Murat Koptur 24 Ağustos 2018
library(GGally) ## Loading required package: ggplot2
library(ggplot2)
library(readr)
library(reshape2)child_data <- read_csv("../data/child_data.csv")## Parsed with column specification:
## cols(
## age = col_double(),
## mem_span = col_double(),
## iq = col_integer(),
## read_ab = col_double()
## )
head(child_data)## # A tibble: 6 x 4
## age mem_span iq read_ab
## <dbl> <dbl> <int> <dbl>
## 1 6.7 4.4 95 7.2
## 2 5.9 4 90 6
## 3 5.5 4.1 105 6
## 4 6.2 4.8 98 6.6
## 5 6.4 5 106 7
## 6 7.3 5.5 100 7.2
child_data_melted <- melt(child_data)## No id variables; using all as measure variables
head(child_data_melted)## variable value
## 1 age 6.7
## 2 age 5.9
## 3 age 5.5
## 4 age 6.2
## 5 age 6.4
## 6 age 7.3
ggplot(data = child_data_melted, aes(x = value)) +
geom_histogram(aes(y = ..ncount..)) +
geom_density(aes(y = ..scaled..)) +
facet_wrap(~variable, scales = "free") +
labs(x = "Values", y = "Frequencies", title = "Histograms")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggpairs(child_data)
child_data_scaled <- scale(child_data)
head(child_data_scaled)## age mem_span iq read_ab
## [1,] 0.6268603 -0.2164352 -0.2376403 1.309125
## [2,] -0.1188471 -0.9090277 -1.0297747 -0.436375
## [3,] -0.4917008 -0.7358796 1.3466285 -0.436375
## [4,] 0.1607932 0.4761574 0.2376403 0.436375
## [5,] 0.3472200 0.8224536 1.5050553 1.018208
## [6,] 1.1861409 1.6881943 0.5544941 1.309125
model1 <- lm(read_ab ~ age + iq, data = as.data.frame(child_data_scaled))
summary(model1)##
## Call:
## lm(formula = read_ab ~ age + iq, data = as.data.frame(child_data_scaled))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.85644 -0.02059 0.04402 0.20506 0.81633
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.302e-16 9.998e-02 0.000 1.00000
## age 9.117e-01 1.047e-01 8.711 1.12e-07 ***
## iq 3.313e-01 1.047e-01 3.165 0.00565 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4471 on 17 degrees of freedom
## Multiple R-squared: 0.8211, Adjusted R-squared: 0.8001
## F-statistic: 39.02 on 2 and 17 DF, p-value: 4.434e-07
model2 <- lm(read_ab ~ age + mem_span, data = as.data.frame(child_data_scaled))
summary(model2)##
## Call:
## lm(formula = read_ab ~ age + mem_span, data = as.data.frame(child_data_scaled))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9536 -0.2206 0.0244 0.1668 1.0719
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.363e-16 1.038e-01 0.000 1.00000
## age 5.296e-01 1.542e-01 3.435 0.00316 **
## mem_span 4.377e-01 1.542e-01 2.839 0.01135 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4643 on 17 degrees of freedom
## Multiple R-squared: 0.8071, Adjusted R-squared: 0.7844
## F-statistic: 35.57 on 2 and 17 DF, p-value: 8.414e-07
model3 <- lm(read_ab ~ age + iq + age:iq, data = as.data.frame(child_data_scaled))
summary(model3)##
## Call:
## lm(formula = read_ab ~ age + iq + age:iq, data = as.data.frame(child_data_scaled))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.82042 -0.08630 -0.01172 0.18550 0.89331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03942 0.09964 0.396 0.69764
## age 0.79560 0.12613 6.308 1.04e-05 ***
## iq 0.38369 0.10642 3.605 0.00237 **
## age:iq 0.20914 0.13667 1.530 0.14549
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4305 on 16 degrees of freedom
## Multiple R-squared: 0.844, Adjusted R-squared: 0.8147
## F-statistic: 28.85 on 3 and 16 DF, p-value: 1.089e-06