plotModel: Plot bestfit models
In lfpdroubi/appraiseR: R Package For Real Estate Appraisals
plotModel R Documentation
Plot bestfit models
Description
plotModel plots bestfit models
Usage
plotModel(object, ...)
## S3 method for class 'bestfit'
plotModel(object, fit = 1, scale = c("transformed", "original"), ...)
## S3 method for class 'lm'
plotModel(object, vars = parameters(object)$predictors, ...)
Arguments
object
Object of class bestfit
...
further arguments passed to predict.lm.
fit
chosen fit
scale
Plot model under original or transformed scale?
interval
the type of interval calculation (provided to predict.lm) to
be ploted with the model.
level
Tolerance/confidence level (provided to predict.lm) to be
ploted.
FUN
function used to transform the response (optional)
at
Data Frame to be used to calculate the predictions, instead of
mean values.
(defaults for center of each variable).
Examples
# 1. Crete random bivariate normal data just for testing
library(MASS)
sample_cov <- matrix(c(1000^2, -25000,
-25000, 50^2),
ncol = 2, byrow = T)
n <- 50
set.seed(4)
dados <- mvrnorm(n = n,
mu = c(5000, 360),
Sigma = sample_cov,
empirical = T)
colnames(dados) <- c("VU", "Area")
dados <- as.data.frame(dados)
fit <- lm(VU ~ Area, data = dados)
plotModel(fit)
plotModel(fit, residuals = T)
plotModel(fit, residuals = T, at = list(Area = 275))
plotModel(fit, residuals = T, at = list(Area = 275),
interval = "both", level = .80,
av = 6725, ca = TRUE,
)
# 2. Random data with variables transformation (log-log)
m <- c(5000, 360)
s <- c(1000, 50)
r <- -0.75
sigma <- sqrt(log(s^2/m^2 + 1))
mu <- log(m) - sigma^2/2
rho <- log(r*prod(s)/prod(m) + 1)
set.seed(1)
dados <- exp(mvrnorm(n = n, mu = mu, Sigma = diag(sigma^2 - rho) + rho,
empirical = TRUE))
colnames(dados) <- c("VU", "Area")
dados <- as.data.frame(dados)
# Wrong fit:
wfit <- lm(VU ~ Area, data = dados)
plotModel(wfit, residuals = T)
# Right fit:
fit <- lm(log(VU)~log(Area), data = dados)
plotModel(fit)
plotModel(fit, residuals = T)
plotModel(fit, residuals = T, at = list(Area = 500))
plotModel(fit, residuals = T, at = list(Area = 500),
interval = "both", level = .80,
ca = TRUE)
plotModel(fit, residuals = T, at = list(Area = 500),
interval = "both", level = .80,
ca = TRUE, av = log(3850))
plotModel(fit, residuals = T, at = list(Area = 500), FUN = "log",
interval = "both", level = .80,
ca = TRUE, av = 3850)
# 3. Random data with 2 covariates
## VU Area Frente
sigma <- matrix(c( 1000^2, -60000, -750,
-60000, 100^2, 240,
-750, 240, 3^2),
ncol = 3, byrow = T)
set.seed(1)
dados <- mvrnorm(n=100,
mu=c(5000, 360, 12),
Sigma=sigma,
empirical = T)
colnames(dados) <- c("VU", "Area", "Frente")
dados <- as.data.frame(dados)
fit <- lm(VU ~ Area + Frente, data = dados)
plotModel(fit, residuals = TRUE)
plotModel(fit, vars = "Area", residuals=T)
plotModel(fit, vars = "Frente", residuals = T)
plotModel(fit, interval = "both", ca = TRUE, residuals = T,
at = list(Area = 360, Frente = 12))
plotModel(fit, interval = "both", ca = TRUE, residuals = T,
at = list(Area = 360, Frente = 12),
av = 5750)
# 4. Random data with 2 covariates and transformations (Cobb-Douglas)
m <- c(5000, 360, 12)
s <- c(1000, 50, 3)
ryx1 <- -0.60
ryx2 <- -0.25
rx1x2 <- .80
sigma <- sqrt(log(s^2/m^2 + 1))
mu <- log(m) - sigma^2/2
rhoyx1 <- log(ryx1*prod(s[1:2])/prod(m[1:2]) + 1)
rhoyx2 <- log(ryx2*prod(s[c(1, 3)])/prod(m[c(1, 3)]) + 1)
rhox1x2 <- log(rx1x2*prod(s[2:3])/prod(m[2:3]) + 1)
## VU Area Frente
Sigma <- matrix(c(sigma[1]^2, rhoyx1, rhoyx2, # VU
rhoyx1, sigma[2]^2, rhox1x2, # Area
