UPDATE: Implements CORREL, SLOPE, INTERCEPT, RSQ and STEYX
These are all functions that follow a very simmilar path code
This commit is contained in:
Nicolás Hatcher
committed by
Nicolás Hatcher Andrés
Nicolás Hatcher Andrés
parent
01b19b9c35
commit
8597d14a4e
227
base/src/functions/statistical/correl.rs
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227
base/src/functions/statistical/correl.rs
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use crate::expressions::types::CellReferenceIndex;
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use crate::{
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calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
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};
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impl Model {
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// CORREL(array1, array2) - Returns the correlation coefficient of two data sets
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pub(crate) fn fn_correl(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
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let (_, _, values_left, values_right) = match self.fn_get_two_matrices(args, cell) {
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Ok(s) => s,
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Err(e) => return e,
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};
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let mut n = 0.0;
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let mut sum_x = 0.0;
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let mut sum_y = 0.0;
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let mut sum_x2 = 0.0;
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let mut sum_y2 = 0.0;
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let mut sum_xy = 0.0;
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for (x_opt, y_opt) in values_left.into_iter().zip(values_right.into_iter()) {
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if let (Some(x), Some(y)) = (x_opt, y_opt) {
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n += 1.0;
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sum_x += x;
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sum_y += y;
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sum_x2 += x * x;
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sum_y2 += y * y;
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sum_xy += x * y;
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}
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}
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// Need at least 2 valid pairs
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if n < 2.0 {
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return CalcResult::new_error(
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Error::DIV,
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cell,
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"CORREL requires at least two numeric data points in each range".to_string(),
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);
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}
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let num = n * sum_xy - sum_x * sum_y;
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let denom_x = n * sum_x2 - sum_x * sum_x;
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let denom_y = n * sum_y2 - sum_y * sum_y;
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let denom = (denom_x * denom_y).sqrt();
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if denom == 0.0 || !denom.is_finite() {
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return CalcResult::new_error(
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Error::DIV,
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cell,
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"Division by zero in CORREL".to_string(),
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);
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}
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let r = num / denom;
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CalcResult::Number(r)
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}
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// SLOPE(known_y's, known_x's) - Returns the slope of the linear regression line
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pub(crate) fn fn_slope(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
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let (_rows, _cols, values_y, values_x) = match self.fn_get_two_matrices(args, cell) {
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Ok(s) => s,
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Err(e) => return e,
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};
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let mut n = 0.0;
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let mut sum_x = 0.0;
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let mut sum_y = 0.0;
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let mut sum_x2 = 0.0;
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let mut sum_xy = 0.0;
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let len = values_y.len().min(values_x.len());
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for i in 0..len {
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if let (Some(y), Some(x)) = (values_y[i], values_x[i]) {
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n += 1.0;
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sum_x += x;
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sum_y += y;
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sum_x2 += x * x;
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sum_xy += x * y;
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}
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}
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if n < 2.0 {
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return CalcResult::new_error(
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Error::DIV,
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cell,
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"SLOPE requires at least two numeric data points".to_string(),
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);
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}
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let denom = n * sum_x2 - sum_x * sum_x;
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if denom == 0.0 || !denom.is_finite() {
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return CalcResult::new_error(
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Error::DIV,
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cell,
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"Division by zero in SLOPE".to_string(),
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);
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}
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let num = n * sum_xy - sum_x * sum_y;
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let slope = num / denom;
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CalcResult::Number(slope)
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}
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// INTERCEPT(known_y's, known_x's) - Returns the y-intercept of the linear regression line
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pub(crate) fn fn_intercept(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
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let (_rows, _cols, values_y, values_x) = match self.fn_get_two_matrices(args, cell) {
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Ok(s) => s,
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Err(e) => return e,
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};
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let mut n = 0.0;
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let mut sum_x = 0.0;
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let mut sum_y = 0.0;
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let mut sum_x2 = 0.0;
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let mut sum_xy = 0.0;
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let len = values_y.len().min(values_x.len());
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for i in 0..len {
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if let (Some(y), Some(x)) = (values_y[i], values_x[i]) {
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n += 1.0;
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sum_x += x;
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sum_y += y;
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sum_x2 += x * x;
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sum_xy += x * y;
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}
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}
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if n < 2.0 {
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return CalcResult::new_error(
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Error::DIV,
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cell,
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"INTERCEPT requires at least two numeric data points".to_string(),
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);
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}
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let denom = n * sum_x2 - sum_x * sum_x;
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if denom == 0.0 || !denom.is_finite() {
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return CalcResult::new_error(
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Error::DIV,
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cell,
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"Division by zero in INTERCEPT".to_string(),
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);
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}
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let num = n * sum_xy - sum_x * sum_y;
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let slope = num / denom;
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let intercept = (sum_y - slope * sum_x) / n;
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CalcResult::Number(intercept)
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}
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// STEYX(known_y's, known_x's) - Returns the standard error of the predicted y-values
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pub(crate) fn fn_steyx(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
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let (_rows, _cols, values_y, values_x) = match self.fn_get_two_matrices(args, cell) {
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Ok(s) => s,
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Err(e) => return e,
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};
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let mut n = 0.0;
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let mut sum_x = 0.0;
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let mut sum_y = 0.0;
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let mut sum_x2 = 0.0;
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let mut sum_xy = 0.0;
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// We need the actual pairs again later for residuals
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let mut pairs: Vec<(f64, f64)> = Vec::new();
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let len = values_y.len().min(values_x.len());
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for i in 0..len {
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if let (Some(y), Some(x)) = (values_y[i], values_x[i]) {
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n += 1.0;
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sum_x += x;
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sum_y += y;
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sum_x2 += x * x;
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sum_xy += x * y;
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pairs.push((x, y));
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}
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}
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// Need at least 3 points for STEYX (n - 2 in denominator)
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if n < 3.0 {
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return CalcResult::new_error(
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Error::DIV,
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cell,
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"STEYX requires at least three numeric data points".to_string(),
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);
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}
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let denom = n * sum_x2 - sum_x * sum_x;
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if denom == 0.0 || !denom.is_finite() {
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return CalcResult::new_error(
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Error::DIV,
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cell,
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"Division by zero in STEYX".to_string(),
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);
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}
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let num = n * sum_xy - sum_x * sum_y;
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let slope = num / denom;
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let intercept = (sum_y - slope * sum_x) / n;
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// Sum of squared residuals: Σ (y - ŷ)^2, ŷ = intercept + slope * x
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let mut sse = 0.0;
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for (x, y) in pairs {
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let y_hat = intercept + slope * x;
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let diff = y - y_hat;
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sse += diff * diff;
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}
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let dof = n - 2.0;
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if dof <= 0.0 {
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return CalcResult::new_error(
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Error::DIV,
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cell,
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"STEYX has non-positive degrees of freedom".to_string(),
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);
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}
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let sey = (sse / dof).sqrt();
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if !sey.is_finite() {
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return CalcResult::new_error(Error::DIV, cell, "Numerical error in STEYX".to_string());
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}
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CalcResult::Number(sey)
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}
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}
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