title: Writing loop/function to generate various mixed model result
body: <p>I am writing loops or functions in R, and I still haven't really understood how to do that. Currently, I need to write a loop/function (not sure which one would be better) to create several results of Mixed models within the same data frame. </p>
  
  <p>sample dataset look like:</p>
  
  <pre><code>dataset &lt;- read.table(text = 
  "ID A_2 B_2 C_2 A_1 B_1 C_1 chkgp
  M1  10  20  60  30  54  33  Treatment
  M1  20  50  40  33  31  44  Placebo
  M2  40  80  40  23  15  66  Placebo
  M2  30  90  40  67  67  66  Treatment
  M3  30  10  20  22  89  77  Treatment
  M3  40  50  30  44  50  88  Placebo
  M4  40  30  40  42  34  99  Treatment
  M4  30  40  50  33  60  80  Placebo",header = TRUE, stringsAsFactors = FALSE)
  
  </code></pre>
  
  <p>I build a model to take<code>Variable _2</code> as the dependent variable and <code>variable _1</code> as independent variable please have "lme4" package to run the mixed model</p>
  
  <pre><code>dep_vars&lt;-grep("_2$",colnames(dataset),value = T) #This selects all variables ending in `_2` which should all be dependent variables.
  
  
  #This removes the `_2` from the dependent variables which should give you the common stem which can be used to select both dependent and independent variables from your data frame.
  
  reg_vars&lt;-gsub("_2$","",dep_vars)
  
  ## To check that we have exact the correct variable which _2
  dep_vars
  
  
  [1] "A_2" "B_2" "C_2"
  </code></pre>
  
  <p>create a loop get all the result </p>
  
  <pre><code>full_results &lt;- lapply(reg_vars, function(i) summary(lmer(paste0("log(",i,"_2)~",i,"_1+chkgp+(1|ID)"),data=dataset)))
  
  </code></pre>
  
  <p>to check the summary of the first model result</p>
  
  <pre><code>full_results[1]
  [[1]]
  Linear mixed model fit by REML ['lmerMod']
  Formula: log(A_2) ~ A_1 + chkgp + (1 | ID)
     Data: dataset
  
  REML criterion at convergence: 16.9
  
  Scaled residuals: 
       Min       1Q   Median       3Q      Max 
  -1.16981 -0.24161  0.04418  0.37744  0.95925 
  
  Random effects:
   Groups   Name        Variance Std.Dev.
   ID       (Intercept) 0.1314   0.3625  
   Residual             0.1188   0.3446  
  Number of obs: 8, groups:  ID, 4
  
  Fixed effects:
                  Estimate Std. Error t value
  (Intercept)     3.293643   0.411924   7.996
  A_1             0.004512   0.009844   0.458
  chkgpTreatment -0.276792   0.253242  -1.093
  
  Correlation of Fixed Effects:
              (Intr) A_1   
  A_1         -0.795       
  chkgpTrtmnt -0.068 -0.272
  
  </code></pre>
  
  <p><strong>Question:</strong> I want to get the result of the chkgpTreatment std.error t value, P-value, upper CI and lower CI of each model  and stored it in data frame like this</p>
  
  <pre><code>
  Depend_var  Indep_var mean difference
                                         p.value Upper ci Lower_ci
  A_2 A_1 chkgpTreatment          
  B_2 B_1 chkgpTreatment          
  C_2 C_1 chkgpTreatment
  </code></pre>