RClase02

datos=c(6,4,5,3,8,0)
A=matrix(data=datos,nrow=3,ncol=2,byrow = FALSE)
print(A*3)

##      [,1] [,2]
## [1,]   18    9
## [2,]   12   24
## [3,]   15    0

matA=matrix(data = c("a", "b", "c", "d", "e", "f"), nrow = 3, ncol = 2, byrow=FALSE)
rownames(matA)=c("fila1","fila2","fila3")
colnames(matA)=c("col1","col2")
print(matA)

##       col1 col2
## fila1 "a"  "d" 
## fila2 "b"  "e" 
## fila3 "c"  "f"

class(matA)

## [1] "matrix" "array"

print(is.matrix(matA))

## [1] TRUE

Fórmulas estadísticas

x=c(2,3,5,6,1,0,5,3,4)
media=mean(x)
desvSTD=sd(x)
varianza=var(x)

print(media)

## [1] 3.222222

print(desvSTD)

## [1] 1.986063

print(varianza)

## [1] 3.944444

x=c(0,1,2,3,4,5)
y=c(0.5,1.4,1.98,3.1,3.8,5.4)
formula1=formula(y~x)
modelo=lm(formula1)
summary(modelo)

## 
## Call:
## lm(formula = formula1)
## 
## Residuals:
##        1        2        3        4        5        6 
##  0.14762  0.10990 -0.24781 -0.06552 -0.30324  0.35905 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.35238    0.20485    1.72 0.160511    
## x            0.93771    0.06766   13.86 0.000157 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.283 on 4 degrees of freedom
## Multiple R-squared:  0.9796, Adjusted R-squared:  0.9745 
## F-statistic: 192.1 on 1 and 4 DF,  p-value: 0.0001571

plot(x,y)
abline(0.35238,0.93771,col="purple")