CODEWITHSUNDEEP
r
KT_1
KT_1

Reputation: 8494

Extracting values from a function output: In R

For an example dataframe:

df <- structure(list(region = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L), .Label = c("a", "b", "c", "d"), class = "factor"), 
    result = c(0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L), weight = c(0.126, 
    0.5, 0.8, 1.5, 5.3, 2.2, 3.2, 1.1, 0.1, 1.3, 2.5)), .Names = c("region", 
"result", "weight"), row.names = c(NA, 11L), class = "data.frame")

I am calculating the relative risk using the function:

#Relative risk function
calcRelativeRisk <- function(mymatrix,alpha=0.05,referencerow=2)
{
  numrow <- nrow(mymatrix)
  myrownames <- rownames(mymatrix)
  for (i in 1:numrow)
  {
    rowname <- myrownames[i]
    DiseaseUnexposed <- mymatrix[referencerow,1]
    ControlUnexposed <- mymatrix[referencerow,2]
    if (i != referencerow)
    {
      DiseaseExposed <- mymatrix[i,1]
      ControlExposed <- mymatrix[i,2]
      totExposed <- DiseaseExposed + ControlExposed
      totUnexposed <- DiseaseUnexposed + ControlUnexposed
      probDiseaseGivenExposed <- DiseaseExposed/totExposed
      probDiseaseGivenUnexposed <- DiseaseUnexposed/totUnexposed

      # calculate the relative risk
      relativeRisk <- probDiseaseGivenExposed/probDiseaseGivenUnexposed
      print(paste("category =", rowname, ", relative risk = ",relativeRisk))

      # calculate a confidence interval
      confidenceLevel <- (1 - alpha)*100
      sigma <- sqrt((1/DiseaseExposed) - (1/totExposed) +
                      (1/DiseaseUnexposed) - (1/totUnexposed))
      # sigma is the standard error of estimate of log of relative risk
      z <- qnorm(1-(alpha/2))
      lowervalue <- relativeRisk * exp(-z * sigma)
      uppervalue <- relativeRisk * exp( z * sigma)
      print(paste("category =", rowname, ", ", confidenceLevel,
                  "% confidence interval = [",lowervalue,",",uppervalue,"]"))
    }
  }
}

First creating the xtab:

df$region <- factor(df$region)
result <- xtabs(weight ~ region + result, data=df)
result

And then using the function to calculate relative risk:

calcRelativeRisk(result,alpha=0.05)
[1] "category = a , relative risk =  1.26904794624327"
[1] "category = a ,  95 % confidence interval = [ 0.751148304223936 , 2.14402759189898 ]"

I want to label the relative risk ("RR") and the confidence intervals "RR_upper" and "RR_lower". This is because I am creating a large table with this code run over multiple dataframes. How can I extract these values from the R output? (and then round them etc.). I presume I could change the function print options but as I didn't create the function, I wondered whether there was another way?

Upvotes: 0

Views: 543

Answers (1)

lmo
lmo

Reputation: 38500

The best thing to do would be to change the print statement into something that outputs data. If you wrap calcRelativeRisk(result,alpha=0.05) in either str or typeof, you get NULL. The print statement does not save the values and returns a NULL object.

After the print statement, in the final line of the function, add a named vector:

c("relative_risk"=relativeRisk,  "lowervalue"=lowervalue, "uppervalue"=uppervalue)

This will return a numeric vector of length 3 that you can then use to build your tables.

So the bottom of your function would look like this:

    lowervalue <- relativeRisk * exp(-z * sigma)
      uppervalue <- relativeRisk * exp( z * sigma)
      # print values of interest
      print(paste("category =", rowname, ", ", confidenceLevel,
                  "% confidence interval = [",lowervalue,",",uppervalue,"]"))


    }
  }
  # return values of interest
  c("relative_risk"=relativeRisk,  "lowervalue"=lowervalue, "uppervalue"=uppervalue)
}

To retrieve these values simply use the following:

myValues <- calcRelativeRisk(result,alpha=0.05)

Upvotes: 2

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