Create stepwise.summary

# stepwiseqtl.summary
# R function
# requires- qtl
# Author: John T. Lovell
# Author email: johntlovell@gmail.com
# Version: 3.2
# Date: 18-Sept 2014


#stepwiseqtl.summary function details
#inputs
# 1. cross- a R/qtl cross object w/ genotype probabilities (only for method="hk" right now)
# 2. phes- a vector of phenotype names that correspond to phenotype columns in the R/qtl cross object
# 3. max.qtls- the maximum number of QTLs to explore via forward model selection
# 4. pens- a dataframe of penalties with column names that match phes (from calc.penalties ideally- have had trouble with other formats)
# 5. covar- a dataframe of covariate(s) with a length that matches that of the phenotypes in the cross object.
#         - interactive covariates are not incorporated. I am working on a post-hoc script to do this
# 6. printout- should the function tell you what is going on?
# 7. drop- the LOD drop interval that should be created. See the R/qtl function lodint

#outputs:
# 1. the function returns a list of stepwiseQTL objects. This includes:
#   a) formula, b) qtl model, c) lodprofiles, d) stepwise trace
# 2. a text file (comma separated) called "stepwisesummary.stats.csv" that contains the following columns:
#   phenotpye, chromosome, position (cM), 
#   type III statistics for each qtl: df, sums of squares, LOD, %variance explained, F statistic, P-value (from chi2), P-value (from F)
#   effect t statistics: estimate, standard error, t. test statistic
#   confidence intervals by position and marker
#   ## Note: for covariates and epistasis, some of these columns return "NA"