eqm_vlambdas = numeric()
eqm_vlambdas_ep = numeric()
mean(eqm_previsao_til)
sd(eqm_previsao_til)
eqm_previsao_hat <- mean(eqm_previsao_til)
eqm_previsao_hat_ep <- sd(eqm_previsao_til)
eqm_vlambdas <- append(eqm_vlambdas, eqm_previsao_hat)
eqm_vlambdas_ep <- append(eqm_vlambdas_ep, eqm_previsao_hat_ep)
eqm_vlambdas
eqm_vlambdas_ep
eqm_vlambdas <- append(eqm_vlambdas, eqm_previsao_hat)
eqm_vlambdas_ep <- append(eqm_vlambdas_ep, eqm_previsao_hat_ep)
eqm_vlambdas
eqm_vlambdas_ep
eqm_vlambdas = numeric()
eqm_vlambdas_ep = numeric()
for(lambda in vlambdas){
eqm_previsao_til = numeric()
for(k in 1:K){
x_treino <- Xpad[as.vector(mgrupos[-k,]), ]
y_treino <- ypad[as.vector(mgrupos[-k,])]
x_teste <- Xpad[as.vector(mgrupos[k,]), ]
y_teste <- ypad[as.vector(mgrupos[k,])]
# Ajuste do lasso
lasso_fit_cv <- glmnet(x_treino, y_treino, family = 'gaussian', alpha = 1,
intercept = FALSE)
beta_til <- coef(lasso_fit_cv, s = vlambdas[i])[-1]
eqm_til_k <- mean((y_teste - x_teste%*%beta_til)^2)
eqm_previsao_til <- append(eqm_previsao_til, eqm_til_k)
}
eqm_previsao_hat <- mean(eqm_previsao_til)
eqm_previsao_hat_ep <- sd(eqm_previsao_til)
eqm_vlambdas <- append(eqm_vlambdas, eqm_previsao_hat)
eqm_vlambdas_ep <- append(eqm_vlambdas_ep, eqm_previsao_hat_ep)
}
plot(vlambdas, eqm_vlambdas)
for(lambda in vlambdas){
eqm_previsao_til = numeric()
for(k in 1:K){
x_treino <- Xpad[as.vector(mgrupos[-k,]), ]
y_treino <- ypad[as.vector(mgrupos[-k,])]
x_teste <- Xpad[as.vector(mgrupos[k,]), ]
y_teste <- ypad[as.vector(mgrupos[k,])]
# Ajuste do lasso
lasso_fit_cv <- glmnet(x_treino, y_treino, family = 'gaussian', alpha = 1,
intercept = FALSE)
beta_til <- coef(lasso_fit_cv, s = lambda)[-1]
eqm_til_k <- mean((y_teste - x_teste%*%beta_til)^2)
eqm_previsao_til <- append(eqm_previsao_til, eqm_til_k)
}
eqm_previsao_hat <- mean(eqm_previsao_til)
eqm_previsao_hat_ep <- sd(eqm_previsao_til)
eqm_vlambdas <- append(eqm_vlambdas, eqm_previsao_hat)
eqm_vlambdas_ep <- append(eqm_vlambdas_ep, eqm_previsao_hat_ep)
}
plot(vlambdas, eqm_vlambdas)
eqm_vlambdas = numeric()
eqm_vlambdas_ep = numeric()
for(lambda in vlambdas){
eqm_previsao_til = numeric()
for(k in 1:K){
x_treino <- Xpad[as.vector(mgrupos[-k,]), ]
y_treino <- ypad[as.vector(mgrupos[-k,])]
x_teste <- Xpad[as.vector(mgrupos[k,]), ]
y_teste <- ypad[as.vector(mgrupos[k,])]
# Ajuste do lasso
lasso_fit_cv <- glmnet(x_treino, y_treino, family = 'gaussian', alpha = 1,
intercept = FALSE)
beta_til <- coef(lasso_fit_cv, s = lambda)[-1]
eqm_til_k <- mean((y_teste - x_teste%*%beta_til)^2)
eqm_previsao_til <- append(eqm_previsao_til, eqm_til_k)
}
eqm_previsao_hat <- mean(eqm_previsao_til)
eqm_previsao_hat_ep <- sd(eqm_previsao_til)
