patch <- read.csv("/Users/andesgomez/Documents/Stanford/Autumn2013-Masters/PayedWork/andres_data/Spring2014/patch_oddone_no_fam_14_may_PATCH.csv",
header=TRUE, sep="\t", row.names=NULL, stringsAsFactors = FALSE)
patch$odd_one <- patch$Answer.choice=="\"odd_one\""
patch$twin_1 <- patch$Answer.choice=="\"twin_1\""
patch$twin_2 <- patch$Answer.choice=="\"twin_2\""
compliant <- patch$Answer.manip_check_target == "\"2\"" & patch$Answer.manip_check_dist == "\"2\"" & patch$Answer.manip_check_foil == "\"2\"" & patch$Answer.name_check_correct == "\"TRUE\""
patch_compliant <- subset(patch,compliant)
length(patch$Answer.item)## [1] 200
## [1] "number of compliant participants"
## [1] 178
# Statistical analysis - (regressions, anova, chi square, factor analysis,
# etc.)
summary(aov(odd_one ~ Answer.linguistic_framing_condition * Answer.item, data = patch_compliant))## Warning: variable 'Answer.item' converted to a factor
## Df Sum Sq Mean Sq F value
## Answer.linguistic_framing_condition 1 0.48 0.479 4.71
## Answer.item 5 0.78 0.157 1.54
## Answer.linguistic_framing_condition:Answer.item 5 0.36 0.073 0.71
## Residuals 166 16.90 0.102
## Pr(>F)
## Answer.linguistic_framing_condition 0.031 *
## Answer.item 0.181
## Answer.linguistic_framing_condition:Answer.item 0.615
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(glm(odd_one ~ Answer.linguistic_framing_condition + twenties, data = patch_compliant))##
## Call:
## glm(formula = odd_one ~ Answer.linguistic_framing_condition +
## twenties, data = patch_compliant)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.2141 -0.1178 -0.1044 -0.0082 0.8956
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.7483 0.4540 -1.65 0.101
## Answer.linguistic_framing_condition 0.0962 0.0476 2.02 0.045 *
## twentiesTRUE -0.1096 0.0480 -2.28 0.024 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1001)
##
## Null deviance: 18.522 on 177 degrees of freedom
## Residual deviance: 17.521 on 175 degrees of freedom
## AIC: 100.5
##
## Number of Fisher Scoring iterations: 2
# Visualization - the reason this is after statistical analysis is that some properties of the graphs (e.g. a regression line or confidence intervals) are in themselves statistical analysis computed in the previous section
library(plyr)## Warning: package 'plyr' was built under R version 2.15.1
library(reshape2)## Warning: package 'reshape2' was built under R version 2.15.1
library(ggplot2)## Warning: package 'ggplot2' was built under R version 2.15.2
library(binom)## Warning: package 'binom' was built under R version 2.15.3
library(bootstrap)## Warning: package 'bootstrap' was built under R version 2.15.3
##
## Attaching package: 'bootstrap'
##
## The following object(s) are masked _by_ '.GlobalEnv':
##
## patch
#statistics for boolean factors; copied from useful.R, with a slightly different mean function to work with the choiceCorrect factor
l.mean <- function(...){mean(as.logical(...))}
l.theta <- function(x,xdata,na.rm=T) {l.mean(xdata[x],na.rm=na.rm)}
l.ci.low <- function(x,na.rm=T) {
l.mean(x,na.rm=na.rm) - quantile(bootstrap(1:length(x),1000,l.theta,x,na.rm=na.rm)$thetastar,.025,na.rm=na.rm)}
l.ci.high <- function(x,na.rm=T) {
quantile(bootstrap(1:length(x),1000,l.theta,x,na.rm=na.rm)$thetastar,.975,na.rm=na.rm) - l.mean(x,na.rm=na.rm)}
# Plot the histogram for the answers given on each item (aggreagating linguistic frame conditions)
#ms <- aggregate(odd_one ~ Answer.linguistic_framing_condition + Answer.item, data = patch_compliant,l.mean)
#ms$cil <- aggregate(odd_one ~ Answer.linguistic_framing_condition + Answer.item, data = patch_compliant, l.ci.low)$odd_one
#ms$cih <- aggregate(odd_one ~ Answer.linguistic_framing_condition + Answer.item, data = patch_compliant, l.ci.high)$odd_one
md <- melt(patch_compliant, measure.vars = c("odd_one","twin_1","twin_2"), variable.name="object", value.name="chosen")
ms <- ddply(md, .(object, Answer.item), #Answer.item,
summarise,
c = mean(chosen),
n = sum(chosen),
l = length(chosen),
sdc = sd(chosen),
c.cih = c + l.ci.high(chosen),
c.cil = c - l.ci.low(chosen))
ms$item <- factor(ms$Answer.item)
levels(ms$item) <- c("boat","friend", "pizza", "snowman", "sundae", "Christmas tree")
ggplot(ms, aes(x= item, y=c, fill=object)) +
geom_bar(position=position_dodge()) +
geom_linerange(aes(ymin=c.cil,ymax=c.cih),
position=position_dodge(width=.9)) +
ylab("Probability of choosing") +
scale_fill_manual(values=cbPalette)## Mapping a variable to y and also using stat="bin".
