blocks|key|1982866|text|您可以通过调用str(summary(fit))查看summary()返回的对象的结构。每一块都可以使用$进行访问。F统计量的p值更容易从anova返回的对象中获得。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1982867|简而言之，您可以这样做：|1982868|rSquared+<-+summary(fit)$r.squared
pVal+<-+anova(fit)$'Pr(>F)'[1]|code-block|syntax|javascript|1982869|entityMap^0|7|H|Q|9|1G|1|1X|5|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@$9|P|A|Q|B|C]|$9|R|A|S|B|C]|$9|T|A|U|B|C]|$9|V|A|W|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|X|8|@]|D|@]|E|$]]|$1|H|3|I|5|J|7|Y|8|@]|D|@]|E|$K|L]]|$1|M|3|-4|5|6|7|Z|8|@]|D|@]|E|$]]]|N|$]]

You can see the structure of the object returned by <code>summary()</code> by calling <code>str(summary(fit))</code>. Each piece can be accessed using <code>$</code>. The p-value for the F statistic is more easily had from the object returned by <code>anova</code>.

Concisely, you can do this:

<pre><code>rSquared &lt;- summary(fit)$r.squared
pVal &lt;- anova(fit)$'Pr(&gt;F)'[1]
</code></pre>

blocks|key|1983024|text|虽然上面的两个答案都很好，但提取对象部分的过程更一般。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1983025|在许多情况下，函数返回列表，可以使用str()访问各个组件，该命令将打印组件及其名称。然后，您可以使用$运算符访问它们，即myobject$componentname。|offset|length|style|CODE|1983026|对于lm对象，有许多预定义的方法可以使用，比如coef()、resid()、summary()等，但你并不总是那么幸运。|1983027|entityMap^0|0|I|5|1P|M|0|N|6|U|7|12|9|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|M|8|@$D|N|E|O|F|G]|$D|P|E|Q|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|R|8|@$D|S|E|T|F|G]|$D|U|E|V|F|G]|$D|W|E|X|F|G]]|9|@]|A|$]]|$1|J|3|-4|5|6|7|Y|8|@]|9|@]|A|$]]]|K|$]]

While both of the answers above are good, the procedure for extracting parts of objects is more general.

In many cases, functions return lists, and the individual components can be accessed using <code>str()</code> which will print the components along with their names. You can then access them using the $ operator, i.e. <code>myobject$componentname</code>. 

In the case of lm objects, there are a number of predefined methods one can use such as <code>coef()</code>, <code>resid()</code>, <code>summary()</code> etc, but you won't always be so lucky.

blocks|key|1769656|text|请注意，summary(fit)会生成一个包含所需所有信息的对象。其中存储了beta、se、t和p向量。通过选择系数矩阵的第4列(存储在摘要对象中)获得p值：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1769657|summary(fit)$coefficients[,4]+
summary(fit)$r.squared|code-block|syntax|javascript|1769658|尝试使用str(summary(fit))查看此对象包含的所有信息。|1769659|编辑:我误读了Chase的答案，它基本上告诉你如何获得我在这里给出的内容。|1769660|entityMap^0|4|C|0|0|4|H|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|T|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|U|8|@$9|V|A|W|B|C]]|D|@]|E|$]]|$1|M|3|N|5|6|7|X|8|@]|D|@]|E|$]]|$1|O|3|-4|5|6|7|Y|8|@]|D|@]|E|$]]]|P|$]]

Notice that <code>summary(fit)</code> generates an object with all the information you need. The beta, se, t and p vectors are stored in it. Get the p-values by selecting the 4th column of the coefficients matrix (stored in the summary object): 

<pre><code>summary(fit)$coefficients[,4] 
summary(fit)$r.squared
</code></pre>

Try <code>str(summary(fit))</code> to see all the info that this object contains. 

Edit: I had misread Chase's answer which basically tells you how to get to what I give here.

blocks|key|1769775|text|另一种选择是使用cor.test函数，而不是lm：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1769776|>+x+<-+c(44.4,+45.9,+41.9,+53.3,+44.7,+44.1,+50.7,+45.2,+60.1)
>+y+<-+c(+2.6,++3.1,++2.5,++5.0,++3.6,++4.0,++5.2,++2.8,++3.8)

>+mycor+=+cor.test(x,y)
>+mylm+=+lm(x~y)

#+r+and+rsquared:
>+cor.test(x,y)$estimate+**+2
++++++cor+
0.3262484+
>+summary(lm(x~y))$r.squared
[1]+0.3262484

#+P.value+

>+lmp(lm(x~y))++#+Using+the+lmp+function+defined+in+Chase's+answer
[1]+0.1081731
>+cor.test(x,y)$p.value
[1]+0.1081731|code-block|syntax|javascript|1769777|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Another option is to use the cor.test function, instead of lm:

<pre><code>&gt; x &lt;- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
&gt; y &lt;- c( 2.6, 3.1, 2.5, 5.0, 3.6, 4.0, 5.2, 2.8, 3.8)

&gt; mycor = cor.test(x,y)
&gt; mylm = lm(x~y)

# r and rsquared:
&gt; cor.test(x,y)$estimate ** 2
 cor 
0.3262484 
&gt; summary(lm(x~y))$r.squared
[1] 0.3262484

# P.value 

&gt; lmp(lm(x~y)) # Using the lmp function defined in Chase's answer
[1] 0.1081731
&gt; cor.test(x,y)$p.value
[1] 0.1081731
</code></pre>

blocks|key|1769817|text|x+=+cumsum(c(0,+runif(100,+-1,+%2B1)))
y+=+cumsum(c(0,+runif(100,+-1,+%2B1)))
fit+=+lm(y+~+x)
>+names(summary(fit))
[1]+"call"++++++++++"terms"++++++++
+[3]+"residuals"+++++"coefficients"+
+[5]+"aliased"+++++++"sigma"++++++++
+[7]+"df"++++++++++++"r.squared"++++
+[9]+"adj.r.squared"+"fstatistic"+++
[11]+"cov.unscaled"+
++++summary(fit)$r.squared|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|1769818|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>x = cumsum(c(0, runif(100, -1, +1)))
y = cumsum(c(0, runif(100, -1, +1)))
fit = lm(y ~ x)
&gt; names(summary(fit))
[1] "call" "terms" 
 [3] "residuals" "coefficients" 
 [5] "aliased" "sigma" 
 [7] "df" "r.squared" 
 [9] "adj.r.squared" "fstatistic" 
[11] "cov.unscaled" 
 summary(fit)$r.squared
</code></pre>

blocks|key|1769872|text|这是提取p值的最简单方法：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1769873|coef(summary(modelname))[,+"Pr(>%7Ct%7C)"]|code-block|syntax|javascript|1769874|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

This is the easiest way to pull the p-values:

<pre><code>coef(summary(modelname))[, "Pr(&gt;|t|)"]
</code></pre>

blocks|key|1983533|text|对于显示在summary()末尾的最终p值，该函数使用pf()根据summary(fit)$fstatistic值进行计算。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1983534|fstat+<-+summary(fit)$fstatistic
pf(fstat[1],+fstat[2],+fstat[3],+lower.tail=FALSE)|code-block|syntax|javascript|1983535|来源：[1]，[2]|1983536|entityMap|0|LINK|mutability|MUTABLE|url|http://r.789695.n4.nabble.com/Extract-p-value-from-lm-for-the-whole-model-tp1470479p1470527.html|1|https://stackoverflow.com/a/28016999/9645167^0|5|9|R|4|X|N|0|0|3|3|0|7|3|1|0^^$0|@$1|2|3|4|5|6|7|W|8|@$9|X|A|Y|B|C]|$9|Z|A|10|B|C]|$9|11|A|12|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|13|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|14|8|@]|D|@$9|15|A|16|1|17]|$9|18|A|19|1|1A]]|E|$]]|$1|M|3|-4|5|6|7|1B|8|@]|D|@]|E|$]]]|N|$O|$5|P|Q|R|E|$S|T]]|U|$5|P|Q|R|E|$S|V]]]]

For the final p-value displayed at the end of <code>summary()</code>, the function uses <code>pf()</code> to calculate from the <code>summary(fit)$fstatistic</code> values.

<pre class="lang-r prettyprint-override"><code>fstat &lt;- summary(fit)$fstatistic
pf(fstat[1], fstat[2], fstat[3], lower.tail=FALSE)
</code></pre>

Source: <a href="http://r.789695.n4.nabble.com/Extract-p-value-from-lm-for-the-whole-model-tp1470479p1470527.html" rel="nofollow noreferrer">[1]</a>, <a href="https://stackoverflow.com/a/28016999/9645167">[2]</a>

How do you pull out the p-value (for the significance of the coefficient of the single explanatory variable being non-zero) and R-squared value from a simple linear regression model? For example...

<pre><code>x = cumsum(c(0, runif(100, -1, +1)))
y = cumsum(c(0, runif(100, -1, +1)))
fit = lm(y ~ x)
summary(fit)
</code></pre>

I know that <code>summary(fit)</code> displays the p-value and R-squared value, but I want to be able to stick these into other variables.

pull out p-values and r-squared from a linear regression

如何从简单的线性回归模型中提取p值(单个解释变量的系数不为零的显着性)和R平方值？例如..。x = cumsum(c(0, runif(100, -1, +1)))y = cumsum(c(0, runif(100, -1, +1)))fit = lm(y ~ x)summary(fit)我知道summary(fit)...

问从线性回归中提取p值和r平方
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