Tag: Coding

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Permutation Pyramid

OK so here’s an update to the permutation pyramid I made.

I had to make a list of different permutations of models but it became hard to keep track of all the different variables and their combinations. Hence, the permutation pyramid. The goal is to make a list of vector combinations and permutations. I found this super useful when generating formulas for AICc/Model selection.

Enjoy!

The function has a few options:

  1. vec
    • The vector to draw the combinations/permutations from
  2. order.matters = FALSE
    • A simple boolean TRUE / FALSE to add in the permutations of each vector combination. This will generate permutations of the combinations and add them to the list. This may only be useful for certain models
  3. req
    • A vector of elements that should be required in the combinations.
  4. interact
    • A list of vector combinations that can be found in the original combinations

Function

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pyramid <- function( vec, order.matters = FALSE, req, interact ){
  # pyramid of variable combinations
  # this doesn't include different
  # arrangements
  vrz <- lapply(
    1:length( vec ),
    function( x ){
      combn( vec, x ) %>% as.data.frame()
    }
  ) %>% purrr::flatten() %>% unname()
   
  # If there are interactions
  if( !missing( interact ) ){
    # possible interaction combos
    intx <- seq( from = 1, to = length( interact ), by = 1 ) %>% knp.perm.pyramid()
     
    # extra interactions
    vrz.int <- list()
     
    # look through vars
    for( v in vrz ){
       
      # make a list of interactions
      # and add them to the list
      vrz.int <- append(
        vrz.int,
        lapply( intx, function( ints ){
          # count number of matches to compare
          # and filter out unaltered var lists
          mt <- 0
           
          # for each interaction
          # check if its in the array
          # then add it if it is
          for ( int in ints ) {
            if(
                length( intersect( v, interact[[ int ]] ) ) == length( interact[[ int ]] ) &
                length( interact[[ int ]] ) > 1
              ){
                v <- c( v, paste0( interact[[ int ]], collapse = ":" ) )
                mt <- mt + 1
            }
          }
           
          # if the number of matches equals
          # the number of interactions then
          # return the altered array
          if( mt == length(ints) ){
            return( v )
          }
          return( NULL )
           
        }) %>% plyr::compact()
      )
    }
     
    # append interactions
    vrz <- append( vrz, vrz.int )
  }
   
  # order matters so lets rearrange
  if( order.matters ){
    vrz <- lapply( vrz, function( x ){
      combinat::permn( x )
    }) %>% purrr::flatten()
  }
   
  # if there's any required variables in each combination
  if( !missing( req ) ){
    vrz <- lapply(vrz, function( x ){
      if( length( intersect( x, req ) ) == length( req ) ){
        return( x )
      }
      return( NULL )
    }) %>% plyr::compact()
  }
   
  # return list of character
  # permutations
  return( vrz )
}

Examples / Usage

Plain vanilla use

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> a <- c( "A", "B", "C" )
> pyramid( a )
[[1]]
[1] "A"
 
[[2]]
[1] "B"
 
[[3]]
[1] "C"
 
[[4]]
[1] "A" "B"
 
[[5]]
[1] "A" "C"
 
[[6]]
[1] "B" "C"
 
[[7]]
[1] "A" "B" "C"

If the order of the elements matters

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> a <- c( "A", "B", "C" )
> pyramid( a, order.matters = TRUE )
[[1]]
[1] "A"
 
[[2]]
[1] "B"
 
[[3]]
[1] "C"
 
[[4]]
[1] "A" "B"
 
[[5]]
[1] "B" "A"
 
[[6]]
[1] "A" "C"
 
[[7]]
[1] "C" "A"
 
[[8]]
[1] "B" "C"
 
[[9]]
[1] "C" "B"
 
[[10]]
[1] "A" "B" "C"
 
[[11]]
[1] "A" "C" "B"
 
[[12]]
[1] "C" "A" "B"
 
[[13]]
[1] "C" "B" "A"
 
[[14]]
[1] "B" "C" "A"
 
[[15]]
[1] "B" "A" "C"

Require variables

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> a <- c( "A", "B", "C", "D", "E" )
> b <- c( "B", "D" )
> pyramid( a, req = b )
[[1]]
[1] "B" "D"
 
[[2]]
[1] "A" "B" "D"
 
[[3]]
[1] "B" "C" "D"
 
[[4]]
[1] "B" "D" "E"
 
[[5]]
[1] "A" "B" "C" "D"
 
[[6]]
[1] "A" "B" "D" "E"
 
[[7]]
[1] "B" "C" "D" "E"
 
[[8]]
[1] "A" "B" "C" "D" "E"

