 s: t(15) = 0.29, p = .774, d = 0.07, 95% CI = [–0.26, 0.20]"
untrainedDifferenceScoreTest0to3 <- t.test(diffScoreUntrainedData0to3$value)
untrainedDifferenceScoreTest0to3##
## One Sample t-test
##
## data: diffScoreUntrainedData0to3$value
## t = -0.29255, df = 15, p-value = 0.7739
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.2609987 0.1979987
## sample estimates:
## mean of x
## -0.0315cohensD(x = diffScoreUntrainedData0to3$value)## [1] 0.07313838reproCheck(reportedValue = '0.29', obtainedValue = -0.29255, valueType = 't') #missing negative sign## [1] "MAJOR_ERROR for t. The reported value (0.29) and the obtained value (-0.29) differed by 200%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.29 -0.29 t 200
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MAJOR_ERROR NAreproCheck(reportedValue = '.774', obtainedValue = 0.7739, valueType = 'p')## [1] "MATCH for p. The reported value (0.774) and the obtained value (0.774) differed by 0%. Note that the obtained value was rounded to 3 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.774 0.774 p 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.07', obtainedValue = 0.07313838, valueType = 'd')## [1] "MATCH for d. The reported value (0.07) and the obtained value (0.07) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.07 0.07 d 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '-0.26', obtainedValue = -0.2609987, valueType = 'ci')## [1] "MATCH for ci. The reported value (-0.26) and the obtained value (-0.26) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE -0.26 -0.26 ci 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NAreproCheck(reportedValue = '0.20', obtainedValue = 0.1979987, valueType = 'ci')## [1] "MATCH for ci. The reported value (0.2) and the obtained value (0.2) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."## dummyRow reportedValue obtainedValue valueType percentageError
## 1 TRUE NA NA <NA> NA
## 2 FALSE 0.2 0.2 ci 0
## comparisonOutcome eyeballCheck
## 1 <NA> NA
## 2 MATCH NA[Please include a text summary describing your findings. If this reproducibility check was a failure, you should note any suggestions as to what you think the likely cause(s) might be.]
INSUFFICIENT INFORMATION ERROR
I was not able to reproduce the t-test of total looking time for the first exposure phase, as the dataset only includes data for the response phase.
INSUFFICIENT INFORMATION ERROR
While I was able to run a paired two-tailed t-test for total looking time in the 3s response phase (the test I presume the authors ran), my values did not match. They were so wildly off that I believe I am not running the same test as the authors, who do not specify which test they ran to achieve these results. Therefore, I categorize this as insufficient information, but include the code that I ran above.
The results of the paired two-tailed t-tests on difference score acorss conditions for the three specified time frames generally matched the reported values. However, the upper confidence interval for the t-test on difference scores for the 0-1s time frame was reported incorrectly; I believe this discrepancy is likely the result of a typo or incorrect copying of data.
INSUFFICIENT INFORMATION ERROR
Similarly to the first test, I could not run this tests as the provided data did not include the exposure time.
INSUFFICIENT INFORMATION ERROR
I did not have enough information to reproduce these tests. I do not know where the specified time range came from (if it is the result of the test or if the data outsde this range is excluded before the test) or how the test was run. My attempt at replicating (code above) is commented out but included.
Similarly to the reponse phase interval difference score t-tests, the results from these tests largely matched those of the reported data. However, the upper confidence interval for the 0-3s interval was rounded incorrectly in the original reported data; this error is likely the result of a typo.
The t-values for all three time intervals were reported as positive when they should have been negative. However, the written explanation of this analysis does reflect these negative values (so perhaps we shoud nto interpret them as incorrect).
[PILOT/COPILOT ENTER RELEVANT INFORMATION BELOW]
Author_Assistance = FALSE # was author assistance provided? (if so, enter TRUE)
Insufficient_Information_Errors <- 4 # how many discrete insufficient information issues did you encounter?
# Assess the causal locus (discrete reproducibility issues) of any reproducibility errors. Note that there doesn't necessarily have to be a one-to-one correspondance between discrete reproducibility issues and reproducibility errors. For example, it could be that the original article neglects to mention that a Greenhouse-Geisser correct was applied to ANOVA outcomes. This might result in multiple reproducibility errors, but there is a single causal locus (discrete reproducibility issue).
locus_typo <- 0 # how many discrete issues did you encounter that related to typographical errors?
locus_specification <- 2 # how many discrete issues did you encounter that related to incomplete, incorrect, or unclear specification of the original analyses?
locus_analysis <- 0 # how many discrete issues did you encounter that related to errors in the authors' original analyses?
locus_data <- 1 # how many discrete issues did you encounter that related to errors in the data files shared by the authors?
locus_unidentified <- 0 # how many discrete issues were there for which you could not identify the cause
# How many of the above issues were resolved through author assistance?
locus_typo_resolved <- NA # how many discrete issues did you encounter that related to typographical errors?