rhoyx2, rhox1x2, sigma[3]^2), # Frente
ncol = 3, byrow = T)
set.seed(1)
dados <- exp(mvrnorm(n=100, mu= mu, Sigma = Sigma,
empirical = T))
colnames(dados) <- c("VU", "Area", "Frente")
dados <- as.data.frame(dados)
fit <- lm(log(VU) ~ log(Area) + log(Frente), data = dados)
plotModel(fit, residuals = TRUE)
plotModel(fit, interval = "both", ca = TRUE, residuals = T,
at = list(Area = 450, Frente = 18), av = log(4850))
plotModel(fit, interval = "both", ca = TRUE, residuals = T, FUN = "log",
at = list(Area = 450, Frente = 18), av = 4850)
# 5. With real data
library(sf)
data(centro_2015)
centro_2015 <- within(centro_2015, VU <- valor/area_total)
fit <- lm(log(VU) ~ log(area_total) + quartos + suites + garagens +
log(dist_b_mar) + padrao,
data = centro_2015)
plotModel(fit)
plotModel(fit, interval = "both", level = .80, ca = TRUE)
plotModel(fit, interval = "both", level = .80, ca = TRUE, residuals = T)
plotModel(fit, at = list(area_total = 205, quartos = 3, suites = 1,
garagens = 2, dist_b_mar = 250, padrao = 'medio'))
plotModel(fit, at = list(area_total = 205, quartos = 3, suites = 1,
garagens = 2, dist_b_mar = 250, padrao = 'medio'),
interval = "both")
plotModel(fit, at = list(area_total = 205, quartos = 3, suites = 1,
garagens = 2, dist_b_mar = 250, padrao = 'medio'),
interval = "both", ca = TRUE)
plotModel(fit, at = list(area_total = 205, quartos = 3, suites = 1,
garagens = 2, dist_b_mar = 250, padrao = 'medio'),
interval = "both", ca = TRUE, residuals = TRUE,
av = log(5650))
## On the original scale
plotModel(fit, interval = "both", level = .80, FUN = "log", ca = TRUE)
plotModel(fit, interval = "both", level = .80, FUN = "log",
at = data.frame(area_total = 205, quartos = 3, suites = 1,
garagens = 2, dist_b_mar = 250, padrao = 'medio'),
ca = TRUE)
plotModel(fit, interval = "both", level = .80, FUN = "log",
at = data.frame(area_total = 205, quartos = 3, suites = 1,
garagens = 2, dist_b_mar = 250, padrao = 'medio'),
ca = TRUE, av = 5650)
# 5.1 Another model, with same data (apostila Prof. Norberto Hochheim)
dados <- within(centro_2015, padrao <- as.numeric(padrao))
fit1 <- lm(log(valor) ~ area_total + quartos + suites + garagens +
log(dist_b_mar) +I(1/padrao),
data = dados, subset = -c(31, 39))
plotModel(fit1, interval = "both", level = .80,
at = data.frame(area_total = 205, quartos = 3, suites = 1,
garagens = 2, dist_b_mar = 250, padrao = 2),
ca = TRUE, av = log(1100000),
residuals = T)
#
# plotModel.besfit()
dados <- st_drop_geometry(centro_2015)
best_fit <- bestfit(valor ~ ., data = dados)
plotModel(best_fit)
plotModel(best_fit, fit = 514, scale = "original", interval = "both",
ca = TRUE,
at = list(area_total = 205, quartos = 3, suites = 1,
garagens = 2, dist_b_mar = 250, padrao = "medio"),
av = 1100000)
best_fit <- bestfit(valor ~ ., data = data)
plotModel(best_fit, fit = 514) # Plots the best fit
plotModel(best_fit, fit = 514, interval = "confidence") # adds CIs
plotModel(best_fit, fit = 514, scale = "original") # plots in the original scale
plotModel(best_fit, fit = 514, scale = "original", interval = "both",
ca = TRUE)
plotModel(best_fit, fit = 514, scale = "original", interval = "both",
ca = TRUE,
at = list(area_total = 205, quartos = 3, suites = 1,
garagens = 2, dist_b_mar = 250, padrao = 2), av = 1100000)
plotModel(best_fit, interval = "confidence") # choose another fit
plotModel(best_fit, interval = "confidence", scale = "original",
at = list(area_total = 205, quartos = 3, suites = 1, garagens = 2,
dist_b_mar = 250, padrao = 2))
lfpdroubi/appraiseR documentation built on April 14, 2024, 10:27 p.m.