Include interactions

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> a <- c( "A", "B", "C")
> b <- list( c( "A", "B" ), c( "A", "B", "C" ) )
> pyramid( a, interact = b )
[[1]]
[1] "A"
 
[[2]]
[1] "B"
 
[[3]]
[1] "C"
 
[[4]]
[1] "A" "B"
 
[[5]]
[1] "A" "C"
 
[[6]]
[1] "B" "C"
 
[[7]]
[1] "A" "B" "C"
 
[[8]]
[1] "A"   "B"   "A:B"
 
[[9]]
[1] "A"   "B"   "C"   "A:B"
 
[[10]]
[1] "A"     "B"     "C"     "A:B:C"
 
[[11]]
[1] "A"     "B"     "C"     "A:B"   "A:B:C"
Posted on

Permutation Pyramid

So I had to make a combination of different values based on elements in a vector. With a few tweaks and what not I made a little function to find all the combinations and permutations of a given vector. Enjoy

The function:

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# creat a list of combinations and
# permutations of elements from
# a single vector. This requires
# a few libraries:
# library( dplyr )
# library( purrr )
# library( combinat )
 
# "order.matters" is means that
# for every combination of elements
# find every order they can be
# arranged.
 
permutation.pyramid <- function( v, order.matters = TRUE ){
 
  # get the unique combinations of elements
  # and flatten them into a one dimensional list
  out <- 1:length(v) %>%
    lapply(function( x ){
      combn( v, x ) %>% as.data.frame()
    }) %>%
    purrr::flatten() %>%
    unname()
   
  # if order.matters then find all the
  # arrangements of each combination
  if( order.matters ){
    out <- out %>%
      lapply(function( x ){
        combinat::permn( x )
      }) %>%
      purrr::flatten()
  }
   
  # return list of permutations
  return( out )
}

Use and examples.

Find all combinations and permutations of a given vector

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> test <- c("A","B","C")
> permutation.pyramid( test )
[[1]]
[1] "A"
 
[[2]]
[1] "B"
 
[[3]]
[1] "C"
 
[[4]]
[1] "A" "B"
 
[[5]]
[1] "B" "A"
 
[[6]]
[1] "A" "C"
 
[[7]]
[1] "C" "A"
 
[[8]]
[1] "B" "C"
 
[[9]]
[1] "C" "B"
 
[[10]]
[1] "A" "B" "C"
 
[[11]]
[1] "A" "C" "B"
 
[[12]]
[1] "C" "A" "B"
 
[[13]]
[1] "C" "B" "A"
 
[[14]]
[1] "B" "C" "A"
 
[[15]]
[1] "B" "A" "C"

Find all combinations of vector

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> test <- c("A","B","C")
> permutation.pyramid( test, order.matters = FALSE )
[[1]]
[1] "A"
 
[[2]]
[1] "B"
 
[[3]]
[1] "C"
 
[[4]]
[1] "A" "B"
 
[[5]]
[1] "A" "C"
 
[[6]]
[1] "B" "C"
 
[[7]]
[1] "A" "B" "C"

Anyways I hope that’ll be useful for someone!

Posted on

Confidence Intervals

A small little function written in R to get the 95% confidence intervals and some quick stats of a vector. I found this useful in error reporting in some stats. I got the concept from this tutorial.

This does require the dplyr library

Function

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library( dplyr )
 
ci <- function( x ){
  cnf <- dplyr::tibble(
    mean = mean( x, na.rm = TRUE),
    st.dev = sd( x, na.rm = TRUE),
    n = length( x ),
    error = qnorm( 0.975 ) * st.dev / sqrt( n ),
    ci05 = mean - error,
    ci95 = mean + error
  )
   
  cat( cnf$mean, "(", cnf$ci05, "-", cnf$ci95, ")\n" )
  return( cnf )
}

Use

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> x <- sample(10)
> ci(x)
5.5 ( 3.623477 - 7.376523 )
# A tibble: 1 x 6
   mean st.dev     n error  ci05  ci95
  <dbl>  <dbl> <int> <dbl> <dbl> <dbl>
1   5.5   3.03    10  1.88  3.62  7.38
Posted on

Quick update to Grade Calculator

Just a quick update to make the Grade Calculator more user friendly.

  1. Added a very simple notification system for the overall grade. It’ll now tell you if you pass a certain grade threshold for the overall mark. Default is 50% for all the Aussie grading system
  2. Added a very simple settings UI to adjust the passing grade threshold and turn notifications on/off

Hope y’all can enjoy that and find it useful.

Posted on

Grade Calculator

As a coding exercise and my first dip into Angular JS I thought I’d make a grade calculator to help me estimate grades. I hope it helps anyone interested in estimating your grades needed to pass a course.

The link to the calculator can be found here.