Calculates the Pearson correlation coefficient
x <- rnorm(10) y <- rnorm(10)
x
[1] -0.40674264 2.41954178 2.42796127 -0.28477615 -0.54452441 0.69373477 [7] 0.85569233 -1.42600450 -0.06299629 0.94166159
y
[1] 0.03117989 1.16086128 0.25655903 1.02594775 -0.82415760 0.32674814 [7] 1.23306169 1.24575192 0.14762524 0.87471457
plot(x,y)
x and y should be numeric vectors
cor.test(x,y)
Pearson's product-moment correlation
data: x and y
t = 0.51281, df = 8, p-value = 0.6219
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.5083266 0.7264281
sample estimates:
cor
0.1783965
z = x + rnorm(10, mean = 0, sd = 0.1)
plot(x,z)
cor.test(x,z)
Pearson's product-moment correlation
data: x and z
t = 60.21, df = 8, p-value = 6.433e-12
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.9951623 0.9997495
sample estimates:
cor
0.9988985
test = cor.test(x,y)
test = cor.test(x,y)
test$statistic
t 0.5128077
test$p.value
[1] 0.6219418
test$estimate
cor 0.1783965
x_matrix <- matrix(rnorm(30), nrow = 10) dim(x_matrix)
[1] 10 3
apply(x_matrix, 2, function(x) cor.test(x,y)$estimate)
[1] -0.2527349 0.2279156 -0.1988432
Calculates Pearson’s correlation coefficient: x and y can be numeric vectors, matrices or data frames
cor(x_matrix,y)
[,1] [1,] -0.2527349 [2,] 0.2279156 [3,] -0.1988432
Calculates Pearson’s correlation coefficient: x and y can be numeric vectors, matrices or data frames
cor(x_matrix,y)
[,1] [1,] -0.2527349 [2,] 0.2279156 [3,] -0.1988432
apply(x_matrix, 2, function(x) cor.test(x,y)$estimate)
[1] -0.2527349 0.2279156 -0.1988432
Calculates Pearson’s correlation coefficient
cor(x,y)
[1] 0.1783965
x and y can be numeric vectors, matrices or data frames
x[2] = NA x
[1] -0.40674264 NA 2.42796127 -0.28477615 -0.54452441 0.69373477 [7] 0.85569233 -1.42600450 -0.06299629 0.94166159
x[2] = NA x
[1] -0.40674264 NA 2.42796127 -0.28477615 -0.54452441 0.69373477 [7] 0.85569233 -1.42600450 -0.06299629 0.94166159
is.na(x)
[1] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
x[2] = NA x
[1] -0.40674264 NA 2.42796127 -0.28477615 -0.54452441 0.69373477 [7] 0.85569233 -1.42600450 -0.06299629 0.94166159
is.na(x)
[1] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
which(is.na(x))
[1] 2
is.na(x)
[1] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
which(is.na(x))
[1] 2
cor(x,y)
[1] NA
cor(x,y,use = "complete.obs")
[1] 0.00559633
x_matrix[1,1] = NA
cor(x_matrix,y)
[,1] [1,] NA [2,] 0.2279156 [3,] -0.1988432
cor(x_matrix,y)
[,1] [1,] NA [2,] 0.2279156 [3,] -0.1988432
cor(x_matrix,y,use = "complete.obs")
[,1] [1,] -0.3989906 [2,] 0.3619255 [3,] -0.2409004
cor(x_matrix[2:10,],y[2:10])
[,1] [1,] -0.3989906 [2,] 0.3619255 [3,] -0.2409004
cor(x_matrix,y,use = "complete.obs")
[,1] [1,] -0.3989906 [2,] 0.3619255 [3,] -0.2409004
use = “complete.obs” - removes the entire row if there is any NA value found in the row
cor(x_matrix[2:10,],y[2:10]) == cor(x_matrix,y,use = "complete.obs")
[,1] [1,] TRUE [2,] TRUE [3,] TRUE
cor(x_matrix,y)
[,1] [1,] NA [2,] 0.2279156 [3,] -0.1988432
cor(x_matrix,y,use = "pairwise.complete.obs")
[,1] [1,] -0.3989906 [2,] 0.2279156 [3,] -0.1988432
use = “complete.obs” - removes the entire row if there is any NA value found in the row
cor(x_matrix[2:10,],y[2:10]) == cor(x_matrix,y,use = "complete.obs")
[,1] [1,] TRUE [2,] TRUE [3,] TRUE
cor(x_matrix[2:10,],y[2:10]) == cor(x_matrix,y,use = "pairwise.complete.obs")
[,1] [1,] TRUE [2,] FALSE [3,] FALSE
lm(formula)
lm(y ~ x)
Call: lm(formula = y ~ x) Coefficients: (Intercept) x 0.478887 0.003394
fit = lm(y ~ x) summary(fit)
Call:
lm(formula = y ~ x)
Residuals:
Min 1Q Median 3Q Max
-1.3012 -0.3311 -0.1545 0.5480 0.7717
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.478887 0.248491 1.927 0.0953 .
x 0.003394 0.229233 0.015 0.9886
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7264 on 7 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 3.132e-05, Adjusted R-squared: -0.1428
F-statistic: 0.0002192 on 1 and 7 DF, p-value: 0.9886
sample = sample.int(10,5) sample
[1] 6 8 5 2 10
sample
[1] 6 8 5 2 10
order(sample)
[1] 4 3 1 2 5
order returns a permutation which rearranges its first argument into ascending or descending order
sample
[1] 6 8 5 2 10
order(sample)
[1] 4 3 1 2 5
order returns a permutation which rearranges its first argument into ascending or descending order
sample[order(sample)]
[1] 2 5 6 8 10
order(sample)
[1] 4 3 1 2 5
order(sample, decreasing = TRUE)
[1] 5 2 1 3 4
order(sample)
[1] 4 3 1 2 5
order(sample, decreasing = TRUE)
[1] 5 2 1 3 4
sample[order(sample, decreasing = TRUE)]
[1] 10 8 6 5 2