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mới đã sửa lại Performance của một số thương vụ (file mdat1)
> mdat<-read.csv("D:thR/mdat1.csv",
sep=";", header=TRUE)
> head(mdat)
Number Acquirer FirmsizeA Target FirmsizeT Year Stake Categories Performance Stake1 X
1 M01 KinhDo listed Wall's foreign 2003 50% acquisition positive above 15
2 M02 KinhDo listed Tribeco listed 2005 35.40% acquisition negative above 13
3 M03 ANZ foreign Sacombank listed 2005 10% acquisition negative under 13
4 M04 StandardCharter foreign ACB listed 2005 10% acquisition positive under 13
5 M05 OCBC foreign VPBank other 2006 10% acquisition negative under 12
6 M06 HSBC foreign Techcombank other 2007 15% acquisition negative under 11
> hieuqua1 <- xtabs(~Performance+FirmsizeA, data=mdat)
> hieuqua1
FirmsizeA
Performance foreign listed other SOE
mix 8 6 1 1
negative 19 10 0 0
positive 16 13 2 3
> hieuqua1 <- read.table("D:thR/hieuqua1.txt", header=TRUE)
> hieuqua1
pos neg
foreign 16 27
domestic 18 18
> chisq.test(hieuqua1)
Pearson's Chi-squared test with Yates' continuity correction
data: hieuqua1
X-squared = 0.83796, df = 1, p-value = 0.36
> fisher.test(hieuqua1)
Fisher's Exact Test for Count Data
data: hieuqua1
p-value = 0.2657
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.2190024 1.5993961
sample estimates:
odds ratio
0.5965731
> hieuqua3 <- read.table("D:thR/hieuqua3.txt", header=TRUE)
> hieuqua3
neg pos
acquisition 40 30
merger 5 4
> fisher.test(hieuqua3)
Fisher's Exact Test for Count Data
data: hieuqua3
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.1940964 5.4276861
sample estimates:
odds ratio
1.065783
> hieuqua4 <- read.table("D:thR/hieuqua4.txt", header=TRUE)
> hieuqua4
pos neg
under35 28 14
above35 6 31
> fisher.test(hieuqua4)
Fisher's Exact Test for Count Data
data: hieuqua4
p-value = 9.702e-06
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
3.160346 36.481411
sample estimates:
odds ratio
9.97676
> chisq.test(hieuqua4)
Pearson's Chi-squared test with Yates' continuity correction
data: hieuqua4
X-squared = 18.417, df = 1, p-value = 1.775e-05
> hieuqua2 <- read.table("D:thR/hieuqua2.txt", header=TRUE)
> hieuqua2
pos neg
foreign 5 1
domestic 29 44
> chisq.test(hieuqua2)
Pearson's Chi-squared test with Yates' continuity correction
data: hieuqua2
X-squared = 2.7057, df = 1, p-value = 0.09999
Warning message:
In chisq.test(hieuqua2) : Chi-squared approximation may be incorrect
> hqmodel1 <- read.table("D:thR/hqmodel1.txt", header=T)
> hqmodel1
FirmsizeA Categories Neg Pos
1 foreign acquisition 26 16
2 foreign merger 1 0
3 domestic acquisition 14 14
4 domestic merger 4 4
> contrasts(hqmodel1$FirmsizeA)=contr.treatment(levels(hqmodel1$FirmsizeA), base=2)
> contrasts(hqmodel1$Categories)=contr.treatment(levels(hqmodel1$Categories), base=2)
> library(VGAM)
Loading required package: stats4
Loading required package: splines
> fit.hqmodel1=vglm(cbind(Pos,Neg)~FirmsizeA+Categories, data=hqmodel1,family=multinomial)
> summary(fit.hqmodel1)
Call:
vglm(formula = cbind(Pos, Neg) ~ FirmsizeA + Categories, family = multinomial,
data = hqmodel1)
Pearson residuals:
[,1]
[1,] 0.1039
[2,] -0.6948
[3,] -0.1231
[4,] 0.2310
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.7282 0.8016 -0.908 0.364
FirmsizeAdomestic 0.5650 0.4831 1.170 0.242
Categoriesacquisition 0.2097 0.7533 0.278 0.781
Number of linear predictors: 1
Name of linear predictor: log(mu[,1]/mu[,2])
Residual deviance: 0.8669 on 1 degrees of freedom
Log-likelihood: -5.7028 on 1 degrees of freedom
Number of iterations: 3
Reference group is level 2 of the response
> hqmodel2 <- xtabs(~Stake1+Categories+Performance, data=mdat)
> hqmodel2
, , Performance = mix
Categories
Stake1 acquisition merger
above 12 2
under 2 0
, , Performance = negative
Categories
Stake1 acquisition merger
above 15 2
under 11 1
, , Performance = positive
Categories
Stake1 acquisition merger
above 24 4
under 6 0
> hqmodel2 <- read.table("D:thR/hqmodel2.txt", header=T)
> hqmodel2
Stake1 Categories Neg Pos
1 above acquisition 27 24
2 under acquisition 13 6
3 above merger 4 4
4 under merger 1 0
> contrasts(hqmodel2$Stake1)=contr.treatment(levels(hqmodel2$Stake1), base=2)
> contrasts(hqmodel2$Categories)=contr.treatment(levels(hqmodel2$Categories), base=2)
> fit.hqmodel2=vglm(cbind(Pos,Neg)~Stake1+Categories, data=hqmodel2,family=multinomial)
> summary(fit.hqmodel2)
Call:
vglm(formula = cbind(Pos, Neg) ~ Stake1 + Categories, family = multinomial,
data = hqmodel2)
Pearson residuals:
[,1]
[1,] -0.08132
[2,] 0.14514
[3,] 0.20564
[4,] -0.63918
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.89512 0.84263 -1.062 0.288
Stake1above 0.74984 0.55674 1.347 0.178
Categoriesacquisition 0.05031 0.72239 0.070 0.944
Number of linear predictors: 1
Name of linear predictor: log(mu[,1]/mu[,2])
Residual deviance: 0.7548 on 1 degrees of freedom
Log-likelihood: -5.5099 on 1 degrees of freedom
Number of iterations: 3
Reference group is level 2 of the response
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