Normal Distribution in R
Normal Distribution
- dnorm()
dnorm(x, mean, sd)
- pnorm()
pnorm(x, mean, sd)
- qnorm()
qnorm(p, mean, sd)
- rnorm()
rnorm(n, mean, sd)
where,
– x represents the data set of values – mean(x) represents the mean of data set x. It’s default value is 0.– sd(x) represents the standard deviation of data set x. It’s default value is 1.– n is the number of observations. – p is vector of probabilities
Functions To Generate Normal Distribution in R
dnorm()
dnorm()
Syntax :
dnorm(x, mean, sd)
Example:
# creating a sequence of values # between -15 to 15 with a difference of 0.1 x = seq(-15, 15, by=0.1) y = dnorm(x, mean(x), sd(x)) # output to be present as PNG file png(file="dnormExample.png") # Plot the graph. plot(x, y) # saving the file dev.off() |
Output:
pnorm()
pnorm()
Syntax:
pnorm(x, mean, sd)
Example:
# creating a sequence of values # between -10 to 10 with a difference of 0.1 x <- seq(-10, 10, by=0.1) y <- pnorm(x, mean = 2.5, sd = 2) # output to be present as PNG file png(file="pnormExample.png") # Plot the graph. plot(x, y) # saving the file dev.off() |
Output :
qnorm()
qnorm()
pnorm()
Syntax:
qnorm(p, mean, sd)
Example:
# Create a sequence of probability values # incrementing by 0.02. x <- seq(0, 1, by = 0.02) y <- qnorm(x, mean(x), sd(x)) # output to be present as PNG file png(file = "qnormExample.png") # Plot the graph. plot(x, y) # Save the file. dev.off() |
Output:
rnorm()
rnorm()
Syntax:
rnorm(x, mean, sd)
Example:
# Create a vector of 1000 random numbers # with mean=90 and sd=5 x <- rnorm(10000, mean=90, sd=5) # output to be present as PNG file png(file = "rnormExample.png") # Create the histogram with 50 bars hist(x, breaks=50) # Save the file. dev.off() |
Output :