eqm_vlambdas <- append(eqm_vlambdas, eqm_previsao_hat)
eqm_vlambdas_ep <- append(eqm_vlambdas_ep, eqm_previsao_hat_ep)
}
plot(vlambdas, eqm_vlambdas)
max(t(Xpad)%*%ypad)/(n*sd(ypad))
vlambdas <- seq(0, 1, length=nlambdas)
eqm_vlambdas = numeric()
eqm_vlambdas_ep = numeric()
for(lambda in vlambdas){
eqm_previsao_til = numeric()
for(k in 1:K){
x_treino <- Xpad[as.vector(mgrupos[-k,]), ]
y_treino <- ypad[as.vector(mgrupos[-k,])]
x_teste <- Xpad[as.vector(mgrupos[k,]), ]
y_teste <- ypad[as.vector(mgrupos[k,])]
# Ajuste do lasso
lasso_fit_cv <- glmnet(x_treino, y_treino, family = 'gaussian', alpha = 1,
intercept = FALSE)
beta_til <- coef(lasso_fit_cv, s = lambda)[-1]
eqm_til_k <- mean((y_teste - x_teste%*%beta_til)^2)
eqm_previsao_til <- append(eqm_previsao_til, eqm_til_k)
}
eqm_previsao_hat <- mean(eqm_previsao_til)
eqm_previsao_hat_ep <- sd(eqm_previsao_til)
eqm_vlambdas <- append(eqm_vlambdas, eqm_previsao_hat)
eqm_vlambdas_ep <- append(eqm_vlambdas_ep, eqm_previsao_hat_ep)
}
plot(vlambdas, eqm_vlambdas)
max(t(Xpad)%*%ypad/n)
vlambdas <- seq(0, 100, length=nlambdas)
vlambdas <- seq(0, 154, length=nlambdas)
eqm_vlambdas = numeric()
eqm_vlambdas_ep = numeric()
for(lambda in vlambdas){
eqm_previsao_til = numeric()
for(k in 1:K){
x_treino <- Xpad[as.vector(mgrupos[-k,]), ]
y_treino <- ypad[as.vector(mgrupos[-k,])]
x_teste <- Xpad[as.vector(mgrupos[k,]), ]
y_teste <- ypad[as.vector(mgrupos[k,])]
# Ajuste do lasso
lasso_fit_cv <- glmnet(x_treino, y_treino, family = 'gaussian', alpha = 1,
intercept = FALSE)
beta_til <- coef(lasso_fit_cv, s = lambda)[-1]
eqm_til_k <- mean((y_teste - x_teste%*%beta_til)^2)
eqm_previsao_til <- append(eqm_previsao_til, eqm_til_k)
}
eqm_previsao_hat <- mean(eqm_previsao_til)
eqm_previsao_hat_ep <- sd(eqm_previsao_til)
eqm_vlambdas <- append(eqm_vlambdas, eqm_previsao_hat)
eqm_vlambdas_ep <- append(eqm_vlambdas_ep, eqm_previsao_hat_ep)
}
plot(vlambdas, eqm_vlambdas)
nlambdas <- 30
vlambdas <- seq(0, max(t(Xpad)%*%ypad/n), length=nlambdas)
eqm_vlambdas = numeric()
eqm_vlambdas_ep = numeric()
plot(vlambdas, eqm_vlambdas_ep)
eqm_vlambdas_ep
sd(eqm_previsao_til)
eqm_vlambdas = numeric()
eqm_vlambdas_ep = numeric()
for(lambda in vlambdas){
eqm_previsao_til = numeric()
for(k in 1:K){
x_treino <- Xpad[as.vector(mgrupos[-k,]), ]
y_treino <- ypad[as.vector(mgrupos[-k,])]
x_teste <- Xpad[as.vector(mgrupos[k,]), ]
y_teste <- ypad[as.vector(mgrupos[k,])]
# Ajuste do lasso
lasso_fit_cv <- glmnet(x_treino, y_treino, family = 'gaussian', alpha = 1,
intercept = FALSE)
beta_til <- coef(lasso_fit_cv, s = lambda)[-1]
eqm_til_k <- mean((y_teste - x_teste%*%beta_til)^2)
eqm_previsao_til <- append(eqm_previsao_til, eqm_til_k)
}
