Chapter 11 Performing Statistical Tests

R is extremely powerful when it comes to performing statistical tests. Here, we will do a Pearson’s correlation between the Log2 ratios of HM and HL. The correlation test will be done with the cor.test() function, as follows:

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HM.ML.cor <- cor.test(protein.df$Ratio.H.M,
                      protein.df$Ratio.M.L)
HM.ML.cor

# 
#   Pearson's product-moment correlation
# 
# data:  protein.df$Ratio.H.M and protein.df$Ratio.M.L
# t = -3, df = 830, p-value = 0.01
# alternative hypothesis: true correlation is not equal to 0
# 95 percent confidence interval:
#  -0.156 -0.021
# sample estimates:
#    cor 
# -0.089

The cor.test() function provides a set of values which can be individually accessed:

# HM.HL.cor
attributes(HM.ML.cor)

# $names
# [1] "statistic"   "parameter"   "p.value"     "estimate"   
# [5] "null.value"  "alternative" "method"      "data.name"  
# [9] "conf.int"   
# 
# $class
# [1] "htest"

class(HM.ML.cor)

# [1] "htest"

names(HM.ML.cor)

# [1] "statistic"   "parameter"   "p.value"     "estimate"   
# [5] "null.value"  "alternative" "method"      "data.name"  
# [9] "conf.int"

HM.ML.cor$p.value

# [1] 0.01

HM.ML.cor$estimate # R

#    cor 
# -0.089

HM.ML.cor$estimate^2 # R-squared

#   cor 
# 0.008

The class of a cor.test() output is htest (hypothesis test). htest objects are lists. They have some unique properties, but you can extract information in a similar way as in data frames.

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43. Note that the default cor.test() function performs a Pearson correlation coefficient. If we wanted to perform a Spearman’s rank-correlation coefficient, we would have to set the method argument to "spearman". Also, in the output, cor refers to \(R\). To calculate the more common \(R^2\), you will have to square it.↩