I tried selecting for cases in a column but could not succeed in generating the summary. I wonder if you can help me

[sky3]

load("CONDO.Rdata")
CONDO.A <- CONDO[CONDO$SOLD=="A",]
CONDO$SOLD=="A"
as.numeric(CONDO$SOLD)
CONDO.A <- CONDO[as.numeric(CONDO$SOLD)==1,]
x1 <- CONDO.A$FLOOR
x2 <- CONDO.A$DIST
x3 <- CONDO.A$VIEW
x4 <- CONDO.A$END
x5 <- CONDO.A$FURNISH
y <- CONDO.A$SOLD
fit1 <- lm(y~x1+x2)

I have this code :fit1 <- lm(y~x1+x2)
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

[HanOostdijk]

Please read Welcome to the RStudio Community!.

You list some statements that have no influence on the fit.
E.g. as.numeric(CONDO$SOLD) only shows the effect of taking that variable and trying to make it numeric. It does not actually change the variable. For that you need to specify CONDO$SOLD<-as.numeric(CONDO$SOLD) .
Probably you meant to execute:

CONDO$SOLD<-as.numeric(CONDO$SOLD)
CONDO.A <- CONDO[CONDO$SOLD==1,]
fit1 <- lm(SOLD~FLOOR+DIST,data=CONDO.A)

?

[sky3]

@HanOostdijk

CONDO$SOLD<-as.numeric(CONDO$SOLD)
CONDO.A <- CONDO[CONDO$SOLD==1,]
fit1 <- lm(SOLD~FLOOR+DIST,data=CONDO.A)

It worked with warning. I added the input you provided washout understanding.
input

or

summary(fit1)

Call:
lm(formula = SOLD ~ FLOOR + DIST, data = CONDO.A)

Residuals:
Min 1Q Median 3Q Max
-9.557e-15 -9.590e-17 5.900e-17 2.657e-16 6.231e-16

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.000e+00 3.088e-16 3.238e+15 <2e-16 ***
FLOOR 1.073e-16 4.851e-17 2.212e+00 0.0292 *
DIST 2.710e-17 2.368e-17 1.144e+00 0.2551

Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 9.77e-16 on 103 degrees of freedom
Multiple R-squared: 0.4999, Adjusted R-squared: 0.4901
F-statistic: 51.47 on 2 and 103 DF, p-value: 3.185e-16

Warning message:
In summary.lm(fit1) : essentially perfect fit: summary may be unreliable

based on all of this
load("CONDO.Rdata")
CONDO.A <- CONDO[CONDO$SOLD=="A",]
CONDO$SOLD=="A"
as.numeric(CONDO$SOLD)
CONDO.A <- CONDO[as.numeric(CONDO$SOLD)==1,]
x1 <- CONDO.A$FLOOR
x2 <- CONDO.A$DIST
x3 <- CONDO.A$VIEW
x4 <- CONDO.A$END
x5 <- CONDO.A$FURNISH
y <- CONDO.A$SOLD
CONDO$SOLD<-as.numeric(CONDO$SOLD)
CONDO.A <- CONDO[CONDO$SOLD==1,]
fit1 <- lm(SOLD~FLOOR+DIST,data=CONDO.A)
summary(fit1)

What should be eliminated ? I am trying to understand. All the idea was to select A (auction) and remove fixed priced (f) from SOLD column ...

[FJCC]

I have added some comments to your code.

@sky3

CONDO.A <- CONDO[CONDO$SOLD=="A",] #This sets SOLD to only have the value A
CONDO$SOLD=="A" #Returns TRUE values due to the previous filtering
as.numeric(CONDO$SOLD) #does nothing as previously noted
#The following line either does nothing (if the numeric value of A is 1)
# or it returns zero rows. I assume it does nothing
CONDO.A <- CONDO[as.numeric(CONDO$SOLD)==1,]

#You do not use x1 through x5 or y in the fit, so these lines are not needed.
x1 <- CONDO.A$FLOOR
x2 <- CONDO.A$DIST
x3 <- CONDO.A$VIEW
x4 <- CONDO.A$END
x5 <- CONDO.A$FURNISH
y <- CONDO.A$SOLD

CONDO$SOLD<-as.numeric(CONDO$SOLD) #Makes SOLD numeric
#The following line either does nothing (if the numeric value of A was 1)
# or it returns zero rows. I assume it does nothing
CONDO.A <- CONDO[CONDO$SOLD==1,]

#You have fixed SOLD to have only the value 1, so it makes no sense to fit its
#value against other variables. As the summary shows, the fit has an intercept of 1
#and other coefficients are VERY small.
fit1 <- lm(SOLD~FLOOR+DIST,data=CONDO.A)

Why are you setting SOLD to have only one value?

[sky3]

Thank you. I am addressing the auction price which is coded as number 1, and taking off the fixed price . This is why we subsetted( or selected cases need to use x1 through x5 when I test the second order model and the interactions with dummies. I basically have three models to test.
This is the complete model
E(y) = β0 + β1x1 + β2x2 + β3x3 + β4x4 + β5x5

X1 floor height , x2: distance from elevator x3: dummies 1 if ocean view 0 if not, x4, 1 if an end unit, 0 if not, x5: 1 if furnished, 0 if not.

[jrkrideau]

We need a reproducible example (reprex)

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

As @ FJCC points out your code is not making sense. There is clearly something we are missing.