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ckcode ⌲ chapter-b3-adding-explanatory

require(coursekata) # find best fitting model Sex_model <- lm(Thumb ~ Sex, data = Fingers) # add code to visualize the new model on the jitter plot gf_jitter(Thumb ~ Sex, data = Fingers, width = .1) # find best fitting model Sex_model <- lm(Thumb ~ Sex, data = Fingers) # add code to visualize the new model on the jitter plot gf_jitter(Thumb ~ Sex, data = Fingers, width = .1) %>% gf_model(Sex_model) ex() %>% { check_function(., "gf_model") %>% check_arg("object") %>% check_equal() check_or(., check_function(., "gf_model") %>% check_arg("model") %>% check_equal(), override_solution(., "gf_jitter(Thumb ~ Sex, data = Fingers) %>% gf_model(Thumb ~ Sex)") %>% check_function(., "gf_model") %>% check_arg("model") %>% check_equal() ) }
CK Code: B3_Code_RtoFit_01
require(coursekata) # we have saved the Sex model for you Sex_model <- lm(Thumb ~ Sex, data = Fingers) # write code to generate predictions using this model # no need to save the predictions # we have saved the Sex model for you Sex_model <- lm(Thumb ~ Sex, data = Fingers) # write code to generate predictions using this model # no need to save the predictions predict(Sex_model) ex() %>% check_function("predict") %>% check_result() %>% check_equal()
CK Code: B3_Code_RtoFit_02
require(coursekata) # we have saved the Sex model for you Sex_model <- lm(Thumb ~ Sex, data = Fingers) # print out the best fitting parameter estimates # we have saved the Sex model for you Sex_model <- lm(Thumb ~ Sex, data = Fingers) # print out the best fitting parameter estimates Sex_model ex() %>% check_output_expr("Sex_model")
CK Code: B3_Code_RtoFit_03
require(coursekata) # this creates the residuals from the Sex_model Sex_model <- lm(Fingers$Thumb ~ Fingers$Sex) Fingers$Sex_resid <- resid(Sex_model) # this creates histograms of Thumb for each Sex # modify it to create histograms of Sex_resid for each Sex gf_histogram(~Thumb, data = Fingers) %>% gf_facet_grid(Sex ~ .) # this creates the residuals from the Sex_model Sex_model <- lm(Fingers$Thumb ~ Fingers$Sex) Fingers$Sex_resid <- resid(Sex_model) # this creates histograms of Thumb for each Sex # modify it to create histograms of Sex_resid for each Sex gf_histogram(~Sex_resid, data = Fingers) %>% gf_facet_grid(Sex ~ .) ex() %>% { check_or(., check_function(., "gf_histogram") %>% { check_arg(., "object") %>% check_equal() check_arg(., "data") %>% check_equal() }, override_solution(., "gf_histogram(Fingers, ~ Sex_resid)") %>% check_function("gf_histogram") %>% { check_arg(., "object") %>% check_equal() check_arg(., "gformula") %>% check_equal() } ) check_function(., "gf_facet_grid") %>% check_arg("...") %>% check_equal(incorrect_msg = "Make sure you keep the code to create a grid faceted by `Sex`") }
CK Code: B3_Code_Graphing_01
require(coursekata) # This codes saves the best fitting models empty_model <- lm(Thumb ~ NULL, data=Fingers) Sex_model <- lm(Thumb ~ Sex, data=Fingers) # This code squares and sums the residuals from the empty model sum(resid(empty_model)^2) # Write code to square and sum the residuals from the Sex model # This codes saves the best fitting models empty_model <- lm(Thumb ~ NULL, data=Fingers) Sex_model <- lm(Thumb ~ Sex, data=Fingers) # This code squares and sums the residuals from the empty model sum(resid(empty_model)^2) # Write code to square and sum the residuals from the Sex model sum(resid(Sex_model)^2) ex() %>% { check_function(., "sum", 1) %>% check_result() %>% check_equal() check_function(., "sum", 2) %>% check_result() %>% check_equal() }
CK Code: B3_Code_UsingSS_01
require(coursekata) empty_model <- lm(Thumb ~ NULL, data = Fingers) Sex_model <- lm(Thumb ~ Sex, data = Fingers) # try running the code as is # then modify to create the ANOVA table for Sex_model supernova(empty_model) empty_model <- lm(Thumb ~ NULL, data = Fingers) Sex_model <- lm(Thumb ~ Sex, data = Fingers) supernova(Sex_model) ex() %>% { check_function(., "lm") %>% check_result() %>% check_equal() check_object(., "Sex_model") %>% check_equal() check_function(., "supernova") %>% check_result() %>% check_equal() }
CK Code: B3_Code_UsingSS_02
require(coursekata) empty_model <- lm(Thumb ~ NULL, data=Fingers) Sex_model <- lm(Thumb ~ Sex, data=Fingers) # creates the differences between the two predictions error_reduced <- predict(Sex_model) - predict(empty_model) # modify this line of code to square and sum these differences error_reduced # creates the differences between the two predictions error_reduced <- predict(Sex_model) - predict(empty_model) # modify this line of code to square and sum these differences sum(error_reduced ^ 2) ex() %>% check_output(1334.2)
CK Code: B3_Code_Partitioning_01
require(coursekata) # run your code here
CK Code: B3_Code_Review2_01
require(coursekata) # run your code here
CK Code: B3_Code_Review2_02
require(coursekata) # run your code here
CK Code: B3_Code_Review2_03
require(coursekata) # run your code here
CK Code: B3_Code_Review2_04
require(coursekata) # run your code here
CK Code: B3_Code_Review2_05

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