#GGally - create a plot matrix for the data visualization.
library(GGally)
## Warning: package 'GGally' was built under R version 4.1.3
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 4.1.3
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
data(women)
head(women)
## height weight
## 1 58 115
## 2 59 117
## 3 60 120
## 4 61 123
## 5 62 126
## 6 63 129
ggpairs(data=women, columns=1:2, title="Death rate")
fit_ex <- lm(height ~ weight, data = women)
ggplot(data=women, aes(fit_ex$residuals)) +
geom_histogram(binwidth = 1, color = "green", fill = "yellow") +
theme(panel.background = element_rect(fill = "red"),
axis.line.x=element_line(),
axis.line.y=element_line()) +
ggtitle("Histogram for women height")
ggplot(data = women, aes(x = height, y = weight)) +
geom_point() +
stat_smooth(method = "lm", col = "blue") +
theme(panel.background = element_rect(fill = "grey"),
axis.line.x=element_line(),
axis.line.y=element_line()) +
ggtitle("Linear Model fitted to above data")
## `geom_smooth()` using formula 'y ~ x'
predict(fit_ex, data.frame(weight = 70.2))
## 1
## 45.88835
summary(fit_ex)
##
## Call:
## lm(formula = height ~ weight, data = women)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.83233 -0.26249 0.08314 0.34353 0.49790
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 25.723456 1.043746 24.64 2.68e-12 ***
## weight 0.287249 0.007588 37.85 1.09e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.44 on 13 degrees of freedom
## Multiple R-squared: 0.991, Adjusted R-squared: 0.9903
## F-statistic: 1433 on 1 and 13 DF, p-value: 1.091e-14
#Example -2
x <- c(680, 8713, 18166, 64287, 71600,
98521, 65324, 152114, 115843,
531267, 896851, 208725, 3072113)
library(lubridate)
## Warning: package 'lubridate' was built under R version 4.1.3
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
library(forecast)
## Warning: package 'forecast' was built under R version 4.1.3
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
cts <- ts(x, start = decimal_date(ymd("2021-02-21")),frequency = 365.25 / 6)
fit <- auto.arima(cts)
forecast(fit, 4)
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 2021.353 3072113 1962765.8 4181460 1375512.9 4768713
## 2021.370 3072113 1503259.2 4640967 672758.1 5471468
## 2021.386 3072113 1150667.3 4993559 133515.4 6010711
## 2021.403 3072113 853418.6 5290807 -321087.2 6465313
plot(forecast(fit, 5), xlab ="Weekly purchase of medicine",ylab ="Total income",main ="purchase vs Income", col.main ="blue")
