I am trying to use tree to partition a data set. The data set has 3924
observations. Partitioning seems to work for small subsets of the data,
but when I use the entire data set, no partitioning occurs. The
variables are:
RESP respondent to a survey (0 = not a respondent, 1 =
respondent)
AGE_P Age (continuous)
ORIGIN_I Hispanic Ethnicity (1 = Hispanic, 2 = non-Hispanic)
RACRECI2 Race Recode (1 = White, 2 = Black, 3 = Other)
parents Parent(s) present in the family (1 = Yes, 2 = No)
educ Education Recode (1 = HS, GED, or less, 5 = some college, 6 =
Bachelor's or AA degree, 9 = Master's & higher
Here are 2 calls to tree and a snip of summary results:
### Use a sample of 100 ####
> set.seed(331)
> nsize = 100
> sam <- sample(1:nrow(nhis), nsize)
>
> t1 <- tree( RESP ~ AGE_P + ORIGIN_I + RACRECI2 + parents + educ,
+ method = "class",
+ control = tree.control(nobs = nsize, minsize = 10),
+ data = nhis[sam,])
> summary(t1)
Classification tree:
tree(formula = RESP ~ AGE_P + ORIGIN_I + RACRECI2 + parents +
educ, data = nhis[sam, ], control = tree.control(nobs = nsize,
minsize = 10), method = "class")
Number of terminal nodes: 13 ##### All vars were used
#### Use entire data set ####
> nsize = 3924
> sam <- sample(1:nrow(nhis), nsize)
>
> t1 <- tree( RESP ~ AGE_P + ORIGIN_I + RACRECI2 + parents + educ,
+ method = "class",
+ control = tree.control(nobs = nsize, minsize = 10),
+ data = nhis[sam,])
> summary(t1)
Classification tree:
.
.
.
Variables actually used in tree construction:
character(0) ##### No vars were used
Number of terminal nodes: 1
It doesn't matter whether I use the categorical vars as factors or not;
I still get the same results. As I increase the subsample from 100
incrementally up to 1200 , fewer vars are used in tree construction. At
1200 the point is reached where none are used.
Is there a way to force tree to do something with the larger sample
sizes and the whole data set?
Package tree version 1.0-26
R 2.6.0
Windows XP, v.5.1, service pack 2
Thanks
Richard Valliant
U of Maryland US
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