eqm_vlambdas <- append(eqm_vlambdas, mean(eqm_previsao_til))
eqm_vlambdas_ep <- append(eqm_vlambdas_ep, sd(eqm_previsao_til))
}
plot(vlambdas, eqm_vlambdas_ep)
plot(vlambdas, eqm_vlambdas)
plot(vlambdas, eqm_vlambdas)
plot(vlambdas, eqm_vlambdas_ep)
max(t(Xpad)%*%ypad/n)
max(t(Xpad)%*%ypad/40)
vlambdas <- seq(0, 50, length=nlambdas)
eqm_vlambdas = numeric()
eqm_vlambdas_ep = numeric()
for(lambda in vlambdas){
eqm_previsao_til = numeric()
for(k in 1:K){
x_treino <- Xpad[as.vector(mgrupos[-k,]), ]
y_treino <- ypad[as.vector(mgrupos[-k,])]
x_teste <- Xpad[as.vector(mgrupos[k,]), ]
y_teste <- ypad[as.vector(mgrupos[k,])]
# Ajuste do lasso
lasso_fit_cv <- glmnet(x_treino, y_treino, family = 'gaussian', alpha = 1,
intercept = FALSE)
beta_til <- coef(lasso_fit_cv, s = lambda)[-1]
eqm_til_k <- mean((y_teste - x_teste%*%beta_til)^2)
eqm_previsao_til <- append(eqm_previsao_til, eqm_til_k)
}
eqm_vlambdas <- append(eqm_vlambdas, mean(eqm_previsao_til))
eqm_vlambdas_ep <- append(eqm_vlambdas_ep, sd(eqm_previsao_til))
}
plot(vlambdas, eqm_vlambdas)
plot(vlambdas, eqm_vlambdas_ep)
max(t(Xpad)%*%ypad)
max(t(Xpad)%*%ypad)/50
nlambdas <- 30
vlambdas <- seq(0, max(t(Xpad)%*%ypad)/50, length=nlambdas)
eqm_vlambdas = numeric()
eqm_vlambdas_ep = numeric()
for(lambda in vlambdas){
eqm_previsao_til = numeric()
for(k in 1:K){
x_treino <- Xpad[as.vector(mgrupos[-k,]), ]
y_treino <- ypad[as.vector(mgrupos[-k,])]
x_teste <- Xpad[as.vector(mgrupos[k,]), ]
y_teste <- ypad[as.vector(mgrupos[k,])]
# Ajuste do lasso
lasso_fit_cv <- glmnet(x_treino, y_treino, family = 'gaussian', alpha = 1,
intercept = FALSE)
beta_til <- coef(lasso_fit_cv, s = lambda)[-1]
eqm_til_k <- mean((y_teste - x_teste%*%beta_til)^2)
eqm_previsao_til <- append(eqm_previsao_til, eqm_til_k)
}
eqm_vlambdas <- append(eqm_vlambdas, mean(eqm_previsao_til))
eqm_vlambdas_ep <- append(eqm_vlambdas_ep, sd(eqm_previsao_til))
}
plot(vlambdas, eqm_vlambdas)
# 95% confidence intervals
ic_low <- eqm_vlambdas - 2*eqm_vlambdas_ep
ic_upp <- eqm_vlambdas + 2*eqm_vlambdas_ep
plot(vlambdas, eqm_vlambdas)
arrows(x,ic_low, ic_upp,
angle=90, code=3, length=0.05, col="blue", lwd=1.5)
plot(vlambdas, eqm_vlambdas)
arrows(vlambdas,ic_low, ic_upp,
angle=90, code=3, length=0.05, col="blue", lwd=1.5)
# 95% confidence intervals
ic_low <- eqm_vlambdas - 2*eqm_vlambdas_ep
ic_upp <- eqm_vlambdas + 2*eqm_vlambdas_ep
plot(vlambdas, eqm_vlambdas)
arrows(vlambdas,ic_low, ic_upp,
angle=90, code=3, length=0.05, col="blue", lwd=1.5)
# 95% confidence intervals
ic_low <- eqm_vlambdas - 2*eqm_vlambdas_ep
ic_upp <- eqm_vlambdas + 2*eqm_vlambdas_ep
ic_low
ic_upp
eqm_vlambdas
K <- 10
mgrupos <- matrix(sample(1:n, n), nrow = K)
nlambdas <- 30
