doparse | R Documentation |
DO
Statements
This function parses a string and expands this string in case of DO
statements which are shortcuts for writing loops. The statement
DO(n,m,k)
increments an index from n
to m
in steps
of k
. The index in the string model
must be defined
as %
. For a nested loop within a loop,
the DO2
statement can be used using %1
and %2
as indices. See Examples for hints on the specification. The loop in DO2
must not be explicitly crossed, e.g. in applications for specifying
covariances or correlations. The formal syntax for
for (ii in 1:(K-1)){ for (jj in (ii+1):K) { ... } }
can be written as DO2(1,K-1,1,%1,K,1
)
. See Example 2.
doparse(model)
model |
A string with |
Parsed string in which DO
statements are expanded.
This function is also used in lavaanify.IRT
and
tamaanify
.
#############################################################################
# EXAMPLE 1: doparse example
#############################################################################
# define model
model <- "
# items I1,...,I10 load on G
DO(1,10,1)
G=~ lamg% * I%
DOEND
I2 | 0.75*t1
v10 > 0 ;
# The first index loops from 1 to 3 and the second index loops from 1 to 2
DO2(1,3,1, 1,2,1)
F%2=~ a%2%1 * A%2%1 ;
DOEND
# Loop from 1 to 9 with steps of 2
DO(1,9,2)
HA1=~ I%
I% | beta% * t1
DOEND
"
# process string
out <- TAM::doparse(model)
cat(out)
## # items I1,...,I10 load on G
## G=~ lamg1 * I1
## G=~ lamg2 * I2
## G=~ lamg3 * I3
## G=~ lamg4 * I4
## G=~ lamg5 * I5
## G=~ lamg6 * I6
## G=~ lamg7 * I7
## G=~ lamg8 * I8
## G=~ lamg9 * I9
## G=~ lamg10 * I10
## I2 | 0.75*t1
## v10 > 0
## F1=~ a11 * A11
## F2=~ a21 * A21
## F1=~ a12 * A12
## F2=~ a22 * A22
## F1=~ a13 * A13
## F2=~ a23 * A23
## HA1=~ I1
## HA1=~ I3
## HA1=~ I5
## HA1=~ I7
## HA1=~ I9
## I1 | beta1 * t1
## I3 | beta3 * t1
## I5 | beta5 * t1
## I7 | beta7 * t1
## I9 | beta9 * t1
#############################################################################
# EXAMPLE 2: doparse with nested loop example
#############################################################################
# define model
model <- "
DO(1,4,1)
G=~ lamg% * I%
DOEND
# specify some correlated residuals
DO2(1,3,1,%1,4,1)
I%1 ~~ I%2
DOEND
"
# process string
out <- TAM::doparse(model)
cat(out)
## G=~ lamg1 * I1
## G=~ lamg2 * I2
## G=~ lamg3 * I3
## G=~ lamg4 * I4
## # specify some correlated residuals
## I1 ~~ I2
## I1 ~~ I3
## I1 ~~ I4
## I2 ~~ I3
## I2 ~~ I4
## I3 ~~ I4
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