Question

我正在执行ANCOVA，以便测试每个性别（导管变量，{变量，logLCC和不同头部测量（响应变量，logLP）之间的关系。 {1}}）。我在sexo得到了每个性别的斜率，我想将它们与1进行比较。更具体地说，我想知道斜率是显着高于还是低于1，或者它们是否等于1因为这在异速生长关系中具有不同的生物学意义。这是我的代码：

lm

我在这里看到了几个主题的整个过程。但我能理解的是，我的斜坡与只是不同的。我怎么能检查他们更大或更小而不是1？

EDITED

解决！

#Modelling my lm#
> lm.logLP.sexo.adu<-lm(logLP~logLCC*sexo, data=ADU)
> anova(lm.logLP.sexo.adu)
Analysis of Variance Table

Response: logLP
         Df Sum Sq Mean Sq  F value    Pr(>F)    
logLCC        1 3.8727  3.8727 3407.208 < 2.2e-16 ***
sexo          1 0.6926  0.6926  609.386 < 2.2e-16 ***
logLCC:sexo   1 0.0396  0.0396   34.829 7.563e-09 ***
Residuals   409 0.4649  0.0011                       
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

#Obtaining slopes#
> lm.logLP.sexo.adu$coefficients
 (Intercept)       logLCC        sexoM logLCC:sexoM 
  -0.1008891    0.6725818   -1.0058962    0.2633595 
> lm.logLP.sexo.adu1<-lstrends(lm.logLP.sexo.adu,"sexo",var="logLCC")
> lm.logLP.sexo.adu1
 sexo logLCC.trend         SE  df  lower.CL  upper.CL
 H       0.6725818 0.03020017 409 0.6132149 0.7319487
 M       0.9359413 0.03285353 409 0.8713585 1.0005241

Confidence level used: 0.95

#Comparing slopes#
> pairs(lm.logLP.sexo.adu1)
 contrast   estimate         SE  df t.ratio p.value
 H - M    -0.2633595 0.04462515 409  -5.902  <.0001

#Checking whether the slopes are different than 1#
#Computes Summary with statistics      
> s1<-summary(lm.logLP.sexo.adu)
> s1

Call:
lm(formula = logLP ~ logLCC * sexo, data = ADU)

Residuals:
 Min       1Q   Median       3Q      Max 
-0.13728 -0.02202 -0.00109  0.01880  0.12468 

Coefficients:
         Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -0.10089    0.12497  -0.807     0.42    
logLCC        0.67258    0.03020  22.271  < 2e-16 ***
sexoM        -1.00590    0.18700  -5.379 1.26e-07 ***
logLCC:sexoM  0.26336    0.04463   5.902 7.56e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.03371 on 409 degrees of freedom
Multiple R-squared:  0.9083,    Adjusted R-squared:  0.9076 
F-statistic:  1350 on 3 and 409 DF,  p-value: < 2.2e-16

#Computes t-student H0: intercept=1. The estimation of coefficients and   their s.d. are in s1$coefficients
> t1<-(1-s1$coefficients[2,1])/s1$coefficients[2,2]
#Calculates two tailed probability
> pval<- 2 * pt(abs(t1), df = df.residual(lm.logLP.sexo.adu), lower.tail = FALSE)
> print(pval)
[1] 3.037231e-24

此外，测试应该在单性别模型中进行。

Answer 1

我如何检查它们是大于还是小于1？

与this post，this post一样，如您所知，您可以通过

进行Wald测试

t1<-(1-s1$coefficients[2,1])/s1$coefficients[2,2]

或者，使用vcov和coef函数使代码更具可读性

fit <- lm.logLP.sexo.adu
t1<-(1-coef(fit)[1])/vcov(fit)[1, 1]

Wald测试为您提供t统计数据，可用于制作双面或单侧test。因此，您可以删除abs并根据要测试的尾部设置lower.tail参数。

如何比较R中的斜率

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