vlambdas <- seq(0, max(t(Xpad)%*%ypad)/50, length=nlambdas)
eqm_vlambdas = numeric()
eqm_vlambdas_ep = numeric()
for(lambda in vlambdas){
eqm_previsao_til = numeric()
for(k in 1:K){
x_treino <- Xpad[as.vector(mgrupos[-k,]), ]
y_treino <- ypad[as.vector(mgrupos[-k,])]
x_teste <- Xpad[as.vector(mgrupos[k,]), ]
y_teste <- ypad[as.vector(mgrupos[k,])]
# Ajuste do lasso
lasso_fit_cv <- glmnet(x_treino, y_treino, family = 'gaussian', alpha = 1,
intercept = FALSE)
beta_til <- coef(lasso_fit_cv, s = lambda)[-1]
eqm_til_k <- mean((y_teste - x_teste%*%beta_til)^2)
eqm_previsao_til <- append(eqm_previsao_til, eqm_til_k)
}
eqm_vlambdas <- append(eqm_vlambdas, mean(eqm_previsao_til))
eqm_vlambdas_ep <- append(eqm_vlambdas_ep, sd(eqm_previsao_til))
}
plot(vlambdas, eqm_vlambdas)
plot(vlambdas, eqm_vlambdas_ep)
# 95% confidence intervals
ic_low <- eqm_vlambdas - eqm_vlambdas_ep
ic_upp <- eqm_vlambdas + eqm_vlambdas_ep
plot(vlambdas, eqm_vlambdas)
arrows(vlambdas,ic_low, ic_upp,
angle=90, code=3, length=0.05, col="blue", lwd=1.5)
plot(vlambdas, eqm_vlambdas, ylim = c(min(ic_low), max(ic_upp)))
arrows(vlambdas,ic_low, ic_upp,
angle=90, code=3, length=0.05, col="blue", lwd=1.5)
?arrows
plot(vlambdas, eqm_vlambdas, ylim = c(min(ic_low), max(ic_upp)))
arrows(vlambdas,ic_low, vlambdas, ic_upp,
angle=90, code=3, length=0.05, col="blue", lwd=1.5)
which.min(eqm_vlambdas)
lambda_cv <= vlambdas[which.min(eqm_vlambdas)]
lambda_cv <- vlambdas[which.min(eqm_vlambdas)]
abline(lambda_cv, lty=2, lwd = 2)
abline(v=lambda_cv, lty=2, lwd = 2)
abline(v=lambda_cv, lty=2, lwd = 2, color='yellow')
abline(v=lambda_cv, lty=2, lwd = 2, col='yellow')
abline(v=lambda_cv, lty=2, lwd = 2, col='green')
plot(vlambdas, eqm_vlambdas, ylim = c(min(ic_low), max(ic_upp)),
ylab='EQM', xlab='lambdas')
arrows(vlambdas,ic_low, vlambdas, ic_upp,
angle=90, code=3, length=0.05, col='blue', lwd=1.5)
lambda_cv <- vlambdas[which.min(eqm_vlambdas)]
abline(v=lambda_cv, lty=2, lwd = 2, col='green')
abline(v=lambda_cv, lty=2, lwd = 4, col='green')
lambda_cv
# escolhendo a penalização para o lasso via validação cruzada
cv_lasso_fit <- cv.glmnet(Xpad, ypad, type.measure = 'mse', nfolds = K)
cv_lasso_fit$lambda.min
# escolhendo a penalização para o lasso via validação cruzada
cv_lasso_fit <- cv.glmnet(Xpad, ypad, type.measure = 'mse', nfolds = K)
cv_lasso_fit$lambda.min
K <- 10
mgrupos <- matrix(sample(1:n, n), nrow = K)
nlambdas <- 30
vlambdas <- seq(0, max(t(Xpad)%*%ypad)/n, length=nlambdas)
eqm_vlambdas = numeric()
eqm_vlambdas_ep = numeric()
for(lambda in vlambdas){
eqm_previsao_til = numeric()
for(k in 1:K){
x_treino <- Xpad[as.vector(mgrupos[-k,]), ]
y_treino <- ypad[as.vector(mgrupos[-k,])]
