问题
I am running a linear mixed model for a dataset Developmental trajectory of 2 groups- assessed at 3 timepoints on a number of different measures. Predictor variables are age and maternal education. I am using nlme and ggplot2 packages. Here is my final model (GM_RAW is the dependent variable)
Model_5<-lme(GM_RAW~timepoint*Group+age+Maternal_Education, data=dat,
random=~timepoint|ID,method="ML", na.action=na.omit,control=list(opt="optim"))
summary(Model_5)
I have plotted individual trajectories based on the raw data but I want to add the information from the predicted lines from the model for my 2 groups. I have tried various suggestions posted on stack overflow but none seem to work
dat$gmpred<-predict(Model_5)
GrossMotor<-ggplot(dat,aes(x=as.numeric(timepoint),y=GM_RAW,colour=Group))+
geom_point()+ geom_line(aes(group=ID))
GrossMotor + geom_line(data=dat, aes(y=gmpred))
I want to show the intercept and slope for my 2 groups on the final graph
some sample data
"","ID","timepoint","Maternal_Education","age","Group","GM_RAW"
"1",3012,"5 months",NA,188,"Typical Group",10
"2",3089,"5 months",NA,182,"Typical Group",9
"3",3012,"10 months",NA,328,"Typical Group",13
"4",3004,"5 months","Tertiary postgraduate",163,"Typical Group",6
"5",3008,"5 months","Tertiary postgraduate",171,"Typical Group",4
"6",3023,"5 months","Tertiary postgraduate",170,"Typical Group",8
"7",3063,"5 months","Tertiary postgraduate",181,"Typical Group",10
"8",3071,"5 months","Tertiary postgraduate",151,"Typical Group",8
"9",3074,"5 months","Tertiary postgraduate",183,"Typical Group",9
"10",3075,"5 months","Tertiary postgraduate",165,"Typical Group",9
"11",3087,"5 months","Tertiary postgraduate",172,"Typical Group",6
"12",3104,"5 months","Tertiary postgraduate",180,"Typical Group",7
"13",3115,"5 months","Tertiary postgraduate",199,"Typical Group",8
"14",3142,"5 months","Tertiary postgraduate",201,"Typical Group",9
"15",3161,"5 months","Tertiary postgraduate",189,"Typical Group",7
"16",3162,"5 months","Tertiary postgraduate",201,"Typical Group",8
"17",4002,"5 months","Tertiary postgraduate",202,"NF1",8
"18",4024,"5 months","Tertiary postgraduate",167,"NF1",8
"19",3004,"10 months","Tertiary postgraduate",315,"Typical Group",9
"20",3008,"10 months","Tertiary postgraduate",341,"Typical Group",9
"21",3023,"10 months","Tertiary postgraduate",358,"Typical Group",14
"22",3063,"10 months","Tertiary postgraduate",293,"Typical Group",17
"23",3071,"10 months","Tertiary postgraduate",302,"Typical Group",12
"24",3074,"10 months","Tertiary postgraduate",333,"Typical Group",12
"25",3075,"10 months","Tertiary postgraduate",318,"Typical Group",11
"26",3078,"10 months","Tertiary postgraduate",304,"Typical Group",9
"27",3087,"10 months","Tertiary postgraduate",335,"Typical Group",13
"28",3104,"10 months","Tertiary postgraduate",294,"Typical Group",10
"29",3115,"10 months","Tertiary postgraduate",305,"Typical Group",11
"30",3142,"10 months","Tertiary postgraduate",327,"Typical Group",11
"31",3161,"10 months","Tertiary postgraduate",328,"Typical Group",12
"32",3162,"10 months","Tertiary postgraduate",333,"Typical Group",10
"33",4002,"10 months","Tertiary postgraduate",335,"NF1",11
"34",4009,"10 months","Tertiary postgraduate",320,"NF1",11
"35",4024,"10 months","Tertiary postgraduate",351,"NF1",13
"36",3004,"14 months","Tertiary postgraduate",438,"Typical Group",21
"37",3008,"14 months","Tertiary postgraduate",460,"Typical Group",9
"38",3023,"14 months","Tertiary postgraduate",471,"Typical Group",18
"39",3063,"14 months","Tertiary postgraduate",445,"Typical Group",20
回答1:
I think the problem is the way you are constructing your model. Based on your data, I had to take away some covariates in order to make the model work. Also, if you include a random effect for "ID", you will have different intercepts for each ID, and thus for each timepoint you will have several y values in your prediction column. ggplot does link them, but of course it looks totally crooked.
