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The hypothesis test functions in the stats package use classic S3 object-orientated programming. You write a function that creates a "htest" object, which is a list with a standard set of components, and R has a built-in print method for objects of that class. The user-level function is traditionally called something like yourname.test but can have any name. It can have any appropriate arguments.

• Type ?t.test to see the definition of a "htest" object.
• See stats:::t.test.default to see an example of function that creates a "htest" object.
• See stats:::print.htest to see how the user-friendly output is created.

Here is a toy example that performs a very simple chisquare test:

demo.test <- function(s2, df=1)
{
  pval <- pchisq(s2, df, lower.tail=FALSE)
  out <- list(
    statistic=s2,
    parameter=NULL,
    p.value=pval,
    null.value=NULL,
    alternative="greater",
    method="demo",
    data.name=NULL)
  class(out) <- "htest"
  out
}

Then

> TEST <- demo.test(30, df=10)
> TEST

        demo

data:  
= 30, p-value = 0.0008566
alternative hypothesis: greater

If you want to be fancier, you can make your function S3 generic (like the stats package functions) in order to handle different types of input (e.g., a formula instead of data vectors). But an ordinary function like the above example might satisfy your needs.

The hypothesis test functions in the stats package use classic S3 object-orientated programming. You write a function that creates a "htest" object, which is a list with a standard set of components, and R has a built-in print method for objects of that class. The user-level function is traditionally called something like yourname.test but can have any name. It can have any appropriate arguments.

• Type ?t.test to see the definition of a "htest" object.
• See stats:::t.test.default to see an example of a function that creates a "htest" object.
• See stats:::print.htest to see how the user-friendly output is created.

Here is a toy example that performs a very simple chisquare test:

demo.test <- function(s2, df=1)
{
  pval <- pchisq(s2, df, lower.tail=FALSE)
  out <- list(
    statistic=s2,
    parameter=NULL,
    p.value=pval,
    null.value=NULL,
    alternative="greater",
    method="demo",
    data.name="s2")
  class(out) <- "htest"
  out
}

Then

> TEST <- demo.test(30, df=10)
> TEST

        demo

data:  s2
= 30, p-value = 0.0008566
alternative hypothesis: greater

If you want to be fancier, you can make your function S3 generic (like the stats package functions) in order to handle different types of input (e.g., a formula instead of data vectors). But an ordinary function like the above example might satisfy your needs.

The hypothesis test functions in the stats package use classic S3 object-orientated programming. You write a function that creates a "htest" object, which is a list with a standard set of components, and R has a built-in print method for objects of that class. The user-level function is traditionally called something like yourname.test but can have any name. It can have any appropriate arguments.

• Type ?t.test to see the definition of a "htest" object.
• See stats:::t.test.default to see an example of function that creates a "htest" object.
• See stats:::print.htest to see how the user-friendly output is created.

Here is a toy example that performs a very simple chisquare test:

demo.test <- function(s2, df=1)
{
  pval <- pchisq(s2,df,lower.tail=FALSE)
  out <- list(
    statistic=s2,
    parameter=NULL,
    p.value=pval,
    null.value=NULL,
    alternative="greater",
    method="demo",
    data.name=NULL)
  class(out) <- "htest"
  out
}

Then

> TEST <- demo.test(30, df=10)
> TEST

        demo

data:  
= 30, p-value = 0.0008566
alternative hypothesis: greater

If you want to be fancier, you can make your function S3 generic (like the stats package functions) in order to handle different types of input (e.g., a formula instead of data vectors). But an ordinary function like the above example might satisfy your needs.

The hypothesis test functions in the stats package use classic S3 object-orientated programming. You write a function that creates a "htest" object, which is a list with a standard set of components, and R has a built-in print method for objects of that class. The user-level function is traditionally called something like yourname.test but can have any name. It can have any appropriate arguments.

• Type ?t.test to see the definition of a "htest" object.
• See stats:::t.test.default to see an example of function that creates a "htest" object.
• See stats:::print.htest to see how the user-friendly output is created.

Here is a toy example that performs a very simple chisquare test:

demo.test <- function(s2, df=1)
{
  pval <- pchisq(s2, df, lower.tail=FALSE)
  out <- list(
    statistic=s2,
    parameter=NULL,
    p.value=pval,
    null.value=NULL,
    alternative="greater",
    method="demo",
    data.name=NULL)
  class(out) <- "htest"
  out
}

Then

> TEST <- demo.test(30, df=10)
> TEST

        demo

data:  
= 30, p-value = 0.0008566
alternative hypothesis: greater

If you want to be fancier, you can make your function S3 generic (like the stats package functions) in order to handle different types of input (e.g., a formula instead of data vectors). But an ordinary function like the above example might satisfy your needs.

The hypothesis test functions in the stats package use classic S3 object-orientated programming. You write a function that creates a "htest" object, which is a list with a standard set of components, and R has a built-in print method for objects of that class. The user-level function is traditionally called something like yourname.test but can have any name. It can have any appropriate arguments.

• Type ?t.test to see the definition of a "htest" object.
• See stats:::t.test.default to see an example of function that creates a "htest" object.
• See stats:::print.htest to see how the user-friendly output is created.

The hypothesis test functions in the stats package use classic S3 object-orientated programming. You write a function that creates a "htest" object, which is a list with a standard set of components, and R has a built-in print method for objects of that class. The user-level function is traditionally called something like yourname.test but can have any name. It can have any appropriate arguments.

• Type ?t.test to see the definition of a "htest" object.
• See stats:::t.test.default to see an example of function that creates a "htest" object.
• See stats:::print.htest to see how the user-friendly output is created.

Here is a toy example that performs a very simple chisquare test:

demo.test <- function(s2, df=1)
{
  pval <- pchisq(s2,df,lower.tail=FALSE)
  out <- list(
    statistic=s2,
    parameter=NULL,
    p.value=pval,
    null.value=NULL,
    alternative="greater",
    method="demo",
    data.name=NULL)
  class(out) <- "htest"
  out
}

Then

> TEST <- demo.test(30, df=10)
> TEST

        demo

data:  
= 30, p-value = 0.0008566
alternative hypothesis: greater

If you want to be fancier, you can make your function S3 generic (like the stats package functions) in order to handle different types of input (e.g., a formula instead of data vectors). But an ordinary function like the above example might satisfy your needs.