CourseKata - ckcode-chapter-d2-multivariate-comparisons

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ckcode ⌲ chapter-d2-multivariate-comparisons

require(coursekata)

# this fits the Neighborhood model
Neighborhood_model <- lm(PriceK ~ Neighborhood, data=Smallville)

# this saves the residuals
PriceK_N_resid <- resid(Neighborhood_model)

# square and sum the residuals

# this fits the Neighborhood model
Neighborhood_model <- lm(PriceK ~ Neighborhood, data=Smallville)

# this saves the residuals
PriceK_N_resid <- resid(Neighborhood_model)

# square and sum the residuals
sum(PriceK_N_resid ^ 2)

ex() %>% check_output_expr("sum(PriceK_N_resid ^ 2)")

CK Code: D2_Code_SumSqu_01

require(coursekata)

# this fits the Neighborhood model
Neighborhood_model <- lm(PriceK ~ Neighborhood, data=Smallville)

# find the ANOVA table

# this fits the Neighborhood model
Neighborhood_model <- lm(PriceK ~ Neighborhood, data=Smallville)

# find the ANOVA table
supernova(Neighborhood_model)

ex() %>%
  check_function("supernova") %>%
  check_result() %>%
  check_equal()

CK Code: D2_Code_SumSqu_02

require(coursekata)

# this saves the multivariate model
multi_model <- lm(PriceK~ Neighborhood + HomeSizeK, data = Smallville)

# write code to generate the model comparisons

# this saves the multivariate model
multi_model <- lm(PriceK~ Neighborhood + HomeSizeK, data = Smallville)

# write code to generate the model comparisons
generate_models(multi_model)

ex() %>%
  check_function("generate_models") %>%
  check_result() %>%
  check_equal()

CK Code: D2_Code_PREF_01

require(coursekata)

# this fits and saves the neighborhood model
Neighborhood_model <- lm(PriceK~ Neighborhood, data = Smallville)

# write code to  save the residuals of the neighborhood model
PriceK_N_resid <-

# this will print a few of these residuals
head(PriceK_N_resid)

# this fits and saves the neighborhood model
Neighborhood_model <- lm(PriceK~ Neighborhood, data = Smallville)

# write code to  save the residuals of the neighborhood model
PriceK_N_resid <- resid(Neighborhood_model)

# this will print a few of these residuals
head(PriceK_N_resid)

ex() %>%
  check_object("PriceK_N_resid") %>%
  check_equal()

CK Code: D2_Code_Shuffle_01

require(coursekata)
Neighborhood_model <- lm(PriceK~ Neighborhood, data = Smallville)

# this saves the residuals of the neighborhood model
PriceK_N_resid <- resid(Neighborhood_model)

# write code to fit the multivariate model again, this time
# using residuals from the neighborhood model as the outcome variable
PriceK_N_resid_multi_model <- lm()

# this will print the ANOVA table for this new model
supernova(PriceK_N_resid_multi_model)

# this saves the residuals of the neighborhood model
PriceK_N_resid <- resid(Neighborhood_model)

# write code to fit the multivariate model again, this time
# using residuals from the neighborhood model as the outcome variable
PriceK_N_resid_multi_model <- lm(PriceK_N_resid ~ Neighborhood + HomeSizeK, data = Smallville)

# this will print the ANOVA table for this new model
supernova(PriceK_N_resid_multi_model)

ex() %>%
  check_object("PriceK_N_resid_multi_model") %>%
  check_equal()

CK Code: D2_Code_Shuffle_02

require(coursekata)

# code to fit neighborhood model and save residuals
Neighborhood_model <- lm(PriceK ~ Neighborhood, data = Smallville)
Smallville$PriceK_N_resids <- resid(Neighborhood_model)

# this code prints sample F for HomeSizeK
f(PriceK_N_resids ~ Neighborhood + HomeSizeK, data = Smallville, predictor = ~HomeSizeK)

# modify the code below to produce the F when residuals are shuffled
f(PriceK_N_resids ~ Neighborhood + HomeSizeK, data = Smallville, predictor = ~HomeSizeK)

# this code prints sample F for HomeSizeK
f(PriceK_N_resids ~ Neighborhood + HomeSizeK, data = Smallville, predictor = ~HomeSizeK)

# modify the code below to produce the F when residuals are shuffled
f(shuffle(PriceK_N_resids) ~ Neighborhood + HomeSizeK, data = Smallville, predictor = ~HomeSizeK)

ex() %>% check_function("f", index = 2) %>% {
  check_arg(., 1) %>% check_equal()
  check_arg(., "data") %>% check_equal()
  check_arg(., "predictor") %>% check_equal()
}

