diff options
Diffstat (limited to 'r.html.markdown')
| -rw-r--r-- | r.html.markdown | 105 |
1 files changed, 102 insertions, 3 deletions
diff --git a/r.html.markdown b/r.html.markdown index b7bd6801..8539b10e 100644 --- a/r.html.markdown +++ b/r.html.markdown @@ -2,6 +2,8 @@ language: R contributors: - ["e99n09", "http://github.com/e99n09"] + - ["isomorphismes", "http://twitter.com/isomorphisms"] + - ["kalinn", "http://github.com/kalinn"] filename: learnr.r --- @@ -196,6 +198,14 @@ class(NaN) # "numeric" # You can do arithmetic on two vectors with length greater than 1, # so long as the larger vector's length is an integer multiple of the smaller c(1,2,3) + c(1,2,3) # 2 4 6 +# Since a single number is a vector of length one, scalars are applied +# elementwise to vectors +(4 * c(1,2,3) - 2) / 2 # 1 3 5 +# Except for scalars, use caution when performing arithmetic on vectors with +# different lengths. Although it can be done, +c(1,2,3,1,2,3) * c(1,2) # 1 4 3 2 2 6 +# Matching lengths is better practice and easier to read +c(1,2,3,1,2,3) * c(1,2,1,2,1,2) # CHARACTERS # There's no difference between strings and characters in R @@ -234,6 +244,9 @@ class(NA) # "logical" TRUE | FALSE # TRUE # AND TRUE & FALSE # FALSE +# Applying | and & to vectors returns elementwise logic operations +c(TRUE,FALSE,FALSE) | c(FALSE,TRUE,FALSE) # TRUE TRUE FALSE +c(TRUE,FALSE,TRUE) & c(FALSE,TRUE,TRUE) # FALSE FALSE TRUE # You can test if x is TRUE isTRUE(TRUE) # TRUE # Here we get a logical vector with many elements: @@ -664,15 +677,101 @@ write.csv(pets, "pets2.csv") # to make a new .csv file ######################### +# Statistical Analysis +######################### + +# Linear regression! +linearModel <- lm(price ~ time, data = list1) +linearModel # outputs result of regression +# => +# Call: +# lm(formula = price ~ time, data = list1) +# +# Coefficients: +# (Intercept) time +# 0.1453 0.4943 +summary(linearModel) # more verbose output from the regression +# => +# Call: +# lm(formula = price ~ time, data = list1) +# +# Residuals: +# Min 1Q Median 3Q Max +# -8.3134 -3.0131 -0.3606 2.8016 10.3992 +# +# Coefficients: +# Estimate Std. Error t value Pr(>|t|) +# (Intercept) 0.14527 1.50084 0.097 0.923 +# time 0.49435 0.06379 7.749 2.44e-09 *** +# --- +# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 +# +# Residual standard error: 4.657 on 38 degrees of freedom +# Multiple R-squared: 0.6124, Adjusted R-squared: 0.6022 +# F-statistic: 60.05 on 1 and 38 DF, p-value: 2.44e-09 +coef(linearModel) # extract estimated parameters +# => +# (Intercept) time +# 0.1452662 0.4943490 +summary(linearModel)$coefficients # another way to extract results +# => +# Estimate Std. Error t value Pr(>|t|) +# (Intercept) 0.1452662 1.50084246 0.09678975 9.234021e-01 +# time 0.4943490 0.06379348 7.74920901 2.440008e-09 +summary(linearModel)$coefficients[,4] # the p-values +# => +# (Intercept) time +# 9.234021e-01 2.440008e-09 + +# GENERAL LINEAR MODELS +# Logistic regression +set.seed(1) +list1$success = rbinom(length(list1$time), 1, .5) # random binary +glModel <- glm(success ~ time, data = list1, + family=binomial(link="logit")) +glModel # outputs result of logistic regression +# => +# Call: glm(formula = success ~ time, +# family = binomial(link = "logit"), data = list1) +# +# Coefficients: +# (Intercept) time +# 0.17018 -0.01321 +# +# Degrees of Freedom: 39 Total (i.e. Null); 38 Residual +# Null Deviance: 55.35 +# Residual Deviance: 55.12 AIC: 59.12 +summary(glModel) # more verbose output from the regression +# => +# Call: +# glm(formula = success ~ time, +# family = binomial(link = "logit"), data = list1) + +# Deviance Residuals: +# Min 1Q Median 3Q Max +# -1.245 -1.118 -1.035 1.202 1.327 +# +# Coefficients: +# Estimate Std. Error z value Pr(>|z|) +# (Intercept) 0.17018 0.64621 0.263 0.792 +# time -0.01321 0.02757 -0.479 0.632 +# +# (Dispersion parameter for binomial family taken to be 1) +# +# Null deviance: 55.352 on 39 degrees of freedom +# Residual deviance: 55.121 on 38 degrees of freedom +# AIC: 59.121 +# +# Number of Fisher Scoring iterations: 3 + + +######################### # Plots ######################### # BUILT-IN PLOTTING FUNCTIONS # Scatterplots! plot(list1$time, list1$price, main = "fake data") -# Regressions! -linearModel <- lm(price ~ time, data = list1) -linearModel # outputs result of regression # Plot regression line on existing plot abline(linearModel, col = "red") # Get a variety of nice diagnostics |