x_teste <- Xpad[as.vector(mgrupos[k,]), ]
y_teste <- ypad[as.vector(mgrupos[k,])]
lasso_fit_cv <- glmnet(x_treino, y_treino, family = 'gaussian', alpha = 1,
intercept = FALSE)
beta_til <- coef(lasso_fit_cv, s = lambda)[-1]
eqm_til_k <- mean((y_teste - x_teste%*%beta_til)^2)
eqm_previsao_til <- append(eqm_previsao_til, eqm_til_k)
}
eqm_vlambdas <- append(eqm_vlambdas, mean(eqm_previsao_til))
eqm_vlambdas_ep <- append(eqm_vlambdas_ep, sd(eqm_previsao_til))
}
plot(vlambdas, eqm_vlambdas)
plot(vlambdas, eqm_vlambdas_ep)
# 95% confidence intervals
ic_low <- eqm_vlambdas - eqm_vlambdas_ep
ic_upp <- eqm_vlambdas + eqm_vlambdas_ep
plot(vlambdas, eqm_vlambdas, ylim = c(min(ic_low), max(ic_upp)),
ylab='EQM', xlab='lambdas')
arrows(vlambdas,ic_low, vlambdas, ic_upp,
angle=90, code=3, length=0.05, col='blue', lwd=1.5)
lambda_cv <- vlambdas[which.min(eqm_vlambdas)]
abline(v=lambda_cv, lty=2, lwd = 3, col='green')
lambda_cv
# escolhendo a penalização para o lasso via validação cruzada
cv_lasso_fit <- cv.glmnet(Xpad, ypad, type.measure = 'mse', nfolds = K)
cv_lasso_fit$lambda.min
set.seed(123)
K <- 10
mgrupos <- matrix(sample(1:n, n), nrow = K)
nlambdas <- 30
vlambdas <- seq(0, max(t(Xpad)%*%ypad)/n, length=nlambdas)
eqm_vlambdas = numeric()
eqm_vlambdas_ep = numeric()
for(lambda in vlambdas){
eqm_previsao_til = numeric()
for(k in 1:K){
x_treino <- Xpad[as.vector(mgrupos[-k,]), ]
y_treino <- ypad[as.vector(mgrupos[-k,])]
x_teste <- Xpad[as.vector(mgrupos[k,]), ]
y_teste <- ypad[as.vector(mgrupos[k,])]
lasso_fit_cv <- glmnet(x_treino, y_treino, family = 'gaussian', alpha = 1,
intercept = FALSE)
beta_til <- coef(lasso_fit_cv, s = lambda)[-1]
eqm_til_k <- mean((y_teste - x_teste%*%beta_til)^2)
eqm_previsao_til <- append(eqm_previsao_til, eqm_til_k)
}
eqm_vlambdas <- append(eqm_vlambdas, mean(eqm_previsao_til))
eqm_vlambdas_ep <- append(eqm_vlambdas_ep, sd(eqm_previsao_til))
}
plot(vlambdas, eqm_vlambdas)
plot(vlambdas, eqm_vlambdas_ep)
# 95% confidence intervals
ic_low <- eqm_vlambdas - eqm_vlambdas_ep
ic_upp <- eqm_vlambdas + eqm_vlambdas_ep
plot(vlambdas, eqm_vlambdas, ylim = c(min(ic_low), max(ic_upp)),
ylab='EQM', xlab='lambdas')
arrows(vlambdas,ic_low, vlambdas, ic_upp,
angle=90, code=3, length=0.05, col='blue', lwd=1.5)
lambda_cv <- vlambdas[which.min(eqm_vlambdas)]
abline(v=lambda_cv, lty=2, lwd = 3, col='green')
# escolhendo a penalização para o lasso via validação cruzada
cv_lasso_fit <- cv.glmnet(Xpad, ypad, type.measure = 'mse', nfolds = K)
cv_lasso_fit$lambda.min
lambda_cv
png(file="eqm_cv_lambdas.png",
width=600, height=500, res = 100)
plot(vlambdas, eqm_vlambdas, ylim = c(min(ic_low), max(ic_upp)),
ylab='EQM', xlab='lambdas')