CK Code: D2_Code_Targeted_01

require(coursekata)

Neighborhood_model <- lm(PriceK~ Neighborhood, data = Smallville)
Smallville$PriceK_N_resids <- resid(Neighborhood_model)
set.seed(runif(1, min = 1, max = 10000))

# This code generates one shuffled HomeSizeK F
# Modify it to make a sampling distribution of 1000 shuffled Fs
# Save them in a data frame called HomeSizeK_sdof
f(shuffle(PriceK_N_resids) ~ Neighborhood + HomeSizeK, data = Smallville, predictor = ~HomeSizeK)

# This code will put these Fs into a histogram
gf_histogram(~ f, data = HomeSizeK_sdof) %>%
  gf_labs(title = "shuffled HomeSizeK Fs")

# This code generates one shuffled HomeSizeK F
# Modify it to make a sampling distribution of 1000 shuffled Fs
# Save them in a data frame called HomeSizeK_sdof
HomeSizeK_sdof <- do(1000) * f(shuffle(PriceK_N_resids) ~ Neighborhood + HomeSizeK, data = Smallville, predictor = ~HomeSizeK)

# This code will put these Fs into a histogram
gf_histogram(~ f, data = HomeSizeK_sdof) %>%
  gf_labs(title = "shuffled HomeSizeK Fs")

ex() %>% check_correct(
  check_object(., "HomeSizeK_sdof") %>% check_equal(),
  check_function(., "do") %>% {
    check_arg(., 1) %>% check_equal()
    check_operator(., "*")
  }
)

CK Code: D2_Code_Targeted_02

require(coursekata)

Neighborhood_model <- lm(PriceK~ Neighborhood, data = Smallville)
Smallville$PriceK_N_resids <- resid(Neighborhood_model)

# This saves the sample HomeSizeK F
HomeSizeK_f <- f(PriceK_N_resids ~ Neighborhood + HomeSizeK, data = Smallville, predictor = ~HomeSizeK)

# This code generates a sampling distribution of shuffled HomeSizeK Fs
HomeSizeK_sdof <- do(1000) * f(shuffle(PriceK_N_resids) ~ Neighborhood + HomeSizeK, data = Smallville, predictor = ~HomeSizeK)

# Paste in the code for tallying the p-value for HomeSizeK


# Modify the code below to generate an ANOVA table from the multivariate model
lm(PriceK ~ Neighborhood + HomeSizeK, data = Smallville)

# This saves the sample HomeSizeK F
HomeSizeK_f <- f(PriceK_N_resids ~ Neighborhood + HomeSizeK, data = Smallville, predictor = ~HomeSizeK)

# This code generates a sampling distribution of shuffled HomeSizeK Fs
HomeSizeK_sdof <- do(1000) * f(shuffle(PriceK_N_resids) ~ Neighborhood + HomeSizeK, data = Smallville, predictor = ~HomeSizeK)

# Paste in the code for tallying the p-value for HomeSizeK
tally(~ f > HomeSizeK_f, data=HomeSizeK_sdof, format="proportion")

# Modify the code below to generate an ANOVA table from the multivariate model
supernova(lm(PriceK ~ Neighborhood + HomeSizeK, data = Smallville))

ex() %>% {
  check_function(., "tally") %>%
    check_result() %>%
    check_equal()
  check_function(., "supernova") %>%
    check_result() %>%
    check_equal()
}

CK Code: D2_Code_Targeted_03

require(coursekata)

# code to make ANOVA table for multivariate model
supernova(lm(PriceK ~ HomeSizeK + HasFireplace, data = Smallville))

# add code to get ANOVA tables for each of the two single-predictor models

# code to make ANOVA table for multivariate model
supernova(lm(PriceK ~ HomeSizeK + HasFireplace, data = Smallville))

# add code to get ANOVA tables for each of the two single-predictor models
supernova(lm(PriceK ~ HasFireplace, data = Smallville))
supernova(lm(PriceK ~ HomeSizeK, data = Smallville))

ex() %>% {
  check_function(., "supernova", index = 2) %>%
    check_result() %>%
    check_equal()
  check_function(., "supernova", index = 3) %>%
    check_result() %>%
    check_equal()
}

CK Code: D2_Code_Deciding_01

require(coursekata)

# find the best-fitting parameter estimates

# find the best-fitting parameter estimates
lm(tip_percent ~ condition + gender, data = tip_exp)

ex() %>%
  check_function("lm") %>%
  check_result() %>%
  check_equal()