Thus, I removed the ID as a random effect and you will see that the plot works.
library(tidyverse)
foo <- foo %>% mutate(timepoint = str_replace(timepoint, " months", ""))
Model_5 <- nlme::lme(GM_RAW ~ Group,
data = foo,
random = ~ 1 | timepoint, method = "ML", na.action = na.omit
)
foo$gmpred <- predict(Model_5)
ggplot(foo, aes(x = as.numeric(timepoint), y = GM_RAW, colour = Group)) +
geom_point() +
geom_line(aes(group = ID)) +
geom_line(aes(y = gmpred, group = Group, linetype = Group), color = "black")
If you really need to add ID as a random effect, you will need to change the grouping variable for the geom_line, I use interaction()
Model_5 <- nlme::lme(GM_RAW ~ Group,
data = foo,
random = ~ timepoint | ID, method = "ML", na.action = na.omit
)
foo$gmpred <- predict(Model_5)
ggplot(foo, aes(x = as.numeric(timepoint), y = GM_RAW, colour = Group)) +
geom_point() +
geom_line(aes(group = ID)) +
geom_line(aes(y = gmpred, group = interaction(Group,ID), linetype = Group), color = "black")
Created on 2020-04-18 by the reprex package (v0.3.0)
data
foo <- readr::read_csv(",ID,timepoint,Maternal_Education,age,Group,GM_RAW
1,3012,5 months,NA,188,Typical Group,10
2,3089,5 months,NA,182,Typical Group,9
3,3012,10 months,NA,328,Typical Group,13
4,3004,5 months,Tertiary postgraduate,163,Typical Group,6
5,3008,5 months,Tertiary postgraduate,171,Typical Group,4
6,3023,5 months,Tertiary postgraduate,170,Typical Group,8
7,3063,5 months,Tertiary postgraduate,181,Typical Group,10
8,3071,5 months,Tertiary postgraduate,151,Typical Group,8
9,3074,5 months,Tertiary postgraduate,183,Typical Group,9
10,3075,5 months,Tertiary postgraduate,165,Typical Group,9
11,3087,5 months,Tertiary postgraduate,172,Typical Group,6
12,3104,5 months,Tertiary postgraduate,180,Typical Group,7
13,3115,5 months,Tertiary postgraduate,199,Typical Group,8
14,3142,5 months,Tertiary postgraduate,201,Typical Group,9
15,3161,5 months,Tertiary postgraduate,189,Typical Group,7
16,3162,5 months,Tertiary postgraduate,201,Typical Group,8
17,4002,5 months,Tertiary postgraduate,202,NF1,8
18,4024,5 months,Tertiary postgraduate,167,NF1,8
19,3004,10 months,Tertiary postgraduate,315,Typical Group,9
20,3008,10 months,Tertiary postgraduate,341,Typical Group,9
21,3023,10 months,Tertiary postgraduate,358,Typical Group,14
22,3063,10 months,Tertiary postgraduate,293,Typical Group,17
23,3071,10 months,Tertiary postgraduate,302,Typical Group,12
24,3074,10 months,Tertiary postgraduate,333,Typical Group,12
25,3075,10 months,Tertiary postgraduate,318,Typical Group,11
26,3078,10 months,Tertiary postgraduate,304,Typical Group,9
27,3087,10 months,Tertiary postgraduate,335,Typical Group,13
28,3104,10 months,Tertiary postgraduate,294,Typical Group,10
29,3115,10 months,Tertiary postgraduate,305,Typical Group,11
30,3142,10 months,Tertiary postgraduate,327,Typical Group,11
31,3161,10 months,Tertiary postgraduate,328,Typical Group,12
32,3162,10 months,Tertiary postgraduate,333,Typical Group,10
33,4002,10 months,Tertiary postgraduate,335,NF1,11
34,4009,10 months,Tertiary postgraduate,320,NF1,11
35,4024,10 months,Tertiary postgraduate,351,NF1,13
36,3004,14 months,Tertiary postgraduate,438,Typical Group,21
37,3008,14 months,Tertiary postgraduate,460,Typical Group,9
38,3023,14 months,Tertiary postgraduate,471,Typical Group,18
39,3063,14 months,Tertiary postgraduate,445,Typical Group,20")
#> Warning: Missing column names filled in: 'X1' [1]
来源：https://stackoverflow.com/questions/61288690/visualising-linear-mixed-model-in-r