locus_specification_resolved <- NA # how many discrete issues did you encounter that related to incomplete, incorrect, or unclear specification of the original analyses?
locus_analysis_resolved <- NA # how many discrete issues did you encounter that related to errors in the authors' original analyses?
locus_data_resolved <- NA # how many discrete issues did you encounter that related to errors in the data files shared by the authors?
locus_unidentified_resolved <- NA # how many discrete issues were there for which you could not identify the cause
Affects_Conclusion <- FALSE # Do any reproducibility issues encounter appear to affect the conclusions made in the original article? TRUE, FALSE, or NA. This is a subjective judgement, but you should taking into account multiple factors, such as the presence/absence of decision errors, the number of target outcomes that could not be reproduced, the type of outcomes that could or could not be reproduced, the difference in magnitude of effect sizes, and the predictions of the specific hypothesis under scrutiny.[PILOT/COPILOT DO NOT EDIT THE CODE CHUNK BELOW]
reportObject <- reportObject %>%
filter(dummyRow == FALSE) %>% # remove the dummy row
select(-dummyRow) %>% # remove dummy row designation
mutate(articleID = articleID) %>% # add variables to report
select(articleID, everything()) # make articleID first column
# decide on final outcome
if(any(reportObject$comparisonOutcome %in% c("MAJOR_ERROR", "DECISION_ERROR")) | Insufficient_Information_Errors > 0){
finalOutcome <- "Failure without author assistance"
if(Author_Assistance == T){
finalOutcome <- "Failure despite author assistance"
}
}else{
finalOutcome <- "Success without author assistance"
if(Author_Assistance == T){
finalOutcome <- "Success with author assistance"
}
}
# collate report extra details
reportExtras <- data.frame(articleID, pilotNames, copilotNames, pilotTTC, copilotTTC, pilotStartDate, copilotStartDate, completionDate, Author_Assistance, finalOutcome, Insufficient_Information_Errors, locus_typo, locus_specification, locus_analysis, locus_data, locus_unidentified, locus_typo_resolved, locus_specification_resolved, locus_analysis_resolved, locus_data_resolved, locus_unidentified_resolved)
# save report objects
if(reportType == "pilot"){
write_csv(reportObject, "pilotReportDetailed.csv")
write_csv(reportExtras, "pilotReportExtras.csv")
}
if(reportType == "final"){
write_csv(reportObject, "finalReportDetailed.csv")
write_csv(reportExtras, "finalReportExtras.csv")
}[This function will output information about the package versions used in this report:]
devtools::session_info()## ─ Session info ──────────────────────────────────────────────────────────
## setting value
## version R version 3.4.3 (2017-11-30)
## os macOS 10.14
## system x86_64, darwin15.6.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Asia/Seoul
## date 2018-11-05
##
## ─ Packages ──────────────────────────────────────────────────────────────
## package * version date lib
## abind 1.4-5 2016-07-21 [1]
## ade4 1.7-13 2018-08-31 [1]
## assertthat 0.2.0 2017-04-11 [1]
## backports 1.1.2 2017-12-13 [1]
## base64enc 0.1-3 2015-07-28 [1]
## bindr 0.1.1 2018-03-13 [1]
## bindrcpp * 0.2.2 2018-03-29 [1]
## broom 0.4.5 2018-07-03 [1]
## callr 3.0.0 2018-08-24 [1]
## car 3.0-2 2018-08-23 [1]
## carData 3.0-1 2018-03-28 [1]
## CARPSreports * 0.1 2018-10-29 [1]
## cellranger 1.1.0 2016-07-27 [1]
## cli 1.0.0 2017-11-05 [1]
## cluster 2.0.6 2017-03-10 [1]
## colorspace 1.3-2 2016-12-14 [1]
## crayon 1.3.4 2017-09-16 [1]
## curl 3.2 2018-03-28 [1]
## data.table 1.11.4 2018-05-27 [1]
## desc 1.2.0 2018-05-01 [1]
## devtools 2.0.1 2018-10-26 [1]
## digest 0.6.15 2018-01-28 [1]
## dplyr * 0.7.6 2018-06-29 [1]
## evaluate 0.11 2018-07-17 [1]
## forcats * 0.3.0 2018-02-19 [1]
## foreign 0.8-69 2017-06-22 [1]
## fs 1.2.6 2018-08-23 [1]
## ggplot2 * 3.0.0 2018-07-03 [1]
## glue 1.3.0 2018-07-17 [1]
## gtable 0.2.0 2016-02-26 [1]
## haven * 1.1.2 2018-06-27 [1]
## hms 0.4.0 2017-11-23 [1]
## htmltools 0.3.6 2017-04-28 [1]
## httr 1.3.1 2017-08-20 [1]
## jsonlite 1.5 2017-06-01 [1]
## knitr * 1.20 2018-02-20 [1]
## lattice 0.20-35 2017-03-25 [1]
## lazyeval 0.2.1 2017-10-29 [1]
## lsr * 0.5 2015-03-02 [1]
## lubridate 1.7.4 2018-04-11 [1]
## magrittr 1.5 2014-11-22 [1]
## MASS 7.3-47 2017-02-26 [1]
## Matrix 1.2-12 2017-11-20 [1]
## memoise 1.1.0 2017-04-21 [1]
## mgcv 1.8-22 2017-09-24 [1]
## mnormt 1.5-5 2016-10-15 [1]
## modelr 0.1.2 2018-05-11 [1]
## munsell 0.4.3 2016-02-13 [1]
## nlme 3.1-131 2017-02-06 [1]
## openxlsx 4.1.0 2018-05-26 [1]
## permute 0.9-4 2016-09-09 [1]
## pillar 1.3.0 2018-07-14 [1]
## pkgbuild 1.0.2 2018-10-16 [1]
## pkgconfig 2.0.1 2017-03-21 [1]
## pkgload 1.0.1 2018-10-11 [1]
## plyr 1.8.4 2016-06-08 [1]
## prettyunits 1.0.2 2015-07-13 [1]
## processx 3.2.0 2018-08-16 [1]
## ps 1.1.0 2018-08-10 [1]
## psych 1.8.4 2018-05-06 [1]
## purrr * 0.2.5 2018-05-29 [1]
## R6 2.2.2 2017-06-17 [1]
## Rcpp 0.12.18 2018-07-23 [1]
## readr * 1.1.1 2017-05-16 [1]
## readxl * 1.1.0 2018-04-20 [1]
## remotes 2.0.1 2018-10-19 [1]
## reshape2 1.4.3 2017-12-11 [1]
## rio 0.5.10 2018-03-29 [1]
## rlang 0.2.2 2018-08-16 [1]
## rmarkdown 1.10 2018-06-11 [1]
## rprojroot 1.3-2 2018-01-03 [1]
## rstudioapi 0.7 2017-09-07 [1]
## RVAideMemoire * 0.9-69-3 2018-05-14 [1]
## rvest 0.3.2 2016-06-17 [1]
## scales 0.5.0 2017-08-24 [1]
## sessioninfo 1.1.0 2018-09-25 [1]
## stringi 1.2.4 2018-07-20 [1]
## stringr * 1.3.1 2018-05-10 [1]
## tibble * 1.4.2 2018-01-22 [1]
## tidyr * 0.8.1 2018-05-18 [1]
## tidyselect 0.2.4 2018-02-26 [1]
## tidyverse * 1.2.1 2017-11-14 [1]
## usethis 1.4.0 2018-08-14 [1]
## vegan 2.5-3 2018-10-25 [1]
## withr 2.1.2 2018-03-15 [1]
## xml2 1.2.0 2018-01-24 [1]
## yaml 2.2.0 2018-07-25 [1]
## zip 1.0.0 2017-04-25 [1]
## source
## CRAN (R 3.4.0)
## CRAN (R 3.4.4)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## CRAN (R 3.4.0)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## Github (METRICS-CARPS/CARPSreports@89db4a9)
## CRAN (R 3.4.0)
## CRAN (R 3.4.2)
## CRAN (R 3.4.3)
## CRAN (R 3.4.0)
## CRAN (R 3.4.1)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## CRAN (R 3.4.3)
## CRAN (R 3.4.3)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## CRAN (R 3.4.3)
## CRAN (R 3.4.3)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## CRAN (R 3.4.0)
## CRAN (R 3.4.4)
## CRAN (R 3.4.3)
## CRAN (R 3.4.0)
## CRAN (R 3.4.1)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## CRAN (R 3.4.3)
## CRAN (R 3.4.2)
## CRAN (R 3.4.0)
## CRAN (R 3.4.4)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## CRAN (R 3.4.3)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## CRAN (R 3.4.0)
## CRAN (R 3.4.4)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## CRAN (R 3.4.4)
## CRAN (R 3.4.0)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## CRAN (R 3.4.0)
## CRAN (R 3.4.4)
## CRAN (R 3.4.0)
## CRAN (R 3.4.0)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## CRAN (R 3.4.0)
## CRAN (R 3.4.4)
## CRAN (R 3.4.0)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## CRAN (R 3.4.3)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## CRAN (R 3.4.3)
## CRAN (R 3.4.1)
## CRAN (R 3.4.4)
## CRAN (R 3.4.0)
## CRAN (R 3.4.1)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## CRAN (R 3.4.3)
## CRAN (R 3.4.4)
## CRAN (R 3.4.3)
## CRAN (R 3.4.2)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## CRAN (R 3.4.4)
## CRAN (R 3.4.3)
## CRAN (R 3.4.4)
## CRAN (R 3.4.0)
##
## [1] /Library/Frameworks/R.framework/Versions/3.4/Resources/library