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| plotModel | R Documentation |
Plot bestfit models
Description
plotModel plots bestfit models
Usage
plotModel(object, ...)
## S3 method for class 'bestfit'
plotModel(object, fit = 1, scale = c("transformed", "original"), ...)
## S3 method for class 'lm'
plotModel(object, vars = parameters(object)$predictors, ...)
Arguments
object |
Object of class |
... |
further arguments passed to predict.lm. |
fit |
chosen fit |
scale |
Plot model under original or transformed scale? |
interval |
the type of interval calculation (provided to predict.lm) to be ploted with the model. |
level |
Tolerance/confidence level (provided to predict.lm) to be ploted. |
FUN |
function used to transform the response (optional) |
at |
Data Frame to be used to calculate the predictions, instead of mean values. (defaults for center of each variable). |
Examples
# 1. Crete random bivariate normal data just for testing
library(MASS)
sample_cov <- matrix(c(1000^2, -25000,
-25000, 50^2),
ncol = 2, byrow = T)
n <- 50
set.seed(4)
dados <- mvrnorm(n = n,
mu = c(5000, 360),
Sigma = sample_cov,
empirical = T)
colnames(dados) <- c("VU", "Area")
dados <- as.data.frame(dados)
fit <- lm(VU ~ Area, data = dados)
plotModel(fit)
plotModel(fit, residuals = T)
plotModel(fit, residuals = T, at = list(Area = 275))
plotModel(fit, residuals = T, at = list(Area = 275),
interval = "both", level = .80,
av = 6725, ca = TRUE,
)
# 2. Random data with variables transformation (log-log)
m <- c(5000, 360)
s <- c(1000, 50)
r <- -0.75
sigma <- sqrt(log(s^2/m^2 + 1))
mu <- log(m) - sigma^2/2
rho <- log(r*prod(s)/prod(m) + 1)
set.seed(1)
dados <- exp(mvrnorm(n = n, mu = mu, Sigma = diag(sigma^2 - rho) + rho,
empirical = TRUE))
colnames(dados) <- c("VU", "Area")
dados <- as.data.frame(dados)
# Wrong fit:
wfit <- lm(VU ~ Area, data = dados)
plotModel(wfit, residuals = T)
# Right fit:
fit <- lm(log(VU)~log(Area), data = dados)
plotModel(fit)
plotModel(fit, residuals = T)
plotModel(fit, residuals = T, at = list(Area = 500))
plotModel(fit, residuals = T, at = list(Area = 500),
interval = "both", level = .80,
ca = TRUE)
plotModel(fit, residuals = T, at = list(Area = 500),
interval = "both", level = .80,
ca = TRUE, av = log(3850))
plotModel(fit, residuals = T, at = list(Area = 500), FUN = "log",
interval = "both", level = .80,
ca = TRUE, av = 3850)
# 3. Random data with 2 covariates
## VU Area Frente
sigma <- matrix(c( 1000^2, -60000, -750,
-60000, 100^2, 240,
-750, 240, 3^2),
ncol = 3, byrow = T)
set.seed(1)
dados <- mvrnorm(n=100,
mu=c(5000, 360, 12),
Sigma=sigma,
empirical = T)
colnames(dados) <- c("VU", "Area", "Frente")
dados <- as.data.frame(dados)
fit <- lm(VU ~ Area + Frente, data = dados)
plotModel(fit, residuals = TRUE)
plotModel(fit, vars = "Area", residuals=T)
plotModel(fit, vars = "Frente", residuals = T)
plotModel(fit, interval = "both", ca = TRUE, residuals = T,
at = list(Area = 360, Frente = 12))
plotModel(fit, interval = "both", ca = TRUE, residuals = T,
at = list(Area = 360, Frente = 12),
av = 5750)
# 4. Random data with 2 covariates and transformations (Cobb-Douglas)
m <- c(5000, 360, 12)
s <- c(1000, 50, 3)
ryx1 <- -0.60
ryx2 <- -0.25
rx1x2 <- .80
sigma <- sqrt(log(s^2/m^2 + 1))
mu <- log(m) - sigma^2/2
rhoyx1 <- log(ryx1*prod(s[1:2])/prod(m[1:2]) + 1)
rhoyx2 <- log(ryx2*prod(s[c(1, 3)])/prod(m[c(1, 3)]) + 1)
rhox1x2 <- log(rx1x2*prod(s[2:3])/prod(m[2:3]) + 1)
## VU Area Frente
Sigma <- matrix(c(sigma[1]^2, rhoyx1, rhoyx2, # VU
rhoyx1, sigma[2]^2, rhox1x2, # Area