CK Code: D2_Code_Categorical_01

require(coursekata)

# write code to save this model as multi_model
lm(formula = tip_percent ~ condition + gender, data = tip_exp)

# this makes a faceted jitter plot of our data
# write code to overlay the multi_model onto it
gf_jitter(tip_percent ~ condition, color = ~condition, data = tip_exp, width = .1)  %>%
  gf_facet_grid(. ~ gender)

# write code to save this model as multi_model
multi_model <- lm(formula = tip_percent ~ condition + gender, data = tip_exp)

# this makes a faceted jitter plot of our data
# write code to overlay the multi_model onto it
gf_jitter(tip_percent ~ condition, color = ~condition, data = tip_exp, width = .1)  %>%
  gf_facet_grid(. ~ gender) %>%
  gf_model(multi_model)

ex() %>% {
  check_object(., "multi_model") %>% check_equal()
  check_function(., "gf_model") %>% {
    check_arg(., "object") %>% check_equal()
    check_arg(., "model") %>% check_equal()
  }
}

CK Code: D2_Code_Categorical_02

require(coursekata)

# here is the code to find the best-fitting model
# modify this to generate the ANOVA table for this model
lm(tip_percent ~ condition + gender, data = tip_exp)

# here is the code to find the best-fitting model
# modify this to generate the ANOVA table for this model
supernova(lm(tip_percent ~ condition + gender, data = tip_exp))

ex() %>%
  check_output_expr("supernova(lm(tip_percent ~ condition + gender, data = tip_exp))")

CK Code: D2_Code_Error_01

require(coursekata)

# add the color argument to this scatterplot
gf_point(FEV ~ HEIGHT, data = fevdata)

# add the color argument to this scatterplot
gf_point(FEV ~ HEIGHT, data = fevdata, color = ~AGE)

ex() %>%
  check_function("gf_point") %>%
  check_arg("color") %>%
  check_equal()

CK Code: D2_Code_Multiple_01

require(coursekata)

# find the best-fitting parameter estimates

# find the best-fitting parameter estimates
lm(FEV ~ HEIGHT + AGE, data = fevdata)

ex() %>%
  check_function("lm") %>%
  check_result() %>%
  check_equal()

CK Code: D2_Code_Multiple_02

require(coursekata)

# this saves the multivariate model
multi_model <- lm(FEV ~ HEIGHT + AGE, data = fevdata)

# add the multi_model to this plot
gf_point(FEV ~ HEIGHT, data = fevdata, color = ~AGE, alpha = .2)

# this saves the multivariate model
multi_model <- lm(FEV ~ HEIGHT + AGE, data = fevdata)

# add the multi_model to this plot
gf_point(FEV ~ HEIGHT, data = fevdata, color = ~AGE, alpha = .2) %>%
  gf_model(multi_model)

ex() %>%
  check_function("gf_model") %>% {
    check_arg(., "object") %>% check_equal()
    check_arg(., "model") %>% check_equal()
  }

CK Code: D2_Code_Multiple_03

require(coursekata)

# preload plotly to prevent the startup messages
require(plotly)

# this loads plotly
library(plotly)

# this saves the predictions of the multivariate model
fevdata$prediction <- predict(lm(FEV ~ HEIGHT + AGE, data = fevdata))

# this makes the 3d plot of model
plot_ly(fevdata, x = ~AGE, y = ~HEIGHT, z = ~prediction, type = "scatter3d", mode = "markers", color = ~AGE, size = 1)

# this loads plotly
library(plotly)

# this saves the predictions of the multivariate model
fevdata$prediction <- predict(lm(FEV ~ HEIGHT + AGE, data = fevdata))

# this makes the 3d plot of model
plot_ly(fevdata, x = ~AGE, y = ~HEIGHT, z = ~prediction, type = "scatter3d", mode = "markers", color = ~AGE, size = 1)

ex() %>% check_error()

CK Code: D2_Code_Multiple_04

require(coursekata)

# saves the multivariate model
multi_model <- lm(FEV ~ HEIGHT + AGE, data = fevdata)

# write code to produce the ANOVA table

# saves the multivariate model
multi_model <- lm(FEV ~ HEIGHT + AGE, data = fevdata)

# write code to produce the ANOVA table
supernova(multi_model)

ex() %>%
  check_function("supernova") %>%
  check_result() %>%
  check_equal()

CK Code: D2_Code_Unpacking_01

ckcode-chapter-d1-intro-multivariate-models ckcode-chapter-d3-models-with-interactions

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ckcode ⌲ chapter-d2-multivariate-comparisons

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