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See the Rmd file here: https://github.com/raw/ralfer/apa_format_and_misc/master/example/example.Rmd
library(apastats)
load.libs(c('data.table','knitr'))## Warning: package 'data.table' was built under R version 3.4.4
## Warning: package 'knitr' was built under R version 3.4.3
opts_chunk$set(message=F)t_res<-t.test(rnorm(20, mean = 10, sd=2))
t_res##
## One Sample t-test
##
## data: rnorm(20, mean = 10, sd = 2)
## t = 32.338, df = 19, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 9.299656 10.586773
## sample estimates:
## mean of x
## 9.943215
One sample Student's t-test demonstrated that X significantly higher than zero, t(19.0) = 32.34, p < .001.
data("sleep")
describe.mean.and.t(sleep$extra, sleep$group, which.mean = 3, paired=T)## [1] "_M_ = 0.75 [-0.25, 1.78] vs. _M_ = 2.33 [1.21, 3.49], _t_(9.0) = -4.06, _p_ = .003"
Student's t-test showed that increase in hours of sleep compared to control was lower in group 1 than in group 2, M = 0.75 [-0.27, 1.73] vs. M = 2.33 [1.22, 3.45], t(9.0) = -4.06, p = .003.
require(MASS)## Warning: package 'MASS' was built under R version 3.4.4
glm_res<-glm(Freq ~ (Age+Sex)*Survived, family = poisson, data = data.frame(Titanic))
describe.glm(glm_res)## B SE Stat p eff
## (Intercept) 2.48 0.14 17.83 < .001 (Intercept)
## AgeAdult 3.32 0.14 23.52 < .001 AgeAdult
## SexFemale -2.38 0.09 -25.58 < .001 SexFemale
## SurvivedYes -0.48 0.20 -2.46 = .014 SurvivedYes
## AgeAdult:SurvivedYes -0.88 0.20 -4.45 < .001 AgeAdult:SurvivedYes
## SexFemale:SurvivedYes 2.32 0.12 19.38 < .001 SexFemale:SurvivedYes
## str
## (Intercept) _Z_ = 17.83, _p_ < .001
## AgeAdult _Z_ = 23.52, _p_ < .001
## SexFemale _Z_ = -25.58, _p_ < .001
## SurvivedYes _Z_ = -2.46, _p_ = .014
## AgeAdult:SurvivedYes _Z_ = -4.45, _p_ < .001
## SexFemale:SurvivedYes _Z_ = 19.38, _p_ < .001
lm_res<-lm(Postwt ~ Prewt + Treat + offset(Prewt), anorexia)
describe.glm(lm_res)## B SE Stat p eff str
## (Intercept) 49.77 13.39 3.72 < .001 (Intercept) _t_ = 3.72, _p_ < .001
## Prewt -0.57 0.16 -3.51 < .001 Prewt _t_ = -3.51, _p_ < .001
## TreatCont -4.10 1.89 -2.16 = .034 TreatCont _t_ = -2.16, _p_ = .034
## TreatFT 4.56 2.13 2.14 = .036 TreatFT _t_ = 2.14, _p_ = .036
describe.glm(glm_res, "SexFemale:SurvivedYes", dtype=1)## [1] "_Z_ = 19.38, _p_ < .001"
describe.glm(glm_res, "SexFemale:SurvivedYes", dtype=2)## [1] "_B_ = 2.32 (0.12), _p_ < .001"
describe.glm(glm_res, "SexFemale:SurvivedYes", dtype=3)## [1] "_B_ = 2.32, _SE_ = 0.12, _Z_ = 19.38, _p_ < .001"
describe.glm(glm_res, "SexFemale:SurvivedYes", dtype=3, test.df=T)## [1] "_B_ = 2.32, _SE_ = 0.12, _Z_(26) = 19.38, _p_ < .001"
Adults had lower chances of survival on Titanic than children, Z = -4.45, p < .001, and women had higher chances than men, B = 2.32, SE = 0.12, Z = 19.38, p < .001.