rhoyx2, rhox1x2, sigma[3]^2), # Frente
ncol = 3, byrow = T)
set.seed(1)
dados <- exp(mvrnorm(n=100, mu= mu, Sigma = Sigma,
empirical = T))
colnames(dados) <- c("VU", "Area", "Frente")
dados <- as.data.frame(dados)
fit <- lm(log(VU) ~ log(Area) + log(Frente), data = dados)
plotModel(fit, residuals = TRUE)
plotModel(fit, interval = "both", ca = TRUE, residuals = T,
at = list(Area = 450, Frente = 18), av = log(4850))
plotModel(fit, interval = "both", ca = TRUE, residuals = T, FUN = "log",
at = list(Area = 450, Frente = 18), av = 4850)
# 5. With real data
library(sf)
data(centro_2015)
centro_2015 <- within(centro_2015, VU <- valor/area_total)
fit <- lm(log(VU) ~ log(area_total) + quartos + suites + garagens +
log(dist_b_mar) + padrao,
data = centro_2015)
plotModel(fit)
plotModel(fit, interval = "both", level = .80, ca = TRUE)
plotModel(fit, interval = "both", level = .80, ca = TRUE, residuals = T)
plotModel(fit, at = list(area_total = 205, quartos = 3, suites = 1,
garagens = 2, dist_b_mar = 250, padrao = 'medio'))
plotModel(fit, at = list(area_total = 205, quartos = 3, suites = 1,
garagens = 2, dist_b_mar = 250, padrao = 'medio'),
interval = "both")
plotModel(fit, at = list(area_total = 205, quartos = 3, suites = 1,
garagens = 2, dist_b_mar = 250, padrao = 'medio'),
interval = "both", ca = TRUE)
plotModel(fit, at = list(area_total = 205, quartos = 3, suites = 1,
garagens = 2, dist_b_mar = 250, padrao = 'medio'),
interval = "both", ca = TRUE, residuals = TRUE,
av = log(5650))
## On the original scale
plotModel(fit, interval = "both", level = .80, FUN = "log", ca = TRUE)
plotModel(fit, interval = "both", level = .80, FUN = "log",
at = data.frame(area_total = 205, quartos = 3, suites = 1,
garagens = 2, dist_b_mar = 250, padrao = 'medio'),
ca = TRUE)
plotModel(fit, interval = "both", level = .80, FUN = "log",
at = data.frame(area_total = 205, quartos = 3, suites = 1,
garagens = 2, dist_b_mar = 250, padrao = 'medio'),
ca = TRUE, av = 5650)
# 5.1 Another model, with same data (apostila Prof. Norberto Hochheim)
dados <- within(centro_2015, padrao <- as.numeric(padrao))
fit1 <- lm(log(valor) ~ area_total + quartos + suites + garagens +
log(dist_b_mar) +I(1/padrao),
data = dados, subset = -c(31, 39))
plotModel(fit1, interval = "both", level = .80,
at = data.frame(area_total = 205, quartos = 3, suites = 1,
garagens = 2, dist_b_mar = 250, padrao = 2),
ca = TRUE, av = log(1100000),
residuals = T)
#
# plotModel.besfit()
dados <- st_drop_geometry(centro_2015)
best_fit <- bestfit(valor ~ ., data = dados)
plotModel(best_fit)
plotModel(best_fit, fit = 514, scale = "original", interval = "both",
ca = TRUE,
at = list(area_total = 205, quartos = 3, suites = 1,
garagens = 2, dist_b_mar = 250, padrao = "medio"),
av = 1100000)
best_fit <- bestfit(valor ~ ., data = data)
plotModel(best_fit, fit = 514) # Plots the best fit
plotModel(best_fit, fit = 514, interval = "confidence") # adds CIs
plotModel(best_fit, fit = 514, scale = "original") # plots in the original scale
plotModel(best_fit, fit = 514, scale = "original", interval = "both",
ca = TRUE)
plotModel(best_fit, fit = 514, scale = "original", interval = "both",
ca = TRUE,
at = list(area_total = 205, quartos = 3, suites = 1,
garagens = 2, dist_b_mar = 250, padrao = 2), av = 1100000)
plotModel(best_fit, interval = "confidence") # choose another fit
plotModel(best_fit, interval = "confidence", scale = "original",
at = list(area_total = 205, quartos = 3, suites = 1, garagens = 2,
dist_b_mar = 250, padrao = 2))
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