library(mosaicCalc)
## Loading required package: mosaic
## Registered S3 method overwritten by 'mosaic':
## method from
## fortify.SpatialPolygonsDataFrame ggplot2
##
## The 'mosaic' package masks several functions from core packages in order to add
## additional features. The original behavior of these functions should not be affected by this.
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## Attaching package: 'mosaic'
## The following objects are masked from 'package:dplyr':
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## count, do, tally
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## mean
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## binom.test, cor, cor.test, cov, fivenum, IQR, median, prop.test,
## quantile, sd, t.test, var
## The following objects are masked from 'package:base':
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## Loading required package: mosaicCore
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## Attaching package: 'mosaicCore'
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## Attaching package: 'mosaicCalc'
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## D
Utils <- read.csv("http://www.mosaic-web.org/go/datasets/utilities.csv")
gf_point(ccf ~ temp, data = Utils) %>%
gf_labs(y = "Natural gas usage (ccf/month)",
x = "Average outdoor temperature (F)")
f <- fitModel(ccf ~ A * temp + B, data = Utils)
gf_point(ccf ~ temp, data = Utils) %>%
slice_plot(f(temp) ~ temp)
f2 <- fitModel(
ccf ~ A * temp + B + C *sqrt(temp),
data = Utils)
gf_point(
ccf ~ temp, data = Utils) %>%
slice_plot(f2(temp) ~ temp)
Hondas <- read.csv("http://www.mosaic-web.org/go/datasets/used-hondas.csv")
head(Hondas)
## Price Year Mileage Location Color Age
## 1 20746 2006 18394 St.Paul Grey 1
## 2 19787 2007 8 St.Paul Black 0
## 3 17987 2005 39998 St.Paul Grey 2
## 4 17588 2004 35882 St.Paul Black 3
## 5 16987 2004 25306 St.Paul Grey 3
## 6 16987 2005 33399 St.Paul Black 2
carPrice1 <- fitModel(
Price ~ A + B * Age + C * Mileage, data = Hondas
)
contour_plot(
carPrice1(Age = age, Mileage = miles) ~ age + miles,
domain(age=2:8, miles=range(0, 60000)))
carPrice2 <- fitModel(
Price ~ A + B * Age + C * Mileage + D * Age * Mileage,
data = Hondas)
contour_plot(
carPrice2(Age=age, Mileage=miles) ~ age + miles,
domain(age = range(0, 8), miles = range(0, 60000)))
logPrice2 <- fitModel(
logPrice ~ A + B * Age + C * Mileage + D * Age * Mileage,
data = Hondas %>% mutate(logPrice = log10(Price)))
contour_plot(
logPrice2(Age=age, Mileage=miles) ~ age + miles,
domain(age = range(0, 8), miles = range(0, 60000)))
carPrice3 <- fitModel(
Price ~ A + B * Age + C * Mileage + D * Age * Mileage +
E * Age^2 + F * Mileage^2 + G * Age^2 * Mileage +
H * Age * Mileage^2,
data = Hondas)
gf_point(Mileage ~ Age, data = Hondas, fill = NA) %>%
contour_plot(
carPrice3(Age=Age, Mileage=Mileage) ~ Age + Mileage)
## Warning: Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.
Utilities = read.csv("http://www.mosaic-web.org/go/datasets/utilities.csv")
gf_point(ccf ~ temp, data = Utilities)
project(ccf ~ temp + 1, data = Utilities)
## (Intercept) temp
## 253.098208 -3.464251
model_fun = makeFun( 253.098 - 3.464*temp ~ temp)
gf_point(ccf ~ temp, data=Utils) %>%
slice_plot(model_fun(temp) ~ temp)
mod2 <- makeFun(447.03 + 1.378*temp - 63.21*sqrt(temp) ~ temp)
gf_point(ccf ~ temp, data=Utils) %>% # the data
slice_plot(mod2(temp) ~ temp) %>%
gf_labs(x = "Temperature (F)",
y = "Natural gas used (ccf)")
project(ccf ~ 1 + temp + I(temp^2) + I(temp^3) + I(temp^4),
data = Utils)
## (Intercept) temp I(temp^2) I(temp^3) I(temp^4)
## 1.757579e+02 8.225746e+00 -4.815403e-01 7.102673e-03 -3.384490e-05
ccfQuad <- makeFun(1.7576e2 + 8.225*T -4.815e-1*T^2 +
7.103e-3*T^3 - 3.384e-5*T^4 ~ T)
gf_point(ccf ~ temp, data = Utils) %>%
slice_plot(ccfQuad(temp) ~ temp) %>%
gf_labs(y = "Natural gas use (ccf)", x = "Temperature (F)")
ccfQuad(32)
## [1] 143.1713
Cars = read.csv("http://www.mosaic-web.org/go/datasets/cardata.csv")
head(Cars)
## mpg pounds horsepower cylinders tons constant
## 1 16.9 3967.60 155 8 2.0 1
## 2 15.5 3689.14 142 8 1.8 1
## 3 19.2 3280.55 125 8 1.6 1
## 4 18.5 3585.40 150 8 1.8 1
## 5 30.0 1961.05 68 4 1.0 1
## 6 27.5 2329.60 95 4 1.2 1
project(mpg ~ pounds + 1, data = Cars)
## (Intercept) pounds
## 43.188646127 -0.007200773
43.1886 - 0.00720*2000
## [1] 28.7886
project(mpg ~ pounds + horsepower + 1, data = Cars)
## (Intercept) pounds horsepower
## 46.932738241 -0.002902265 -0.144930546
mod_fun <- makeFun(46.933 - 0.00290*lbs - 0.1449*hp ~ lbs + hp)
mod_fun(lbs = 2000, hp = 50)
## [1] 33.888