require(ez)
data(ANT)
rt_anova = ezANOVA(
data = ANT[ANT$error==0,]
, dv = rt
, wid = subnum
, within = .(cue,flank)
, between = group
)## Warning: Collapsing data to cell means. *IF* the requested effects are a
## subset of the full design, you must use the "within_full" argument, else
## results may be inaccurate.
rt_anova## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 group 1 18 18.430592 4.377562e-04 * 0.07633358
## 3 cue 3 54 516.605213 1.005518e-39 * 0.89662286
## 5 flank 2 36 1350.598810 1.386546e-34 * 0.92710583
## 4 group:cue 3 54 2.553236 6.497492e-02 0.04110445
## 6 group:flank 2 36 8.768499 7.900829e-04 * 0.07627434
## 7 cue:flank 6 108 5.193357 9.938494e-05 * 0.11436699
## 8 group:cue:flank 6 108 6.377225 9.012515e-06 * 0.13686958
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 3 cue 0.7828347 0.5366835
## 4 group:cue 0.7828347 0.5366835
## 5 flank 0.8812738 0.3415406
## 6 group:flank 0.8812738 0.3415406
## 7 cue:flank 0.1737053 0.1254796
## 8 group:cue:flank 0.1737053 0.1254796
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF]
## 3 cue 0.8652559 1.115029e-34 * 1.0239520 1.005518e-39
## 4 group:cue 0.8652559 7.472046e-02 1.0239520 6.497492e-02
## 5 flank 0.8938738 3.763312e-31 * 0.9858964 3.964046e-34
## 6 group:flank 0.8938738 1.297752e-03 * 0.9858964 8.438369e-04
## 7 cue:flank 0.6022111 1.546166e-03 * 0.7721473 4.745714e-04
## 8 group:cue:flank 0.6022111 3.424499e-04 * 0.7721473 7.170939e-05
## p[HF]<.05
## 3 *
## 4
## 5 *
## 6 *
## 7 *
## 8 *
Repeated measures ANOVA demonstrated a significant influence of cue on RT, F(3, 54) = 516.61, p < .001, G = .90.
You can plot within-subject CIs with the help of summarySEwithin from http://www.cookbook-r.com/Graphs/Plotting_means_and_error_bars_%28ggplot2%29/
require(nlme)## Warning: package 'nlme' was built under R version 3.4.4
require(gridExtra)## Warning: package 'gridExtra' was built under R version 3.4.2
data(ergoStool)
p1<-plot.pointrange(ergoStool, aes(x=Type, y=effort))+ggtitle('Usual CIs')## Warning: package 'ggplot2' was built under R version 3.4.3
p2<-plot.pointrange(ergoStool, aes(x=Type, y=effort), within_subj=T, wid='Subject')+ggtitle('Within-subject CIs')
grid.arrange(p1, p2, ncol=2)
Sometimes it is useful to analyze data log-transformed (e.g., in reaction time analyses), but to show it untransformed. scale_y_exp comes in handy.
library(scales)
temp <- tempfile()
download.file("http://cogjournal.org/2/1/files/ChetverikovRJCS2015SOMdata.zip",temp)
faces <- data.table(read.csv(unz(temp, "faces_data.csv"), header = T))
unlink(temp)
faces[,logAT:=log(answerTime)]
p0<-plot.pointrange(faces[correct==1, ], aes(x=user_gender, color=stim_gender, y=logAT), wid='uid')+ylab('Log RT')
p0
p1<-plot.pointrange(faces[correct==1, ], aes(x=user_gender, color=stim_gender, y=logAT), wid='uid')+scale_y_exp(digits=2)+labs(x="Participant's gender", color="Face Gender", y='Untransformed RT')
p1
It is not very easy to share legend or axis title between plots. I modified a function from https://github.com/hadley/ggplot2/wiki/Share-a-legend-between-two-ggplot2-graphs to make it easier.
p2<-plot.pointrange(faces, aes(x=user_gender, color=stim_gender, y=correct), wid='uid')+labs(x="Participant's gender", color="Face Gender" ,y='Accuracy')
p2
grid_arrange_shared_legend(p1 + theme(legend.direction="horizontal"),p2)
grid_arrange_shared_legend(p1, p2, stack = 'horizontal')
grid_arrange_shared_legend(p1, p2, stack = 'horizontal', one_sub = T)