[DOC] Documentation for builtin mice function
Closes #953.
diff --git a/dev/docs/builtins-reference.md b/dev/docs/builtins-reference.md
index ecbdf13..afc501f 100644
--- a/dev/docs/builtins-reference.md
+++ b/dev/docs/builtins-reference.md
@@ -30,6 +30,7 @@
* [`lmDS`-Function](#lmds-function)
* [`lmCG`-Function](#lmcg-function)
* [`lmpredict`-Function](#lmpredict-function)
+ * [`mice`-Function](#mice-function)
* [`scale`-Function](#scale-function)
* [`sigmoid`-Function](#sigmoid-function)
* [`steplm`-Function](#steplm-function)
@@ -353,6 +354,37 @@
yp = lmpredict(X, w)
```
+## `mice`-Function
+
+The `mice`-function implements Multiple Imputation using Chained Equations (MICE) for nominal data.
+
+### Usage
+```r
+mice(F, cMask, iter, complete, verbose)
+```
+
+### Arguments
+| Name | Type | Default | Description |
+| :------- | :------------- | -------- | :---------- |
+| F | Frame[String] | required | Data Frame with one-dimensional row matrix with N columns where N>1. |
+| cMask | Matrix[Double] | required | 0/1 row vector for identifying numeric (0) and categorical features (1) with one-dimensional row matrix with column = ncol(F). |
+| iter | Integer | `3` | Number of iteration for multiple imputations. |
+| complete | Integer | `3` | A complete dataset generated though a specific iteration. |
+| verbose | Boolean | `FALSE` | Boolean value. |
+
+### Returns
+| Type | Description |
+| :------------- | :---------- |
+| Frame[String] | imputed dataset. |
+| Frame[String] | A complete dataset generated though a specific iteration. |
+
+### Example
+```r
+F = as.frame(matrix("4 3 2 8 7 8 5", rows=1, cols=7))
+cMask = round(rand(rows=1,cols=ncol(F),min=0,max=1))
+[dataset, singleSet] = mice(F, cMask, iter = 3, complete = 3, verbose = FALSE)
+```
+
## `scale`-Function
The scale function is a generic function whose default method centers or scales the column of a numeric matrix.
diff --git a/scripts/builtin/mice.dml b/scripts/builtin/mice.dml
index b00d542..a06a357 100644
--- a/scripts/builtin/mice.dml
+++ b/scripts/builtin/mice.dml
@@ -40,13 +40,13 @@
# singleSet Double --- A complete dataset generated though a specific iteration
# Assumption missing value are represented with empty string i.e ",," in csv file
-# variables with suffix n are storing continous/numeric data and variables with suffix c are storing categorical data
+# variables with suffix n are storing continuous/numeric data and variables with suffix c are storing categorical data
s_mice= function(Frame[String] F, Matrix[Double] cMask, Integer iter = 3, Integer complete = 3, Boolean verbose = FALSE)
return(Frame[String] dataset, Frame[String] singleSet)
{
if(ncol(F) == 1)
- stop("invalid aregument: can not apply mice on single column")
+ stop("invalid argument: can not apply mice on single column")
if(complete > iter)
complete = iter
@@ -78,7 +78,7 @@
XO = replace(target=X, pattern=NaN, replacement=0);
- # remove categorical features and impute continous features with mean
+ # remove categorical features and impute continuous features with mean
eX_n = removeEmpty(target=X, margin="cols", select=(cMask==0))
col_n = ncol(eX_n);
# storing the mask/address of missing values
@@ -150,7 +150,7 @@
{
Mask_Filled_n = Mask_n;
Mask_Filled_c = Mask_c
- in_n = 1; in_c = 1; i=1; j=1; # varibales for index selection
+ in_n = 1; in_c = 1; i=1; j=1; # variables for index selection
while(i <= ncol(dX))
{
if(as.scalar(dXMask[1,i]) == 0)
@@ -197,7 +197,7 @@
test_X = removeEmpty(target = slice2, margin = "cols", select = selX);
test_Y = slice2a[,in_c]
- # train clasification model
+ # train classification model
beta = multiLogReg(X=train_X, Y=train_Y, icpt = 1, tol = 0.00000001, reg = 0.001, maxi = 100, maxii=0, verbose=FALSE)
# predicting missing values
[prob,pred,acc] = multiLogRegPredict(X=test_X, B=beta, Y = test_Y)
