Using R tm to find trend between terms/entities

Question

I have a Corpus of text document on the subject of pollutant fate and transport. I did the termdocumentmatrix and term association. However, I would like to find our "trend association" between terms. For example, I would like to find out if more ambient light would increase hydrolysis of a chemicalX. I have already have 'light', 'hydrolysis', 'increase', and 'chemicalX' in the termdomumentmatrix, what is a good way to answer above question I posed? Please note that I have already done the findAssocs among these terms and they are to certain degree positively linked together (all above 0.5).

Please advise. Thanks

Below is the rough tm process I used, please note that I have many other docs and I just made an excerpt of a small text for example:

> require(tm)
> my.docs <- c("These experiments showed that the ordinary and the polarized 
+ lights had a stimulating effect on the hydrolytic process, and 
+ both of about the same magnitude. When hydrolysis goes on 
+ (Curves I and II in Figs. 3 and 4) in the presence of light, a larger 
+ amount of the starch substrate is hydrolyzed. The differences 
+ between the two curves (ordinary light and polarized light) are 
+ quite insignificant; they are of the magnitude of twice the probable 
+ error of the mean and so far as it is consistent it can be attributed 
+ to the slight differences existing in the spectral composition of the 
+ lights. 
+ 
+ The situation regarding the effect of radiation on the starch- 
+ diastase system is, in brief: 
+ 1. Ordinary light and polarized light, of the same intensity and 
+ as closely as possible similar in spectral composition, have the 
+ same effect. 
+ 2. Light falling on the starch-diastase system as described, increases 
+ the rate of hydrolysis over that of the same reaction in the 
+ dark. 
+ ")
> funcs <- list(tolower, removePunctuation, stripWhitespace, removeNumbers)
> lightC <- Corpus(VectorSource(my.docs))
> lightCC <- tm_map(lightC, FUN=tm_reduce, tmFuns=funcs)
> my.dictionary.terms <- tolower(c("light","hydrolysis","increases","decreases","reduce","starch"))
> my.dictionary <- Dictionary(my.dictionary.terms)
> tdmLight <- TermDocumentMatrix(lightCC, control=list(weight=weightTfIdf, stopwords=stopwords("english"), dictionary=my.dictionary))
> findAssocs(tdmLight, "light", 0.5)