arrows(vlambdas,ic_low, vlambdas, ic_upp,
angle=90, code=3, length=0.05, col='blue', lwd=1.5)
dev.off()
# lambda que minimiza o EQM de previsao
lambda_cv <- vlambdas[which.min(eqm_vlambdas)]
lambda_cv
png(file="eqm_cv_lambdas.png",
width=600, height=500, res = 100)
plot(vlambdas, eqm_vlambdas, ylim = c(min(ic_low), max(ic_upp)),
ylab='EQM', xlab='lambdas')
arrows(vlambdas,ic_low, vlambdas, ic_upp,
angle=90, code=3, length=0.05, col='blue', lwd=1.5)
abline(v=lambda_cv, lty=2, lwd = 3, col='green')
dev.off()
max(t(Xpad)%*%ypad)/n
limiar_suave = function(x, lambda){
sinal_x <- ifelse(x>0, 1, -1)
return( sinal_x*max(0, abs(x) - lambda) )
}
x <- seq(-3, 3, length=1000)
limiar_suave = function(x, lambda){
sinal_x <- 1*(x>0)
return( sinal_x*max(0, abs(x) - lambda) )
}
x <- seq(-3, 3, length=10)
limiar_suave(x, 2)
limiar_suave = function(x, lambda){
sinal_x <- 2*(x>0) - 1
return( sinal_x*max(0, abs(x) - lambda) )
}
x <- seq(-3, 3, length=10)
limiar_suave(x, 2)
x
lambda = 2
sinal_x*max(0, abs(x) - lambda)
sinal_x <- 2*(x>0) - 1
sinal_x
abs(x)
max(0, abs(x) - lambda)
max(rep(0, length(x)), abs(x) - lambda)
sapply(x, function(y) max(y, abs(y)-lambda))
sapply(x, function(y) max(0, abs(y)-lambda))
limiar_suave = function(x, lambda){
sinal_x <- 2*(x>0) - 1
return( sinal_x*sapply(x, function(y) max(0, abs(y)-lambda)) )
}
lambda = 2
x <- seq(-3, 3, length=10)
limiar_suave(x, 2)
lambda = 1
x <- seq(-3, 3, length=10)
limiar_suave(x, 1)
plot(x, limiar_suave(x, 1), lty=2)
x <- seq(-3, 3, length=10000)
plot(x, limiar_suave(x, 1), lty=2)
x <- seq(-3, 3, length=1000)
plot(x, limiar_suave(x, 1), lty=2)
plot(x, limiar_suave(x, 1), type='l', lty=2)
abline(a=0)
abline(b=0)
abline(a=0,b=0)
abline(a=0,b=1)
plot(x, limiar_suave(x, 1), type='l', lty=2)
abline(a=0,b=1)
plot(x, limiar_suave(x, 1), type='l', lty=2, lwd=2, col='blue')
abline(a=0,b=1, lwd=2)
plot(x, limiar_suave(x, 1), type='l', lty=2, lwd=2, col='blue',
ylab = expression(cal(S)_lambda))
plot(x, limiar_suave(x, 1), type='l', lty=2, lwd=2, col='blue',
ylab = expression(cal(S)[lambda]))
plot(x, limiar_suave(x, 1), type='l', lty=2, lwd=2, col='blue',
ylab = expression(S[lambda](x)))
abline(a=0,b=1, lwd=2)
arrows(1.5,0.5, 1.5, 1.5,
angle=90, code=3, length=0.05, col='blue', lwd=1, lty=3)
text(1.7, .7, expression(lambda), col='blue')
text(1.7, 1.7, expression(lambda), col='blue')
text(1.7, 1.5, expression(lambda), col='blue')
text(1.7, 1.3, expression(lambda), col='blue')
text(1.7, 1.2, expression(lambda), col='blue')
text(1.7, 1.2, expression(lambda), col='blue', cex=2)
text(1.7, 1.2, expression(lambda), col='blue', cex=1.5)
plot(x, limiar_suave(x, 1), type='l', lty=2, lwd=2, col='blue',
