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1.  final def !=(arg0: Any): Boolean

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2.  final def ##(): Int

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3.  final def ==(arg0: Any): Boolean

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4.  def arraySum(arr: Column): Column
5.  final def asInstanceOf[T0]: T0

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6.  def clone(): AnyRef

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7.  final def eq(arg0: AnyRef): Boolean

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8.  def equals(arg0: Any): Boolean

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9.  def finalize(): Unit

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10.  def gLocal(dataframe: DataFrame, x: String, weights: String = "weights", permutations: Int = 0, star: Boolean = false, islandWeight: Double = 0.0): DataFrame

    Performs the Gi or Gi* statistic on the x column of the dataframe.

    Performs the Gi or Gi* statistic on the x column of the dataframe.

    Weights should be the neighbors of this row. The members of the weights should be comprised of structs containing a value column and a neighbor column. The neighbor column should be the contents of the neighbors with the same types as the parent row (minus neighbors). You can use wherobots.weighing.add_distance_band_column to achieve this. To calculate the Gi* statistic, ensure the focal observation is in the neighbors array (i.e. the row is in the weights column) and star=true. Significance is calculated with a z score. Permutation tests are not yet implemented and thus island weight does nothing. The following columns will be added: G, E[G], V[G], Z, P.

    dataframe

    the dataframe to perform the G statistic on

    x

    The column name we want to perform hotspot analysis on

    weights

    The column name containing the neighbors array. The neighbor column should be the contents of the neighbors with the same types as the parent row (minus neighbors). You can use wherobots.weighing.add_distance_band_column to achieve this.

    permutations

    Not used. Permutation tests are not supported yet. The number of permutations to use for the significance test.

    star

    Whether the focal observation is in the neighbors array. If true this calculates Gi*, otherwise Gi

    islandWeight

    Not used. The weight for the simulated neighbor used for records without a neighbor in perm tests

    returns

    A dataframe with the original columns plus the columns G, E[G], V[G], Z, P.

11.  final def getClass(): Class[_]

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12.  def hashCode(): Int

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13.  final def isInstanceOf[T0]: Boolean

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14.  final def ne(arg0: AnyRef): Boolean

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15.  final def notify(): Unit

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16.  final def notifyAll(): Unit

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17.  final def synchronized[T0](arg0: ⇒ T0): T0

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18.  def toString(): String

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19.  final def wait(): Unit

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20.  final def wait(arg0: Long, arg1: Int): Unit

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21.  final def wait(arg0: Long): Unit

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