Dear R-users and experts
I want to create to analyse my data which looks like follows:( I have show
only 8 variables but original variables much more number >1000)
*sub*
*ydata*
*X1*
*X2*
*X3*
*X4*
*X5*
*X6*
*X7*
*X8*
1
12
1
1
1
2
1
1
1
1
2
13
2
2
1
2
2
1
1
1
3
11
1
1
1
2
1
2
1
2
4
12
1
1
2
1
1
2
2
2
5
14
1
2
2
1
1
2
2
2
6
12
2
1
1
2
2
1
1
1
7
8
2
2
2
2
1
1
2
1
8
19
1
1
1
1
1
2
2
1
9
6
2
2
2
1
1
1
1
2
I want to create look to fit models like the following:
lme(ydata ~ X1+ X2, random= intercept)
lme(ydata ~ X3+X4, random= intercept)
lme(ydata ~ X5+ X6, random= intercept)
lme(ydata ~ X7 + X8, random= intercept)
means that the loop should read all the X variables in files, but I do not
want to avoid litting lme(ydata ~ X2+ X3) or lme(ydata ~ X4+X5) or lme(ydata
~ X6+X7). Also I want only output the P value to a vector so that I can plot
the graph with X versus P value.
I have hard time with picking lme output to a vector. If it is impossible
please just suggest in term of linear model (lm), in this type I had no
option than adopt the lm nor lme.
I am sorry to bother you all but it only option remaining !
Thanks;
John
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