stats0_help: Compute Statistics Ignoring 'NA' Calues

In j-martineau/uj: Utilities by JAM

Compute Statistics Ignoring NA Calues

Description

Simple wrappers for calculating statistics ignoring any NA values.

Usage

stats0_help()

sum0(...)

sd0(...)

cor0(x, y = NULL)

cov0(x, y = NULL)

min0(...)

max0(...)

var0(...)

pmin0(...)

pmax0(...)

mean0(...)

csds0(x)

rsds0(x)

cvars0(x)

rvars0(x)

csums0(x)

rsums0(x)

cmeans0(x)

rmeans0(x)

Arguments

...	Scalars, vectors, or matrices. Reduced a single vector of atomic values for sd0, min0, max0, and mean0.
x	A numeric vector or a numeric matrix.
y	An optional numeric vector or numeric matrix.

Value

A numeric vector

⁠cmeans0, csums0, cvars0, csds0⁠
⁠rmeans0, rsums0, rvars0, rsds0⁠
⁠mean0, sum0, var0, sd0⁠
⁠pmin0, min0⁠
⁠pmax0, max0⁠

A numeric vector or matrix

⁠cor0, cov0⁠

Functions

• sum0(): Calculate sum ignoring NA values.

• sd0(): Calculate standard deviation ignoring NA values.

• cor0(): Calculate correlation ignoring NA values.

• cov0(): Calculate covariance ignoring NA values.

• min0(): Calculate minimum value ignoring NA values.

• max0(): Calculate maximum ignoring NA values.

• var0(): Calculate variance ignoring NA values.

• pmin0(): Calculate pairmin values ignoring NA values.

• pmax0(): Calculate pairmax values ignoring NA values.

• mean0(): Calculate mean value ignoring NA values.

• csds0(): Calculate column standard deviations ignoring NA values.

• rsds0(): Calculate row standard deviations ignoring NA values.

• cvars0(): Calculate column variances ignoring NA values.

• rvars0(): Calculate row variances ignoring NA values.

• csums0(): Calculate column sums ignoring NA values.

• rsums0(): Calculate row sums ignoring NA values.

• cmeans0(): Calculate column means ignoring NA values.

• rmeans0(): Calculate row means ignoring NA values.

See Also

Other missingness: na_help()

Examples

egVec1 <- sample(0:99, 10)
egVec2 <- sample(0:99, 20)
egVec3 <- sample(0:99, 20)
egMat1 <- sample(0:99, 100)

egVec1[sample(1:10, 1)] <- NA
egVec2[sample(1:20, 1)] <- NA
egVec3[sample(1:20, 2)] <- NA
egMat1[sample(1:100, 5)] <- NA
egMat1 <- matrix(egMat1, nrow = 10)
rownames(egMat1) <- paste0("R", 1:10)
colnames(egMat1) <- paste0("C", 1:10)
egDtf1 <- as.data.frame(egMat1)
egX <- list(v1 = egVec1, v2 = egVec2, v3 = egVec3, m = egMat1, d = egDtf1)

egVec1
egVec2
egVec3
egMat1
egDtf1
egX

matrix(c(cmeans0(egMat1), cmeans0(egDtf1)), ncol = 2)
matrix(c(rmeans0(egMat1), rmeans0(egDtf1)), ncol = 2)
matrix(c(csums0( egMat1), csums0( egDtf1)), ncol = 2)
matrix(c(rsums0( egMat1), rsums0( egDtf1)), ncol = 2)
matrix(c(csds0(  egMat1), csds0(  egDtf1)), ncol = 2)
matrix(c(rsds0(  egMat1), rsds0(  egDtf1)), ncol = 2)

pmin0(egVec1, egVec2, egVec3)
pmax0(egVec2, egVec2, egVec3)

cor0(egVec2, egVec3)
cov0(egVec2, egVec3)

cor0(egMat1)
cov0(egMat1)

list(mean = mean0(egX), min = min0(egX), max = max0(egX),
      sum = sum0( egX), var = var0(egX), sd  = sd0( egX))

j-martineau/uj documentation built on Sept. 14, 2024, 4:40 a.m.