[英]R: how to extract information from summary model fit
library(nlme)
fm1 <- nlme(height ~ SSasymp(age, Asym, R0, lrc),
data = Loblolly,
fixed = Asym + R0 + lrc ~ 1,
random = Asym ~ 1,
start = c(Asym = 103, R0 = -8.5, lrc = -3.3))
> summary(fm1)
Nonlinear mixed-effects model fit by maximum likelihood
Model: height ~ SSasymp(age, Asym, R0, lrc)
Data: Loblolly
AIC BIC logLik
239.4856 251.6397 -114.7428
Random effects:
Formula: Asym ~ 1 | Seed
Asym Residual
StdDev: 3.650642 0.7188625
Fixed effects: Asym + R0 + lrc ~ 1
Value Std.Error DF t-value p-value
Asym 101.44960 2.4616951 68 41.21128 0
R0 -8.62733 0.3179505 68 -27.13420 0
lrc -3.23375 0.0342702 68 -94.36052 0
Correlation:
Asym R0
R0 0.704
lrc -0.908 -0.827
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.23601930 -0.62380854 0.05917466 0.65727206 1.95794425
Number of Observations: 84
Number of Groups: 14
我有興趣從NLME擬合的摘要輸出中提取信息。
我想提取一下
fm1$apVar
但沒有運氣。 fixef(fm1)
提取 sqrt(diag(fm1$varFix))
但這些值與固定效果下的Std.Error列下的值不完全匹配? fm1$logLik
提取) fm1$Residuals
提取) 我的最終目標是適應多個模型並將其各自的摘要估計存儲到有組織的數據data.frame
。
fm1 <- nlme(height ~ SSasymp(age, Asym, R0, lrc),
data = Loblolly,
fixed = Asym + R0 + lrc ~ 1,
random = Asym ~ 1,
start = c(Asym = 103, R0 = -8.5, lrc = -3.3))
fm2 <- nlme(height ~ SSasymp(age, Asym, R0, lrc),
data = Loblolly,
fixed = Asym + R0 + lrc ~ 1,
random = Asym ~ 1,
start = c(Asym = 103, R0 = -5.4, lrc = -3.3))
summary(fm1)
summary(fm2)
mylist = list(NULL, summary(fm1), NULL, summary(fm2), NULL, NULL)
假設我的列表對象看起來像是mylist
。 現在我想創建一個看起來像這樣的data.frame
:
model FixedAsym FixedAsymStdError FixedR0 ... Residual
1 101.44960 2.4616951 -8.62733 0.7188625
2 101.44934 2.4616788 -8.62736 ... 0.7188625
並創建此data.frame(行數對應於我在mylist
有多少個模型摘要)我需要系統地從模型摘要輸出中提取這些值(編號為1-5)。
這里還有幾件......
as.numeric(VarCorr(fm1)[,2])
# [1] 3.6506418 0.7188625
summary(fm1)$tTable[,2]
# Asym R0 lrc
# 2.46169512 0.31795045 0.03427017
# looks like you don't need this one anymore, but here's a way of getting it
summary(fm1)$corFixed
# Asym R0 lrc
# Asym 1.0000000 0.7039498 -0.9077793
# R0 0.7039498 1.0000000 -0.8271022
# lrc -0.9077793 -0.8271022 1.0000000
抱歉這不是一個完整的答案 - 可能很難創建一個像你所描述的匯總表,因為每個潛在行的結構將是不同的,並且將取決於包含哪些變量作為固定和隨機效應。
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