7/solutions7.r (6bffa98) - SS2012-StatisticalMethods.git

solutions7.r

#Problem 1

#definition of a straight line function
linrel=function(x,m,b) m*x+b

#definition x-Vctor
x_vec=seq(-1,3,0.1)

#definition y-vector
y_vec=linrel(x_vec,1,0)
#plotting x-vector vs. y-vector
plot(x_vec, y_vec, type="l", col="black", xlab='x',ylab='f(x)=y')

#generating mock data point for 0 <= x <= 2 for 10 equaly spaced points
equ_points=10
mock_vec = rep(NA,10)
x_vec_2 =seq(0,2, 2/(equ_points-1))
mock_vec = rnorm({1:10},mean = x_vec_2, sd =  0.4)
points(x_vec_2,mock_vec, col="red", type="p")

##Problem2
conrel =function(b) b
quarel =function(x,a,m,b) a*x**2 + m*x+b

reso = 10  #the resolution
a_vec = seq(-4,4,length.out = reso)
m_vec = seq(-4,8,length.out = reso)
b_vec = seq(-2,2,length.out = reso)

# Define function chi2_quad
chi2_qua = function(a,m,b) sum((mock_vec - quarel(mock_vec,a,m,b))**2/(0.4)**2)
chi2_lin = function(m,b) sum((mock_vec - linrel(mock_vec,m,b))**2/(0.4)**2)
chi2_con = function(b) sum((mock_vec - conrel(b))**2/(0.4)**2)

# Calculate 3-dim grid for values a=-4..4, m=-4..8, b-2..2
qua_arr = array(NA, dim=c(length(b_vec),length(m_vec),length(a_vec)))
lin_arr = array(NA, dim=c(length(b_vec),length(m_vec)))
con_arr = array(NA, dim=c(length(b_vec)))
for (i in (1:length(b_vec))){
    con_arr[i] = chi2_con(b_vec[i])
	for (j in (1:length(m_vec))){
        lin_arr[i,j] = chi2_lin(m_vec[j],b_vec[i])
		for (k in (1:length(a_vec))){
			qua_arr[i,j,k] = chi2_qua(a_vec[k],m_vec[j],b_vec[i])
		}
	}
}

minimum_qua=min(qua_arr)
best_m_qua = m_vec[which(qua_arr == minimum_qua,arr.ind=TRUE)[2]]
best_b_qua = b_vec[which(qua_arr == minimum_qua,arr.ind=TRUE)[1]]
best_a_qua = a_vec[which(qua_arr == minimum_qua,arr.ind=TRUE)[3]]
minimum_lin=min(lin_arr)
best_m_lin = m_vec[which(lin_arr == minimum_lin,arr.ind=TRUE)[2]]
best_b_lin = b_vec[which(lin_arr == minimum_lin,arr.ind=TRUE)[1]]
minimum_con=min(lin_arr)
best_b_con = b_vec[which(con_arr == minimum_con,arr.ind=TRUE)[1]]

SS2012-StatisticalMethods.git

add 2.2