Let's say you want to extract a p-value and save it as a variable for future use from a linear or generalized linear model – mixed or non! This is something you might want to do if, say, you were calculating Fisher's C from an equation-level Structural Equation Model. Here's how to extract the effect of a variable from multiple different fit models. We'll start with a data set with x, y, z, and a block effect (we'll see who in a moment).

```
x <- rep(1:10, 2)
y <- rnorm(20, x, 3)
block <- c(rep("a", 10), rep("b", 10))
mydata <- data.frame(x = x, y = y, block = block, z = rnorm(20))
```

Now, how would you extract the p-value for the parameter fit for z from a linear model object? Simply put, use the t-table from the lm object's summary

```
alm <- lm(y ~ x + z, data = mydata)
summary(alm)$coefficients
```

```
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1833 1.3496 0.8768 0.392840
## x 0.7416 0.2190 3.3869 0.003506
## z -0.4021 0.8376 -0.4801 0.637251
```

```
# Note that this is a matrix.
# The third row, fourth column is the p value
# you want, so...
p.lm <- summary(alm)$coefficients[3, 4]
p.lm
```

```
## [1] 0.6373
```

That's a linear model, what about a generalized linear model?

```
aglm <- glm(y ~ x + z, data = mydata)
summary(aglm)$coefficients
```

```
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1833 1.3496 0.8768 0.392840
## x 0.7416 0.2190 3.3869 0.003506
## z -0.4021 0.8376 -0.4801 0.637251
```

```
# Again, is a matrix.
# The third row, fourth column is the p value you
# want, so...
p.glm <- summary(aglm)$coefficients[3, 4]
p.glm
```

```
## [1] 0.6373
```

That's a linear model, what about a generalized linear model?

```
anls <- nls(y ~ a * x + b * z, data = mydata,
start = list(a = 1, b = 1))
summary(anls)$coefficients
```

```
## Estimate Std. Error t value Pr(>|t|)
## a 0.9118 0.1007 9.050 4.055e-08
## b -0.4651 0.8291 -0.561 5.817e-01
```

```
# Again, is a matrix.
# The second row, fourth column is the p value you
# want, so...
p.nls <- summary(anls)$coefficients[2, 4]
p.nls
```

```
## [1] 0.5817
```

Great. Now, what if we were running a mixed model? First, let's look at the nlme package. Here, the relevant part of the summary object is the tTable

```
library(nlme)
alme <- lme(y ~ x + z, random = ~1 | block, data = mydata)
summary(alme)$tTable
```

```
## Value Std.Error DF t-value p-value
## (Intercept) 1.1833 1.3496 16 0.8768 0.393592
## x 0.7416 0.2190 16 3.3869 0.003763
## z -0.4021 0.8376 16 -0.4801 0.637630
```

```
# Again, is a matrix.
# But now the third row, fifth column is the p value
# you want, so...
p.lme <- summary(alme)$tTable[3, 5]
p.lme
```

```
## [1] 0.6376
```

Last, what about lme4? Now, for a linear lmer object, you cannot get a p value. But, if this is a generalizes linear mixed model, you are good to go (as in Shipley 2009). Let's try that here.

```
library(lme4)
almer <- lmer(y ~ x + z + 1 | block, data = mydata)
# no p-value!
summary(almer)@coefs
```

```
## Estimate Std. Error t value
## (Intercept) 4.792 0.5823 8.231
```

```
# but, for a genearlined linear mixed model
# and yes, I know this is a
# bad model but, you know, demonstration!
aglmer <- lmer(y + 5 ~ x + z + (1 | block),
data = mydata, family = poisson(link = "log"))
summary(aglmer)@coefs
```

```
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.90813 0.16542 11.535 8.812e-31
## x 0.07247 0.02471 2.933 3.362e-03
## z -0.03193 0.09046 -0.353 7.241e-01
```

```
# matrix again! Third row, fourth column
p.glmer <- summary(aglmer)@coefs[3, 4]
p.glmer
```

```
## [1] 0.7241
```