标题: 恳求各位大侠指导一下 [打印本页] 作者: shiyiming 时间: 2010-6-10 13:17 标题: 恳求各位大侠指导一下 data xifang;
do treat = 1 to 3;
do r = 1 to 8;
input x y @@;output;
end;
end;
cards;
47 54 58 66 53 63 46 51 49 56 56 66 54 61 44 50
52 54 53 53 64 67 58 62 59 62 61 63 63 64 66 69
44 52 48 58 46 54 50 61 59 70 57 64 58 69 53 66
;
proc univariate data=xifang normal;/*过程步1 对协变量x做正态性检验*/
var x;by treat;run ;
proc univariate data=xifang normal;/*过程步2 对因变量y做正态性检验*/
var y ;by treat ;run ;
proc anova data=xifang; class treat;/*过程步3 对协变量x做方差分析*/
model x = treat ;run ;
proc anova data=xifang ; class treat ;/*过程步4 对因变量y做方差分析*/
model y = treat ;
run ;
proc glm data=xifang; class treat ; /*过程步5 以x 为协变量做协方差分析*/
model y = x treat/ solution ;
lsmeans treat/ stderr pdiff tdiff ;
run;
quit;
在这段程序中“lsmeans treat/ stderr pdiff tdiff ;”是做什么的啊？作者: shiyiming 时间: 2012-8-5 18:36 标题: Re: 恳求各位大侠指导一下 [color=#FF0040:2savz1nm]lsmeans [/color:2savz1nm]
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Least-squares means (LS-means) are computed for each effect listed in the LSMEANS statement. You may specify only classification effects in the LSMEANS statement - that is, effects that contain only classification variables. You may also specify options to perform multiple comparisons. In contrast to the MEANS statement, the LSMEANS statement performs multiple comparisons on interactions as well as main effects.
LS-means are predicted population margins; that is, they estimate the marginal means over a balanced population. In a sense, LS-means are to unbalanced designs as class and subclass arithmetic means are to balanced designs. Each LS-mean is computed as L'b for a certain column vector L, where b is the vector of parameter estimates - that is, the solution of the normal equations.
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[color=#FF0040:2savz1nm] pdiff [/color:2savz1nm]
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requests that p-values for differences of the LS-means be produced. The optional difftype specifies which differences to display. Possible values for difftype are ALL, CONTROL, CONTROLL, and CONTROLU. The ALL value requests all pairwise differences, and it is the default. The CONTROL value requests the differences with a control that, by default, is the first level of each of the specified LS-mean effects.
To specify which levels of the effects are the controls, list the quoted formatted values in parentheses after the keyword CONTROL. For example, if the effects A, B, and C are class variables, each having two levels, '1' and '2', the following LSMEANS statement specifies the '1' '2' level of A*B and the '2' '1' level of B*C as controls:
lsmeans A*B B*C / pdiff=control('1' '2', '2' '1');
For multiple effect situations such as this one, the ordering of the list is significant, and you should check the output to make sure that the controls are correct.
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[color=#FF0040:2savz1nm]stderr [/color:2savz1nm]
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produces the standard error of the LS-means and the probability level for the hypothesis H0: LS-mean = 0.
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[color=#FF0040:2savz1nm]tdiff[/color:2savz1nm]
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produces the t values for all hypotheses H0: LS-mean(i) = LS-mean(j) and the corresponding probabilities.
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