ylab = expression(S[lambda](x)))
abline(a=0,b=1, lwd=2)
arrows(1.5,0.5, 1.5, 1.5,
angle=90, code=3, length=0.05, col='blue', lwd=1, lty=3)
text(1.7, 1.2, expression(lambda), col='blue', cex=1.5)
abline(v=1, lwd=1, lty=3)
abline(v=-11, lwd=1, lty=3)
abline(v=-11, lwd=1, lty=3)
abline(v=-1, lwd=1, lty=3)
plot(x, limiar_suave(x, 1), type='l', lty=2, lwd=2,
ylab = expression(S[lambda](x)))
abline(a=0,b=1, lwd=2, col='blue')
arrows(1.5,0.5, 1.5, 1.5,
angle=90, code=3, length=0.05, lwd=1, lty=3)
text(1.7, 1.2, expression(lambda), cex=1.5)
abline(v=1, lwd=1, lty=3)
abline(v=-1, lwd=1, lty=3)
plot(x, limiar_suave(x, 1), type='l', lty=2, lwd=2,
ylab = expression(S[lambda](x)), cex.lab=1.5, cex.axis=1.5)
par(mgp = c(4, 1, 0), mar=c(5,5.5,2,0))
plot(x, limiar_suave(x, 1), type='l', lty=2, lwd=2,
ylab = expression(S[lambda](x)), cex.lab=1.5, cex.axis=1.5)
par(mgp = c(4, 1, 1), mar=c(5,5.5,2,0))
plot(x, limiar_suave(x, 1), type='l', lty=2, lwd=2,
ylab = expression(S[lambda](x)), cex.lab=1.5, cex.axis=1.5)
par(mar=c(5,5.5,2,0))
plot(x, limiar_suave(x, 1), type='l', lty=2, lwd=2,
ylab = expression(S[lambda](x)), cex.lab=1.5, cex.axis=1.5)
par(mgp = c(4, 0, 1), mar=c(5,5.5,2,0))
plot(x, limiar_suave(x, 1), type='l', lty=2, lwd=2,
ylab = expression(S[lambda](x)), cex.lab=1.5, cex.axis=1.5)
par(mgp = c(4, 1, 0), mar=c(5,5.5,2,0))
plot(x, limiar_suave(x, 1), type='l', lty=2, lwd=2,
ylab = expression(S[lambda](x)), cex.lab=1.5, cex.axis=1.5)
par(mgp = c(4, 1, 0), mar=c(5,5.5,2,1))
plot(x, limiar_suave(x, 1), type='l', lty=2, lwd=2,
ylab = expression(S[lambda](x)), cex.lab=1.5, cex.axis=1.5)
par(mgp = c(4, 1, 0), mar=c(5,5.5,1,1))
plot(x, limiar_suave(x, 1), type='l', lty=2, lwd=2,
ylab = expression(S[lambda](x)), cex.lab=1.5, cex.axis=1.5)
par(mgp = c(2, 1, 0), mar=c(5,5.5,1,1))
plot(x, limiar_suave(x, 1), type='l', lty=2, lwd=2,
ylab = expression(S[lambda](x)), cex.lab=1.5, cex.axis=1.5)
par(mgp = c(3, 1, 0), mar=c(5,5.5,1,1))
plot(x, limiar_suave(x, 1), type='l', lty=2, lwd=2,
ylab = expression(S[lambda](x)), cex.lab=1.5, cex.axis=1.5)
abline(a=0,b=1, lwd=2, col='blue')
arrows(1.5,0.5, 1.5, 1.5,
angle=90, code=3, length=0.05, lwd=1, lty=3)
text(1.7, 1.2, expression(lambda), cex=1.5)
abline(v=1, lwd=1, lty=3)
abline(v=-1, lwd=1, lty=3)
png(file="limiar_suave.png",
width=600, height=500, res = 100)
par(mgp = c(3, 1, 0), mar=c(5,5.5,1,1))
plot(x, limiar_suave(x, 1), type='l', lty=2, lwd=2,
ylab = expression(S[lambda](x)), cex.lab=1.5, cex.axis=1.5)
abline(a=0,b=1, lwd=2, col='blue')
arrows(1.5,0.5, 1.5, 1.5,
angle=90, code=3, length=0.05, lwd=1, lty=3)
text(1.7, 1.2, expression(lambda), cex=1.5)
abline(v=1, lwd=1, lty=3)
abline(v=-1, lwd=1, lty=3)
dev.off()
