Can I use a summary function on lm? - r

Question

I am working with election survey data and have a dataset loaded into R and I have objects created. Right now I am working in tidyverse. I am trying to run a regression with male and another variable. However, male is under gender and I am trying to isolate just male from the gender overall. In the data male comes up as 1 and female is 2.
Warning messages:
1: In model.response(mf, "numeric") :
using type = "numeric" with a factor response will be ignored
2: In Ops.factor(y, z$residuals) : ‘-’ not meaningful for factors
I do get coefficients, but then I try to get summary:
gender_disgust_set<-lm(gender~dem_disgusted, data=my_data_set)
summary(gender_disgust_set)
then I get this warning message:
Error in quantile.default(resid) : (unordered) factors are not allowed
In addition: Warning message:
In Ops.factor(r, 2) : ‘^’ not meaningful for factors
lm(gender~dem_disgusted, data=my_data_set)
subset(my_data_set, gender = male)
male_total<-subset(my_data_set, gender = male)
summary(male_total)
lm(gender~dem_disgusted, data=my_data_set)

Answer 1

The lm() function is designed for linear regression, which generally assumes a continuous response.
From the lm() details page:
A typical model has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response.
Your gender variable is a factor (not continuous; more information about data types here). If you really wanted to predict gender (a factor), you would need to use glm() for logistic regression.
Yes, you can use summary() on lm objects, but whether linear (or logistic) regression is best for your specific research question is a different question.
library(tidyverse)
set.seed(123)
gender <- sample(1:2, 10, replace = TRUE) %>% factor()
x1 <- sample(1:12, 10, replace = TRUE) %>% as.numeric()
x2 <- sample(1:100, 10, replace = TRUE) %>% as.numeric()
x3 <- sample(50:75, 10, replace = TRUE) %>% as.numeric()
my_data_set <- data.frame(gender, x1, x2, x3)
sapply(my_data_set, class)
#> gender x1 x2 x3
#> "factor" "numeric" "numeric" "numeric"
# error
# gender_disgust_set <- lm(gender ~ x1, data = my_data_set)
# summary(gender_disgust_set)
# logistic regression
gender_disgust_set1 <- glm(gender ~ x1, data = my_data_set, family = "binomial")
summary(gender_disgust_set1)
#>
#> Call:
#> glm(formula = gender ~ x1, family = "binomial", data = my_data_set)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -1.2271 -0.9526 -0.8296 1.1571 1.5409
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 0.6530 1.7983 0.363 0.717
#> x1 -0.1342 0.2149 -0.625 0.532
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 13.46 on 9 degrees of freedom
#> Residual deviance: 13.06 on 8 degrees of freedom
#> AIC: 17.06
#>
#> Number of Fisher Scoring iterations: 4
# or flip it around
# while this model works, please look into dummy-coding before using
# factors to predict continuous responses
gender_disgust_set2 <- lm(x1 ~ gender, data = my_data_set)
summary(gender_disgust_set2)
#>
#> Call:
#> lm(formula = x1 ~ gender, data = my_data_set)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -4.500 -2.438 0.500 2.688 3.750
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 8.500 1.371 6.199 0.00026 ***
#> gender2 -1.250 2.168 -0.577 0.58010
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 3.359 on 8 degrees of freedom
#> Multiple R-squared: 0.03989, Adjusted R-squared: -0.08012
#> F-statistic: 0.3324 on 1 and 8 DF, p-value: 0.5801