2. • What is central limit theorem?
x <- rnorm(30, mean = 1, sd = 2)
hist(x)
xmean <- numeric(100)
for (i in 1:100)
{
x <- rnorm(30, mean = 1, sd = 2)
xmean[i] <-mean(x)
}
hist(xmean)
3. • What is central limit theorem?
y <- rexp(100, rate = 1)
hist(y)
ymean <- numeric(100)
for (i in 1:100)
{
y <- rexp(100, rate = 1)
ymean[i] <-mean(y)
}
hist(ymean)
4. rnorm() 產生常態分布的隨機變數
dnorm() probability density
pnorm() cumulative probability function
qnorm() the value of quantile
rnorm(n=30,mean=0,sd=1)
dnorm(1)== 1/sqrt(2*pi)*exp(-1/2)
pnorm(1.645, mean=0,sd=1)
qnorm(0.95,mean=0,sd=1)
7. 建立 R documents 的好習慣
• 多做注解 (##)
• 留意套件和主程式的版本 (R-news)
• 在documents 的開頭交代基本環境
– e.g.:
### This is for …. By xxx at 2014/7/06
library(ez)
setwd(“c:/data/”)
load(“myexample.Rdata”)
rm(list=ls())
8. quasif data set in languageR package
Source: Raaijmakers et al., 1999, Table2
9. data(lexicalMeasures)
Lexical distributional measures for 2233 English
monomorphemic words. This dataset provides a
subset of the data available in the dataset
english.
Baayen, R.H., Feldman, L. and Schreuder, R.
(2006) Morphological influences on the
recognition of monosyllabic monomorphemic
words, Journal of Memory and Language, 53,
496-512.
16. > B=B[order(B$Item, B$SOA), ];B
Subj Item SOA RT
1 s1 w1 Long 466
2 s1 w2 Long 520
3 s1 w3 Long 502
4 s1 w1 Short 475
5 s1 w2 Short 494
6 s1 w3 Short 490
7 s2 w1 Long 516
8 s2 w2 Long 566
9 s2 w3 Long 577
10 s2 w1 Short 491
11 s2 w2 Short 544
12 s2 w3 Short 526
13 s3 w1 Long 484
14 s3 w2 Long 529
15 s3 w3 Long 539
16 s3 w1 Short 470
17 s3 w2 Short 511
18 s3 w3 Short 528
17. > B$RT=B$RT/1000;B
Subj Item SOA RT
1 s1 w1 Long 0.466
2 s1 w2 Long 0.520
3 s1 w3 Long 0.502
4 s1 w1 Short 0.475
5 s1 w2 Short 0.494
6 s1 w3 Short 0.490
7 s2 w1 Long 0.516
8 s2 w2 Long 0.566
9 s2 w3 Long 0.577
10 s2 w1 Short 0.491
11 s2 w2 Short 0.544
12 s2 w3 Short 0.526
13 s3 w1 Long 0.484
14 s3 w2 Long 0.529
15 s3 w3 Long 0.539
16 s3 w1 Short 0.470
17 s3 w2 Short 0.511
18 s3 w3 Short 0.528
18. > B.xtab=xtabs(~ SOA+Item, data=B);B.xtab
Item
SOA w1 w2 w3
Long 3 3 3
Short 3 3 3
> B.xtab.g500=xtabs(~ SOA+Item,
+ data=B,subset=B$RT>500);B.xtab.g500
Item
SOA w1 w2 w3
Long 1 3 3
Short 0 2 2
19. > bysub=aggregate(B$RT, list(B$SOA, B$Subj),
+ mean); bysub
Group.1 Group.2 x
1 Long s1 496.0000
2 Short s1 486.3333
3 Long s2 553.0000
4 Short s2 520.3333
5 Long s3 517.3333
6 Short s3 503.0000
> colnames(bysub) = c(“SOA”, “Subj”, “meanRT”)
> bysub
SOA Subj meanRT
1 Long s1 496.0000
2 Short s1 486.3333
3 Long s2 553.0000
4 Short s2 520.3333
5 Long s3 517.3333
6 Short s3 503.0000
20. > byitem=aggregate(B$RT, list(B$SOA, B$Item),
+ mean); byitem
Group.1 Group.2 x
1 Long w1 488.6667
2 Short w1 478.6667
3 Long w2 538.3333
4 Short w2 516.3333
5 Long w3 539.3333
6 Short w3 514.6667
> colnames(byitem) = c(“SOA”, “Subj”, “meanRT”)
> byitem
SOA Subj meanRT
1 Long s1 496.0000
2 Short s1 486.3333
3 Long s2 553.0000
4 Short s2 520.3333
5 Long s3 517.3333
6 Short s3 503.0000
21. • By subject analysis
bysub=aggregate(B$RT, list(B$SOA, B$Subj), mean);
bysub
names(bysub) <- c("SOA", "Subj", "RT”)
rt_anova = ezANOVA(
data = B #### 用aggregate 之前的 data frames
, dv = RT
, wid = Subj
, within = .(SOA)
)
print(rt_anova)
rt_anova3 = ezANOVA(
data = bysub #### 用by subject mean 的 data frames
, dv = RT
, wid = Subj
, within = .(SOA)
)
print(rt_anova3)
22. • By item analysis
byitem=aggregate(B$RT, list(B$SOA, B$Item), mean);
byitem
names(byitem) <- c("SOA", "items", "RT")
rt_anova2 = ezANOVA(
data = byitem
, dv = RT
,wid = items
, between = SOA
)
print(rt_anova2)
23. • data(ANT)
– ANT{ez}
– Simulated data from the Attention Network Test
– J Fan, BD McCandliss, T Sommer, A Raz, MI Posner
(2002). Testing the efficiency and independence of
attentional networks. Journal of Cognitive
Neuroscience, 14, 340-347.
• 2 within-Ss variables (“cue” and “flank”)
• 1 between-Ss variable (“group”)
• 2 dependent variables (“rt”, and “error”)
24. > data(ANT) ### A data frame with 5760
observations on the following 10 variables
> head(ANT, 20)
25.
26. aov.rt = ezANOVA(
data = ANT[ANT$error==0,]
, dv = rt
, wid = subnum
, within = .(cue,flank)
, between = group
)
print(aov.rt)
27.
28. aov.rt = ezANOVA(
data = ANT[ANT$error==0,]
, dv = rt
, wid = subnum
, within = .(cue,flank)
, between = group
, detailed = T
)
print(aov.rt)