修复在 R 中类似 lm() 的函数后打印的呼叫通知
Fixing call notice printed after an lm() like function in R
我在自己的函数 foo
中使用库 robumeta
中名为 robu()
的类似 lm()
的函数。
但是,我正在处理 formula
参数,当它缺少默认公式时,默认公式为:formula(dint~1)
或用户定义的任何公式。
它工作正常,但是,在 foo
的输出中,打印的公式调用始终是:Model: missing(f) if formula(dint ~ 1)
无论在 foo
.[=20= 中输入什么公式]
我能否更正这部分输出,使其仅显示所使用的确切公式? (见下面的例子)
dat <- data.frame(dint = 1:9, SD = 1:9*.1,
time = c(1,1,2,3,4,3,2,4,1),
study.name = rep(c("bob", "jim", "jon"), 3))
library(robumeta)
# MY FUNCTION:
foo <- function(f, data){
robu(formula = if(missing(f)) formula(dint~1) else formula(f), data = data, studynum = study.name, var = SD^2)
}
# EXAMPLES OF USE:
foo(data = dat) ## HERE I expect: `Model: dint ~ 1`
foo(dint~as.factor(time), data = dat) ## HERE I expect: `Model: dint ~ time`
一个选项是更新 'ml' 对象
foo <- function(f, data){
fmla <- if(missing(f)) {
formula(dint ~ 1)
} else {
formula(f)
}
model <- robu(formula = fmla, data = data, studynum = study.name, var = SD^2)
model$ml <- fmla
model
}
-正在检查
foo(data = dat)
RVE: Correlated Effects Model with Small-Sample Corrections
Model: dint ~ 1
Number of studies = 3
Number of outcomes = 9 (min = 3 , mean = 3 , median = 3 , max = 3 )
Rho = 0.8
I.sq = 96.83379
Tau.sq = 9.985899
Estimate StdErr t-value dfs P(|t|>) 95% CI.L 95% CI.U Sig
1 X.Intercept. 4.99 0.577 8.65 2 0.0131 2.51 7.48 **
---
Signif. codes: < .01 *** < .05 ** < .10 *
---
Note: If df < 4, do not trust the results
foo(dint~ as.factor(time), data = dat)
RVE: Correlated Effects Model with Small-Sample Corrections
Model: dint ~ as.factor(time)
Number of studies = 3
Number of outcomes = 9 (min = 3 , mean = 3 , median = 3 , max = 3 )
Rho = 0.8
I.sq = 97.24601
Tau.sq = 11.60119
Estimate StdErr t-value dfs P(|t|>) 95% CI.L 95% CI.U Sig
1 X.Intercept. 3.98 2.50 1.588 2.00 0.253 -6.80 14.8
2 as.factor.time.2 1.04 4.41 0.236 1.47 0.842 -26.27 28.3
3 as.factor.time.3 1.01 1.64 0.620 1.47 0.617 -9.10 11.1
4 as.factor.time.4 2.52 2.50 1.007 2.00 0.420 -8.26 13.3
---
Signif. codes: < .01 *** < .05 ** < .10 *
我在自己的函数 foo
中使用库 robumeta
中名为 robu()
的类似 lm()
的函数。
但是,我正在处理 formula
参数,当它缺少默认公式时,默认公式为:formula(dint~1)
或用户定义的任何公式。
它工作正常,但是,在 foo
的输出中,打印的公式调用始终是:Model: missing(f) if formula(dint ~ 1)
无论在 foo
.[=20= 中输入什么公式]
我能否更正这部分输出,使其仅显示所使用的确切公式? (见下面的例子)
dat <- data.frame(dint = 1:9, SD = 1:9*.1,
time = c(1,1,2,3,4,3,2,4,1),
study.name = rep(c("bob", "jim", "jon"), 3))
library(robumeta)
# MY FUNCTION:
foo <- function(f, data){
robu(formula = if(missing(f)) formula(dint~1) else formula(f), data = data, studynum = study.name, var = SD^2)
}
# EXAMPLES OF USE:
foo(data = dat) ## HERE I expect: `Model: dint ~ 1`
foo(dint~as.factor(time), data = dat) ## HERE I expect: `Model: dint ~ time`
一个选项是更新 'ml' 对象
foo <- function(f, data){
fmla <- if(missing(f)) {
formula(dint ~ 1)
} else {
formula(f)
}
model <- robu(formula = fmla, data = data, studynum = study.name, var = SD^2)
model$ml <- fmla
model
}
-正在检查
foo(data = dat)
RVE: Correlated Effects Model with Small-Sample Corrections
Model: dint ~ 1
Number of studies = 3
Number of outcomes = 9 (min = 3 , mean = 3 , median = 3 , max = 3 )
Rho = 0.8
I.sq = 96.83379
Tau.sq = 9.985899
Estimate StdErr t-value dfs P(|t|>) 95% CI.L 95% CI.U Sig
1 X.Intercept. 4.99 0.577 8.65 2 0.0131 2.51 7.48 **
---
Signif. codes: < .01 *** < .05 ** < .10 *
---
Note: If df < 4, do not trust the results
foo(dint~ as.factor(time), data = dat)
RVE: Correlated Effects Model with Small-Sample Corrections
Model: dint ~ as.factor(time)
Number of studies = 3
Number of outcomes = 9 (min = 3 , mean = 3 , median = 3 , max = 3 )
Rho = 0.8
I.sq = 97.24601
Tau.sq = 11.60119
Estimate StdErr t-value dfs P(|t|>) 95% CI.L 95% CI.U Sig
1 X.Intercept. 3.98 2.50 1.588 2.00 0.253 -6.80 14.8
2 as.factor.time.2 1.04 4.41 0.236 1.47 0.842 -26.27 28.3
3 as.factor.time.3 1.01 1.64 0.620 1.47 0.617 -9.10 11.1
4 as.factor.time.4 2.52 2.50 1.007 2.00 0.420 -8.26 13.3
---
Signif. codes: < .01 *** < .05 ** < .10 *