# poly.regression

0th

Percentile

##### Local Polynomial Regression

Calculates a Local Polynomial Regression for smoothing or imputation of missing data.

This is a wrapper function for loess that simplifies data smoothing and imputation of missing values. The function allows for smoothing a vector, based on an index (derived automatically) or covariates. If the impute option is TRUE NA values are imputed, otherwise the returned vector will still have NA's present. If impute and na.only are both TRUE the vector is returned, without being smoothed but with imputed NA values filled in. The loess weight function is defined using the tri-cube weight function w(x) = (1-|x|^3)^3 where; x is the distance of a data point from the point the curve being fitted.

##### Usage
poly.regression(
y,
x = NULL,
s = 0.75,
impute = FALSE,
na.only = FALSE,
ci = FALSE,
...
)
##### Arguments
y

Vector to smooth or impute NA values

x

Optional x covariate data (must match dimensions of y)

s

Smoothing parameter (larger equates to more smoothing)

impute

(FALSE/TRUE) Should NA values be inputed

na.only

(FALSE/TRUE) Should only NA values be change in y

ci

(FALSE/TRUE) Should confidence intervals be returned

...

##### Value

If ci = FALSE, a vector of smoothed values, otherwise a list object with:

• loess - A vector, same length of y, representing the smoothed or inputed data

• lower.ci - Lower confidence interval

• upper.ci - Upper confidence interval

loess for loess ... model options

##### Aliases
• poly.regression
##### Examples
# NOT RUN {
x <- seq(-20, 20, 0.1)
y <- sin(x)/x + rnorm(length(x), sd=0.03)
p <- which(y == "NaN")
y <- y[-p]
r <- poly.regression(y, ci=TRUE, s=0.30)

plot(y,type="l", lwd=0.5, main="s = 0.10")
y.polygon <- c((r$lower.ci)[1:length(y)], (r$upper.ci)[rev(1:length(y))])
x.polygon <- c(1:length(y), rev(1:length(y)))
polygon(x.polygon, y.polygon, col="#00009933", border=NA)
lines(r\$loess, lwd=1.5, col="red")

# Impute NA values, replacing only NA's
y.na <- y
y.na[c(100,200,300)] <- NA
p.y <- poly.regression(y.na, s=0.10, impute = TRUE, na.only = TRUE)
y - p.y

plot(p.y,type="l", lwd=1.5, col="blue", main="s = 0.10")
lines(y, lwd=1.5, col="red")

# }

Documentation reproduced from package spatialEco, version 1.3-2, License: GPL-3

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