## With stat="bin", it will attempt to set the y value to the count of cases in each group.
## This can result in unexpected behavior and will not be allowed in a future version of ggplot2.
## If you want y to represent counts of cases, use stat="bin" and don't map a variable to y.
## If you want y to represent values in the data, use stat="identity".
## See ?geom_bar for examples. (Deprecated; last used in version 0.9.2)
# Visualizing the two conditions separately: Show the histogram by item and by condition
ms <- ddply(patch_compliant, .(Answer.linguistic_framing_condition,Answer.item),
function(x) {
y <- data.frame(choice=c("odd_one","twin_1","twin_2"),
proportion=c(mean(x$odd_one),mean(x$twin_1),mean(x$twin_2)))
return(y)
})
ms$frame <- factor(ms$Answer.linguistic_framing_condition)
levels(ms$frame) <- c("patch","word")
ms$item <- factor(ms$Answer.item)
qplot(item, proportion, fill=choice, facets = . ~ frame,
geom="bar",
data=ms)## Mapping a variable to y and also using stat="bin".
## With stat="bin", it will attempt to set the y value to the count of cases in each group.
## This can result in unexpected behavior and will not be allowed in a future version of ggplot2.
## If you want y to represent counts of cases, use stat="bin" and don't map a variable to y.
## If you want y to represent values in the data, use stat="identity".
## See ?geom_bar for examples. (Deprecated; last used in version 0.9.2)
## Mapping a variable to y and also using stat="bin".
## With stat="bin", it will attempt to set the y value to the count of cases in each group.
## This can result in unexpected behavior and will not be allowed in a future version of ggplot2.
## If you want y to represent counts of cases, use stat="bin" and don't map a variable to y.
## If you want y to represent values in the data, use stat="identity".
## See ?geom_bar for examples. (Deprecated; last used in version 0.9.2)
patch_table <- aggregate(cbind(odd_one,
twin_1,
twin_2) ~
Answer.linguistic_framing_condition , data=patch_compliant, sum)
Complete Basic info:
May 14th 2014
200 subjects | 3FC | NO familiarization | random out of 6 | Linguistic framing: One word or color patch | Participants asked to count the tshree elements scale, level 1 Code: PATCH
patch_oddone_no_fam_14_may_PATCH.csv
var participant_response_type = 0;
var participant_feature_count = 2; // They count all of the props
var linguistic_framing = random(9,10); //
var question_type = 0;
var target_filler_sequence = 0;
var familiarization_status = 0;
var stim_index = random(0,5);
var scale_and_level = 8;
var img_size = 200; // needs to be implemented, currently just a placeholder
var cond = random(1,4);
var file_number_to_use_for_referents = '3'
render("odd_one.Rmd", "pdf_document")
#/Users/andesgomez/Documents/Stanford/Autumn2013-Masters/PayedWork/RMarkdown_files #Rscript -e "require(knitr); require(markdown); knit('$odd_one.rmd', '$odd_one.md'); #markdownToHTML('$odd_one.md', '$odd_one.html')"
pandoc -s odd_one.html -o odd_one.html.pdf