Extracting t-stat p values from lm in R
Here is an example with comments of how you can extract just the t-values.
# Some dummy data
n <- 1e3L
df <- data.frame(x = rnorm(n), z = rnorm(n))
df$y <- with(df, 0.01 * x^2 + z/3)
# Run regression
lr1 <- lm(y ~ x + z, data = df)
# R has special summary method for class "lm"
summary(lr1)
# Call:
# lm(formula = y ~ x + z, data = df)
# Residuals:
# Min 1Q Median 3Q Max
# -0.010810 -0.009025 -0.005259 0.003617 0.096771
# Coefficients:
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 0.0100122 0.0004313 23.216 <2e-16 ***
# x 0.0008105 0.0004305 1.883 0.06 .
# z 0.3336034 0.0004244 786.036 <2e-16 ***
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# Residual standard error: 0.01363 on 997 degrees of freedom
# Multiple R-squared: 0.9984, Adjusted R-squared: 0.9984
# F-statistic: 3.09e+05 on 2 and 997 DF, p-value: < 2.2e-16
# Now, if you only want the t-values
summary(lr1)[["coefficients"]][, "t value"]
# Or (better practice as explained in comments by Axeman)
coef(summary(lr1))[, "t value"]
# (Intercept) x z
# 23.216317 1.882841 786.035718
You could try this:
summary(fit)