inst/examples/ExRegress.r

In assaforon/table1xls: Exports Reproducible Summary Tables to Multi-Tab Spreadsheet Files (.xls or .xlsx)

book4<-XLwriteOpen("attenu.xls") 

quakenames=c("Magnitude (Richter), per unit","Distance (log km), per x10")

# Ground acceleration as a function of magnitude and distance, all log scale.
quakemod1=summary(lm(log10(accel)~mag+log10(dist),data=attenu))


## Model-scale summaries; we don't care for the intercept.
# First (wrongly) using Normal distribution for inference/CIs

XLregresSummary(book4,"ModelScale",varnames=quakenames,
                betas=quakemod1$coef[-1,1],SE=quakemod1$coef[-1,2],
                ,title="Log-Ground Acceleration Effects, Normal CIs")

# Now using t-distribution as befits linear regression

XLregresSummary(book4,"ModelScale",varnames=quakenames,
                betas=quakemod1$coef[-1,1],SE=quakemod1$coef[-1,2],
                ,title="Log-Ground Acceleration Effects",df=quakemod1$df[2],col1=6)

## Same thing, but using matrix input; no need to provide SE and names. 
## It is arguably still nicer to provide your own names - but could be a reproducibility risk. 
## Also, increasing the p-value resolution by changing 'pround'.

XLregresSummary(book4,"ModelScale",betas=quakemod1$coef[-1,],
                pround=6,title="Log-Ground Acceleration Effects",
                ,df=quakemod1$df[2],row1=8)

## Effects are arguably more meaningful as percent change. 
## So... still same model, but different summaries. 
## Also, note the combination of matrix input with names over-written via 'varnames':

XLregresSummary(book4,"PercentChange",varnames=quakenames,
                betas=quakemod1$coef[-1,],
                roundig=1,pround=6,title="Relative Ground Acceleration Effects",
                transfun=function(x) 100*(10^x-1),
                effname="Percent Change",df=quakemod1$df[2])

cat("Look for",paste(getwd(),"attenu.xls",sep='/'),"to see the results!\n")

### lm() does not take account of station or event level grouping.
### So we use a mixed model, losing 16 data points w/no station data:
### Run this on your own... and ask the authors of "lme4" about p-values at your own risk :)

# library(lme4)
# quakemod2=lmer(log10(accel)~mag+log10(dist)+(1|event)+(1|station),data=attenu)
# 
# XLregresSummary(book4,"MixedModel",varnames=quakenames,betas=fixef(quakemod2)[-1],
# SE=sqrt(diag(vcov(quakemod2)))[-1],
# roundig=1,pround=6,
# title="Relative Ground Acceleration Effects",
# transfun=function(x) 100*(10^x-1),effname="Percent Change",df=160)