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use https://stats.idre.ucla.edu/stat/stata/examples/mlm_ma_hox/pupcross, clear
xtmixed achiev pupsex pupses || _all: R.sschool ||_all: R.pschool, reml var
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proc mixed data=WORK.IMPORT covtest CL;
class sschool pschool;
model achiev = pupsex pupses/solution CL;
random sschool pschool / solution;
run;
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recode achiev (min/5.9 = 0)(6/max = 1),gen(ach)
xtmelogit ach pupsex pupses || _all: R.sschool || _all: R.pschool, var
*等价于
meglm ach pupsex pupses || _all: R.sschool || _all: R.pschool, family(binomial) link(logit)
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meglm ach pupsex pupses , family(binomial) link(logit)
Wald chi2(2) = 17.24
Log likelihood = -626.12645 Prob > chi2 = 0.0002
------------------------------------------------------------------------------
ach | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
pupsex | 0.338 0.137 2.47 0.013 0.070 0.606
pupses | 0.170 0.048 3.54 0.000 0.076 0.264
_cons | -0.140 0.218 -0.65 0.519 -0.567 0.286
------------------------------------------------------------------------------
meglm ach pupsex pupses i.sschool || _all: R.pschool, family(binomial) link(logit)
Log likelihood = -536.82395 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
ach | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
pupsex | 0.457 0.164 2.78 0.005 0.134 0.779
pupses | 0.212 0.058 3.68 0.000 0.099 0.325
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dis ((626.12645-536.82395)*2/30)/((536.82395*2)/(1000-32))
2.2397328
*Ftail(df1,df2,f-value)
gen p = Ftail(30,968,5.3676704)
sum p
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
p | 1,000 4.32e-18 0 4.32e-18 4.32e-18
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meqrlogit ach pupsex pupses || _all: R.sschool || _all: R.pschool, var technique(nr)
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meqrlogit be_poverty1 c_age c_age2 gender birth_p edu2 edu3 fam2 fam3 fam4 ihs_tincome_h_cpi || _all: R.cohort1 ||_all: R.wave, var technique(nr)
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meglm ach pupsex pupses || _all: R.sschool || _all: R.pschool, family(binomial) link(probit)
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melogit ach pupsex pupses || _all: R.sschool ||_all: R.pschool
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#https://www.rensvandeschoot.com/tutorials/lme4/
library(lme4)
library(haven)
https://osf.io/7xnzp/
school <- read_dta("/Users/ginglam/Downloads/临时/sample.dta",encoding = "GB2312”)
school2 <- read_dta("/Users/ginglam/Downloads/临时/sample2.dta",encoding = "GB2312")
model<- lmer(achiev ~ 1 + pupsex + pupses + (1|sschool) + (1|pschool), REML = TRUE, data=school)
model2 <- glmer(ach ~ 1 + pupsex + pupses + (1|sschool) + (1|pschool), family=binomial, data=school2)
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PROC GLIMMIX DATA=WORK.IMPORT maxopt=25000;
class sschool pschool;
model ach(event='1') = pupsex pupses /solution CL
dist=binary;
random sschool pschool / solution;
covtest GLM / WALD;
NLOPTIONS TECHNIQUE=NRRIDG;
run;
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