我希望使用 let x = yourClassname.setStringAsCardNumberWithSartNumber(4, withString: str!, withStrLenght: 8)
### output:- 4111XXXXXXXX1111
let x = yourClassname.setStringAsCardNumberWithSartNumber(0, withString: str!, withStrLenght: 12)
### output: - XXXXXXXXXXXX1111
*/
func setStringAsCardNumberWithSartNumber(Number:Int,withString str:String ,withStrLenght len:Int ) -> String{
//let aString: String = "41111111111111111"
let arr = str.characters
var CrediteCard : String = ""
if arr.count > (Number + len) {
for (index, element ) in arr.enumerate(){
if index >= Number && index < (Number + len) {
CrediteCard = CrediteCard + String("X")
}else{
CrediteCard = CrediteCard + String(element)
}
}
return CrediteCard
}else{
print("\(Number) plus \(len) are grether than strings chatarter \(arr.count)")
}
print("\(CrediteCard)")
return str
}
拟合线性混合效果模型，而不会丢弃缺少数据的观察结果。也就是说，我希望lme4::lmer继续使用所有数据来最大化可能性。
我认为使用lmer产生这种行为是否正确？ This unanswered question让我想知道这可能是错的。
答案 0 :(得分：0)
lmer（与大多数模型函数一样）无法处理缺失的数据。为了说明这一点：
data(Orthodont,package="nlme")
Orthodont$nsex <- as.numeric(Orthodont$Sex=="Male")
Orthodont$nsexage <- with(Orthodont, nsex*age)
Orthodont[1, 2] <- NA
lmer(distance ~ age + (age|Subject) + (0+nsex|Subject) +
(0 + nsexage|Subject), data=Orthodont, na.action = na.pass)
#Error in lme4::lFormula(formula = distance ~ age + (age | Subject) + (0 + :
# NA in Z (random-effects model matrix): please use "na.action='na.omit'" or "na.action='na.exclude'"