如何测试r中均值和方差之间的差异?

下面的代码是测试均值和方差之间差异的正确方法吗?

short<-function(t) {
    str(t)
    print("model")
    m<-lm(y~x*r,data=t,na.action=na.omit,weights=t$weights)
    print("summary(m)")
    print(summary(m))
    print("summary(aov)")
    print(summary(aov(m)))
    print("test for equal means")
    print((t.test(y ~ r, data=t))) 
    print("test for equal variances")
    print((var.test(y ~ r, data=t)))
}

它打印出:

Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   81 obs. of  10 variables:
 $ a      : num  7.5 6.8 6.74 6.97 7.3 7.08 7.05 7.2 7.8 8.08 ...
 $ x      : num  144 103 106 110 122 ...
 $ y      : num  0.42 0.68 0.64 0.55 0.49 0.431 0.421 0.53 0.164 0.084 ...
 $ n      : num  265 250 122 122 296 439 439 188 455 459 ...
 $ tx     : num  24 24 20 20 52 26 26 26 24 24 ...
 $ dm2    : num  4.8 3.9 7.9 9 13 10.1 9.6 9.5 12.6 12.3 ...
 $ race   : num  1 1 1 1 1 1 1 1 1 1 ...
 $ size   : num  0.0124 0.0117 0.00571 0.00571 0.01385 ...
 $ weights: num  0.0124 0.0117 0.00571 0.00571 0.01385 ...
 $ r      : Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...
[1] "model"
[1] "summary(m)"

Call:
lm(formula = y ~ x * r, data = t, weights = t$weights, na.action = na.omit)

Weighted Residuals:
      Min        1Q    Median        3Q       Max 
-0.028252 -0.007268  0.002292  0.008745  0.029854 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.246985   0.102363  12.182  < 2e-16 ***
x           -0.006364   0.000808  -7.877 1.76e-11 ***
r2          -0.280859   0.241185  -1.164    0.248    
x:r2         0.001786   0.002005   0.891    0.376    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.0123 on 77 degrees of freedom
Multiple R-squared:  0.4713,    Adjusted R-squared:  0.4507 
F-statistic: 22.88 on 3 and 77 DF,  p-value: 1.084e-10

[1] "summary(aov)"
            Df   Sum Sq  Mean Sq F value   Pr(>F)    
x            1 0.009681 0.009681  63.968 1.03e-11 ***
r            1 0.000588 0.000588   3.882   0.0524 .  
x:r          1 0.000120 0.000120   0.793   0.3758    
Residuals   77 0.011654 0.000151                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
[1] "test for equal means"

    Welch Two Sample t-test

data:  y by r
t = 1.1229, df = 35.039, p-value = 0.2691
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.03406304  0.11838766
sample estimates:
mean in group 1 mean in group 2 
      0.4479123       0.4057500 

[1] "test for equal variances"

    F test to compare two variances

data:  y by r
F = 2.3944, num df = 64, denom df = 15, p-value = 0.06285
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
 0.9514814 4.9001925
sample estimates:
ratio of variances 
          2.394376 

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