系数表中不存在NA列不足的行；如何插入? [英] Coefficient table does not have NA rows in rank-deficient fit; how to insert them?
问题描述
library(lmPerm)
x <- lmp(formula = a ~ b * c + d + e, data = df, perm = "Prob")
summary(x) # truncated output, I can see `NA` rows here!
#Coefficients: (1 not defined because of singularities)
# Estimate Iter Pr(Prob)
#b 5.874 51 1.000
#c -30.060 281 0.263
#b:c NA NA NA
#d1 -31.333 60 0.633
#d2 33.297 165 0.382
#d3 -19.096 51 1.000
#e 1.976 NA NA
我想提取所有内容的Pr(Prob)结果，但是
I want to pull out the Pr(Prob) results for everything, but
y <- summary(x)$coef[, "Pr(Prob)"]
#(Intercept) b c d1 d2
# 0.09459459 1.00000000 0.26334520 0.63333333 0.38181818
# d3 e
# 1.00000000 NA
这不是我想要的.我也需要在正确的位置b:c行.
This is not what I want. I need b:c row, too, in the right position.
我想从上面得到的输出示例是:
An example of the output I would like from the above would be:
# (Intercept) b c b:c d1 d2
# 0.09459459 1.00000000 0.26334520 NA 0.63333333 0.38181818
# d3 e
# 1.00000000 NA
我还想提取与每个变量相对应的Iter列.谢谢.
I also would like to pull out the Iter column that corresponds to each variable. Thanks.
推荐答案
lmp基于lm，并且summary.lmp的行为也类似于summary.lm，因此我将首先使用lm进行说明，然后显示我们可以对lmp做同样的事情.
lmp is based on lm and summary.lmp also behaves like summary.lm, so I will first use lm for illustration, then show that we can do the same for lmp.
lm和summary.lm
lm and summary.lm
请阅读?summary.lm并注意以下返回值:
Have a read on ?summary.lm and watch out for the following returned values:
coefficients: a p x 4 matrix with columns for the estimated
coefficient, its standard error, t-statistic and
corresponding (two-sided) p-value. Aliased coefficients are
omitted.
aliased: named logical vector showing if the original coefficients are
aliased.
当具有秩不足的模型时，NA系数在系数表中被省略，它们被称为aliased变量.考虑以下小的可复制示例:
When you have rank-deficient models, NA coefficients are omitted in the coefficient table, and they are called aliased variables. Consider the following small, reproducible example:
set.seed(0)
zz <- xx <- rnorm(10)
yy <- rnorm(10)
fit <- lm(yy ~ xx + zz)
coef(fit) ## we can see `NA` here
#(Intercept) xx zz
# 0.1295147 0.2706560 NA
a <- summary(fit) ## it is also printed to screen
#Coefficients: (1 not defined because of singularities)
# Estimate Std. Error t value Pr(>|t|)
#(Intercept) 0.1295 0.3143 0.412 0.691
#xx 0.2707 0.2669 1.014 0.340
#zz NA NA NA NA
b <- coef(a) ## but no `NA` returned in the matrix / table
# Estimate Std. Error t value Pr(>|t|)
#(Intercept) 0.1295147 0.3142758 0.4121051 0.6910837
#xx 0.2706560 0.2669118 1.0140279 0.3402525
d <- a$aliased
#(Intercept) xx zz
# FALSE FALSE TRUE
如果您要将NA行填充到系数表/矩阵中，我们可以
If you want to pad NA rows to coefficient table / matrix, we can do
## an augmented matrix of `NA`
e <- matrix(nrow = length(d), ncol = ncol(b),
dimnames = list(names(d), dimnames(b)[[2]]))
## fill rows for non-aliased variables
e[!d] <- b
# Estimate Std. Error t value Pr(>|t|)
#(Intercept) 0.1295147 0.3142758 0.4121051 0.6910837
#xx 0.2706560 0.2669118 1.0140279 0.3402525
#zz NA NA NA NA
lmp和summary.lmp
lmp and summary.lmp
什么都不需要更改.
library(lmPerm)
fit <- lmp(yy ~ xx + zz, perm = "Prob")
a <- summary(fit) ## `summary.lmp`
b <- coef(a)
# Estimate Iter Pr(Prob)
#(Intercept) -0.0264354 241 0.2946058
#xx 0.2706560 241 0.2946058
d <- a$aliased
#(Intercept) xx zz
# FALSE FALSE TRUE
e <- matrix(nrow = length(d), ncol = ncol(b),
dimnames = list(names(d), dimnames(b)[[2]]))
e[!d] <- b
# Estimate Iter Pr(Prob)
#(Intercept) -0.0264354 241 0.2946058
#xx 0.2706560 241 0.2946058
#zz NA NA NA
如果要提取Iter和Pr(Prob)，只需执行
If you, want to extract Iter and Pr(Prob), just do
e[, 2] ## e[, "Iter"]
#(Intercept) xx zz
# 241 241 NA
e[, 3] ## e[, "Pr(Prob)"]
#(Intercept) xx zz
# 0.2946058 0.2946058 NA
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