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

[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:
 Groups    Name        Variance 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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