Geek Answers Handbook

Linear regression in R (normal and logarithmic data)

I want to do a linear regression in R for data in normal and double logarithmic graphs.

For normal data, the data set may be as follows:

lin <- data.frame(x = c(0:6), y = c(0.3, 0.1, 0.9, 3.1, 5, 4.9, 6.2))
plot (lin$x, lin$y)

There I want to calculate the draw line for linear regression only for data points 2, 3 and 4.

For double logarithmic data, the data set may be as follows:

data = data.frame(
    x=c(1:15),
    y=c(
        1.000, 0.742, 0.623, 0.550, 0.500, 0.462, 0.433,
        0.051, 0.043, 0.037, 0.032, 0.028, 0.025, 0.022, 0.020
      )
    )
plot (data$x, data$y, log="xy")

Here I want to draw a regression line for datasets 1: 7 and 8:15.

Ho can you calculate the slope and y-offset als, as well as the fit parameters (R ^ 2, p-value)?

How is this done for normal and for logarithmic data?

Thanks for the help,

Sven

+5
2

R lm(). , subset , , :

lin <- data.frame(x = c(0:6), y = c(0.3, 0.1, 0.9, 3.1, 5, 4.9, 6.2))
linm <- lm(y ~ x, data = lin, subset = 2:4)

:

R> linm

Call:
lm(formula = y ~ x, data = lin, subset = 2:4)

Coefficients:
(Intercept)            x  
     -1.633        1.500  

R> fitted(linm)
         2          3          4 
-0.1333333  1.3666667  2.8666667

, , ; i) , , ii) ANCOVA. log().

:

logm1 <- lm(log(y) ~ log(x), data = dat, subset = 1:7)
logm2 <- lm(log(y) ~ log(x), data = dat, subset = 8:15)

ANCOVA,

dat <- transform(dat, ind = factor(1:15 <= 7))
logm3 <- lm(log(y) ~ log(x) * ind, data = dat)

, ? , , .

R> coef(logm1)
  (Intercept)        log(x) 
-0.0001487042 -0.4305802355 
R> coef(logm2)
(Intercept)      log(x) 
  0.1428293  -1.4966954

, -0.4306 -1.4967 . ANCOVA:

R> coef(logm3)
   (Intercept)         log(x)        indTRUE log(x):indTRUE 
     0.1428293     -1.4966954     -0.1429780      1.0661152

? , , ind, logm3, , , logm2; logm2 logm3 , log(x). , logm1, , :

R> coefs[1] + coefs[3]
  (Intercept) 
-0.0001487042

indTRUE - 1 2. :

R> coefs[2] + coefs[4]
    log(x) 
-0.4305802

logm1 2 (coefs[2]), 1 (coefs[4]).

, - abline() . . :

plot(y ~ x, data = lin)
abline(linm)

, :

pdat <- with(dat, data.frame(x = seq(from = head(x, 1), to = tail(x,1), 
                                     by = 0.1))
pdat <- transform(pdat, yhat = c(predict(logm1, pdat[1:70,, drop = FALSE]), 
                                 predict(logm2, pdat[71:141,, drop = FALSE])))

, yhat

plot(y ~ x, data = dat)
lines(exp(yhat) ~ x, dat = pdat, subset = 1:70, col = "red")
lines(exp(yhat) ~ x, dat = pdat, subset = 71:141, col = "blue")

:

plot(log(y) ~ log(x), data = dat)
lines(yhat ~ log(x), dat = pdat, subset = 1:70, col = "red")
lines(yhat ~ log(x), dat = pdat, subset = 71:141, col = "blue")

...

ANCOVA. pdat, ,

pdat <- with(dat, data.frame(x = seq(from = head(x, 1), to = tail(x,1), 
                                     by = 0.1)[1:140],
                             ind = factor(rep(c(TRUE, FALSE), each = 70))))
pdat <- transform(pdat, yhat = predict(logm3, pdat))

, , predict(), - ANCOVA logm3. -:

plot(y ~ x, data = dat)
lines(exp(yhat) ~ x, dat = pdat, subset = 1:70, col = "red")
lines(exp(yhat) ~ x, dat = pdat, subset = 71:141, col = "blue")
+9
#Split the data into two groups
data1 <- data[1:7, ]
data2 <- data[8:15, ]

#Perform the regression
model1 <- lm(log(y) ~ log(x), data1)
model2 <- lm(log(y) ~ log(x), data2)
summary(model1)
summary(model2)

#Plot it
with(data, plot(x, y, log="xy"))
lines(1:7, exp(predict(model1, data.frame(x = 1:7))))
lines(8:15, exp(predict(model2, data.frame(x = 8:15))))

, , , .

data$group <- factor(rep(letters[1:2], times = 7:8))

- , ,

model_all <- lm(log(y) ~ log(x) * group, data)
summary(model_all)
+2

More articles:


All Articles