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list College / Accelerated Statistics with R (XCD)

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  • High School / Advanced Statistics and Data Science I (ABC)
  • High School / Statistics and Data Science I (AB)
  • High School / Statistics and Data Science II (XCD)
  • High School / Algebra + Data Science (G)
  • College / Introductory Statistics with R (ABC)
  • College / Advanced Statistics with R (ABCD)
  • College / Accelerated Statistics with R (XCD)
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8.6 Using shuffle() for Targeted Model Comparisons (Part 2)

Having saved the residuals from Neighborhood model, let’s now see how we can use them, along with the shuffle() function, to create a sampling distribution for the unique effect of HomeSizeK.

Step Two: Create the Sampling Distribution of F for Home Size

A sampling distribution of Fs provides us a way to calculate how likely it would be for the simple model of the DGP (i.e., the one with no unique effect of HomeSizeK) to generate an F for HomeSizeK as large or larger than the one found in the data (11.626).

Before we create the sampling distribution of F for the HomeSizeK effect, we will show you how to get the sample F for HomeSizeK. Our previous method, using the f() function, won’t work; it only gives us the overall F for the full model. To get the F for HomeSizeK you can run this code:

f(PriceK_N_resids ~ Neighborhood + HomeSizeK, data = Smallville, predictor = ~HomeSizeK)

The first part of this code creates a supernova table for the multivariate model using PriceK_N_resids as the outcome. The highlighted part above then reads the sample F for HomeSizeK out of the table (without ever printing it out). We’ve put this code in the window below, so you have this F available.

In the window below, modify the code where indicated, using the shuffle() function, to produce a single F for HomeSizeK that assumes a DGP with 0 effect of home size. Run the code a few times just to see what it does.

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() }

Now let’s add some code to create a sampling distribution of 1000 Fs for HomeSizeK assuming no effect of home size in the DGP. Save these Fs into a data frame called HomeSizeK_sdof.

require(coursekata) Neighborhood_model <- lm(PriceK~ Neighborhood, data = Smallville) Smallville$PriceK_N_resids <- resid(Neighborhood_model) # 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_object("HomeSizeK_sdof") %>% check_equal()

Below we have graphed out the sampling distribution of 1000 shuffled Fs for the HomeSizeK effect. We also have added to the plot, as a black dot, the sample F for the HomeSizeK row of the ANOVA table (11.63). We’ll save this value as HomeSizeK_f. As you can see, the sample F is far out in the tail of the sampling distribution.

Histogram of the sampling distribution of f from the data frame HomeSizeK_sdof. A black dot is plotted on the x-axis at the sample F value of 11.63.

To calculate the exact p-value for the HomeSizeK F, we can use tally.

Try copying and pasting the appropriate code into the code block below. Also generate an ANOVA table – to check out whether the p-value obtained from tally() is similar to the p-value for HomeSizeK in the ANOVA table.

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() }

The p-value we got from tally() is close to the p-value reported on the HomeSizeK row of the multivariate ANOVA table: 0.0019.

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