SAS mixed 程序及其结果解释。请哥哥姐姐们帮我解释sas运行的结果，谢谢。越详细越好。我是想看看dp2这个变量对e_size的影响，结果如何解释，谢谢。其中dp2分为3类，低、中、高，分别为1、2、3. 标蓝色部分都要解释，特别是Pr>0.05或<0.05都说明什么问题，谢谢。

proc mixed data=demo method=ml covtest;
class studyno dp2;
weight wgt;
model e_size= dp2/ chisq s df=10000;
random studyno;
repeated;
parms (1) (1) / eqcons=2;
lsmeans dp2/ cl df=10000;
run;



The Mixed Procedure

Model Information

Data Set HHCC.META411
Dependent Variable e_size
Weight Variable wgt
Covariance Structure Variance Components
Estimation Method ML
Residual Variance Method Parameter
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Containment


Class Level Information

Class Levels Values

No 161 1 2 3 4 5 6 7 8 9 10 11 12 13
14 15 16 17 18 19 20 21 22 23
24 25 26 27 28 29 30 31 32 33
34 35 36 37 38 39 40 41 42 43
44 45 46 47 48 49 50 51 52 53
54 55 56 57 58 59 60 61 62 63
64 65 66 67 68 69 70 71 72 73
74 75 76 77 78 79 80 81 82 83
84 85 86 87 88 89 90 91 92 93
94 95 96 97 98 99 100 101 102
103 104 105 106 107 108 109
110 111 112 113 114 115 116
117 118 119 120 121 122 123
124 125 126 127 128 129 130
131 132 133 134 135 136 137
138 139 140 141 142 143 144
145 146 149 150 151 152 153
155 156 157 158 159 160 161
162 163 164
dp2 3 1 2 3


Dimensions

Covariance Parameters 2
Columns in X 4
Columns in Z 161
Subjects 1
Max Obs Per Subject 161
Observations Used 161
Observations Not Used 0
Total Observations 161

Demo of univariate meta-analysis with PROC MIXED in SAS 8
ML analysis, RANDOM effects of study, CLASS moderator
08:11 Tuesday, April 11, 2000

The Mixed Procedure

Parameter Search

CovP1 CovP2 Log Like -2 Log Like

1.0000 1.0000 -193.4497 386.8993


Iteration History

Iteration Evaluations -2 Log Like Criterion

1 4 253.05162385 .
2 1 242.09167389 0.16581583
3 1 236.40129095 0.05510638
4 1 234.41664611 0.00938611
5 1 234.09535945 0.00036124
6 1 234.08390106 0.00000061
7 1 234.08388225 0.00000000


Convergence criteria met.


Covariance Parameter Estimates

Standard Z
Cov Parm Estimate Error Value Pr Z

No 0.03043 0.01189 2.56 0.0053
Residual 1.0000 0 . .

Fit Statistics

-2 Log Likelihood 234.1
AIC (smaller is better) 242.1
AICC (smaller is better) 242.3
BIC (smaller is better) 254.4


PARMS Model Likelihood Ratio Test

DF Chi-Square Pr > ChiSq

1 152.82 <.0001
Demo of univariate meta-analysis with PROC MIXED in SAS 9
ML analysis, RANDOM effects of study, CLASS moderator
08:11 Tuesday, April 11, 2000

The Mixed Procedure

Solution for Fixed Effects

Standard
Effect dp2 Estimate Error DF t Value Pr > |t|

Intercept -0.3238 0.05370 158 -6.03 <.0001
dp2 1 -0.05019 0.08912 1E4 -0.56 0.5733
dp2 2 0.2473 0.09527 1E4 2.60 0.0095
dp2 3 0 . . . .


Type 3 Tests of Fixed Effects

Num Den
Effect DF DF Chi-Square F Value Pr > ChiSq Pr > F

dp2 2 1E4 9.09 4.55 0.0106 0.0106


Least Squares Means

Standard
Effect dp2 Estimate Error DF t Value Pr > |t| Alpha Lower Upper

dp2 1 -0.3740 0.07113 1E4 -5.26 <.0001 0.05 -0.5134 -0.2346
dp2 2 -0.07654 0.07870 1E4 -0.97 0.3308 0.05 -0.2308 0.07773
dp2 3 -0.3238 0.05370 1E4 -6.03 <.0001 0.05 -0.4291 -0.2185