Implement Percentiles to Z-scores in R
To convert a percentile to a Z-score in R ‘qnorm()’ function is use, which calculates the Z-score corresponding to a given percentile in a standard normal distribution.
The formula is
z_score <- qnorm(percentile / 100)
# Define the percentile
percentile <- 90
# Convert percentile to Z-score
z_score <- qnorm(percentile / 100)
# Display the Z-score
z_score
Output:
[1] 1.281552
The ‘qnorm()’ function is also use to convert an entire vector of percentiles to Z-scores in R.
# Example vector of percentiles
percentiles <- c(0.1, 0.25, 0.5, 0.75, 0.9)
# Convert percentiles to Z-scores
z_scores <- qnorm(percentiles)
# Display the result
z_scores
Output:
[1] -1.2815516 -0.6744898 0.0000000 0.6744898 1.2815516
First define a vector percentiles containing the percentiles and we want to convert to Z-scores.
- Second we use the ‘qnorm()’ function to convert the entire vector of percentiles to Z-scores.
- The resulting z_scores vector contains the Z-scores corresponding to each percentile in the percentiles vector.
How to Convert Between Z-Scores and Percentiles in R
In statistical analysis, converting between Z-scores and percentiles helps researchers understand data distribution clearly. R, a powerful programming language, simplifies this conversion process, making it accessible to analysts. This guide offers a simple walkthrough of how to perform these conversions in R Programming Language enabling users to interpret data effectively and make informed decisions.