[R] Help w/ variable names in loop with lmer

2015-03-29 Thread David Crow 
Hi, R users-

I'm estimating random effects models with cross-level interactions; I want
to interact each of a vector of level-1 variables with each of a vector of
level-2 variables.  Here's the code:


#create data frame with level-1 variables
k - as.data.frame(cbind(robo, asalto, secuestro, asesinato))

#create data frame with level-2 variables
l - as.data.frame(cbind(IDH_IDH, IDH_ingpc, eco_pb, IM_indice, tasa_robo,
hom_tasa, totdelitos1, totdelitos2, total, pri, pan, prd))

#get cross-level interactions

for (i in 1:length(k)) {
for (j in 1:length(l)) {
print(summary(lmer(hrprotcrim ~ k[,i]*l[,j] + (k[,i] | Municipio
}
}
==

The code works and produces 48 (4 level-1 x 12 level-2) sets of output.
The problem is, the output is illegible because instead of the variable
names, I get the indices:

[output]
==
Linear mixed model fit by REML ['lmerMod']
Formula: hrprotcrim ~ k[, i] * l[, j] + (k[, i] | Municipio)

REML criterion at convergence: 8801.4

Scaled residuals:
Min  1Q  Median  3Q Max
-2.4447 -0.7017 -0.2639  0.6766  3.0835

Random effects:
 GroupsNameVariance Std.Dev. Corr
 Municipio (Intercept) 1.067868 1.0334
   k[, i]  0.005387 0.0734   1.00
 Residual  2.976150 1.7252
Number of obs: 2163, groups:  Municipio, 180

Fixed effects:
   Estimate Std. Error t value
(Intercept)2.710847   0.101715  26.651
k[, i]-0.056720   0.355802  -0.159
l[, j] 0.002701   0.002289   1.180
k[, i]:l[, j]  0.006510   0.006340   1.027

Correlation of Fixed Effects:
(Intr) k[, i] l[, j]
k[, i]  -0.048
l[, j]  -0.514  0.028
k[,i]:l[,j]  0.034 -0.566 -0.072
==

Two questions:

1)  How can I get variable names instead of indices in the above output
2)  How can I estimate this with mapply instead of the double loop?

Here's the code for mapply

M4 - mapply(function(k,l){summary(lmer(hrprotcrim ~ k*l + (k |
Municipio)))})

And here's what I get:

list()

I'd be grateful for any pointers.

Best,
David



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Re: [R] Help w/ variable names in loop with lmer

2015-03-29 Thread Ben Bolker 
David Crow david.crow at cide.edu writes:

 
 Hi, R users-
 
 I'm estimating random effects models with cross-level interactions; I want
 to interact each of a vector of level-1 variables with each of a vector of
 level-2 variables.  Here's the code:
 
 
 #create data frame with level-1 variables
 k - as.data.frame(cbind(robo, asalto, secuestro, asesinato))
 
 #create data frame with level-2 variables
 l - as.data.frame(cbind(IDH_IDH, IDH_ingpc, eco_pb, IM_indice, tasa_robo,
 hom_tasa, totdelitos1, totdelitos2, total, pri, pan, prd))
 
 #get cross-level interactions
 
 for (i in 1:length(k)) {
 for (j in 1:length(l)) {
 print(summary(lmer(hrprotcrim ~ k[,i]*l[,j] + (k[,i] | Municipio
 }
 }
 ==
 
 The code works and produces 48 (4 level-1 x 12 level-2) sets of output.
 The problem is, the output is illegible because instead of the variable
 names, I get the indices:
 
 [output]
 ==
 Linear mixed model fit by REML ['lmerMod']
 Formula: hrprotcrim ~ k[, i] * l[, j] + (k[, i] | Municipio)
 

[snip]

 Two questions:
 
 1)  How can I get variable names instead of indices in the above output
 2)  How can I estimate this with mapply instead of the double loop?
 
 Here's the code for mapply
 
 M4 - mapply(function(k,l){summary(lmer(hrprotcrim ~ k*l + (k |
 Municipio)))})
 
 And here's what I get:
 
 list()
 

I would suggest appropriate use of ?reformulate

Assume dd is a data frame containing all variables

lev1vars - c(robo, asalto, secuestro, asesinato)
lev2vars - c(IDH_IDH, IDH_ingpc, ...)
ffun - function(L1var,L2var) {
ff - reformulate(paste(L1var,L2var,sep=*),
  paste(L1var,Municipio,sep=|),
  response=hrprotcrim)
environment(ff) - parent.frame()  ## trickiness
return(summary(lmer(ff,data=dd)))
}
mapply(ffun,lev1vars,lev2vars)

?

If you had given a reproducible example I could have tested this ...

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