# (One-Sample) Kolmogorov-Smirnov Test

`spark.kstest.Rd`

`spark.kstest`

Conduct the two-sided Kolmogorov-Smirnov (KS) test for data sampled from a
continuous distribution.

By comparing the largest difference between the empirical cumulative distribution of the sample data and the theoretical distribution we can provide a test for the the null hypothesis that the sample data comes from that theoretical distribution.

Users can call `summary`

to obtain a summary of the test, and `print.summary.KSTest`

to print out a summary result.

## Arguments

- data
a SparkDataFrame of user data.

- ...
additional argument(s) passed to the method.

- testCol
column name where the test data is from. It should be a column of double type.

- nullHypothesis
name of the theoretical distribution tested against. Currently only

`"norm"`

for normal distribution is supported.- distParams
parameters(s) of the distribution. For

`nullHypothesis = "norm"`

, we can provide as a vector the mean and standard deviation of the distribution. If none is provided, then standard normal will be used. If only one is provided, then the standard deviation will be set to be one.- object
test result object of KSTest by

`spark.kstest`

.- x
summary object of KSTest returned by

`summary`

.

## Value

`spark.kstest`

returns a test result object.

`summary`

returns summary information of KSTest object, which is a list.
The list includes the `p.value`

(p-value), `statistic`

(test statistic
computed for the test), `nullHypothesis`

(the null hypothesis with its
parameters tested against) and `degreesOfFreedom`

(degrees of freedom of the test).

## Examples

```
if (FALSE) {
data <- data.frame(test = c(0.1, 0.15, 0.2, 0.3, 0.25))
df <- createDataFrame(data)
test <- spark.kstest(df, "test", "norm", c(0, 1))
# get a summary of the test result
testSummary <- summary(test)
testSummary
# print out the summary in an organized way
print.summary.KSTest(testSummary)
}
```